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VÉRONIQUE THÉRIAULT
ÉVOLUTION DES TACTIQUES ALTERNATIVES CHEZ L’OMBLE DE FONTAINE
Patrons de reproduction, héritabilité et pêche sélective
Thèse présentée à la Faculté des études supérieures de l’Université Laval
dans le cadre du programme de doctorat en biologie pour l’obtention du grade de Philosophia Doctor (Ph.D.)
DÉPARTEMENT DE BIOLOGIE FACULTÉ DES SCIENCES
UNIVERSITÉ LAVAL QUÉBEC
OCTOBRE 2007 © Véronique Thériault, 2007
i
Résumé L’objectif principal de cette thèse était de déterminer les bases génétiques des tactiques
alternatives de vie chez l’omble de fontaine, Salvelinus fontinalis. L’anadromie, la
migration en eau salée et le retour vers l’eau douce pour la reproduction, ainsi que la
résidence, la réalisation du cycle de vie entièrement en eau douce, sont deux formes de vie
communes chez les salmonidés souvent retrouvées en sympatrie. Ces formes sont ici
considérées comme des tactiques alternatives au sein d’une stratégie conditionnelle et
étudiées sous le modèle de traits seuils. Tout d’abord, à l’aide de marqueurs moléculaires
et d’analyses d’assignation parentale, la reproduction entre les deux formes, assurée par les
mâles résidents, a été mise en évidence. De plus, le succès reproducteur individuel était lié
à la taille chez les femelles, mais pas chez les mâles, suggérant l’emploi d’une tactique de
reproduction furtive par ces derniers. Ensuite, des méthodes de reconstruction de groupes
d’individus apparentés couplées à un « modèle animal » ont permis d’estimer l’héritabilité
de la tactique (gamme variant entre 0.53 et 0.56) ainsi que la corrélation génétique entre la
taille et la tactique (-0.52 ou -0.61), suggérant une évolution conjointe de ces deux traits.
Finalement, à l’aide d’un modèle éco-génétique, les conséquences de la pêche sportive sur
l’évolution de l’anadromie et la résidence ont été évaluées. Après 100 ans de pression de
pêche, la norme de réaction de migration est déplacée, entrainant une diminution dans la
probabilité de migrer avec une augmentation de l’intensité de pêche. Ce changement est
accompagné par une augmentation dans l’âge à la migration. La proportion de poissons
adoptant la tactique anadrome diminue dans la population à mesure que l’intensité de pêche
augmente, tout comme le nombre absolu de poissons retrouvé en eau salée. Ces
changements se traduisent en de plus bas âges et tailles à maturité. Cette thèse contribue à
notre compréhension du déterminisme des phénotypes alternatifs et se distingue par sa
réalisation complète sous conditions naturelles. En mettant en lumière les bases génétiques
de l’anadromie et la résidence, ce travail suggère qu’une réponse évolutive est possible face
à des pressions de sélection, anthropiques ou naturelles, et une telle réponse est démontrée
grâce à une approche de modélisation innovatrice.
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Abstract The central objective of this thesis was to assess the genetic basis of alternative life-history
tactics in brook charr, Salvelinus fontinalis. Anadromy, defined as migration to sea before
returning to freshwater to spawn, and residency, the completion of the entire life-cycle in
freshwater, are two tactics commonly found in sympatry in salmonids. These two life-
history forms are considered here as alternative tactics within a conditional strategy and are
studied according to the threshold model of quantitative genetics. First, molecular markers
and parentage analysis revealed that reproduction frequently occurred between the two
forms, and was mediated by the resident males. Moreover, individual reproductive success
was linked to body size in females, but not in males, which suggest that smaller males
make use of the alternative sneaker reproductive tactic. Second, sib-reconstruction
methods coupled to an “animal model” allowed the estimation of a significant heritability
for the life-history tactic (between 0.53 and 0.56) and a significant genetic correlation
between body size and tactic (-0.52 and -0.61), suggesting a joint evolution of these two
traits. Finally, the evolutionary consequences of sportfishery on the evolution of anadromy
and residency were assessed with the use of an eco-genetic model. After a hundred years
of fishing-induced selection directed toward anadromous fish, the migration reaction norms
shifted, resulting in a decrease in the probability of migrating with increasing harvest rate.
This change was accompanied by a higher mean age at migration. The proportion of fish
adopting the anadromous tactic decreased in the population as harvest rate increased, as did
the absolute number of fish found in saltwater. These changes resulted in a lower mean age
and size at maturity. This thesis contributes to our understanding of the determinism of
alternative phenotypes and stands out because of its realization under completely natural
conditions. By highlighting the genetic basis of anadromy and residency, this work
suggests that an evolutionary response is expected in face of anthropogenic or natural
selective forces, and such consequences are presented through an innovative modeling
approach.
Avant-propos
« Ce n'est point dans l'objet que réside le sens des choses, mais dans la démarche.»
Antoine de Saint-Exupéry
Remerciements
Ah! L’avant-propos! Quand on est rendu là ça veut dire que la fin est là, pour vrai. À en
croire Saint-Exupéry, ma longue démarche n’aura pas été vaine, et m’aura permis à tout le
moins de trouver un certain sens.
Mes premiers remerciements vont spontanément à Julian Dodson, mon directeur, qui m’a
offert en 1998 un projet de maîtrise sur l’omble de fontaine. Après presque une décennie
de collaboration, je ne peux qu’admirer et louanger toute la liberté qu’il nous donne, à
nous, ses étudiants. J’ai apprécié le scientifique et le chercheur, qui, par son intérêt, ses
conseils et ses encouragements m’ont permis d’avancer. J’ai aussi apprécié l’homme, qui
est d’une grande générosité et sensibilité.
Louis Bernatchez, mon co-directeur, me surprendra toujours par sa capacité de garder le fil.
Sans préambule, il arrivait à « sauter » dans mes résultats, à y relever les forces et les
faiblesses et à soulever d’importantes questions. Son aide me fut inestimable et ses
réflexions et commentaires ont sans contredit enrichi cette thèse. Merci!
Merci aussi à Helga Guderley et Steeve Côté d’avoir suivi mes travaux tout au long de ces
années en tant que membres de mon comité d’encadrement. Un merci particulier à Helga,
qui a accepté, même en sabbatique à l’autre bout du monde, d’être examinatrice de ma
thèse. Ce sont des femmes comme elle qui me donnent le goût de continuer en sciences.
J’ai côtoyé des gens dans mes « deux » labos durant ces années qui ont aidé mes réflexions,
qui sont devenus de bons amis et/ou qui m’ont tout simplement permis de passer du bon
temps : Stéphane Plourde, Nadia Aubin-Horth, Dany Garant, Christian Landry, Fred
Lecomte, Jeff Bourque, Robert St-Laurent, Vincent Castric, Dylan Fraser, Sean Rogers,
iv Guy Perry, Vincent Bourret, Jonatan Blais, Lucie Papillon, Simon Blanchet, David Paez,
Gesche Winkler, Pierre-Philippe Dupont, Marc Ringuette.
J’ai passé les 4 dernières années de ma vie entourée de 6 filles : Dju, Chunky, Chât, Caro,
Isa, Cath, les filles de mon équipe de canot à glace. Sans elles la vie aurait été un peu plus
difficile, et surtout, beaucoup plus ennuyante! Elles m’ont apporté un brin d’équilibre dans
cette longue démarche doctorale, et m’ont fait plus souvent qu’autrement sombrer dans le
vice! Merci pour ces moments de pur délire, et même si vous avez significativement ralenti
la progression de mes travaux (!), je ne vous en tiens pas rigueur et je ferais exactement la
même chose si c’était à recommencer. Je vous adore!
Merci à mon père, Jos, et ma mère, Jacqueline, pour leur admiration et leur support
inconditionnel. Merci à ma sœur et grande amie, Annie-Claude, complice de mes
réflexions scientifiques et existentielles. Manu, ma cousine, toujours là pour me
dévergonder, pourra enfin m’appeler à juste titre « doc virus ». Un clin d’œil à Yan, qui a
fait preuve de compréhension et de patience sans égal durant les 3 derniers mois de ma
rédaction. Je souhaite à chacun d’avoir quelqu’un d’aussi apaisant dans sa vie.
Le CIRSA (Centre Interuniversitaire de Recherche sur le Saumon Atlantique) a changé ma
vie. J’ai passé près de 6 saisons de terrain aux abords de la rivière Sainte-Marguerite au
Saguenay, et je m’y suis sentie tout de suite chez-moi. Plus que des collègues, assistants et
techniciens, j’y ai rencontré des personnes formidables qui ont fait de ce séjour une
expérience humaine inoubliable. Dans le désordre : Albertine Gauthier, André Boivin,
Geneviève Morinville, François Martin, Annie Ménard, Christian Langlois, Mélanie
Carrier, Olivier Higgins, Jean-Guillaume Marquis, Mylène Levasseur, Louis Vincent St-
Hilaire Gravel. Certaines continuent d’agrémenter ma vie, d’autres ne sont plus que
souvenir. Mais elles font toutes désormais partie de ma mémoire, et ont contribué chacune
à leur façon à la personne que je suis aujourd’hui.
v Organisation de la thèse Les chapitres 2, 3 et 4, qui constituent le corps de la thèse, sont présentés sous un format
approprié pour publication scientifique. Mon directeur de thèse, Julian Dodson, ainsi que
mon co-directeur, Louis Bernatchez, sont co-auteurs pour chacun des chapitres publiés et
soumis car ils ont contribué de façon substantielle à l’élaboration et au raffinement de
chacun de ces chapitres par d’innombrables commentaires et suggestions.
Le chapitre 2 est publié sous la référence : Thériault V., Bernatchez L. et Dodson J.J. 2007.
Mating system and individual reproductive success of sympatric anadromous and resident
brook charr, Salvelinus fontinalis, under natural conditions, Behavioural Ecology and
Sociobiology, doi:10.1007/s00265-007-0437-8.
Le chapitre 3 est publié sous la référence: Thériault V., Garant D., Bernatchez L. et Dodson
J.J. 2007. Heritability of life-history tactics and genetic correlation with body size in a
natural population of brook charr (Salvelinus fontinalis). Journal of Evolutionary Biology,
doi:10.1111/j.1420-9101.2007.01417.x.
Le co-auteur Dany Garant (Université de Sherbrooke, Département de biologie, 2500 boul.
de l’Université, Sherbrooke, Qc, J1K 2R1) a contribué à l’analyse statistique et à l’écriture
de ce chapitre par ses commentaires et suggestions.
Le chapitre 4 est soumis à la revue Evolutionary Applications comme contribution pour un
numéro spécial portant sur l’évolution des salmonidés qui sera publié en 2008 : Thériault
V., Dunlop E., Dieckmann U., Bernatchez L. et Dodson J.J. The impact of fishing-induced
mortality on the evolution of alternative life-history tactics in brook charr.
La co-auteure Erin Dunlop (Institute of Marine Research, P. O. Box 1870 Nordnes, N-5817
Bergen, Norway) a développé et adapté le modèle utilisé dans ce chapitre et a contribué
grandement à sa qualité par ses commentaires et suggestions. Le co-auteur Ulf Dieckmann
(International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg,
Austria) a aussi fournit des commentaires lors de la rédaction.
À Louis Vincent 1978-2003
Table des matières Résumé....................................................................................................................................i
Abstract................................................................................................................................. ii
Avant-propos....................................................................................................................... iii
Table des matières ............................................................................................................. vii
Liste des tableaux..................................................................................................................x
Liste des figures.................................................................................................................. xii
Chapitre 1. Introduction générale.......................................................................................1
1.1 Diversité et plasticité : les phénotypes alternatifs.........................................................1 1.1.1 Phénotypes alternatifs comme exemple extrême de plasticité...............................1 1.1.2 Phénotypes alternatifs comme traits seuils ............................................................3
1.2 La migration et la résidence chez les salmonidés .........................................................5 1.3 Héritabilité : son estimation en milieu naturel..............................................................8
1.3.1 Milieu contrôlé versus milieu naturel ....................................................................8 1.3.2 Les marqueurs moléculaires comme outils............................................................9 1.3.3 Les erreurs de génotypage et leurs effets.............................................................12
1.4 Mortalité sélective : effet de la pêche .........................................................................13 1.4.1 Réponse plastique vs. évolutive...........................................................................13 1.4.2 La norme de réaction probabiliste .......................................................................15 1.4.3 Une approche de modélisation.............................................................................18
1.5 Le cas de l’omble de fontaine au Québec, Salvelinus fontinalis ................................19 1.6 Objectifs et contributions............................................................................................22
Chapitre 2. Mating system and individual reproductive success of sympatric
anadromous and resident brook charr, Salvelinus fontinalis, under natural conditions
..............................................................................................................................................25
2.1 Résumé........................................................................................................................26 2.2 Abstract.......................................................................................................................27 2.3 Introduction.................................................................................................................28 2.4 Material and Methods .................................................................................................31
2.4.1 Study site and brook charr reproductive behavior ...............................................31 2.4.2 Sampling ..............................................................................................................31 2.4.3 Microsatellite polymorphism analyses ................................................................33 2.4.4 Statistical analyses ...............................................................................................33
2.5 Results.........................................................................................................................36 2.5.1 Characteristics of fish sampled ............................................................................36 2.5.2 Standard genetic statistics ....................................................................................37 2.5.3 Population genetic structure.................................................................................37 2.5.4 Parentage analysis................................................................................................38 2.5.5 Mating patterns ....................................................................................................39 2.5.6 Individual reproductive success...........................................................................39
viii
2.6 Discussion...................................................................................................................40 2.6.1 Gene flow and mating patterns ............................................................................40 2.6.2 Reproductive success...........................................................................................42 2.6.3 Where have all the parents gone? ........................................................................43 2.6.4 Conclusion ...........................................................................................................44
2.7 Acknowledgments ......................................................................................................46 2.8 Tables..........................................................................................................................47 2.9 Figures ........................................................................................................................53
Chapitre 3. Heritability of life-history tactics and genetic correlation with body size in
a natural population of brook charr (Salvelinus fontinalis)............................................58
3.1 Résumé........................................................................................................................59 3.2 Abstract.......................................................................................................................60 3.3 Introduction.................................................................................................................61 3.4 Material and Methods .................................................................................................64
3.4.1 Study Site and Sampling......................................................................................64 3.4.2 Pedigree reconstruction........................................................................................65 3.4.3 Quantitative genetic analyses...............................................................................66 3.4.4 Power and sensitivity analysis .............................................................................68
3.5 Results.........................................................................................................................70 3.5.1 Pedigree reconstruction........................................................................................70 3.5.2 Heritability and genetic correlations....................................................................71
3.6 Discussion...................................................................................................................72 3.7 Acknowledgments ......................................................................................................79 3.8 Tables..........................................................................................................................80 3.9 Figures ........................................................................................................................85
Chapitre 4. The impact of fishing-induced mortality on the evolution of alternative
life-history tactics in brook charr......................................................................................87
4.1 Résumé........................................................................................................................88 4.2 Abstract.......................................................................................................................89 4.3 Introduction.................................................................................................................90 4.4 Material and Methods .................................................................................................93
4.4.1 Migration .............................................................................................................94 4.4.2 Somatic growth ....................................................................................................94 4.4.3 Maturation............................................................................................................95 4.4.4 Reproduction........................................................................................................95 4.4.5 Inheritance and expression...................................................................................96 4.4.6 Natural mortality..................................................................................................97 4.4.7 Fishing-induced mortality....................................................................................97 4.4.8 Initial population structure and parameterization ................................................98
4.5 Results.........................................................................................................................99 4.5.1 Impact of different exploitation rates over 100 years ..........................................99 4.5.2 Impact of different natural mortality rates .........................................................100
ix
4.6 Discussion.................................................................................................................100 4.7 Acknowledgments ....................................................................................................104 4.8 Table .........................................................................................................................105 4.9 Figures ......................................................................................................................107 4.10 Supplemental material ............................................................................................111
4.10.1 Heritability through time .................................................................................111 4.10.2 Comparison of results with growth included as an evolving trait ...................112 4.10.3 The effect of density-dependent growth ..........................................................114
Chapitre 5. Conclusion générale......................................................................................116
5.1 Rappel des principaux résultats ................................................................................116 5.2 Contributions ............................................................................................................118 5.3 La valeur et la signification de l’héritabilité .............................................................119 5.4 Pour ou contre les réponses évolutives ? ..................................................................120 5.5 La perte ou la cachette de l’anadromie? ...................................................................121 5.6 Perspectives ..............................................................................................................125
Bibliographie .....................................................................................................................129
Liste des tableaux Chapitre 2. Mating system and individual reproductive sucess of sympatric
anadromous and resident brook charr, Salvelinus fontinalis, under natural conditions
Table 2-1. PCR conditions for the 13 loci amplified. ...........................................................47 Table 2-2. Number of samples (N), number of alleles (A), FIS, observed (HO) and expected
(HE) heterozygosities, and probabilities of conforming to Hardy-Weinberg equilibrium (P(HW), score U test) for each locus and each temporal sample (RES = resident, ANA = anadromous) separately. Bold values are significant at ∝ = 0.0096 following Bonferroni corrections. .................................................................................................48
Table 2-3. Genetic differentiation between anadromous and resident brook charr estimated by θST for each locus. N = 32 anadromous and 98 resident fish in 2000; N = 15 anadromous and 323 resident fish in 2001. ** P < 0.001 following 2,000 permutations with alpha adjusted for multiple testing using Bonferroni correction (∝ = 0.0038 for single-locus comparisons and ∝= 0.0125 for multilocus comparisons). ......................51
Table 2-4. Number of samples (N), mean, variance, and range of individual reproductive success (RS - number of offspring assigned to individuals) for males and females together (overall) and separately according to life-history form. Means and variances of life-history forms are compared with ANOVA and Levene's test respectively. ......52
Chapitre 3. Heritability of life-history tactics and genetic correlation with body size in
a natural population of brook charr (Salvelinus fontinalis)
Table 3-1. Number of alleles (N), observed (Ho) and expected (He) heterozygosity at each locus. * indicates significant departure from Hardy-Weinberg equilibrium. SfoB52, SfoC113, SfoC129, SfoC28, SfoC88, SfoC115, SfoD100, SfoD75, T. L. King, US Geological Survey, unpublished; SCO204, SCO216, SCO218 from DeHaan & Ardren (2005); Sfo262Lav, Sfo266Lav from Perry et al. (2005b). ...........................................80
Table 3-2. Sample size (N), trait means with their standard deviation (SD), estimates of residual (VR), additive (VA) and phenotypic (VP) variance components and heritability (h2) with their standard error (SE) for life-history tactic (anadromy/residency: Tactic), body size (fork length: FL) and six morphological traits (body depth: DEP, maximum body width: WID, peduncle depth: PED, caudal fin height: CAUD, pectoral fin length: PECT and pelvic fin length: PELV, all transformed following PCA, see methods) in brook charr. Pedigree 1 is the best one obtained with a weight of 1, Pedigree 2 is the best one obtained with a weight of 5, and Pedigree 3 is the same as Pedigree 1, but where half-sib relationships were added (see methods). h2 of the life-history tactic is given transformed to the liability scale (see methods). P-values are those obtained from likelihood ratio tests to assess the significance of the additive genetic component.......................................................................................................................................81
Table 3-3. Power analysis for a range of simulated heritabilities (h2) for a continuous and a binomial trait. The mean heritability estimate and the mean standard error of 20 simulations are presented. Simulations were ran in order to mimic the actual data.
xi
Heritabilities estimated with a sample size of 349 individuals refer to body size and tactic, while a sample size of 215 relates to morphological traits. The heritability of tactic is given transformed to the liability scale for comparison purposes (see methods). Power is assessed by dividing the number of simulations that gave a significant additive variance estimate over a total number of 20 simulations..............82
Table 3-4. Genetic correlations with their standard errors between life-history tactic (anadromy/residency) and body size (FL) and the six morphological traits for the 3 pedigrees used (as in Table 2). Life-history tactic was coded as 0 for anadromous and 1 for resident. P-values are those obtained from likelihood ratio tests to assess the significance of the covariance genetic component. ......................................................83
Table 3-5 Mean heritability (h2) and genetic correlation (rG) estimates as well as mean standard error (SE) obtained over 20 simulations using a full-sib pedigree along with simulated phenotype data from a half-sib pedigree. Phenotype data were simulated according to h2 and rG similar to those estimated in the present study.........................84
Chapitre 4. The impact of fishing-induced mortality on the evolution of alternative
life-history tactics in brook charr
Table 4-1. Model parameters and their values...................................................................105
Liste des figures Chapitre 1. Introduction générale
Figure 1-1. Illustration schématique du modèle de trait seuil. Le graphique du haut représente le cas simple à un locus, où la valeur du trait sous-jacent est déterminée par l’action additive de 2 allèles à un locus. Les barres indiquent la valeur de ce trait sous-jacent (et non sa fréquence dans la population). Les génotypes AA et Aa ont des valeurs qui excèdent le seuil, et se traduisent par la forme 2. Le génotype aa est quant à lui associé à la forme 1. Le graphique du bas représente le modèle polygénique, où plusieurs locus agissent de façon additive pour déterminer la valeur du trait sous-jacent, qui est distribué normalement dans la population. Les parties hachurées et non-hachurées représentent la fréquence dans la population. Les individus qui ont des valeurs de traits sous-jacents qui dépassent le seuil adoptent la forme 2, alors que les autres adoptent la forme 1. D’après Roff 1996. ..............................................................4
Figure 1-2. Interprétation de la norme de réaction pour la taille et l’âge à maturité. Les individus à croissance plus rapide (trajectoire de croissance avec une pente plus élevée) deviennent matures à un âge plus jeune. Plus la croissance est lente (trajectoires de croissance où les pentes sont moins élevées), plus l’âge à maturité augmente. La norme de réaction est définie par la ligne qui joint la taille à maturité à chaque âge. L’interprétation de cette courbe en tant que norme de réaction est basée sur la supposition que les différences dans les taux de croissance sont en grande partie dues aux différences dans les conditions environnementales. D’après Heino et al. 2002. .............................................................................................................................16
Figure 1-3. Distribution de taille à l’âge de 1 an pour les individus résidents (barres noires) et migrants (barres blanches). Parmi les résidents se retrouvent les individus qui migreront à 2 ans et qui sont les plus petits de leur cohorte à l’âge de 1 an (ligne noire). D’après Thériault et Dodson (2003). ................................................................21
Chapitre 2. Mating system and individual reproductive sucess of sympatric
anadromous and resident brook charr, Salvelinus fontinalis, under natural conditions
Figure 2-1. Location of study area, sampling traps, and electrofishing sites. .....................53 Figure 2-2. Rate of juvenile allocation obtained with PASOS as a function of cumulative
number of loci, and using a maximum offset tolerance set to zero (MOT 0 – see text). A plateau is starting to appear at the end of the cumulative curve and the 20% rate attained at SfoC88 was used as a first estimate of the proportion of sampled spawners.......................................................................................................................................54
Figure 2-3. (a) Number of fish caught in the upstream migration trap in 2000 (black) and 2001 (grey) and (b) corresponding length distribution.................................................55
Figure 2-4. Reproductive success (number of offspring assigned per individual) (a) and number of mates (b), as a function of body size for female spawners. Open circles are resident life-history fish, while closed circles are anadromous life-history fish. .........56
xiii Figure 2-5. Reproductive success (number of offspring assigned per individual) as a
function of body size for male spawners. Open circles are resident life-history fish, while closed circles anadromous life-history fish. .......................................................57
Chapitre 3. Heritability of life-history tactics and genetic correlation with body size in
a natural population of brook charr (Salvelinus fontinalis)
Figure 3-1. Distribution of family size for Pedigree 1 and 3 (black bars) and Pedigree 2 (white bars). These families are those that were kept in the final analyses, i.e. the ones that were deemed significant by the randomization procedures (see methods). ..........85
Chapitre 4. The impact of fishing-induced mortality on the evolution of alternative
life-history tactics in brook charr
Figure 4-1. Schematic of the life-cycle of brook charr showing the sequence of events in the eco-genetic model. ......................................................................................................107
Figure 4-2. Empirically derived functions used in the model. (a) Probabilistic migration reaction norms estimated for Morin creek (reproduced from Thériault and Dodson 2003); (b) Probabilistic maturation reaction norms estimated for anadromous and resident individuals showing the 50% (midpoint) 1% and 99% probability curves; (c) Relationship between fecundity and body size for anadromous (open circles) and resident individuals (closed circles) reproduced from Lenormand (2003); (d) Stock-recruitment relationship derived from Elliott (1993); (e) Harvest probabilities curves according to different maximal harvest probabilities increasing from 0.05 to 1 in increments of 0.05.......................................................................................................108
Figure 4-3. Model results after 100 years of fishing-induced mortality, according to different maximum harvest probabilities. The top two panels show the age-specific migration reaction norms (line thickness increases with increasing maximum harvest probabilities between 0 and 1). Results are averaged for 30 independent model runs.....................................................................................................................................109
Figure 4-4. Model results after 100 years of fishing-induced mortality, according to varying survival conditions in freshwater (poor, normal and good, see methods). The maximum harvest probability was 0.5. Results are averaged for 30 independent model runs..............................................................................................................................110
Chapitre 5. Conclusion générale
Figure 5-1. Représentation schématique de l’anadromie et la résidence en tant que trait seuil. (a) Un individu adopte la tactique qui lui procure le plus haut fitness selon la valeur du trait sous-jacent. L’intersection des fonctions de fitness représente le seuil, et la position de celui-ci changera si les fonctions de fitness se déplacent, influençant directement les proportions de chaque morphe dans la population (zone noire pour Anadrome, grise pour Résidents). Les cas où seule la tactique R est exprimée sont présentés en b, c et d. (b) Les conditions présentes (zone grise) font en sorte que le seuil est toujours dépassé. Les différents seuils représentent la variabilité génétique
xiv
individuelle. (c) Les seuils sont déplacés sous l’effet de la sélection. (d) Assimilation génétique, menant à la canalisation du trait, où seule la résidence peut être exprimée, peu importe la valeur du trait sous-jacent. ..................................................................124
Chapitre 1. Introduction générale « Évolution inéluctable qui, parallèlement à ce grand
courant partant du singe pour aboutir à l'homme, part de l'homme pour aboutir à l'imbécile.»
Boris Vian
1.1 Diversité et plasticité : les phénotypes alternatifs Le monde vivant nous fascine de par sa diversité. Que ce soit à une échelle temporelle ou
spatiale, au niveau phénotypique ou génétique, entre les espèces ou à l’intérieur même
d’une population, elle est partout. Des dizaines d’espèces présentes dans un mètre carré de
la forêt amazonienne aux différentes grosseurs de becs chez les pinsons des îles Galápagos
en passant par la variété de couleurs de cichlidés du Lac Victoria, les biologistes n’ont
cessé de s’intéresser à la genèse et la dynamique de la diversité du vivant. Outre une
fascination pour le monde vivant en tant que tel, cet intérêt soutenu pour la biodiversité
découle de Darwin lui-même. Selon la théorie darwinienne de l’évolution, la diversité est à
la base de la sélection naturelle, elle est une condition préalable à son action. La sélection
serait définie en ce sens comme la survie et la reproduction différentielles (i.e. fitness
différent) liées à des différences phénotypiques entre des individus appartenant à une même
entité reproductrice (West-Eberhard 2003). Lorsque ces différences phénotypiques sont
causées par des différences génétiques héritables (additives), transmises de génération en
génération, une réponse à la sélection est attendue, et nous parlons alors d’évolution. Il
n’est donc pas étonnant de voir la biodiversité en premier plan de l’écologie évolutive et
des stratégies de conservation : en la conservant, on s’assure du potentiel évolutif des
espèces, de leur capacité d’adaptation à des changements environnementaux, qu’ils soient
causés ou accélérés de façon anthropique ou naturelle.
1.1.1 Phénotypes alternatifs comme exemple extrême de plasticité Un cas frappant de diversité est la présence de plusieurs formes, ou morphes, au sein d’une
même population, d’où l’appellation « polymorphisme » (ou « polyphenisme », dépendant
des auteurs). Les exemples sont nombreux et couvrent toutes les facettes de l’histoire de
vie : induction ou non de défenses contre un prédateur, utilisation de niches trophiques
différentes, morphologies variées, couleurs différentes, tactiques reproductrices
2 alternatives, migration saisonnière ou résidence, etc. (Roff 1996). Devant tant de diversité,
nous ne pouvons nous empêcher de nous questionner : pourquoi et comment ces formes se
sont-elle créées, quels sont les mécanismes qui les sous-tendent et les maintiennent, que
représentent-elles dans le cours de l’évolution? Ces différents phénotypes sont-ils le
résultat de différents génotypes, le résultat d’influences environnementales différentes, ou
bien une combinaison des deux?
Quelques exemples de polymorphismes génétiques ont été démontrés, où les formes
alternatives seraient caractérisées par un système impliquant un seul locus et deux ou
plusieurs allèles (isopodes, Shuster et Wade 1991; poecilidés, Zimmerer et Kallman 1989;
oiseaux, Lank et al. 1995). Par contre, la plupart des cas de phénotypes alternatifs seraient
en fait le résultat d’une plasticité phénotypique plutôt que d’un déterminisme purement
génétique. La plasticité est cette capacité qu’a un organisme à répondre à des changements
dans l’environnement en altérant sa forme, son état, ses mouvements ou son taux d’activité
(West-Eberhard 2003). Dans le cas des phénotypes alternatifs, cette plasticité serait
adaptative, c’est-à-dire que le phénotype exprimé dans un environnement donné permet de
maximiser le fitness de l’individu dans les conditions où il est exprimé, mais pas dans les
autres situations. Les notions d’hétérogénéité de l’environnement et de compromis sont
centrales à l’origine de la plasticité phénotypique (Moran 1992; Hazel et al. 2004). Des
environnements variables spatialement et/ou temporellement qui provoquent un
renversement des fitness relatifs des phénotypes alternatifs est un prérequis pour l’évolution
de la plasticité adaptative (Moran 1992). Un polymorphisme est favorisé lorsque qu’un seul
phénotype n’est pas « le meilleur » dans tous les environnements. Des compromis sont
donc présents, et ce phénomène est bien illustré dans les cas des polymorphismes de
défense contre les prédateurs : les coûts associés à la production de structures de défenses
(i.e. diminution de croissance) sont contrebalancés par une meilleure survie dans un
environnement où les prédateurs sont présents, mais en absence de prédateurs, les individus
ayant développé ces défenses seraient défavorisés comme ils n’obtiennent plus assez
d’avantages en survie pour pallier leur faible croissance (Roff 1996). L’avantage de la
plasticité réside dans la capacité de produire un meilleur couplage phénotype-
3 environnement à travers plus de conditions différentes qu’il ne serait possible de le faire
avec un seul phénotype dans tous les environnements (DeWitt et al. 1998).
Qui dit plasticité ne dit pas nécessairement influence génétique nulle. Un individu pourrait
avoir une tendance, une « prédisposition » génétique pour l’adoption d’un phénotype plutôt
que de son alternative. Les conditions environnementales viendraient alors ultimement
décider. Un contexte théorique applicable (et appliqué! voir Hazel et al. 1990; Gross 1996;
Roff 1996; Hazel et al. 2004) pour comprendre et étudier les phénotypes alternatifs, dans
lequel l’influence conjointe de l’environnement et de la génétique est prise en compte, est
celui du modèle de traits seuils (threshold trait model of quantitative genetics, aussi
équivalent à la stratégie conditionnelle sensus Gross 1996).
1.1.2 Phénotypes alternatifs comme traits seuils Les traits quantitatifs sont des traits que l’on peut mesurer, qui varient d’une façon
continue, comme la croissance, et qui sont influencés par plusieurs gènes ayant chacun de
petits effets. Les traits qui montrent une variation discontinue seraient aussi dans la plupart
des cas des traits quantitatifs, déterminés par l’effet additif de plusieurs locus (Roff 1994a).
Pour comprendre l’expression et la transmission de ces caractères d’une génération à
l’autre, on fait appel au modèle de traits seuils (Roff 1996). Ce modèle suppose l’existence
d’un caractère continu (ou d’un ensemble de caractères) sous-jacent au trait discontinu, où
un seuil est présent (Falconer et Mackay 1996; Hazel et al. 2004). Lorsque le caractère
sous-jacent est en dessous d’un certain seuil, l’individu développe le phénotype A, tandis
qu’il développe le B si le seuil est dépassé (Figure 1-1). L’appellation stratégie
conditionnelle est souvent employée et fait référence au fait que le phénotype adopté ne soit
pas fixé et ne dépende pas que du génotype : le phénotype est conditionnel à
l’environnement dans lequel l’individu se développe (Gross 1996).
Ce contexte théorique de traits seuils a été appliqué à divers organismes, notamment les
insectes où la littérature abonde (coléoptère, Emlen 1996; cricket Roff et al. 1999), mais
aussi les poissons (saumon, Gross 1996; Hazel et al. 2004; Hutchings et Myers 1994) et les
4 invertébrés (escargot, Ostrowski et al. 2000; limule, Brockmann 2001), de même que les
oiseaux (Berthold 1991). L’influence environnementale sur l’adoption d’un phénotype
alternatif a été mise en évidence de façon convaincante à l’aide d’études en milieu contrôlé.
Roff (1996) dresse d’ailleurs un tableau assez complet où des variations dans les conditions
Figure 1-1. Illustration schématique du modèle de trait seuil. Le graphique du haut représente le cas simple à un locus, où la valeur du trait sous-jacent est déterminée par l’action additive de 2 allèles à un locus. Les barres indiquent la valeur de ce trait sous-jacent (et non sa fréquence dans la population). Les génotypes AA et Aa ont des valeurs qui excèdent le seuil, et se traduisent par la forme 2. Le génotype aa est quant à lui associé à la forme 1. Le graphique du bas représente le modèle polygénique, où plusieurs locus agissent de façon additive pour déterminer la valeur du trait sous-jacent, qui est distribué normalement dans la population. Les parties hachurées et non-hachurées représentent la fréquence dans la population. Les individus qui ont des valeurs de traits sous-jacents qui dépassent le seuil adoptent la forme 2, alors que les autres adoptent la forme 1. D’après Roff 1996.
biotiques ou abiotiques telles la densité des conspécifiques, la présence de prédateurs, les
substances sécrétées, la qualité et quantité de nourriture, la photopériode ou la température
sont autant de facteurs qui peuvent induire l’adoption d’une forme ou l’autre. L’influence
génétique a aussi été démontrée à deux niveaux. Tout d’abord, une héritabilité
5 significative (i.e. la variance phénotypique observée dépend de la variance génétique
additive héritée des parents) a été trouvée à plusieurs reprises pour des traits sous-jacents,
soit 1) en estimant directement l’héritabilité des traits sous-jacents spécifiques soupçonnés,
par exemple la croissance (Garant et al. 2003) ou 2) en estimant l’héritabilité du trait
binomial lui-même, et en normalisant cet estimé pour le ramener sur l’échelle continue
sous-jacente (Ostrowski et al. 2000, voir aussi revue par Roff 1996, pour la transformation
discontinue-continue, se référer à Roff et al. 1997). Deuxièmement, il est aussi postulé que
l’influence génétique résiderait dans la valeur seuil, celle-ci étant génétiquement influencée
et héritable (Hazel et al. 1990; Hutchings et Myers 1994; Hazel et al. 2004; Aubin-Horth et
al. 2005). Cette variation génétique dans la valeur seuil a été mise en évidence chez les
insectes (Emlen 1996; Moczek et Nijhout 2003; Tomkins et Brown 2004).
La question du maintien des tactiques alternatives au sein d’une même population revient
souvent. Contrairement au polymorphisme déterminé génétiquement, les fitness des
tactiques alternatives déterminées par un mode de régulation conditionnel n’ont pas besoin
d’être égaux pour que les deux options se maintiennent dans la population (Gross 1996).
L’élément essentiel est qu’un seul phénotype ne doit pas être toujours avantagé. La
tactique A peut avoir un meilleur fitness, mais seulement dans certaines circonstances: dans
d’autres circonstances, c’est la tactique B qui fait mieux. Tant et aussi longtemps que les
circonstances qui favorisent B reviennent, et que les coûts associés au changement de
phénotype et à l’évaluation des signaux environnementaux qui induisent l’adoption de ce
phénotype ne sont pas trop élevés, la capacité de produire le phénotype B peut être
maintenue (Moran 1992; West-Eberhard 2003).
1.2 La migration et la résidence chez les salmonidés L’anadromie, soit la migration en eau salée et le retour vers l’eau douce pour la
reproduction, est un phénomène commun chez plusieurs espèces de poissons des latitudes
élevées. Pour qu’un comportement migrateur voit le jour, il faut que les bénéfices encourus
à utiliser un second habitat (augmentation des ressources alimentaires, évitement des
conditions environnementales défavorables, augmentation du succès reproducteur), moins
6 les coûts associés au mouvement vers un autre habitat (coût énergétique, augmentation de
la prédation, coût développemental des adaptations requises), surpassent les bénéfices à
n’utiliser qu’un seul habitat (Gross 1987). Deux hypothèses peuvent expliquer l’évolution
de l’anadromie, toutes deux visant ultimement à l’augmentation du fitness. Tout d’abord,
l’anadromie pourrait avoir évolué en vue de l’exploitation de l’eau douce pour la
reproduction, ce dernier habitat procurant de meilleures conditions de survie pour les
jeunes stades de vie (Dodson 1997). Cette hypothèse s’applique à des espèces
ancestralement marines, tells certains membres de la famille des Clupeidae (aloses),
Gadidae (poulamons, lottes), Percichthydae (bars) et Gasterosteidae (épinoches), où les
gamètes sont dispersés et non confinés dans des nids (sauf épinoches, McDowall 1993).
L’anadromie pourrait par contre aussi être adoptée en vue d’une augmentation de la
croissance des stades adultes. Cette hypothèse repose sur l’augmentation des ressources en
eau salée comparativement à celles en eau douce. Une telle augmentation est généralement
observée dans les régions froides et tempérées (Gross 1987). Pour des poissons tels les
salmonidés, où le stade ancestral pourrait être la résidence en eaux douces (voir discussion
par McDowall 2002 qui stipule que l’ancêtre des salmonidés était déjà diadrome) et où le
succès reproducteur dépend beaucoup de la taille tant pour l’accès aux partenaires que pour
la fécondité, l’augmentation de la taille par l’exploitation de l’eau salée serait l’avantage
principal de l’anadromie (Dodson 1997).
Plusieurs espèces de salmonidés comportent à la fois des individus anadromes et des
individus non-anadromes (résidents), cer derniers passant leur vie entière en eau douce
(l’omble de fontaine Salvelinus fontinalis, Castonguay et Fitzgerald 1982, Doyon et al.
1991; l’omble chevalier Salvelinus alpinus, Nordeng 1983, Svenning et al. 1992,
Kristoffersen et al. 1994, Rikardsen et al. 1997; la truite arc-en-ciel Oncorhynchus mykiss,
Pascual et al. 2001; la truite brune Salmo trutta; le saumon sockeye Oncorhynchus nerka,
Jonsson, 1985 et le grand corégone Coregonus clupeaformis, Lambert et Dodson 1990).
Dans certains cas, ces deux formes constituent deux populations distinctes, avec peu
d’échanges génétiques (Foote et al. 1989; Wood et Foote 1990; Wood et Foote 1996).
Dans d’autres, les deux phénotypes font partie de la même population (Hindar et al. 1991;
7 Northcote 1992). Dans ce dernier cas, un mode conditionnel de régulation est alors
l’hypothèse avancée pour expliquer la coexistence de ces deux formes. D’ailleurs, les
phénotypes alternatifs liés à la reproduction (précocité sexuelle versus anadromie) ont déjà
été modélisés dans le passé comme traits seuils (Myers et Hutchings 1986; Hazel et al.
1990; Hutchings et Myers 1994).
La plasticité dans l’adoption de l’anadromie et la résidence a été mise en évidence par
plusieurs études expérimentales qui démontrent que le phénotype adopté change selon les
conditions environnementales, notamment celles qui affectent le taux de croissance
(Saunders et al. 1982; Nordeng 1983; Rowe et Thorpe 1990; Olsson et al. 2006). Aussi,
des différences ont été trouvées dans la quantité de réserves lipidiques, l’efficacité de
croissance, et les coûts métaboliques entre les futurs anadromes et les futurs résidents
(Rowe et al. 1991; Forseth et al. 1999; Bohlin et al. 1994; Morinville et Rasmussen 2003).
Les traits sous-jacents à l’adoption de l’anadromie et la résidence semblent donc être reliés
au budget énergétique.
L’influence génétique dans l’adoption d’une ou l’autre des tactiques alternatives a aussi été
mise de l’avant expérimentalement, où la progéniture des croisements anadrome-anadrome
comportait plus d’individus anadromes, alors que la progéniture des croisements résident-
résident comportait plus de résidents (Nordeng 1983; Glebe et Saunders 1986). Une
héritabilité de la maturité sexuelle précoce, associée à la résidence chez les saumons
atlantique (Salmo salar), chinook (Oncorhynchus tshawytscha) et coho (Oncorynchus
kisutch), a de plus été trouvée à plusieurs reprises, et revêt une importance pratique en
aquaculture (Silverstein et Hershberger 1992; Heath et al. 1994; Wild et al. 1994;
Mousseau et al. 1998). Des traits potentiels sous-jacents à l’adoption d’une tactique
alternative, telle la croissance, ont aussi des héritabilités significatives, et ce, autant en
laboratoire que sous conditions naturelles (Garant et al. 2003; Wilson et al. 2003a; Thrower
et al. 2004). Ces résultats suggèrent une réponse possible des tactiques alternatives à une
pression de sélection. Par contre, aucune étude connue à ce jour n’a estimé l’héritabilité de
8 l’anadromie et la résidence spécifiquement, par opposition à la maturation sexuelle précoce
qui s’exprime chez les mâles seulement et ce, ni en laboratoire, ni en milieu naturel.
1.3 Héritabilité : son estimation en milieu naturel
1.3.1 Milieu contrôlé versus milieu naturel La notion d’héritabilité revient constamment lorsque l’on tente de comprendre l’évolution
des phénotypes alternatifs (et de tout autre trait en fait…). L’héritabilité procure une mesure
du degré avec lequel le phénotype des jeunes est déterminé par le génotype de leurs parents.
Un trait doit être héritable si l’on veut que la sélection change sa valeur de génération en
génération et cela est illustré dans l’équation de l’éleveur, où la réponse à la sélection, R,
est déterminée par le produit de l’héritabilité, h2, et du différentiel de sélection, S (R = h2S).
Plus la variation phénotypique observée, VP, est influencée par la variance génétique
additive, VA (h2 = VA/VP), plus on s’attend à ce que le trait réponde rapidement à la
sélection. L’héritabilité est donc centrale en écologie évolutive : elle aide à comprendre la
nature des variations phénotypiques, et nous éclaire sur la réponse possible d’un phénotype
face à une pression de sélection.
La très grande majorité des études de génétique quantitative et d’héritabilité ont été
effectuées en laboratoire et en milieux contrôlés, tout d’abord parce que les techniques
d’estimation de paramètres quantitatifs ont été développées pour des programmes de
croisements et aussi parce que le milieu naturel est en plusieurs points « incontrôlable », ce
qui pose des contraintes pratiques et théoriques. Il est alors légitime de se demander si les
estimés en laboratoire peuvent être exportés en milieu naturel si on veut prédire (ou
simplement comprendre) l’évolution des traits quantitatifs dans la « vraie vie ». La
tendance générale veut que les estimés en laboratoire surestiment l’héritabilité. Ceci est dû
au fait que la variance environnementale est grandement sous-estimée en laboratoire, où les
conditions sont contrôlées. La variance environnementale est incluse dans la variance
phénotypique totale, donc si elle augmente en nature relativement à la variance additive, VP
augmente et h2 diminuera (h2=VA/VP) (Weigensberg et Roff 1996). Par contre, cette
9 généralisation s’est vue un peu démentie par l’étude de Weigensberg et Roff (1999) qui
démontre en comparant 165 estimés d’héritabilité sur le terrain avec 189 estimés en
laboratoire que ces derniers sont de bons estimés, du moins de la magnitude, des premiers.
Les héritabilités estimées sur le terrain ne seraient donc pas grandement réduites par une
inflation relative de la variance environnementale (Roff 1997). Néanmoins, il reste que
l’héritabilité dépende des conditions particulières d’une population en particulier (Falconer
et Mackay 1996) : son estimation n’est valable que pour la population où elle a été
mesurée, et au moment où elle l’a été. Il va donc sans dire que les extrapolations du
laboratoire au milieu naturel sont à prendre avec précaution, tout autant qu’une
extrapolation d’une population à l’autre, et voir, d’une année à l’autre.
1.3.2 Les marqueurs moléculaires comme outils L’estimation de l’héritabilité repose sur le degré de ressemblance entre individus
apparentés, et c’est là que se trouve le défi à relever en milieu naturel. À moins de pouvoir
déterminer la paternité et la maternité par observations directes, comme c’est le cas chez les
espèces apportant des soins parentaux, les liens de parenté ne sont pas faciles à retracer en
nature. Même dans les cas d’observations directes sur le terrain, souvent, c’est seulement
l’identité d’un des sexes qui peut être déterminée avec certitude. L’avènement de
marqueurs moléculaires performants, de pair avec le raffinement des méthodes
mathématiques au cours de la dernière décennie, a ouvert les portes à une multitude de
possibilités. Les microsatellites, de par leur grande accessibilité et variabilité, sont les
marqueurs moléculaires de choix pour les études d’apparentement (Estoup et Angers 1998).
Il est maintenant possible grâce à ces marqueurs de reconstruire les pedigrees en milieu
naturel, et, en plus de rendre l’estimation de l’héritabilité possible, la structure sociale et les
patrons de reproduction peuvent être définis, le succès reproducteur quantifié, et les
programmes de croisements améliorés (Bernatchez et Duchesne 2000; Neff et al. 2000;
Olsen et al. 2001; van de Castelle et al. 2001).
Il existe plusieurs approches pour estimer des paramètres de génétique quantitative à l’aide
de marqueurs moléculaires (revu par Garant et Kruuk 2005). Mise à part une approche
10 développée par Ritland (1996) qui s’est malheureusement avérée peu fiable en milieu
naturel (Thomas et al. 2002; Coltman 2005), la reconstruction d’un pedigree, d’une façon
ou d’une autre, est essentielle. Une première approche est utilisée lorsque les parents ne
sont pas connus et consiste à reconstruire des groupes d’individus apparentés (revu par
Blouin 2003). Les individus sont donc regroupés ensemble selon la prémisse qu’ils sont
plein-frères ou non-apparentés. Certains algorithmes sont maintenant aussi utilisés pour
reconstruire des groupes d’individus apparentés plus complexe, en incluant plein-frères,
demi-frères ou cousins (Herbinger et al. 2006). Ces méthodes de reconstruction sont en
général basées sur des chaînes Monte Carlo de Markov (MCMC), qui identifient les
groupes d’individus les plus probables selon leur génotype et la fréquence allèlique dans la
population (Thomas et Hill 2000). Différents algorithmes sont disponibles, chacun
performant mieux dans des conditions particulières (Butler et al. 2004). Une fois les
individus regroupés ensemble, ce jeu de données peut être utilisé pour l’estimation de
paramètres de génétique quantitative, soit en utilisant des analyses de variance, plus
traditionnelles, soit à l’aide de « modèles animal », la dernière tendance dans le domaine de
la génétique quantitative en milieu naturel (voir plus bas). Les études ayant comparé les
estimés quantitatifs obtenus avec des pedigrees reconstruits par groupes d’individus
apparentés et ceux obtenus avec des pedigree plus complets, où les parents sont identifiés,
trouvent en général une bonne correspondance (Thomas et al. 2002; Wilson et al. 2003b).
Par contre, Thomas et al. (2002) ont mis en évidence qu’un bon nombre d’individus
apparentés ainsi que beaucoup d’information provenant des marqueurs moléculaires sont
nécessaires pour que ces méthodes soient applicables. Dans la même veine, Wilson et al.
(2003b) ont trouvés une sous-estimation des paramètres quantitatifs en employant des
pedigree reconstruits à partir de groupes d’individus apparentés. Ces auteurs blâment la
structure complexe du vrai pedigree pour expliquer cette sous-estimation (beaucoup de
demi-frères ne seraient pas pris en compte dans le processus de reconstruction).
La façon la plus fiable pour estimer des paramètres quantitatifs est toujours d’avoir le plus
d’information possible sur les liens de parenté. En milieu naturel, quand aucune
information comportementale n’est disponible, c’est l’assignation parentale qui est la
11 méthode la plus performante. Cette approche implique évidemment un échantillonnage des
parents et de leurs jeunes, pour ensuite utiliser l’information moléculaire et retracer les
paternités et maternités, en se basant sur les lois héréditaires de Mendel. Ces approches
utilisent soient des méthodes d’exclusion, ou de maximum de vraisemblance, ou les deux
(revu par Jones et Ardren 2003). Le pedigree obtenu peut ensuite être utilisé pour estimer
les paramètres quantitatifs qui nous intéressent, et c’est alors le modèle animal qui prime.
Le modèle animal n’est pas une approche nouvelle en génétique quantitative : c’est une
méthode statistique utilisée depuis les années 50 dans le domaine de l’élevage (revu dans
Kruuk 2004). Elle a fait son apparition dans les années 90 en milieu naturel (Konigsberg et
Cheverud 1992) et a vraiment pris son essor dans les années 2000, avec plus d’une dizaine
d’études l’employant sur des populations naturelles de moutons et moufflons (Réale et al.
1999; Coltman et al. 2001; Milner et al. 2000; Coltman et al. 2003), d’écureuils (McAdam
et al. 2002; Réale et al. 2003), de mésanges (MacColl et Hatchwell 2003; Charmantier et al.
2004; Garant et al. 2004; McCleery et al. 2004; Garant et al. 2005b), de moineaux
domestiques (Jensen et al. 2003), de saumon Atlantique (Garant et al. 2003) et de cerf
(Kruuk et al. 2000). Le modèle animal est en fait un modèle linéaire mixte, permettant
l’incorporation de plusieurs effets fixes, qui influencent la moyenne d’un trait, et aléatoires,
lesquels influencent la variance d’un trait. Ce modèle permet une utilisation plus efficace
des données provenant de pedigrees complexes, multi-générationnels, parce qu’il exploite
toute la matrice de ressemblance entre les individus, et non seulement l’information entre
les paires, ou entre les groupes plein-frères (Garant et Kruuk 2005). De plus, ce modèle
peut s’appliquer à des jeux de données non-balancés, où des données sont manquantes, ce
qui est très fréquent en milieu naturel. Il peut aussi être utilisé en présence de reproduction
non-aléatoire, de sélection et de consanguinité. Finalement, la grande flexibilité
d’incorporation de divers effets fixes et aléatoires supplémentaires, tel l’effet maternel ou
l’environnement commun, en font un outil très apprécié des écologistes évolutifs sur le
terrain (Kruuk 2004).
12 1.3.3 Les erreurs de génotypage et leurs effets Toutes les méthodes permettant d’élucider les liens de parenté entre individus mentionnées
plus haut, de la régression de Ritland à l’assignation parentale, font appel au génotypage à
de nombreux locus microsatellites. Or, les données de génotypage sont sujettes à des
erreurs qui peuvent avoir des conséquences lors de la reconstruction de groupes apparentés
et de l’assignation parentale. L’amplification de fragments non-spécifiques (stutters), la
dominance d’amplification des petits allèles sur les gros (large allele dropout), les allèles
nuls, la mutation et les erreurs humaines lors du génotypage sont autant de facteurs à
considérer en entreprenant ce genre d’analyse. Wang (2004) démontre que si les erreurs
sont ignorées dans le processus de reconstruction d’individus apparentés, les individus
formant de vraies familles se trouvent séparés. Il est donc suggèré de toujours inclure un
taux d’erreur lors d’analyses d’apparentement pour ainsi augmenter le pourcentage de
familles adéquatement reconstruites (Wang 2004). Butler et al. (2004) ont analysé la
performance de plusieurs algorithmes utilisés pour les analyses d’apparentement et
concluent qu’aucun n’est très robuste aux erreurs de génotypage, aux erreurs humaines et
aux mutations. Par contre, en présence d’allèles nuls, ces algorithmes avaient tendance à
encore regrouper les individus dans les bons groupes limitant donc les conséquences de ce
type d’erreur (Butler et al. 2004). Il semblerait qu’en utilisant l’information associée aux
nombreux autres locus, les génotypes qui sont affectés par un allèle nul sont encore
compatibles avec leurs vrais frères et suivent toujours les lois de transmission de Mendel
(Butler et al. 2004). En ce qui concerne les analyses d’assignation parentale, le danger face
aux erreurs est d’exclure de vrais parents, comme le génotype d’un jeune ne correspond
plus à celui de son vrai parent (Dakin et Avise 2004). En incluant l’erreur dans les
analyses, le succès d’assignation se retrouve donc augmenté, comme certaines
dissimilitudes sont alors acceptées (Marshall et al. 1998). Par contre, un compromis doit
être établi entre succès d’assignation et confiance dans ces assignations, car en étant plus
tolérant sur la possibilité d’erreurs, on permet aussi à de faux parents de se voir attribuer la
paternité/maternité. Les logiciels d’analyses d’apparentement et d’assignation parentale
développés à ce jour permettent pour la plupart d’inclure des erreurs de génotypage
potentielles et ainsi d’être plus près de la réalité. Malheureusement, cela ne règle pas tout,
et la réalité nous rappelle que d’autres sources d’inquiétude existent lors d’analyses
13 parentales, notamment le déséquilibre de liaison entre locus, créant une redondance
d’information, de même que la ressemblance entre géniteurs. Tous deux viennent diminuer
le potentiel discriminatoire de notre jeu de locus et ainsi notre confiance dans les
assignations.
Il n’y a pas encore de méthode simple et éprouvée permettant d’inclure le facteur erreur qui
vient avec les pedigrees reconstruits dans les analyses de génétique quantitative
subséquentes (par exemple dans le modèle animal). Pour l’instant, la façon d’évaluer
l’influence de cette erreur est par simulations, ou encore en comparant les estimés
quantitatifs obtenus avec des pedigrees ayant différents taux de confiance dans les
assignations parentales. En simulant la présence d’allèles nuls sur 10% des locus lors d’une
analyse d’apparentement, Thomas et Hill (2000) ont trouvé une héritabilité plus faible que
lorsque aucune erreur n’était tolérée. Cette diminution dans l’héritabilité a aussi été
observée lors d’assignation parentale en utilisant un pedigree où la confiance dans les
paternités était de 80% comparativement à celui où la confiance était de 95% (Milner et al.
2000).
1.4 Mortalité sélective : effet de la pêche
1.4.1 Réponse plastique vs. évolutive Face à des changements dans le régime de mortalité, le paysage adaptatif change, menant à
différentes solutions optimales (Gasser et al. 2000). Que la pêche représente une force de
sélection capable de changer les traits d’histoire de vie est une idée qui n’est plus remise en
doute (Stokes et Law 2000; Conover 2000). L’évolution par sélection naturelle implique
des pressions qui font en sorte que les individus avec des traits héritables « non-avantagés »
ont une probabilité de survie réduite, alors que ceux avec des caractéristiques plus
« favorables » survivent et se reproduisent mieux, contribuant ainsi davantage à la
prochaine génération (Hutchings 2004b). La pêche peut produire ce genre de pression de
sélection, comme elle cible souvent certains individus d’une population plutôt que d’autres,
par exemple les plus gros et les plus âgés. Par contre, face à un changement phénotypique
14 causé par des pressions naturelles ou anthropiques, il n’est pas toujours clair quelle est la
nature de ce changement : est-ce un changement évolutif (i. e. génétique) ou est-ce une
manifestation de plasticité phénotypique? Comme la pêche influence aussi les conditions
environnantes dans lesquelles les stocks se développent, ces changements
environnementaux sont souvent pointés comme les responsables de la réponse
phénotypique, plutôt que des changements dans la structure génétique de la population.
L’exemple le plus cité et démontré est une diminution dans l’âge à maturité (Law 2000).
Une explication intuitive est que la pêche réduit grandement la biomasse du stock exploité,
diminuant la compétition intraspécifique, laissant plus de nourriture pour les survivants, qui
grandissent alors plus vite. Une croissance rapide est souvent associée à une maturation
rapide, à un plus jeune âge (Stearns 2002). Cette diminution dans l’âge à maturité pourrait
donc être une réponse purement plastique face aux conditions environnementales
changeantes qui augmentent les taux de croissance. Par contre, ce type de réponse
compensatoire n’explique pas tout, et de plus en plus d’évidences laissent croire qu’un
changement génétique est aussi en cause dans bien des pêcheries (Rijnsdorp 1993; Conover
et Munch 2002; Barot et al. 2004; Olsen et al. 2004). Comme pour beaucoup d’espèces, la
pêche chez les salmonidés capture souvent les individus de plus grandes tailles, et agit sur
des traits tels que la croissance et la taille à maturité en plus de l’âge à maturité (Ricker
1995; Haugen et Vollestad 2001). De par sa variation temporelle, la pêche peut aussi
sélectionner les individus basé sur leur moment de migration (run timing, Quinn et al.
2007). La croissance et le moment de migration sont des traits héritables chez les
salmonidés (Garant et al. 2003; Quinn et al. 2007), ce qui fait de la mortalité induite par la
pêche une force sélective capable de créer une réponse évolutive, c’est-à-dire d’occasionner
des changements génétiques.
Que des changements évolutifs aient lieu ne représente pas en soi un problème : l’évolution
par sélection est d’ailleurs vue comme une adaptation favorable à de nouvelles conditions.
Par contre, les mortalités induites par la pêche peuvent représenter des pressions
supplémentaires substantielles sur les populations naturelles, et les conséquences évolutives
à plus ou moins longs termes sont peu connues. Les populations naturelles peuvent-elles
15 s’adapter si vite à de telles perturbations, avant que la mortalité n’excède le potentiel
d’accroissement démographique (Hendry et Kinnison 1999)? De plus, il ne suffit pas de
fermer la pêche pour que la population exploitée retrouve son état original. Des
changements évolutifs peuvent prendre beaucoup plus de temps à renverser que des
réponses plastiques (Law 2000). Les individus qui survivent à une pression de pêche sont
probablement ceux dont les génotypes confèrent un meilleur fitness sous ces conditions de
sélection anthropiques, par exemple les poissons avec de faibles croissances et de bas âges
à maturité. Lorsque cette sélection artificielle n’est plus, les individus restants ne sont plus
optimaux face aux forces de sélection naturelle, ce qui mène à des temps de rétablissement
des stocks très longs (Conover 2000). De plus, un arrêt de la pêche ne produit pas
nécessairement une intensité de sélection égale dans la direction opposée. Rowell (1993) a
démontré qu’en absence de pression de pêche, l’âge à maturité avait très peu d’effet sur la
production totale d’œufs sur toute la durée de vie : le plus grand nombre d’œufs chez les
individus plus âgés compensait pour le fait qu’ils aient retardé la maturation. Par contre, la
pêche favorise un âge à maturité plus faible et peut induire un changement génétique dans
ce trait. Si on ferme la pêche en espérant renverser le processus de sélection, il est peu
probable que la sélection favorisant un âge à maturité élevé soit très forte. Cet exemple
démontre bien l’asymétrie potentielle entre les pressions anthropiques et naturelles.
1.4.2 La norme de réaction probabiliste Récemment, plusieurs études ont adopté une nouvelle méthode pour départager les
réponses plastiques des réponses génétiques dans l’âge et la taille à maturité face à la pêche
(Olsen et al. 2004, 2005; Barot et al. 2004, 2005). L’âge et la taille à maturité sont deux
trait clés de l’histoire de vie souvent étudiés comme covariables, et la relation entre la taille
à maturité et l’âge est habituellement présentée graphiquement comme une norme de
réaction (Figure 1-2). Strictement parlant, une norme de réaction illustre, pour un
génotype, le phénotype adopté sous différentes conditions environnementales (Stearns
1992). La relation entre la taille et l’âge à maturité n’implique pas directement de variable
environnementale, ce n’est donc pas une norme de réaction dans le sens strict du terme. Par
contre, l’emploi du terme « norme de réaction » est justifié car on présume que ce sont des
16 variations dans les taux de croissance, fortement dépendant des conditions
environnementales, qui influencent l’âge et la taille à maturité (Stearns et Koella 1986).
Figure 1-2. Interprétation de la norme de réaction pour la taille et l’âge à maturité. Les individus à croissance plus rapide (trajectoires de croissance avec une pente plus élevée) deviennent matures à un âge plus jeune. Plus la croissance est lente (trajectoires de croissance où les pentes sont moins élevées), plus l’âge à maturité augmente. La norme de réaction est définie par la ligne qui joint la taille à maturité à chaque âge. L’interprétation de cette courbe en tant que norme de réaction est basée sur la supposition que les différences dans les taux de croissance sont en grande partie dues aux différences dans les conditions environnementales. D’après Heino et al. 2002.
Donc, chaque point de la relation taille et âge à maturité correspond à une trajectoire de
croissance caractérisée par un taux de croissance moyen, déterminé par les conditions
environnementales précédentes (Figure 1-2, Barot et al. 2004). En d’autres mots, la norme
de réaction de maturation (MRN – maturation reaction norm) décrit comment les
variations dans la croissance, reflétées dans la taille à l’âge, influencent la maturation (Grift
et al. 2003). Traditionnellement, la MRN implique que la maturation est entièrement
déterminée par l’âge et la taille (TMRN – traditional maturation reaction norm), et qu’elle
a lieu aussitôt qu’une trajectoire de croissance intercepte la norme de réaction. Or, en
nature, il existe une variabilité dans la taille à maturité pour un âge donné, mettant en
évidence la nature stochastique et probabiliste de la maturation. Étant un processus
physiologique complexe, la maturation est influencée par d’autres facteurs tels que la
disponibilité des ressources ou les réserves énergétiques, lesquels, à leur tour, sont
influencées par l’environnement local et l’expérience individuelle (Bernardo 1993; Grift et
al. 2003). Heino et al. (2002) ont donc proposé une nouvelle approche pour prendre en
considération cette variation inexpliquée dans la maturation : la norme de réaction
probabiliste (PMRN – probabilistic maturation reaction norm).
17
La PMRN représente donc la probabilité pour un individu immature qui a survécu et atteint
un certain âge et taille, de devenir mature à cet âge. Elle exprime la tendance à la
maturation comme une probabilité qui est conditionnelle à l’atteinte d’un certain âge et
taille. En ce sens, le processus de maturation en tant que tel est séparé des processus
démographiques (croissance et mortalité) qui déterminent la probabilité d’atteindre un
certain âge et taille : la PMRN est donc indépendante de la croissance et de la mortalité.
C’est cette caractéristique qui la différencie de la TMRN et qui permet à la PMRN d’être
utilisée pour séparer la réponse génétique d’une réponse phénotypique. Un changement
dans la PMRN représenterait donc plus probablement un changement génétique qu’un
changement plastique. En observant des changements dans la PMRN sur des données de
séries temporelles, l’hypothèse d’un changement génétique causé par la pêche a été avancée
pour la morue Atlantique (Gadus morhua, Barot et al. 2004; Olsen et al. 2004, 2005) et la
plie d’Amérique (Hippoglossoides platessoides, Barot et al. 2005). Cette conclusion repose
entre autres sur la présomption que la MRN est un caractère héritable. Des évidences dans
la littérature suggèrent qu’une héritabilité significative est présente pour diverses normes de
réactions (Ostroswki et al. 2000; Brommer et al. 2005; Nussey et al. 2005), et donc qu’une
réponse évolutive est possible.
Le principe de l’approche de la norme de réaction probabiliste peut être appliqué à d’autres
transitions ontogénétiques que la maturation. Une telle transition chez les salmonidés
anadromes, qui a lieu avant la maturation, est la migration d’alimentation en mer. L’âge à la
maturation pourrait être subtilisé par l’âge à la migration, en gardant exactement la même
logique, et l’approche probabiliste utilisée pour le processus de migration. Proposons donc
une PMigRN (probabilistic migration reaction norm). Tout comme pour la taille à
maturité, il existe des variations dans la taille à la migration à un âge donné, parce que
d’autres facteurs, tels la condition ou les taux métaboliques, influencent le processus de
migration (Thorpe 1994). L’approche probabiliste semble donc appropriée et pourrait être
un moyen intéressant d’évaluer si des changements évolutifs ont lieu dans l’âge et la taille à
la migration pour des populations de salmonidés anadromes exposées à la pêche. Il est
18 surprenant de constater que si peu d’études explorant les effets évolutifs de la pêche sur les
salmonidés ne soient disponibles, étant donné leur grande valeur économique, tant
commerciale que récréative (mais voir Ricker 1995; Haugen et Vollestad 2001). Peut-être
est-ce la difficulté d’acquérir les données suffisantes et adéquates pour montrer de tels
changements en milieu naturel qui limite ce genre d’étude.
1.4.3 Une approche de modélisation C’est d’ailleurs en partie pour pallier ce manque de données empiriques qu’une approche
de modélisation est de plus en plus employée pour évaluer les changements induits par la
pêche au sein des populations naturelles. En plus de l’avantage évident à utiliser et créer
des données virtuelles, le pouvoir prédictif des modèles en font d’excellents outils de
gestion. Une approche de modélisation permet d’identifier les pressions de sélection
passées (naturelles ou anthropiques) qui furent responsables d’un changement adaptatif, et
permet aussi de prédire de futurs changements en fonction des pressions de sélection
actuelles (Ernande et al. 2004). Il est par exemple possible d’évaluer, à l’aide de modèle de
dynamique des populations, comment les stratégies de récolte influenceraient l’évolution
d’un trait et par conséquent quelles seraient les stratégies les plus souhaitables pour
maximiser le rendement à long terme (Heino 1998; Ratner et Lande 2001). Des méthodes
ont maintenant vu le jour qui intègrent à la fois des principes de génétique quantitative pour
prédire l’évolution de certains traits héritables et des aspects clés de la dynamique
écologique (par exemple la structure en âge et en taille). Certains incluent explicitement la
plasticité phénotypique en modélisant entre autres les normes de réaction (Ernande et al.
2004; Baskett et al. 2005; Dunlop et al. 2007). Notamment, le modèle récemment
développé par Dunlop et al. (2007) présente plusieurs avancées relativement aux autres
modèles. Tout d’abord, une approche basée sur l’individu (individual-based model, IBM) a
permis d’inclure des effet tels la croissance dépendante de la densité, une caractéristique
importante pour plusieurs populations de poissons, qui influence les prédictions face à des
pressions de sélection. Aussi, contrairement à des modèles plus traditionnels
d’optimisation, où on doit définir les critères de fitness à optimiser (Law 1979), un IBM
laisse le concept de fitness émerger naturellement : des individus avec certains traits seront
plus aptes à survivre, se reproduire, et laisser plus de descendants. Ensuite, la plasticité
19 phénotypique dans l’âge et la taille à maturité fut modélisée au moyen d’une PMRN, ce qui
inclut donc la dépendance de l’âge et la taille à maturité aux conditions environnementales,
de même que la nature probabiliste de la maturation. De plus, les auteurs ont pris en
compte la force sélective engendrée par les soins parentaux, et démontrent ainsi comment
les caractéristiques spécifiques d’une espèce peuvent influencer la réponse évolutive sous
mortalité sélective (Dunlop et al. 2007). L’approche employée par Dunlop et al. (2007)
permet de suivre l’évolution temporelle des traits quantitatifs, et non seulement son résultat
final : on peut ainsi évaluer à quelle vitesse les traits évoluent. En plus de toutes ces
caractéristiques, de telles approches « éco-génétiques » sont alléchantes par le fait qu’en
même temps qu’elles prédisent les changements dans les traits quantitatifs, les
conséquences démographiques sont aussi évaluées (abondance, biomasse).
1.5 Le cas de l’omble de fontaine au Québec, Salvelinus fontinalis L’omble de fontaine est natif du nord-est de l’Amérique du Nord et on le retrouve
communément sous plusieurs formes, en général confiné à l’eau douce en lac (landlocked),
mais aussi résidant en rivière ou encore sous la forme anadrome (Smith et Saunders 1958;
Dutil et Power 1980; Castonguay et Fitzgerald 1982; McCormick et al. 1985; Naiman et al.
1987; Lesueur 1993). L’omble de fontaine aurait colonisé le nord-est de l’Amérique du
Nord du sud au nord, en suivant le retrait des glaciers il y a plus de 10 000 ans. Les
fondateurs des populations nordiques auraient donc été anadromes, leur séjour en eau salée
en dehors de leur rivière d’origine leur permettant de se disperser (Castric et Bernatchez
2003). Ces individus anadromes coexistent maintenant avec des individus résidents dans
plusieurs rivières s’écoulant dans le golfe et le fleuve Saint-Laurent.
Le CIRSA (Centre Interuniversitaire de Recherche sur le Saumon Atlantique) a entrepris il
y a près de 10 ans une étude exhaustive du cycle de vie de l’omble de fontaine de la rivière
Sainte-Marguerite, au Saguenay, où des individus anadromes et résidents vivent en
sympatrie. Malgré la popularité générale de l’omble de fontaine au Québec (pêche
récréative et aquaculture), peu est connu sur son cycle de vie anadrome, de même que sur
20 les différences génétiques et comportementales entre ces formes. Au cours des 10 dernières
années, une multitude de données ont été récoltées et plusieurs hypothèses ont été testées.
Les patrons de migration sont maintenant bien connus (Lenormand et al. 2004). Les jeunes
ombles migrants quittent la rivière pour la première fois en grande majorité à l’âge de 1 ou
2 ans entre la mi-mai et la mi-juin. Après 1, 2 ou 3 saisons en eau salée, avec plusieurs
retours fréquents vers l’eau douce, les ombles remontent en rivière pour une fraie
automnale en octobre. Pendant ce temps, les ombles résidents, eux, n’ont pas bougé.
L’âge médian à la première reproduction est de 3 ans, pour les deux formes, et les multi-
frayeurs sont fréquents. Des croisements en laboratoire et des observations sur le terrain
ont également suggèré que la reproduction est possible entre les formes (obs. personnelles).
Une étude précédente a étudié l’importance de la taille atteinte avant la migration sur la
décision de migrer dans le cadre d’une stratégie conditionnelle (Thériault et Dodson 2003).
Les résultats obtenus ont mis en évidence deux moments de décision : le premier, à un an,
montre que la taille est légèrement plus grande pour les individus qui entreprendront la
migration à cet âge. Cette différence s’explique par le fait qu’à 1 an, les individus qui
restent sont composés de deux groupes : ceux qui migreront l’année suivante, et ceux qui
resteront en eau douce toute leur vie (Figure 1-3). Lorsqu’on retire le groupe des futurs
migrants, qui sont les plus petits, la taille ne diffère plus entre les migrants à 1 an et les
résidents. L’influence de la taille à 1 an sur la décision de migrer n’est donc pas très nette :
les individus de même taille adoptent ultimement une ou l’autre des tactiques. Le
deuxième moment de décision, à 2 ans, est quant à lui relié plus fortement à la taille : les
plus petits individus migrent, alors que les plus gros restent en eau douce pour y compléter
leur cycle vital (Thériault et Dodson 2003).
21
0
10
20
30
40
50 60 70 80 90 100 110 120
Taille (mm)N
ombr
e de
poi
sson
s
Figure 1-3. Distribution de taille à l’âge de 1 an pour les individus résidents (barres noires) et migrants (barres blanches). Parmi les résidents se retrouvent les individus qui migreront à 2 ans et qui sont les plus petits de leur cohorte à l’âge de 1 an (ligne noire). D’après Thériault et Dodson (2003).
Parallèlement à l’étude des taux de croissance, les bases énergétiques de l’anadromie et de
la résidence ont aussi été investiguées (Morinville et Rasmussen 2003). Les résultats
révèlent un taux de consommation plus élevé ainsi que des efficacités de croissance
moindres l’année précédant la migration pour les ombles migrants. Les ombles migrants
ont donc des coûts métaboliques plus élevés que les résidants et ce, avant la migration. De
plus, les ombles qui entreprendront la migration sont retrouvés dans des habitats à courants
plus élevés que les ombles qui adopteront la résidence (Morinville et Rasmussen 2006).
Les différences morphologiques trouvées entre les deux formes dans leur stade de vie
juvéniles vont dans le même sens : les futurs migrants sont plus fusiformes, ce qui
correspond bien à un habitat à plus forte vélocité (Morinville et Rasmussen 2007). Mais
alors, c’est l’œuf ou la poule? Les futurs migrants seraient-ils les individus qui au départ
ont des coûts métaboliques génétiquement plus élevés, ce qui les poussent à utiliser un
habitat où l’apport de nourriture est augmenté (courants rapides), ou bien se retrouvent-ils
dans cet habitat de façon aléatoire en regard de leur génotype, et voient leur coûts
métaboliques augmenter en réponse aux coûts élevés associés à l’activité de nage pour
maintenir une position dans les courants rapides? De façon similaire, ces futurs migrants
auraient-ils au préalable une forme plus allongée, ce qui leur permettrait de tenter leurs
chances dans les courants rapides, étant pré-adaptés, ou bien est-ce le fait de se retrouver
dans un courant rapide qui « moule » leur morphologie?
22
Quoi qu’il en soit, ces études précédentes ouvrent la voie à la question fondamentale de
l’influence relative des facteurs génétiques et environnementaux dans le déterminisme des
tactiques alternatives chez les salmonidés, et dans notre cas particulier, chez l’omble de
fontaine.
1.6 Objectifs et contributions Ce projet de doctorat se veut un pas de plus vers la compréhension de l’évolution des
cycles vitaux et de la diversité phénotypique intraspécifique, en particulier des facteurs qui
déterminent et maintiennent les phénotypes alternatifs au sein d’une population. J’ai choisi
d’étudier l’anadromie et la résidence chez l’omble de fontaine dans une perspective de
stratégie conditionnelle, puisque cette hypothèse est largement appliquée aux phénotypes
alternatifs en général (West-Eberhard 2003) et aux salmonidés en particulier (Gross 1996;
Hazel et al. 1990; Hutchings et Myers 1994). En considérant ce dimorphisme comme un
trait seuil, il devient possible de considérer à la fois les composantes environnementales et
génétiques (Hutchings 2004a).
Le premier objectif de mon projet de doctorat est d’élucider et de décrire les patrons de
reproduction des ombles anadromes et résidents. Les études et observations précédentes
sur le même système supposent et suggèrent que les ombles anadromes et résidents font
partie de la même population, mais cette hypothèse n’a jamais été formellement testée.
Être en présence d’une seule population est une exigence des modèles de stratégie
conditionnelle et de traits seuils. À l’aide de marqueurs moléculaires microsatellites et
d’analyse d’assignation parentale, les patrons de reproduction peuvent être reconstruits et il
devient alors possible de déterminer s’il y a reproduction entre les deux formes. Comme
sous-objectif, je voulais de plus estimer le succès reproducteur individuel et son association
avec la taille. Ceci n’a jamais été réalisé pour les formes anadromes et résidentes où les
deux sexes peuvent adopter une ou l’autre des tactiques.
23 Le second objectif a pour but d’estimer l’héritabilité de l’anadromie et de la résidence, ainsi
que de traits sous-jacents, soient la taille et la forme du corps. De plus, je voulais établir si
les corrélations phénotypiques préalablement observées entre la tactique adoptée et la taille
et la forme du corps sont en partie causées par des corrélations génétiques. Ces estimations
ont été faites en milieu naturel, à l’aide de pedigrees reconstruits basés sur l’information
des marqueurs moléculaires, sans aucune manipulation expérimentale ni croisement
artificiels, ce qui est, à ma connaissance, une première en ce qui concerne l’anadromie et la
résidence.
Le troisième et dernier objectif se voulait un exercice de modélisation pour explorer les
conséquences d’une mortalité sélective engendrée par la pêche sportive. Si l’anadromie et
la résidence sont caractérisées par une variance additive sous-jacente et donc une
héritabilité significative, une réponse évolutive est possible face à des pressions de
sélection anthropiques. Une approche « éco-génétique », incluant des principes de
génétique quantitative et une modélisation des normes de réactions, a été employée dans un
but prédictif, voulant évaluer quelles pourraient être les conséquences évolutives et
démographiques d’une pression de pêche exercée sur les anadromes.
Cette étude revêt une importance fondamentale dans la compréhension de l’évolution des
cycles vitaux, du maintien des stratégies alternatives chez les poissons et de leur réponse à
la sélection. De plus, ce travail représente un pas de plus vers le développement d’outils
moléculaires puissants et de nouvelles méthodes d’assignation parentale et de calcul
d’héritabilité en nature, sujets tout chauds. Cette étude a de plus une importance pratique
pour la gestion et la conservation de l’omble de fontaine au Québec. Depuis la chute de la
plupart des stocks de saumons atlantique il y a plus de 10 ans, l’exploitation de l’omble de
fontaine anadrome s’est intensifiée. Il est évident qu’une bonne compréhension de la
biologie de cette espèce est synonyme d’une bonne gestion. La démonstration qu’ombles
anadromes et résidents font partie de la même population reproductrice viendrait justifier
une gestion conjointe de ces deux formes. De plus, la compréhension du déterminisme
24 génétique et environnemental de ces tactiques alternatives viendrait nous éclairer sur ce qui
influence la proportion d’ombles anadromes disponibles pour les pêcheurs sportifs. De
plus, la quantification de l’héritabilité de l’anadromie et de la résidence peut contribuer à
prédire et comprendre la réponse à la récolte sélective des anadromes potentiellement
exercée par la pêche sportive. Enfin, l’omble de fontaine est le poisson le plus important
pour l’aquaculture au Québec, sa production comptant pour plus de 50% de toute la
production piscicole. Les programmes de croisements sélectifs ont besoin de
connaissances génétiques de bases sur les traits associés à l’anadromie et à la résidence,
connaissances auxquelles ce projet contribue.
Chapitre 2. Mating system and individual reproductive success of sympatric anadromous and resident brook charr, Salvelinus fontinalis, under natural conditions
26
2.1 Résumé Les salmonidés sont bien connus pour leurs deux formes de vie souvent présentes dans une
même rivière, une qui entreprend une migration vers la mer avant la reproduction
(anadrome) et une qui reste toute sa vie en eau douce (résidente). Plusieurs études
génétiques ont suggéré que ces deux formes peuvent appartenir à une seule population
reproductrice, mais les patrons de reproduction sont rarement décrits, particulièrement dans
les populations où les deux sexes sont présents sous les deux formes. En utilisant des
marqueurs microsatellites et des analyses parentales dans une population naturelle d’omble
de fontaine (Salvelinus fontinalis), nous avons mis en évidence l’occurrence de
reproduction entre les deux formes, principalement assurée par les mâles résidents qui se
reproduisent autant avec des femelles anadromes que résidentes. Les déterminants du
succès reproducteur, estimé par le nombre de juvéniles ayant survécu jusqu’à 1 ou 2 ans,
diffèrent entre les sexes. Ainsi, la taille chez les mâles ne semble pas être associée au
succès reproducteur, alors que chez les femelles, les plus grandes ont un meilleur succès.
Le plus haut succès reproducteur individuel des anadromes comparés aux résidents est
principalement expliqué par le plus grand succès des femelles anadromes. Nous suggérons
que les mâles résidents adoptent une tactique de reproduction furtive comme façon
d’augmenter leur succès reproducteur en pouvant féconder les œufs des femelles de toutes
les tailles, dans tous les habitats. La persistance de la tactique résidente chez les femelles
pourrait être associée à leur avantage à accéder à des sites spatialement limités dans de
petits tributaires, non accessibles aux plus grandes femelles.
27
2.2 Abstract Salmonids are known for the occurrence in sympatry of two life-history forms, one that
undergoes migration to sea before returning to freshwater to reproduce (anadromous) and
one that inhabits freshwater without a migratory phase (resident). Whereas one breeding
population is often suggested by population genetic studies, mating patterns have rarely
been directly assessed, especially when both sexes are found within each life-history form.
By using highly polymorphic microsatellite loci and parentage analysis in a natural
population of sympatric anadromous and resident brook charr (Salvelinus fontinalis), we
found that gene flow occurred between the two forms and was mediated by resident males
mating with both resident and anadromous females. Determinants of reproductive success,
estimated by the number of surviving juveniles (ages 1 and 2 years), differed between the
sexes. No strong evidence of the influence of size on individual reproductive success was
found for males, whereas larger females (hence most likely to be anadromous) were more
successful. The higher individual reproductive success of anadromous fish compared to
residents was mainly explained by this higher reproductive success of anadromous females.
We suggest that resident males adopt a “sneaking” reproductive tactic as a way of
increasing their reproductive success by mating with females of all sizes in all habitats.
The persistence of the resident tactic among females may be linked to their advantage in
accessing spatially constrained spawning areas in small tributary streams unavailable to
larger females.
28
2.3 Introduction The study of mating systems provides insights into the reproductive strategies adopted by
both sexes to maximize reproductive success and contributes to our understanding of
selection and local adaptation (Reynolds 1996). The use of highly polymorphic genetic
markers along with parentage assignment analyses has made major contributions to the
field, namely by revealing important discrepancies between behavioral- and genetic-based
definitions of mating systems. Early genetic work on birds and mammals showed that
many species were far more polygamous than previously thought (Westneat 1987; Hughes
1998; Coltman et al. 1999). More recent work on fishes, known for their great diversity of
mating systems and reproductive tactics, has also provided a more complete picture of
mating patterns than that previously described based on behavioral observations alone
(DeWoody and Avise 2001; Avise et al. 2002). In addition to the discovery of
unsuspected elevated levels of polygamy in both sexes (Garant et al. 2001; Feldheim et al.
2004), field studies have provided genetic evidence for cuckoldry, extra-pair paternity,
nest-takeover events, and egg thievery in nest-tending species (Conrad et al. 2001;
DeWoody and Avise 2001; Blomqvist et al. 2002) and have provided more direct measures
of individual reproductive success (Garant et al. 2001; Neff 2001; Blanchfield et al. 2003;
Garant et al. 2005a). This body of work has emphasized the existence of alternative mating
tactics in many taxa and species as a way of achieving substantial mating success (Scott
and Williams 1993; Jones et al. 1998; Coltman et al. 1999; Kempenaers et al. 2001).
Salmonid mating systems have been the focus of many studies (Fleming 1998; Blanchfield
et al. 2003; Dickerson et al. 2004; Seamons et al. 2004b), especially that of Atlantic salmon
(Salmo salar), that involves alternative mating tactics among males (Thomaz et al. 1997;
Garant et al. 2001; Garcia-Vazquez et al. 2001; Jones and Hutchings 2002; Garant et al.
2003). In this species, a proportion of male parr (a term referring to the juvenile
freshwater stages of salmon) mature in freshwater at younger ages and smaller sizes than
anadromous males that migrate to sea before returning to spawn. Whereas anadromous
males fight among themselves to control access to female salmon, mature parr sneak into
29 females’ nests to gain access to mating. However, in many other species of salmonids,
both males and females may adopt residency (maturation without going to sea) and are
often found in sympatry with the anadromous form. Population genetic analyses have
generally failed to demonstrate significant genetic differentiation between sympatric forms
(but see Foote et al. 1989; Wood and Foote 1990; Wood and Foote 1996). Most studies
have revealed greater genetic differentiation between geographical localities than between
coexisting life-history forms within any one locality (brown trout Salmo trutta, Hindar et al.
1991, Schreiber and Diefenbach 2005; brook charr Salvelinus fontinalis, Jones et al. 1997,
Boula et al. 2002, Castric and Bernatchez 2003; rainbow trout Oncorhynchus mykiss
Docker and Heath 2003, Narum et al. 2004). Moreover, experimental crosses and
transplant studies have shown that parr from “pure” anadromous or resident crosses can
either become one form or the other and that transplanted resident fish have given rise to
anadromous stock, or vice versa (Nordeng 1983; Morita et al. 2000; Olsson and Greenberg
2004; Schreiber and Diefenbach 2005). Behavioral observations also provided evidence
for reproduction between anadromous and resident fish (Jonsson 1985; Schreiber and
Diefenbach 2005). Altogether, these studies strongly suggest that in most circumstances,
sympatric resident and anadromous forms of salmonids belong to a single gene pool. Yet,
population genetic analyses and behavioral observations provide little insight into mating
patterns: The actual reproduction is not seen and, even if it was, would not reveal which
fishes were successful in reproducing. No studies have yet documented the mating system
and associated reproductive success of sympatric forms of anadromous and resident
salmonids, other than Atlantic salmon, either in controlled or natural environments.
Adult size in salmonids has often been identified as an important determinant of
reproductive success (Fleming 1996). Size certainly represents a major discrepancy
between anadromous and resident forms. Anadromous individuals benefit from higher
growth rates at sea (Gross 1987; Morita and Takashima 1998), and a bigger size confers a
fecundity advantage to females (Fleming 1996) and a dominance advantage in males
(Blanchfield et al. 2003). Furthermore, because males are able to achieve substantial
reproductive success by adopting alternative reproductive tactics (Hutchings and Myers
30 1988), females often predominate within the anadromous part of the population
(Kristoffersen et al. 1994; Rikardsen et al. 1997; Doucett et al. 1999), and in some
instances, only males adopt the resident tactic (Bohlin et al. 1994). Reproductive success
and its correlation with size have never been compared between sympatric anadromous and
resident individuals where females, and not just males, are adopting both life-history forms.
The brook charr is native of northeast North America and commonly occurs as landlocked,
freshwater-river resident and anadromous forms. Colonization of northeast North America
is believed to have taken place from south to north, after the retreat of glaciers 10,000 years
ago. Founders of extant populations are thus considered to have been anadromous (Castric
and Bernatchez 2003) and now coexist with resident individuals in many populations
occupying tributaries of the Gulf of St. Lawrence and the St. Lawrence R. Little is known
about the life history of sympatric anadromous and resident brook charr. However, it has
been shown that growth rate and growth efficiency differ between future migrants and
future residents and are proximate factors linked to the form adopted (Morinville and
Rasmussen 2003; Thériault and Dodson 2003). This suggests that anadromy and residency
in this species may be under a conditional mode of regulation, where a threshold exists that
must be exceeded to adopt one form or the other (Hazel et al. 1990; Roff 1996).
The objective of this study was to use highly polymorphic microsatellite loci and parentage
analysis to document the mating systems of sympatric resident and anadromous brook charr
in a tributary of the Sainte-Marguerite River, Québec, Canada, under natural conditions.
By means of parentage analysis, we first determined if reproduction occurs between the two
forms and if so, if it is mediated by resident males only. We then estimated individual
reproductive success and its variance for both resident and anadromous fish based on the
number of surviving young (ages 1 and 2 years). We further investigated the mating system
by documenting and comparing the relationship between reproductive success, size, and
number of mates for both sexes and forms.
31 2.4 Material and Methods
2.4.1 Study site and brook charr reproductive behavior The Sainte-Marguerite River system in Quebec, Canada, sustains a large population of
anadromous brook charr that migrates into the Sainte-Marguerite Bay and the Saguenay
River before returning to the freshwater to reproduce (Fig. 2-1). They migrate at 1 or 2+
years of age and to a lesser extent at age 3+, and sex ratio is 1:1 at both downstream and
upstream migrations (Lenormand 2003; Thériault and Dodson 2003). Male and female
resident brook charr also occur in the river and are mainly found in tributaries of the main
river branch. Sampling as well as underwater observations at different sites of the river
showed that anadromous charr use tributaries for reproduction, and thus that both forms are
present on the same spawning grounds (Thériault, personal observation). Reproductive
behavior of brook charr is typical of other river salmonids: Females excavate their nests in
gravel substrates during the fall where they deposit their eggs. Males compete for access to
females and hence for opportunities to fertilize the eggs. Sneaking and satellite behavior by
small males has previously been observed in lacustrine populations of this species
(Blanchfield et al. 2003).
2.4.2 Sampling The study was performed on a small tributary of the Sainte-Marguerite River, Morin Creek
(average 5.6 m wide, 0.3 m deep, Fig. 2-1). An impassable waterfall (75 m high) is located
4 km upstream from the mouth of the tributary. The study was conducted in a 2.5 km
section below this waterfall, accessible to anadromous fish.
An upstream migration trap was installed at 1 km from the mouth of the stream and was
operated from the end of June to the end of October in 2000 and 2001 for intercepting
upstream-migrating anadromous spawners (Fig. 2-1). The trap covered the entire width of
the stream, except for some periods of high flood where fish could pass either over or under
it (see “Results” section). Traps were visited twice daily, fish were measured (fork length)
and marked individually with a T-bar Floy Tag, the adipose fins were taken and preserved
32 in 95% ethanol, and subsequently, fish were released 200 m upstream. Fish caught in the
upstream migration trap were differentiated into anadromous or resident forms based on
length, morphological identification, maturation stage (when available) and/or recapture
information (Thériault and Dodson 2003; Lenormand et al. 2004). Thus, fish bigger than
250 mm were always classified as anadromous as this length approaches the maximum size
of residents observed in this stream. Only three resident fish recorded during 7 years of
survey of this stream exceeded 250 mm (253, 256 and 258 mm fork length respectively)
(Thériault, unpublished data). Fish between 170 and 250 mm were either mature resident
when signs of maturation were present (coloration, body shape, sperm release, oviposition
pore visible) or immature anadromous when no such signs were detected. Although some
mature resident fish were caught in the upstream migration trap, most were captured with
electrofishing gear from June to September 2000 and 2001, in three distinct areas along the
2.5-km section (Fig. 2-1). All fish greater than 120 mm were considered as potential
resident spawners as this was approximately the minimum observed length at sexual
maturity in this stream (114 mm, Lenormand 2003). Fish were measured (with their
adipose fins sampled) and then released not more than 50 m from their capture site. Sex
was noted when external signs of maturation were present.
In 2002, 2003, and 2004, 1+ and 2+ juvenile fish (progeny from the 2000 and 2001
spawners sampled) were collected either by means of a downstream migration trap
intercepting first time migrants in May and June, or by electrofishing from June to October
in the same 2.5-km section as for resident spawners (Fig. 2-1). Fish were classified as
either 1+ or 2+ based on length distribution, previously validated with age determination
based on otoliths from the same system (Thériault and Dodson 2003). All fish were
measured (with their adipose fins sampled) and subsequently released near their site of
capture.
33 2.4.3 Microsatellite polymorphism analyses Total DNA was extracted from the adipose fin tissue using Qiagen® DNeasy™ extraction
kit. Microsatellite polymorphism was analyzed at 13 loci using fluorescent-labeled primers
(SfoB52, SfoC113, SfoC129, SfoC28, SfoC88, SfoC115, SfoD100, and SfoD75, T. L. King,
US Geological Survey, unpublished; SCO204, SCO216, and SCO218; DeHaan and Ardren
2005; Sfo262Lav and Sfo266Lav; Perry et al. 2005b). Five polymerase chain reactions
(PCR) were carried out using either a Perkin-Elmer 9600 thermocycler v.2.01 or a
Biometra® T1 thermocycler (Table 2-1). PCR products 1 and 2 (Table 2-1) were purified
with a PCR96 Cleanup Plate Manu30 from Millipore and were subsequently separated
electrophoretically using a BaseStation™ DNA Fragment Analyzer (MJ Research) (gel 1:
quintuplex; gel 2: triplex). Allelic sizes were scored against the size standard GENESCAN
ROX-500 (Applied Biosystems) using CARTOGRAPHER™ analysis software v.1.2.0.
PCR products 3, 4, and 5 (Table 2-1) were pooled and run together on an ABI™ 3100
automated capillary sequencer (Applied Biosystems). Allelic sizes were scored against the
size standard GENESCAN ROX-500 (Applied Biosystems) using GENESCAN™ analysis
v.3.7 and GENOTYPER™ v.3.7 NT software. Allelic sizes for each locus were
standardized with the software ALLELOGRAM v.1.
2.4.4 Statistical analyses
2.4.4.1 Standard genetic statistics Genetic diversity in the adult population was quantified by the number of alleles per locus
and by observed and expected heterozygosities. Hardy-Weinberg equilibrium (HWE) was
tested separately for each year and form (anadromous and resident), using the score test (U
test), implemented in GENEPOP v.3.4 (Raymond and Rousset 1995). Significance level
was adjusted to account for multiple testing using Bonferroni correction procedure (k = 52
for single-locus comparisons, ∝ = 0.05/k = 0.00096). As we subsequently used a
parentage allocation procedure (see below), several features were investigated that could
affect our analysis, namely the presence of null alleles, linkage disequilibrium, and pairwise
relatedness in the adult population (Pemberton et al. 1995; Dakin and Avise 2004). The
potential occurrence of null alleles was tested by estimating their frequency using the
34 Brookfield (1996) null allele estimator implemented in MICRO-CHECKER (van
Oosterhout et al. 2004). Linkage disequilibrium among loci was examined using the
genotypic linkage disequilibrium option implemented in GENPEOP v.3.4 (Raymond and
Rousset 1995). Finally, pairwise relatedness in the adult population was estimated using
the identity index implemented in IDENTIX (Belkhir et al. 2002) and was compared to its
random expectation under the hypothesis of a panmictic association after 1,000
permutations.
2.4.4.2 Population genetic structure The hypothesis of genic (allelic frequency) differentiation between anadromous and
resident spawners for each year of sampling was tested using a Fisher exact test at
individual loci as well as over multiple loci using GENEPOP v.3.4 (Raymond and Rousset
1995). The extent of genetic differentiation was quantified by FST estimated by θ (Weir
and Cockerham 1984) using GENETIX v.4.02 (Belkhir et al. 2000). Significance level for
single as well as multilocus FST was tested by 2,000 permutations and was adjusted to
account for multiple testing using Bonferroni correction procedure (k = 13 for single-locus
comparisons, ∝ = 0.05/13 = 0.0038; k = 4 for multilocus comparisons, ∝ = 0.05/4 =
0.0125).
2.4.4.3 Parentage analysis Parental allocation was performed using PASOS 1.0, a software package allowing the
allocation of progeny to either one or two parents in an open system, where parents are
potentially missing (Duchesne et al. 2005). Allocation can be performed whether the sex of
putative parents is known. Using a sequential allocation and simulation procedure, PASOS
provides an estimate of the overall allocation correctness rate as well as an estimate of the
proportion of parents that contributed to reproduction but were not collected. Briefly, to
allocate an offspring, PASOS first searches for the most likely pairs among all potential
pairs of collected parents. The likelihoods are computed according to a fixed error model,
wherein the transmission probability from allele X to X equals 0.98 and the remaining 0.02
is evenly distributed over all remaining offspring alleles for any given locus. At least one
most likely pair is always obtained this way. False parents are filtered out by building the
35 most likely transmission scenario and by subsequently computing the distances between
each transmitted parental allele and its presumed offspring counterpart. If the transmission
distance for one putative parent is not within the maximum offset tolerance (MOT – see
below) determined a priori, this parent is rejected (only a single mismatch – one locus – is
needed for rejection). The MOT is a user-defined parameter (set to either 0, 1, or 2) and
refers to the maximum number of offsets between a parental and an offspring allele that
PASOS accepts as possibly due to a scoring error. For example, 254 and 258 are two
offsets apart within a dinucleotide locus, or one offset apart within a tetranucleotide locus.
Allocation with PASOS was performed following two steps. We first allocated the
offspring using the sequence allocation option, starting with the most informative loci, with
MOT set to 0. Adults sampled in 2000 and 2001 were treated as potential parents for all
juveniles, allowing for repeat spawning and incomplete adult sampling. The resulting curve
provided an estimate of the percentage of parents collected among the pool of spawners
needed to produce the juveniles analyzed, which typically corresponds to the point where
the curve reaches a plateau (Fig. 2-2). The second step consists of performing simulations
with this estimate to produce a simulated curve that fits the true allocation curve. This
provides an estimate of the overall correctness rate (confidence). Simulated offspring were
created from the true parental file with a 2% error model and an offset of 2, the
transmission probability being 0.98 for the focal allele, 0.008 at one offset, and 0.002 at
two offsets. The simulated allocation was performed at MOT 0, to better reflect the true
allocation. True and simulated allocation were also performed at MOT 1 and 2, but we
opted for a conservative approach by keeping MOT at 0, as this was the value that provided
the maximal confidence, albeit at the cost of lowering allocation rate (see “Results” and
“Discussion” sections).
For each allocated offspring, we thus obtained the identity of either one or both parents as
well as the number of offspring assigned to each parental pair and, hence, to each
individual. When a single parent fathered or mothered two juveniles or more, those
36 offspring were further partitioned into full-sib families using COLONY v.1.2 (Wang 2004).
COLONY uses a maximum likelihood method to assign individuals sampled into full-sib
families nested within half-sib families based on offspring genotype, allowing for typing
errors. Thus, even if the identity of the mating partners was not known (i.e., in the cases
where only one parent was found), this exercise provided an estimation of the number of
mates involved in a particular mating event.
2.4.4.4 Analyses from the parental allocation results We first used the results of the allocation procedure to assess if reproduction occurred
between anadromous and resident life-history forms. We then performed an analysis of
variance (ANOVA) to detect any significant difference in individual reproductive success
(mean number of juveniles produced per individual) as well as number of mates between
forms. This analysis was performed by considering both each sex separately and both
sexes combined. We used linear regression analysis and ANCOVA to assess the
relationship between reproductive success, length, and form, as well as between number of
mates (estimated from COLONY), size, and form, for each sex separately. Those tests
were done using JMP v.5.0.1a (SAS Institute Software).
2.5 Results
2.5.1 Characteristics of fish sampled Upstream migration of spawners occurred principally in August and to a lesser extent in
September (Fig. 2-3a). Four heavy floods in 2000 interrupted the monitoring of upstream-
migrating fish and probably resulted in the undersampling of the spawning run. In 2000, 55
fishes were classified as anadromous, either mature (N = 31, 255 to 398 mm fork length,
mean = 312.16 mm) or immature (N = 24, 177 to 244 mm fork length, mean = 207.92 mm)
(Fig. 2-3b). In 2001, 30 were anadromous, with 15 being mature (254 to 394 mm fork
length, mean = 310.71) and 15 immature (range 179 to 241 mm fork length, mean= 201.73
mm) (Fig. 2-3b). A total of 153 and 334 potential resident spawners were caught in 2000
and 2001 respectively, mainly by electrofishing but also in the upstream or downstream
traps (2000, 120 to 258 mm fork length, mean = 155.53 mm; 2001, 120 to 256 mm fork
length, mean = 154.94 mm). The significantly higher number of captures in 2001 reflects
37 an increased electrofishing sampling effort in that year. A total of 981 juveniles were
sampled from 2002 to 2004, with 828 aged 1+ (56 to 115 mm fork length, mean = 84.41
mm) and 153 aged 2+ (80 to 140 mm fork length, mean = 121.54 mm).
2.5.2 Standard genetic statistics The 13 loci used showed moderate to high degrees of polymorphism in the adult
population, with 6 to 30 alleles observed per locus and HE ranging from 0.30 (SfoC28) to
0.89 (SCO216) for an overall expected heterozygosity level of 0.76 (Table 2-2). One
temporal sample (RES 2001) displayed significant departures from HWE with a
heterozygote deficiency observed at five loci (Table 2-2). Three of these, SfoC115,
SfoD100 and SfoD75, showed evidence of null alleles with estimated frequencies of 0.045,
0.052, and 0.061 respectively (see parentage analysis results to see how they were dealt
with for parental allocation). Furthermore, the magnitude of the difference between
observed and expected heterozygosities for those five loci was small, suggesting that non-
conformation to HW equilibrium was not of main concern in this sample. Exact test of
genotypic linkage disequilibrium revealed a higher proportion of significant P values than
expected by chance (17 out of 78 observed, 3.9 out of 78 expected by chance at ∝ = 0.05),
again for the temporal sample RES 2001 only. The mean observed pairwise identity
coefficient did not depart significantly from its expected distribution under the hypothesis
of a random association for any of the temporal samples (ANA 2000, P = 0.117; RES 2000,
P = 0.06; ANA 2001, P = 0.998; RES 2001, P = 0.136). Adults were thus not composed of
individuals more related than expected by chance.
2.5.3 Population genetic structure Significant temporal variation in allele frequency was detected among years within the
adult resident sample (exact test of genic differentiation, P < 0.001), although this variation
was mainly due to the effect of two loci out of 13 (SfoC28 and SfoC115). Population
differentiation between anadromous and resident samples was estimated separately for each
year. No evidence for differentiation was found in 2000, with none of the 13 loci showing
significant allele frequency differences among forms (Table 2-3). In 2001, the
differentiation between anadromous and resident samples was significant (FST = 0.012, P =
38 0.003). However, FST values differed greatly among loci (four loci had negative values,
Table 2-3) and the significance of the differentiation was entirely due to a single locus
(SfoD100). Estimating FST and correcting for the presence of null alleles using FreeNA
(Chapuis and Estoup 2007) did not change any of our results (not shown). Overall, these
analyses do not refute the null hypothesis that anadromous and resident adult fish are part
of the same breeding population.
2.5.4 Parentage analysis A total of 494 potential spawners (85 anadromous and 409 resident) and 974 juveniles (1+
and 2+) were used for parentage allocation. A total of 315 juveniles were assigned to a
single parent, whereas 42 were assigned to both parents, leading to a total allocation rate of
21%. These 42 juveniles were either half-sibs or unrelated, suggesting that they were
offspring of 42 different couples. That is, no more than one offspring was assigned to the
same couple. The sequential allocation curve did not reach a true plateau but decreased
more slowly toward the end of the sequence (Fig. 2-2). The allocation rate estimated from
the last loci added was used to estimate the fraction of sampled spawners (20%, see Fig. 2-
2) even though the plateau was not attained because simulation suggested stabilization of
the curve after adding one or two more loci (data not shown). However, parental allocation
was complicated by the occurrence of null alleles, which may cause the false exclusion of a
true parent. Any such bias would be conservative (i.e., excluding true parents rather than
including false parents, Marshall et al. 1998; Dakin and Avise 2004). On the other hand,
this can have an impact on the estimation of the proportion of sampled/missed spawners
and, consequently, on the estimated confidence in our allocations. PASOS is not designed
to account for null alleles as the error model used in the simulation procedure allows error
at a maximum of two offsets apart from the focal allele, whereas the error associated with
null alleles is not necessarily restricted to alleles close to the focal allele. Because removal
of loci with null alleles from our parentage analysis decreased confidence significantly
(results not shown), we chose to retain them in the analysis, making an upward correction
to the estimated proportion of sampled spawners as follows. We estimated the probability
of a false exclusion given by the three loci showing null alleles to be 12% (see formula
developed by Dakin and Avise 2004). This 12% was added to the 20% of sampled parents
39 (estimated by the allocation curve given by PASOS). We thus concluded that a maximum
of 32% of the parents were sampled, and therefore that 68% of the spawners were missing.
This estimate was subsequently used in the simulation procedure, which yielded an
allocation correctness rate of 87%. A total of 72 spawners had two or more offspring
(maximum of 19), and their progeny was divided into full-sib families with COLONY.
Family sizes were small, ranging from one to four individuals (mean = 1.37). That is, no
more than four individuals shared the same unsampled parent. Analysis with COLONY
allowed us to find full-sib individuals in our sample, which could not have been detected
with PASOS because of the high proportion of unsampled spawners.
2.5.5 Mating patterns Out of the 42 couples reconstructed from the parentage allocation analysis, half (21)
involved an anadromous and a resident spawner, clearly showing reproduction and viability
of offspring between the life-history forms. The sex of the adults was known for 13 of those
21 interform crosses and in every case, the male was the resident fish. The remaining
crosses were anadromous-anadromous, producing five offspring, and resident-resident,
producing 16 offspring. The mean number of partners estimated with COLONY did not
differ between life-history form and sex (ANOVA, F1,26 = 0.995, P = 0.33 for form; F1,26 =
0.024, P = 0.88 for sex) and ranged from one to eight (mean = 3.25) for anadromous and
one to seven (mean = 2.52) for resident fish. When anadromous females were found
mating with multiple partners, males were either all resident or they were composed of a
single anadromous fish and many resident fish (three resident males in one mating event,
four in the other). No more than one anadromous male was found mating with the same
female. However, the number of partners must be viewed as conservative as it is limited by
the total number of offspring assigned to each individual.
2.5.6 Individual reproductive success Globally, individual reproductive success of anadromous fish (total number of offspring
assigned to each spawner, 1+ and 2+ combined) was significantly higher than reproductive
success of resident fish (ratio anadromous/resident = 2.9) and the same was true for
variance in individual reproductive success (Table 2-4). This difference was due to
40 anadromous females having a higher reproductive success than resident females. No
difference was observed among males (only four anadromous males were available for this
analysis, Table 2-4).
2.5.6.1 Female determinants of reproductive success Reproductive success was positively related to length for females and explained 46% of the
variation (linear regression, F = 20.75, P = 0.0001, R2 = 0.46, Fig. 2-4a). This relationship
seems to be driven by a few anadromous females that dominated spawning, but a quadratic
regression did not fit the data better than the linear one (F-test, F = 2.19, P = 0.15). An
analysis of covariance revealed that the effect of the life-history form on reproductive
success was not significant when length was included (F1,22 = 4.28, P = 0.05 for length;
F1,22 = 0.07, P = 0.79 for form). The interaction term was not significant and thus the effect
of length on reproductive success was the same within each form (length by form, F1,22 =
1.19, P = 0.25). The number of mates was not related to length in either form (ANCOVA,
F1,12 = 0.13, P = 0.73 for length; F1,12 = 0.02, P = 0.90 for form; F1,12 = 0.09, P = 0.77 for
the interaction term; Fig. 2-4b).
2.5.6.2 Male determinants of reproductive success For males, neither length nor life-history form had a significant effect on reproductive
success (ANCOVA, F1,28 = 1.61, P = 0.21 for length; F1,28 = 1.35, P = 0.26 for form; F1,28 =
1.47, P = 0.24 for the interaction term; Fig. 2-5). However, the low number of anadromous
males (four) in this analysis limits the strenght of our conclusions. Analyses involving
numbers of mates were omitted because mate numbers for anadromous males were
available for two individuals only.
2.6 Discussion
2.6.1 Gene flow and mating patterns Our results showed that anadromous and resident brook charr in this system most likely
belonged to the same breeding population based on both FST and parentage analysis.
Reproduction between the forms occurred principally through anadromous females and
resident males, and confirmed previous behavioral observations in this system and in other
41 species (Wood and Foote 1996; Schreiber and Diefenbach 2005). For instance, Wood and
Foote (1996), despite extensive observations, never observed male sockeye (anadromous,
Oncorhynchus nerka) orienting to female kokanee (resident), but male kokanees were
frequently observed as sneak males to sockeye females, either in the absence or presence of
a sockeye male. Schreiber and Diefenbach (2005) observed anadromous female brown trout
and resident males over the same spawning grounds, and the unequal sex ratio found in
favor of females led them to suggest a frequent interbreeding of anadromous females with
resident males.
Although anadromous males did not appear to mate with resident females in this study,
reproduction between bigger males (normal phenotype) and smaller females (dwarf
phenotype) was suggested in a lacustrine Arctic charr population where normal males
appear to fertilize eggs of normal females first, then those of dwarf females, whose
spawning period was delayed (Jonsson and Hindar 1982). In our study system, the resident
females spawn first- about two weeks before anadromous females- and it is the resident
males that take part in both reproductive events, exhibiting an extended spawning window
overlapping the two spawning periods. It is possible that the predominance of the pairing of
anadromous females with resident males, as well as the few offspring assigned to
anadromous-anadromous mating (only five) can be explained in part by the apparent rarity
of males within the anadromous run. Biased sex ratio in favor of anadromous females is
commonly seen among salmonids, notably in Arctic charr and brown trout (Kristoffersen et
al. 1994; Rikardsen et al. 1997; Doucett et al. 1999; Schreiber and Diefenbach 2005). Sex
was known for 13 anadromous fish, of which only four were males. Indeed, the fact that
sex was most of the time undetermined could argue in favor of a bias in sex ratio favoring
females. Most anadromous males are usually already differentiated in August, having a
more laterally compressed reddish belly and a small kype. Most anadromous fish caught in
the upstream trap exhibited few morphological signs enabling us to clearly identify the sex.
We believe that these fish were most likely females. Moreover, sizes of reproducing
anadromous fish entering the Morin Creek (from 254 mm to 398 mm fork length) were at
the low end and almost outside the size range of anadromous charr in the Sainte-Marguerite
42 River system (measuring from 312 mm to 561 mm fork length, data not shown). Females in
this system are typically smaller than males (females, mean = 421.46 mm fork length;
males, mean = 448.42 mm fork length, t test, t97= -2.5 , P = 0.01). It is noteworthy that
such a possible biased sex ratio toward females occurs in Morin Creek as we know that sex
ratio at downstream migration is 1:1 in this stream (Thériault and Dodson 2003) and that
the sex ratio is also 1:1 during both the downstream and the upstream migration in the main
stem of the river (Lenormand 2003). We hypothesize that availability of adequate
spawning grounds or resting pools is size-limiting for anadromous charr in small tributary
creeks, selecting for smaller anadromous fish and, consequently, mostly females.
2.6.2 Reproductive success Anadromous fish had a higher individual reproductive success than residents, and this was
due to the fact that anadromous females were bigger and thus producing more juveniles
than resident fish. Size in salmonid females has often been reported as an important
determinant of reproductive success (Fleming and Gross 1994; Fleming et al. 1997),
accounting for up to 80% of the variability in the reproductive success of Atlantic salmon
(Fleming 1996). This positive relationship is often related to higher fecundity and larger
eggs among bigger females (Morita and Takashima 1998; Hendry et al. 2001), to better
access to preferential breeding sites (Foote 1990) and to the digging of deeper nests,
providing increased protection against scouring (Steen and Quinn 1999). A greater number
of mates has also been proposed as a determinant of higher reproductive success for
females through genetic and ecological benefits in unstable environments (see the paper of
Garant et al. 2001 for discussion about Atlantic salmon). In our study, females were found
to have multiple partners (one to eight, mean 3.38), regardless of size and form, and this
range must be viewed as conservative because the number of partners was limited by the
number of offspring assigned to each parent. Analyses linking the number of mates and
reproductive success were omitted due to the probable artifact related to the very small
numbers of offspring sampled for the majority of individuals, inevitably resulting in a
positive relationship.
43 Anadromous and resident males were found to have similar reproductive success, but this
result must be considered with caution as only four anadromous males were available for
this analysis. However, none of the four anadromous males, although bigger, produced a
number of offspring outside the resident male’s range. Therefore, no association of size
with reproductive success was found. Size is expected to be a determinant of reproductive
success in males due to its relation to dominance: Dominant males usually win in
intrasexual competition for access to females (Quinn and Foote 1994; Fleming 1998;
Blanchfield et al. 2003; Dickerson et al. 2005). However, in many salmonid studies, little
or no relationship has been found between male size and reproductive success (Garant et al.
2001; Jones and Hutchings 2002; Blanchfield et al. 2003; Seamons et al. 2004a; Dickerson
et al. 2005). Other factors such as arrival timing, ripeness of available females, numbers of
mates, or operational sex ratio, allowing even small males to mate in years where more
females are available, may influence reproductive success (Seamons et al. 2004a;
Dickerson et al. 2005). In our study system, it is likely that resident males use alternative
tactics to increase their reproductive success, being less selective or more opportunistic, as
illustrated by individual males fertilizing eggs of both resident and anadromous females.
This suggests that they might adopt an alternative behavior (for example, sneaking instead
of fighting) in the presence of bigger anadromous males to gain access to mating.
2.6.3 Where have all the parents gone? Parentage allocation in an entirely wild, open system without partial complementary
behavioral information on mating events, has rarely been attempted (but see the studies of
Seamons et al. 2004 a, b; Dickerson et al. 2005). The parentage allocation procedure used
in PASOS, combined with a correction owing to null alleles, allowed us to estimate that
approximately 68% of the spawners needed to have produced the pool of juveniles
analyzed were missing from the pool of putative parents. This high proportion of missing
spawners limits our allocation success by forcing us to perform conservative allocations
(MOT set to 0 and hence no mismatch tolerated) to achieve an acceptable correctness rate
(87%). First, we may have missed anadromous spawners because of flooding events that
were seen in both years, principally in 2000. Those events probably allowed anadromous
spawners to pass by without being intercepted at the trap, as seen in a similar study with
44 steelhead trout (Seamons et al. 2004b). Second, resident spawners were probably missed
because we electrofished in a single pass in open sections, which is certainly not the most
efficient electrofishing method (Rosenberger and Dunham 2005). Moreover, the deepest
sections had to be omitted. Spawners breeding outside the study stream where their
progeny may have immigrated into the stream during their first year of life could also
explain in part the high proportion of missing spawners. However, the observation that
most of the juveniles successfully allocated were done so to only one parent suggests that
the missing parent must have been reproducing somewhere in the stream, and thus it is not
consistent with the hypothesis of juvenile immigration from outside the study system.
Although an allocation success rate of 21% seems small, the allocated juveniles are
nevertheless representative of the reproductive outcome in the creek. We sampled a large
proportion of the stream (49% by stream length), and the sections omitted were not
preferential habitat of juvenile brook charr. Moreover, half-sib offspring were not clustered
in one or adjacent sampling sections. Qualitative analysis of our data showed that only
members of five half-sib families out of 44 were sampled in the same 300 m sampling
section. All other juveniles sharing one parent were found all along the stream, from
several hundred meters to 3 km apart, suggesting considerable dispersal during the first
and/or second year of life or, at least in some cases, by movement between spawning events
by the shared parent. We thus concluded that we had equal chances of sampling members
of different families.
2.6.4 Conclusion This study directly assessed for the first time the genetic mating system and individual
reproductive success of a population of salmonids composed of both anadromous and
resident forms, under natural conditions, where males and females may adopt either tactic.
Gene flow occurred between the two forms and was mediated by resident males mating
with both resident and anadromous females. Reproduction between anadromous males and
resident females was not seen in this study. Discrepancies were found between the sexes in
terms of determinants of reproductive success. Size in males did not have any influence on
45 individual reproductive success, whereas larger females (and hence most likely to be
anadromous) were more successful. This finding corroborates other studies suggesting that
anadromy would be more beneficial to females than males (Jonsson and Jonsson 1993;
Fleming 1998) and provides evolutionary explanations of why residency is not observed
among females in some populations or species (especially Atlantic salmon, Hutchings and
Jones 1998; and Pacific salmon species as chinook salmon, Unwin et al. 1999; and masu
salmon, Tsiger et al. 1994). However, for females to persist in some populations as
residents, benefits must be provided. One such benefit is the higher survival rate associated
with residency, which can be as low as 10% in saltwater in the first year following
migration and as high as 50% in freshwater for the same cohort that remains in freshwater
(Dodson and Lenormand, unpublished data). However, such an advantage would apply
equally to both sexes. Whereas resident males, by adopting a “sneaking” reproductive
tactic, increased their reproductive success by mating with females of all sizes in all
habitats, females may also adopt different tactics in selecting spawning sites (Holtby and
Healey 1986; Seamons et al. 2004a). We suggest that resident females would enjoy an
advantage in accessing spatially constrained spawning areas in small tributary streams
unavailable to larger females. Structural complexity has recently been shown to influence
the competitive ability of males pursuing alternative reproductive tactics in mites, and thus
it is likely to influence the tactic frequency in different species or populations (Lukasik et
al. 2006). Spatial complexity associated with small and higher-order tributaries could
explain why residency is virtually absent within the main stem of the river and why only
small anadromous fish composed the Morin anadromous run. Larger anadromous females
would probably outcompete and exclude smaller females from larger spawning areas,
typically associated with larger rivers. However, large females would be at a disadvantage
in the more restricted spawning habitats associated with tributaries. Small tributary streams
may thus be viewed as a refuge for residency or, at least, a way of ensuring that the resident
tactic persists within females.
46
2.7 Acknowledgments We acknowledge A. Boivin, S. Bordeleau, M. Foy-Guitard, S.-P. Gingras, Fannie Martin,
François Martin, A. Ménard, G. Morinville, L. Papillon, L. V. St-Hilaire Gravel, and R. St-
Laurent for the field assistance and laboratory work. The authors would like to thank P.
Duchesne for the helpful advice and assistance in the use of the software PASOS and D.
Garant for the helpful comments on a draft version of this manuscript. Funding of this
project was provided to J.J.D. and L.B. by NSERC of Canada (Strategic Grant and
Collaborative Special Projects), the Fondation de la Faune du Québec, the Government of
Québec (FAPAQ), the Government of Canada (Economic development), and the financial
partners of AquaSalmo R&D. This study is a contribution to the program of CIRSA
(Centre interuniversitaire de recherche sur le saumon Atlantique) and Quebec-Ocean. V.T.
was financially supported by funding from NSERC and FQRNT. The experiments
conducted comply with the current Canadian laws.
47
2.8
Tab
les
Tabl
e 2-
1. P
CR
con
ditio
ns fo
r the
13
loci
am
plifi
ed.
PCR
Loci
A
mpl
ifica
tion
stat
e
Rx
volu
me
(µL)
Rx
buff
er1
(µL)
dN
TPs2
(µL)
Ta
q (U
) D
NA
(n
g)
Cyc
le (t
empe
ratu
re in
Cel
cius
deg
res)
1 Sf
oC11
3/Sf
oC28
/Sfo
B52/
SfoC
129
quin
tupl
ex
20
2 0.
8 0.
3 8
5min
at 9
5, 3
5x(4
5s a
t 95,
45s
at 5
6, 4
5s a
t 72)
, 10m
in a
t 72
2 Sf
oC11
5/Sf
oD10
0/Sf
oD75
tri
plex
20
2
0.8
0.3
8 5m
in a
t 95,
35x
(45s
at 9
5, 4
5s a
t 58,
45s
at 7
2), 1
0min
at 7
2 3
SCO
204/
SCO
216/
SCO
218
sim
plex
15
1.
5 0.
3 0.
2 8
3min
at 9
4, 3
8x(3
0s a
t 94,
30s
at 6
0, 3
0s a
t 72)
, 7m
in a
t 72
4 Sf
o262
Lav
sim
plex
10
1
0.3
0.2
8 3m
in a
t 94,
35x
(30s
at 9
4, 3
0s a
t 60,
45s
at 7
2), 7
min
at 7
2 5
Sfo2
66La
v si
mpl
ex
10
1 0.
3 0.
2 8
3min
at 9
4, 3
5x(3
0s a
t 94,
30s
at 5
2, 4
5s a
t 72)
, 7m
in a
t 72
1 co
mpr
ise
10 m
M T
ris-H
CL
[ph
9.0]
, 1.5
mM
MgC
l2, 0
.1%
Trit
onX
-100
, 50m
M K
Cl
2
10 m
M e
ach
dNTP
48
Table 2-2. Number of samples (N), number of alleles (A), FIS, observed (HO) and expected (HE) heterozygosities, and probabilities of conforming to Hardy-Weinberg equilibrium (P(HW), score U test) for each locus and each temporal sample (RES = resident, ANA = anadromous) separately. Bold values are significant at ∝ = 0.0096 following Bonferroni corrections.
2000 2001 Locus A ANA RES ANA RES
SfoB52 12 N 32 95 15 323 A 7 9 7 12 FIS -0.054 0.160 0.048 0.044 HO 0.81 0.65 0.80 0.77 HE 0.76 0.77 0.81 0.81 P(HW) 0.8465 0.0191 0.4848 0.0066 SfoC113 9 N 32 95 15 323 A 6 7 6 9 FIS -0.093 0.013 -0.043 0.005 HO 0.84 0.73 0.80 0.74 HE 0.76 0.73 0.74 0.75 P(HW) 0.7546 0.2372 0.5893 0.0008 SfoC129 7 N 32 95 15 322 A 5 6 5 7 FIS -0.021 0.128 0.000 0.019 HO 0.75 0.62 0.73 0.73 HE 0.72 0.71 0.71 0.74 P(HW) 0.2523 0.0120 0.1192 0.0890 SfoC28 9 N 32 95 15 323 A 6 8 5 8 FIS -0.108 0.020 0.138 0.079 HO 0.59 0.54 0.27 0.51 HE 0.53 0.54 0.30 0.56 P(HW) 0.7300 0.4056 0.3206 0.0918 SfoC88 6 N 32 95 15 322 A 5 6 4 6 FIS 0.026 -0.097 -0.213 0.024 HO 0.63 0.68 0.73 0.64 HE 0.63 0.62 0.59 0.66 P(HW) 0.3889 0.7596 0.9427 0.0230
49 SfoC115 16 N 32 94 15 322 A 7 12 5 15 FIS 0.146 0.063 -0.212 0.115 HO 0.47 0.44 0.60 0.59 HE 0.54 0.46 0.48 0.67 P(HW) 0.2163 0.4483 1.0000 0.0003 SfoD100 10 N 32 93 15 322 A 8 10 6 10 FIS 0.206 0.078 -0.183 0.118 HO 0.59 0.75 0.80 0.72 HE 0.73 0.81 0.66 0.81 P(HW) 0.0791 0.1036 0.9509 < 0.001 SfoD75 13 N 32 94 15 321 A 10 11 8 13 FIS 0.090 0.080 0.058 0.136 HO 0.75 0.79 0.73 0.71 HE 0.81 0.85 0.75 0.83 P(HW) 0.1699 0.0249 0.1482 < 0.001 SCO204 15 N 32 90 15 322 A 7 9 6 15 FIS 0.113 0.062 -0.197 0.002 HO 0.69 0.74 0.87 0.81 HE 0.76 0.79 0.70 0.81 P(HW) 0.2271 0.2531 0.9715 0.2030 SCO216 18 N 32 92 15 322 A 15 13 9 18 FIS 0.091 0.069 -0.057 0.042 HO 0.81 0.82 0.93 0.86 HE 0.89 0.87 0.86 0.89 P(HW) 0.1305 0.0954 0.6490 0.0264 SCO218 14 N 32 90 15 322 A 7 9 6 14 FIS -0.007 0.007 -0.170 -0.023 HO 0.81 0.79 0.87 0.82 HE 0.79 0.79 0.72 0.80 P(HW) 0.6297 0.7035 0.9580 0.3894 Sfo262Lav 19 N 31 87 15 319 A 12 13 7 19 FIS -0.033 -0.074 0.115 0.008 HO 0.87 0.91 0.73 0.84 HE 0.83 0.84 0.80 0.84
50 P(HW) 0.6626 0.9460 0.2865 0.5147 Sfo266Lav 30 N 32 92 15 322 A 13 21 7 29 FIS 0.002 -0.008 0.239 0.048 HO 0.88 0.86 0.60 0.83 HE 0.86 0.85 0.76 0.87 P(HW) 0.0688 0.1244 0.0611 < 0.001 Global N 31-32 87-95 15 319-323 A moy 8.3 10.3 6.3 13.5 A tot 108 134 81 175 HO 0.73 0.72 0.73 0.73 HE 0.74 0.74 0.68 0.77 P(HW) 0.0709 0.0052 0.6102 < 0.001
51
Table 2-3. Genetic differentiation between anadromous and resident brook charr estimated by θST for each locus. N = 32 anadromous and 98 resident fish in 2000; N = 15 anadromous and 323 resident fish in 2001. ** P < 0.001 following 2,000 permutations with alpha adjusted for multiple testing using Bonferroni correction (∝ = 0.0038 for single-locus comparisons and ∝= 0.0125 for multilocus comparisons).
Locus θST 2000 θST 2001
SfoB52 -0.0035 -0.0057 SfoC113 0.0071 -0.0098 SfoC129 -0.0046 0.0146 SfoC28 -0.0007 0.0284 SfoC88 -0.0023 -0.0067 SfoC115 -0.0005 0.0176 SfoD100 0.0006 0.0573 ** SfoD75 0.0027 0.0097 SCO204 -0.0062 0.0227 SCO216 0.0027 0.0116 SCO218 -0.0035 0.0126 Sfo262Lav 0.0147 -0.0105 Sfo266Lav -0.0010 0.0172
Multilocus 0.0007 0.0124 **
52
Tabl
e 2-
4. N
umbe
r of s
ampl
es (N
), m
ean,
var
ianc
e, a
nd ra
nge
of in
divi
dual
repr
oduc
tive
succ
ess (
RS
- num
ber o
f off
sprin
g as
sign
ed to
in
divi
dual
s) fo
r mal
es a
nd fe
mal
es to
geth
er (o
vera
ll) a
nd se
para
tely
acc
ordi
ng to
life
-his
tory
form
. Mea
ns a
nd v
aria
nces
of l
ife-h
isto
ry
form
s are
com
pare
d w
ith A
NO
VA
and
Lev
ene's
test
resp
ectiv
ely.
R
S O
vera
ll
RS
Fem
ales
RS
Mal
es
N
M
ean
Var
ianc
eR
ange
N
M
ean
Var
ianc
e R
ange
N
M
ean
Var
ianc
eR
ange
A
nadr
omou
s 31
4.
48
18.5
21-
19
98.
2235
.94
2-19
4
3.25
4.25
1-5
Res
iden
t 13
8 1.
88
2.15
1-10
17
2.18
3.9
1-9
28
2.39
3.73
1-10
F-Va
lue
(df)
33
.62
(1, 1
67)
39.9
7(1
, 167
)
14
.75
(1, 2
4)17
.96
(1, 2
4)
0.
68(1
, 30)
0.37
(1, 3
0)
P-Va
lue
<0
.000
1 <0
.000
1
0.01
660.
0003
0.41
610.
5466
53
2.9 Figures
Figure 2-1. Location of study area, sampling traps, and electrofishing sites.
54
0
0.2
0.4
0.6
0.8
1
Sfo266
Lav
Sfo262
Lav
Sco21
8
Sco21
6
Sco20
4
SfoC11
5
SfoD10
0
SfoD75
SfoB52
SfoC11
3
SfoC12
9
SfoC28
SfoC88
Loci
Rat
e of
juve
nile
allo
catio
n (%
)
Figure 2-2. Rate of juvenile allocation obtained with PASOS as a function of cumulative number of loci, and using a maximum offset tolerance set to zero (MOT 0 – see text). A plateau is starting to appear at the end of the cumulative curve and the 20% rate attained at SfoC88 was used as a first estimate of the proportion of sampled spawners.
55 (a)
0
4
8
12
16
20
30/6
10/7
20/7
30/7 9/8 19
/829
/8 8/9 18/9
28/9
8/10
18/10
28/10
Date (dd/mm)
Num
ber o
f fis
h
(b)
0
2
4
6
8
10
12
14
120 160 200 240 280 320 360 400Length (mm)
Num
ber o
f fis
h
Figure 2-3. (a) Number of fish caught in the upstream migration trap in 2000 (black) and 2001 (grey) and (b) corresponding length distribution.
56 (a)
0
4
8
12
16
20
100 150 200 250 300 350 400Length (mm)
Num
ber o
f offs
prin
g
(b)
0
1
2
3
4
5
6
7
8
9
100 150 200 250 300 350 400Length (mm)
Num
ber o
f mat
es
Figure 2-4. Reproductive success (number of offspring assigned per individual) (a) and number of mates (b), as a function of body size for female spawners. Open circles are resident life-history fish, while closed circles are anadromous life-history fish.
57
0
2
4
6
8
10
12
100 150 200 250 300 350 400Length (mm)
Num
ber o
f offs
prin
g
Figure 2-5. Reproductive success (number of offspring assigned per individual) as a function of body size for male spawners. Open circles are resident life-history fish, while closed circles anadromous life-history fish.
Chapitre 3. Heritability of life-history tactics and genetic correlation with body size in a natural population of brook charr (Salvelinus fontinalis)
59
3.1 Résumé La présence d’une forme anadrome, qui entreprend une migration vers l’eau salée avant la
reproduction, de pair avec une forme entièrement résidente, qui passe tout sa vie en eau
douce, est un dimorphisme bien connu chez les salmonidés. Quoique ce dimorphisme soit
commun, l’influence génétique et environnementale sur l’adoption d’une tactique de vie en
particulier a rarement été étudiée sous conditions naturelles. Nous avons utilisé des
pedigrees reconstruits à partir de marqueurs microsatellites couplés à un « modèle animal »
pour estimer la variance génétique additive de la tactique adoptée (anadrome vs. résident)
dans une population naturelle d’omble de fontaine, Salvelinus fontinalis. Nous avons de
plus estimé la corrélation génétique entre la tactique adoptée et des traits phénotypiquement
corrélés, la taille et la forme du corps. Une héritabilité significative a été mise en évidence
pour la tactique d’histoire de vie (entre 0.53 et 0.56, tout dépendant du pedigree utilisé)
ainsi que pour la taille (0.44 à 0.50). Il y avait de plus une corrélation génétique
significative entre ces deux traits, où les anadromes étaient génétiquement associés à de
plus grandes tailles à l’âge de 1 an (rG = -0.52 et -0.61). Nos résultats indiquent que la
tactique d’histoire de vie dans cette population a le potentiel de répondre à une sélection qui
agirait soit directement sur la tactique elle-même, soit indirectement sur la taille. Cette
étude est une des seules à avoir utilisé des pedigrees basés sur des relations plein-frères
pour estimer avec succès des paramètres de génétique quantitative sous conditions
naturelles.
60
3.2 Abstract A common dimorphism in life-history tactic in salmonids is the presence of an anadromous
pathway involving a migration to sea followed by a freshwater reproduction, along with an
entirely freshwater resident tactic. Although common, the genetic and environmental
influence on the adoption of a particular life-history tactic has rarely been studied under
natural conditions. Here, we used sibship-reconstruction based on microsatellite data and an
‘animal model’ approach to estimate the additive genetic basis of the life-history tactic
adopted (anadromy vs. residency) in a natural population of brook charr, Salvelinus
fontinalis. We also assess its genetic correlation with phenotypic correlated traits, body size
and body shape. Significant heritability was observed for life-history tactic (varying from
0.52 to 0.56 depending on the pedigree scenario adopted) as well as for body size (from
0.44 to 0.50). There was also a significant genetic correlation between these two traits,
whereby anadromous fish were genetically associated with bigger size at age 1 (rG = -0.52
and -0.61). Our findings thus indicate that life-history tactics in this population have the
potential to evolve in response to selection acting on the tactic itself or indirectly via
selection on body size. This study is one of the very few to have successfully used sibship-
reconstruction to estimate quantitative genetic parameters under wild conditions.
61
3.3 Introduction Organisms that face environmental heterogeneity commonly adjust their phenotype in
response to cues that give information about the current or future state of the environment
(Roff and Bradford 2000). Such phenotypic plasticity is shown in an extreme fashion by
the existence of discrete morphs within a population, such as dimorphism in defensive
structures (horned vs. hornless beetle, Moczek et al. 2002), in feeding specializations
(omnivorous and carnivorous morphs of toad tadpoles, Frankino and Pfennig 2001), in life
cycle (wing dimorphism in insects, Roff 1994b) or in mating tactics (satellite vs. territorial
males in fish, Aubin-Horth and Dodson 2004). Many of those discrete phenotypes
generally involve environmentally cued threshold traits. Threshold traits are thought to be
based on underlying characteristics that vary in a continuous way, called the ‘liability’, with
a threshold of sensitivity (Roff 1996). Individuals lying above the threshold develop into
one morph while individuals below the threshold develop into the alternate morph (Hazel et
al. 1990; Falconer and Mackay 1996; Roff 1996). Characteristics such as the concentration
of juvenile hormone, lipid storage or growth efficiency have been shown to affect the
adoption of alternate morphs, and are hypothesized as potential underlying traits (Roff et al.
1997; Thorpe et al. 1998; Forseth et al. 1999; Emlen and Nijhout 2000). Because it reflects
and/or influences many other factors related to the adoption of alternate morphs, body size
is the most commonly reported liability trait (Moczek et al. 2002; Aubin-Horth and Dodson
2004). The threshold trait framework, associated with the conditional strategy theory,
relies on the basis of fitness trade-offs, where an individual expresses the phenotype that
yields the higher fitness payoffs for its particular condition (i.e. depending on the value of
its liability trait), even though it may have a lower overall average fitness (Gross 1996).
The heritable basis of a given phenotypic trait, and its genetic correlation with other traits,
must be established to predict its response to selection and thus its evolutionary potential
(Falconer and Mackay 1996; Kruuk 2004). Threshold traits are likely to have a polygenic
basis, and most polygenic traits bear significant levels of genetic variation (Roff 1996).
Indeed, many studies have reported a heritable basis for threshold traits, mainly through
62 heritability of their liability traits (reviewed in Roff 1996; see also Mousseau et al. 1998;
Ostrowski et al. 2000; Garant et al. 2003). Thresholds of sensitivity may also harbor
genetic variation themselves, such that threshold values that signal the switch between
alternative phenotypes also differ among genotypes (Hazel et al. 1990; Emlen 1996;
Hutchings and Myers 1994; Hazel et al. 2004). However, most studies that have
documented the quantitative genetics of threshold traits were performed in laboratory or
controlled experiments (but see Garant et al. 2003; Wilson et al. 2003a). Since there is
accumulating evidence that heritability and genetic correlations of traits are influenced by
their environments (reviewed in Charmantier and Garant 2005 and in Sgrò and Hoffmann
2004), direct measures of quantitative genetic parameters in nature are essential and may
improve our understanding of how evolution takes place in the wild.
Following hatching in freshwater, many populations of salmonid fishes (trouts, salmons
and charrs) may either remain in freshwater during their entire lives (residency tactic) or
undertake a feeding migration to sea before returning to freshwater to spawn (anadromy
tactic). The conditional strategy framework and threshold trait hypothesis have been
employed to understand the evolutionary basis of this common dimorphism in salmonids
(Hutchings and Myers 1994; Gross 1996; Thorpe et al. 1998; Aubin-Horth and Dodson
2004). Factors such as body size, growth rate, lipid reserves (Rowe and Thorpe 1990;
Rikardsen et al. 2004), and growth efficiency (Forseth et al. 1999; Morinville and
Rasmussen 2003) have all been suggested as potential underlying traits influencing the
adoption of one form or the other. There may be a critical time when a particular threshold
value that is genetically influenced and variable among individuals must be exceeded in
order for reproduction to occur (Hutchings and Myers 1994; Thorpe et al. 1998). If this
threshold is not exceeded, migration occurs and anadromy is expressed. Some studies have
estimated the heritability of adopting a resident life-history, by documenting the genetic
basis of early sexual maturation, in controlled experiments for aquaculture purposes
(Silverstein and Hershberger 1992; Heath et al. 1994; Wild et al. 1994; Mousseau et al.
1998). To our knowledge, however, no studies have attempted to estimate heritability of
anadromy and residency in the wild.
63
The development of hypervariable genetic markers and analytical tools related to kinship
and parentage, have eased the estimation of quantitative genetic parameters in nature
(Garant and Kruuk 2005). Namely, relationships among wild individuals, which are
essential to build reliable pedigrees and estimate quantitative genetic parameters, can now
be accurately established (Garant and Kruuk 2005). Moreover, the so-called ‘animal
model’ is being increasingly used in natural conditions for estimating quantitative genetic
parameters (reviewed in Kruuk 2004; see also Garant et al. 2005b; Charmantier et al. 2006;
Wilson et al. 2006). However, very few studies have yet combined sibship-reconstruction
based on genetic data and an ‘animal model’ approach to estimate quantitative genetic
parameters in the wild.
Using this framework, our main objective is to elucidate the mechanisms influencing the
expression of a dichotomous life-history in a natural population of brook charr, Salvelinus
fontinalis, which contains both anadromous and resident individuals living in sympatry.
Recent studies have demonstrated frequent mating between anadromous and resident
individuals in this population, mainly through resident males mating with anadromous
females (Thériault et al. 2007a). Also, body size in this population has been shown to be
correlated with the adoption of anadromy and residency (Thériault and Dodson 2003).
Growth efficiencies also differ between anadromous and resident individuals; before
migration, future migrants exhibit lower growth efficiencies than future residents and
higher associated metabolic costs (Morinville and Rasmussen 2003). Also, future migrants
inhabit habitats of faster water currents, and thus it is not clear if their higher metabolic
costs reflect higher standard metabolic rate or higher swimming costs related to the
exploitation of a more energetically costly habitat (Morinville and Rasmussen 2006).
Morphological analyses confirmed previous results on differential habitat use: migrants
have a more streamlined morphology than resident fish, which is what is expected in faster
current velocity habitat because such an elongated morphology incurs lower swimming
costs (Boily and Magnan 2002; Morinville and Rasmussen 2007).
64
Altogether, these results raise the hypothesis that dimorphism in life-history tactics in this
brook charr population represents a threshold trait with potential underlying characters
related to energetic budget. Here, we aimed to estimate the heritability of this threshold
trait (residency/anadromy) by means of pedigree reconstruction assisted by molecular
markers and the ‘animal model’ approach. We also quantified heritability of morphological
traits (body size and shape) and tested whether phenotypic correlations previously observed
among these traits and the life-history tactics (Thériault and Dodson 2003; Morinville and
Rasmussen 2006 and 2007) translate into significant genetic correlations.
3.4 Material and Methods
3.4.1 Study Site and Sampling Fishes were collected from the Morin Creek (average 5.6 m wide, 0.3 m deep, See Figure
3-S1), a tributary of the Sainte-Marguerite River, Quebec, Canada. An impassable
waterfall (75 m high) is located 4 km upstream from the mouth of the tributary. Sampling
was conducted in a 2.5 km section below this waterfall. Previous studies have shown that
anadromous brook charr undergo upstream migration into this tributary for spawning, and
that reproduction between anadromous and resident fish is common (Thériault et al.
2007a).
No obvious external expression of smoltification occurs in migrant brook charr
(McCormick et al. 1985 and V. Thériault, personal observation) making it very difficult to
differentiate a migrant from a resident until the moment of migration. We identified
migrants as fish captured in trap nets during downstream migration; previous mark-
recapture studies on the same system have shown that these fish were true migrants
(Thériault and Dodson 2003). Fish captured in streams following the migration period
were defined as residents. Outstream migration of first-time migrants occurs between mid-
May to mid-June and involves 1 and 2 year old fish in this system (Thériault and Dodson
2003). Two cohorts were sampled: cohort 1 was composed of 1+ fish (designating
65 individuals somewhat older than 1 year of age as they are hatched in early May) sampled in
2002 and 2+ fish in 2003, and cohort 2 was composed of 1+ sampled in 2003 and 2+
sampled in 2004. The trap nets were installed 1 km from the mouth of the stream and were
operated from mid-May to mid-June for the three years of sampling. They were visited
twice daily and the following morphological measures were taken in the field in 2002 and
2003 (see Morinville and Rasmussen, 2007, for detailed methodology): fork length (FL),
standard length (SL), body depth (DEP), maximum body width (WID), peduncle depth
(PED), caudal fin height (CAUD), pectoral fin length (PECT) and pelvic fin length
(PELV). The adipose fin was also clipped and preserved in 95% ethanol for subsequent
genetic analyses, and all fish were released. Resident juveniles were captured using a
backpack electro-fisher following the migration period beginning mid-June for the 3 years
of sampling. Tissue sampling and body measurements of resident fish followed the same
protocols as described for migrant juveniles. Fish were classified as either 1+ or 2+ based
on the frequency distribution of body lengths, previously validated with age determination
using otoliths (Thériault and Dodson 2003).
3.4.2 Pedigree reconstruction Individuals of age 1+ and 2+ were partitioned into groups of putative full siblings using
PEDIGREE 2.2 (Herbinger 2005) based on data from 13 microsatellite loci (see Thériault
et al. 2007a, for methodological details). Six loci showed departure from Hardy-Weinberg
equilibrium (Table 3-1), which could be explained in part by the presence of null alleles at
three loci (Thériault et al. 2007a, see discussion about the consequence of null alleles on
sib-reconstruction). Genotypic linkage disequilibrium was also found between pairs of loci
and we believe that this might lower the power of our dataset to reconstruct pedigrees.
The partitioning of full-sibs was done for each cohort separately since we assumed that
full-sibling relationship between fish of different cohorts is very unlikely. In the complete
absence of parental information, PEDIGREE uses a Markov Chain Monte Carlo (MCMC)
approach to partition sibling by maximizing an overall likelihood score on the basis of
pairwise likelihood ratios of being full siblings or unrelated (Smith et al. 2001; Butler et al.
2004; Herbinger et al. 2006). The algorithm is constrained such that within a group of
putative siblings the genotypes at each locus must be able to be derived from a single
66 parental pair (Herbinger 2005). Multiple runs (from 20 to 60) were performed to find the
“best” sibship configurations, i.e. the one yielding the highest score. This configuration is
then retained for significance testing using genotype randomizations followed by full-sib
reconstruction. Thus, a total of 100 sets of the same number of unrelated individuals as
used for the actual pedigree reconstruction was created, sampled from populations with the
same genotypic frequencies as in our original dataset. The overall significance (P-value) of
the pedigree retained was estimated by the proportion of the 100 randomized trials with a
partition score as high or higher than the observed score. Significance of each full-sib
family was also assessed. A specific full-sib family was deemed significant and kept in the
analysis when its internal cohesion score, the average of the Log of the pairwise likelihood
ratios in that group, was higher than the cohesion scores seen in at least 95 full-sib groups
of the same size out of the 100 produced by the randomization procedure. All other full-sib
groups were discarded from subsequent analyses, as well as all individuals not related to
any other (see also Herbinger et al. 2006).
3.4.3 Quantitative genetic analyses Single cohort analysis did not yield significantly different quantitative genetic estimates
(not shown). Consequently, pedigrees obtained for cohorts 1 and 2 were combined in
subsequent analyses. Traits of interest included life-history tactic, scored as 0 for
anadromous (fish captured as migrants in trap nets) and 1 for resident (fish captured in the
stream by electro-fishing), body size (fork length, FL) and six morphological measures:
body depth (DEP), maximum body width (WID), peduncle depth (PED), caudal fin height
(CAUD), pectoral fin length (PECT) and pelvic fin length (PELV). Principal component
analysis (PCA, SAS system v.8) was performed to remove the size- dependent effect on
these morphological measures. In the presence of a size effect, the PCA grouped all
morphometric measures along one factorial axis, which was largely explained by variation
in size among fish (data not shown). To investigate the variation in body shape
independently of size variation, we removed the size effect by regressing the value of each
variable for each fish on the first factorial axis. The residual values obtained were used to
analyze morphological diversity. A general linear model (GLM, JMP™ 5.0.1a, SAS
Institute inc.) for continuous morphological traits, and a logistic regression for the
67 dichotomous trait (life-history tactic) were conducted to determine which factors needed to
be included as fixed effects in subsequent quantitative genetic analyses. This was done in
order to account for temporal heterogeneity in environmental effects on the phenotype.
Because of the small number of 2+ individuals in our pedigree (N = 41), analyses could not
be calculated separately by age, which was thus fitted as a fixed effect. Other fixed factors
were year of sampling (2002, 2003 and 2004) and day of capture. Age had a significant
influence only on FL (F1,214 = 115.22, P < 0.0001). Year was included as a fixed effect for
every trait except PECT (life-history tactic, c22, N=349 = 14.94, P = 0.001; WID, F1,214 =
7.11, P = 0.008; DEP, F1,214 = 8.36, P = 0.004; PED, F1,214 = 31.84, P < 0.0001; CAUD,
F1,214 = 30.78, P < 0.0001; PELV, F1,214 = 8.53, P = 0.004). Given its effect on FL (F1,214
= 7.45, P = 0.0069), DEP (F1,214 = 16.88, P < 0.0001), PED (F1,214 = 38.39, P < 0.0001),
CAUD (F1,214 = 22.75, P < 0.0001) and PELV (F1,214 = 5.76, P = 0.02), day of capture was
fitted as a fixed effect for these traits.
Heritability of each trait was estimated using a mixed model REML estimation procedure
using the software package ASReml (1.10, VSN International Ltd.). Pedigree information
was used to fit a univariate animal model. The model had the following form:
y = Xb +Za + e
where y is a vector of phenotypic values, b and a are the vector of fixed and random
additive effects, e is the vector of residual values, and X and Z are the corresponding design
matrices which relate the effects to y. Total phenotypic variance (VP) of each trait was
partitioned into additive genetic variance (VA) and residual variance (VR). The narrow-
sense heritability (h2) was estimated as the ratio of the additive genetic variance to the total
phenotypic variance: h2 = VA/VP. Significance of the additive genetic component of each
model was assessed by comparing the full model with a reduced model lacking the additive
genetic component using a likelihood ratio test (following a χ2 distribution, where χ2 = -
2*difference in log likelihood and the change in degrees of freedom between models =1).
Since life-history tactic was scored as a binary trait, the heritability estimate and its
associated standard error were transformed to the underlying liability scale (see Falconer
68 and Mackay 1996; Roff 2001). Heritability of life-history tactic thus refers to the
heritability of the liability of the trait, but for convenience, we refer simply to its
phenotypic manifestation (i.e. h2 of life-history tactic, see Roff et al. 1997).
Genetic correlation between life-history tactic and length, as well as between life-history
tactic and the six morphological traits related to body shape were calculated using pairwise
multivariate animal models. The same fixed and random effects used for heritability
estimation were used in these analyses. Genetic correlations were calculated as
rG = COVAB / VAVB
using the program ASReml. Significance of the genetic covariance was assessed by
comparing the likelihood of the model containing the genetic covariance component with
the reduced model in which the genetic covariance was fixed at 0, again using likelihood
ratio tests.
3.4.4 Power and sensitivity analysis
3.4.4.1 Power analysis We used the software package PEDANTIX (Morrissey et al. 2007) to assess the power of
the resolved pedigree to detect significant quantitative genetic parameters. This power
analysis wants to determine if enough information is found in our dataset to detect
quantitative genetic parameters, particularly because data availability is limited for
morphological measures (see results).
PEDANTIX uses a given pedigree to simulate phenotypic data for two traits
simultaneously (continuous data, PHENSIM application) according to a user-defined
variance-covariance matrix. Continuous data can be converted afterwards to binomial data
using the application ADVPHESIM. The phenotypic data obtained are then used for
quantitative genetic analyses, following the same procedure as for real data (i.e. animal
69 model implemented in ASReml). We used Pedigree 1 (see below) and simulated
phenotypic data for a continuous trait and a binomial trait, according to different values of
heritability and genetic correlation (from 0.2 to 0.5) and assessed the power of our pedigree
to detect the expected quantitative genetic parameters. Power was expressed by the number
of simulations that gave a significant heritability estimate as a fraction of the total number
of simulations (N = 20).
3.4.4.2 Varying parameters in PEDIGREE PEDIGREE can yield different full-sib arrangements depending on the numbers of runs and
the parameters used by the user, all of which can be equally probable. PEDIGREE does
not provide any strict criterion to choose the reconstruction that represents the most
plausible pedigree. Here, we assessed the consequences of varying one parameter, the
weight, on the quantitative genetic analyses by comparing two different pedigrees
reconstructed using the procedure described above. The weight is an ad hoc parameter in
the range of 1 to 10, 1 being neutral and the default value used in most cases (Smith et al.
2001; Butler et al. 2004; Herbinger et al. 2006). A higher weight promotes the coalescence
of individuals into larger groups, which is useful since PEDIGREE tends to split very large
full-sib families into subgroups (Smith et al. 2001; Butler et al. 2004). Here, we compared
the quantitative genetic parameters estimated using pedigrees reconstructed with a weight
of 1 (Pedigree 1) and a weight of 5 (Pedigree 2).
3.4.4.3 Full-sib assumption We assumed a full-sib structure while using PEDIGREE, but this assumption is likely to be
violated owing to complex mating patterns in our system, where both sexes have many
partners (Thériault et al. 2007a). PEDIGREE allows the reconstruction of kin-groups
(mixtures of full-sibs, half-sibs and sometimes higher degrees of relationship such as
cousins), and to mix kin pedigree with full-sib pedigree in order to reconstruct half-sib
structure, thus potentially resolving a more complete pedigree (Herbinger et al. 2006).
However, this procedure was too complex to be powerful in our system, and we chose to
assess the consequence of using a full-sib constraint on the estimation of quantitative
genetic parameters using two alternative methods. First, we added several half-sib
70 relationships to Pedigree 1 by combining results from parentage assignments previously
obtained with PASOS (Duchesne et al. 2005; Thériault et al. 2007a) with those from
PEDIGREE, such that the identity of one parent was added to a full-sib family when one or
more juveniles in that particular full-sib family had a known parent. This exercise resulted
in 12 full-sib families sharing four different known parents (involving a total of 60 progeny,
Pedigree 3). Second, we used the software package PEDANTIX (Morrissey et al. 2007) to
simulate half-sib structure in Pedigree 1 and to evaluate the consequences on quantitative
genetic parameters. We first added some half-sib relationships to full-sib Pedigree 1 by
assigning a common parent to full-sib families on a pairwise basis. For example, full-sib
families 1 and 2 now shared a common parent, as well as families 3 and 4, and so on. This
half-sib pedigree was then used by PEDANTIX to simulate phenotypic data according to
different scenarios where heritabilities and genetic correlations ranged from 0.2 to 0.5. The
quantitative genetic parameters were then estimated with ASReml using the simulated
phenotypes, along with the actual full-sib pedigree. This allowed assessing the
consequences of using a full-sib pedigree even if our true pedigree had an significant half-
sib structure.
3.5 Results
3.5.1 Pedigree reconstruction A total of 974 age 1+ and 2+ juveniles were available for sib-reconstruction for 2002, 2003
and 2004 combined (anadromous form N = 440, resident form N = 534). The best
pedigrees retained yielded scores that were not reached by any of the 100 randomizations
performed, indicating that we could reject the null hypothesis that the full-sib partitions
could have been seen in sets of unrelated individuals (P < 0.01). From the initial 974
juveniles, we sorted out those not grouping with any other based on the assumption of full-
sibs as well as those clustering into families that were not significant at a 0.05 level. This
left 349 juveniles grouped into 91 full-sib families for the quantitative genetic analyses for
Pedigrees 1 and 3. When using a weight of 5 (Pedigree 2), this same process left 351
juveniles sorted into 89 full-sib families. Family size ranged from two to 12 individuals,
with a mean (± SE) of 3.84 ± 0.17 and 3.94 ± 0.19 individuals per family for Pedigree 1+3
71 and Pedigree 2, respectively (Figure 3-1). Thus, the use of a higher weight did not translate
into the coalescence of individuals into larger families.
3.5.2 Heritability and genetic correlations Significant additive genetic variance and heritability were obtained with the three pedigrees
for the life-history tactic, body size (FL), as well as body depth (DEP), the heritability
values ranging from 0.39 to 0.56 (Table 3-2). Power analysis involving a binomial trait
showed that a moderate to high value of heritability was needed in order to be detected with
our pedigree structure; power dropped from 0.9 to 0.6 when heritability values of 0.5 and
0.4 were simulated, respectively (Table 3-3). The threshold transformation proved to be
reliable, as estimated values following the threshold transformation were generally in good
agreement with expected simulated values on the continuous scale (Table 3-3). This
analysis also revealed that the type of pedigree structure that we used was capable of
detecting a heritability of 0.2 for body size with a power of 0.75, but that heritabilities
below 0.4 were only detected in 60% of the cases for morphological traits (sample size is
smaller for morphological traits, Table 3-2 and 3-3). It thus appear that data availability
was of concern in this system and that the pedigree used for morphological data was not
adequate to detect small to moderate heritabilities. As a result, large standard errors were
associated with all quantitative genetic estimates of traits other than body size. Also,
estimates of heritabilitiy of morphological traits varied slightly depending on the pedigree
used. Namely, marginally significant heritability was detected for PED with Pedigree 2 but
not with the two other pedigrees (Table 3-2). Moreover, Pedigree 1 and 2 led to marginally
significant heritability for PELV, but not Pedigree 3 (Table 3-2).
A negative genetic correlation between life-history tactic and body size was significant for
pedigree 1 and 3, whereby anadromous fish were genetically associated with bigger size at
age (Table 3-4). Pedigree 2 yielded a significant genetic correlation between life-history
tactic and PECT (Table 3-4). None of the other genetic correlations between life-history
tactic and morphological traits were significant, and all the estimates were associated with
large standard errors, showing considerable variation between pedigrees (Table 3-4).
72 Power analysis revealed that genetic correlations were harder to detect than heritabilities.
Assuming a heritability of 0.5 for both continuous and binomial traits, a genetic correlation
of 0.5 could be detected in 65% of the cases between life-history tactic and body size (N =
349, mean estimated rG = 0.52, mean standard error = 0.22) and in 55% of the cases
between tactic and morphological data (N = 215, mean estimated rG = 0.68, mean standard
error = 0.32).
Adding information on half-sib relationships resolved using PASOS to the reconstructed
pedigree did not change any of the estimates of heritability or genetic correlation as these
were not significantly different between Pedigree 1 and 3 for any of the traits (z-scores
from 0.01 to 0.78, P-values from 0.43 to 0.99). The same conclusion was obtained using
PEDANTIX as quantitative genetic parameters estimated using the full-sib pedigree
yielded results that agreed well with the expected values assuming a half-sib structure
(Table 3-5).
3.6 Discussion The main objective of this study was to estimate the heritability of life-history tactics
(residency/anadromy) in brook charr under natural conditions by means of pedigree
reconstruction assisted by molecular markers. Our results demonstrate that the adoption of
either anadromy or residency involves a significant amount of additive genetic variance.
To our knowledge, this is the first study to provide an estimate of heritability for these
tactics under natural conditions.
The heritability value obtained for life-history tactic is within the range of heritability
estimates generally reported for threshold traits (reviewed in Roff 1996), where
approximately one-half of the phenotypic variation can be attributed to additive genetic
variance. It thus suggests that tactics have considerable potential to respond to selection and
evolve, but it also implies a relatively important environmental influence on the adoption of
a tactic. Other studies have provided estimates of heritability of threshold traits linked to
73 anadromy and residency in salmonids under controlled experiments (early maturity, Wild et
al. 1994; precocious maturity, Silverstein and Hershberger 1992; jacking, Heath et al. 1994;
Mousseau et al. 1998; Heath et al. 2002, smolting and maturing, Thrower et al. 2004) and
the values obtained were highly variable (from low and non-significant to high). These
wide differences highlight the fact that it is unwarranted to compare absolute values of
heritability as they are a property of the population under study and the conditions where
they were measured (Stearns 1992; Falconer and Mackay 1996).
We also found significant heritability for body size, our estimates being higher (0.44 to
0.50) than other reported estimates for body size (at age 1+) in salmonids under natural
conditions (e.g. 0.001 ± 0.04, and 0.26 ± 0.12 in brook charr for two populations
respectively, Wilson et al. 2003a; 0.04 ± 0.15, in Atlantic salmon, Salmo salar, Garant et al.
2003). Body size is an important liability trait for early sexual maturity and anadromy in
salmonids, especially in Atlantic salmon where early-maturing males are bigger in early life
stages than anadromous males (Whalen and Parrish 1999; Garant et al. 2002; Aubin-Horth
and Dodson 2004). In a previous study from the same tributary, back-calculated length-at-
age revealed that smaller brook charr at age 1+ delay migration to the following year,
resulting in age 2+ migrants being smaller than age 2+ resident fish (Thériault and Dodson
2003). At age 1+ however, when removing these smaller future migrants from the sample
of resident fish, these authors observed no difference in body size between migrant and
resident fish. Thériault and Dodson (2003) thus proposed that body size was not the only
trait associated with the adoption of the life-history tactic at that age. Physiological traits
related to the energetic budget are more likely to influence tactic choice (Morinville and
Rasmussen 2003 and 2006). Heritability for body size (0.44-0.50) was similar to that of the
liability itself (the life-history tactic, 0.52-0.56) suggesting that body size could be a major
component of the liability, although other components might also be involved.
The covariance analyses suggest a genetic correlation between life-history tactic and body
size, although larger sample sizes would have been needed to yield more accurate
74 estimations of genetic correlation. Another potential concern is the reliability of the
correlation between a binomial trait and a continuous one using the animal model method,
as this has rarely been done (but see Wilson et al. 2003a). However, simulation analysis
using PEDANTIX showed that the estimated genetic correlation between a binomial trait
and a continuous trait corresponded to the expected one assuming two continuously
distributed traits. Our results thus suggest that differences in body size observed previously
between anadromous and resident brook charr (phenotypic correlation of -0.15, P < 0.01;
Thériault and Dodson 2003) are partly due to the genetic correlation between size and life-
history tactic. Since anadromy was coded 0 and residency 1, the negative correlation we
documented implies that bigger fish would be genetically more prone to be anadromous.
This conclusion must be constrained to age 1+ fish and is consistent with the phenotypic
correlation: at age 1+, migrants appear to be bigger because the sample of resident fish
includes small fish, which would ultimately migrate the following year (Thériault and
Dodson 2003). Also, the genetic correlation between these two traits suggests that
selection acting on one of them would likely have an effect on the other. For example,
given continued selection against fish migrating at age 1+ (for instance because of a more
pronounced fishing pressure on anadromous than resident fish), one would predict a
correlated response in body size, 1+ individuals being smaller in subsequent generations. A
similar result was obtained in a breeding experiment with steelhead trout (Oncorhynchus
mykiss), where the proportion maturing at age 2 was negatively genetically correlated with
mass at age 1 (Thrower et al. 2004). Yet, the intermediate values of the correlation found
in the present study (-0.20 to -0.61) give support to the hypothesis stated above that body
size is not the only component of the liability underlying life-history tactic expression. If
so, one would expect body size and the liability trait to reflect more or less the same genetic
character and the genetic correlation obtained to be closer to unity (Falconer and Mackay
1996).
Estimating genetic quantitative parameters in the wild is a difficult task and it is not always
feasible to estimate the variance components that are likely to influence phenotypic
covariance between relatives. In this study, we lacked the information required to estimate
75 maternal or common environmental effects, which, if present, could potentially inflate
heritabilities values (Falconer and Mackay 1996). First, we had no information on the
parental generation of our fish, and thus mother identity was unknown. However, maternal
effects on body size have been shown to be non-significant at 0, 1 and 2 years old in
Atlantic salmon of the Sainte-Marguerite River (Garant et al. 2003). More generally, it has
also been showed that when maternal effects are detected in salmonids, they are more
important at early larval stages and seem to decrease with age (Heath et al. 1999; Perry et
al. 2005a). Because our study focused on later life history stage, we assumed that maternal
effects were negligible. Common environment effects are another concern that cannot be
easily addressed in our system. The sampling location of each resident fish was recorded,
but because the migrants were all sampled in one downstream trap during their
outmigration in spring, their section of origin was unknown. However, animal models with
sampling location as a fixed effect for residents have been fitted for body size, and gave
similar results to those without sampling location (results not shown). Moreover,
qualitative analysis of our results from parentage analyses (see Thériault et al. 2007a)
showed that members of only five half-sib families out of 44 were sampled in the same 300
meter sampling section. All other juveniles sharing one parent were found all along the
stream, from several hundred meters to 3 kilometers apart, suggesting considerable
dispersal during the first and/or second year of life. Similar qualitative analysis of our data
with the full-sib information obtained here with PEDIGREE revealed that only 30% of the
members of a same family were found in the same 300 meters section. There is thus little
evidences suggesting that common environment effects might be inflating our quantitative
genetics parameters.
Estimates of quantitative genetic parameters obtained in nature have mainly been obtained
from long-term studies that used large pedigrees (reviewed in Kruuk 2004). While the
number of recent studies that use highly polymorphic genetic markers to assess pedigree
information is growing (reviewed in Garant and Kruuk 2005), the limitations of such tools
need to be acknowledged. Here, we used a sib-reconstruction method assuming unrelated
full-sib families only, although this assumption is likely to be violated in the study system.
76 Indeed, mating patterns in brook charr involve both sexes mating with many different
partners and thus a more realistic pedigree would necessarily involve an important half-sib
structure (Thériault et al. 2007a). A procedure nesting full-sib families with kin groups has
been used elsewhere to provide a more accurate pedigree reconstruction (see Herbinger et
al. 2006), but the limited numbers of large families in our study prevented us from using
this approach. Moreover, we attempted to use a more complete pedigree (obtained from
parentage analysis), but ended up with a pedigree that did not contain enough related
individuals (see Thériault et al. 2007a). This prevented model convergence when estimating
quantitative genetic parameters. Nevertheless, assuming a full-sib pedigree structure may
have only a limited impact since most of the information used in estimating quantitative
genetic parameters stems from close relatives (Thomas and Hill 2000). This was supported
here by the absence of significant differences in quantitative genetic parameter estimates
between the full-sib pedigree (Pedigree 1) and the pedigree including some half-sib
relationships (Pedigree 3). Furthermore, simulations showed no significant effect when
using phenotypes created from a half-sib pedigree along with our full-sib pedigree to
estimate quantitative genetic parameters. This result also argue in favor of additive genetic
effects not being confounded with non-additive ones owing to the use of full-sibs only
(Falconer and Mackay 1996).
Sib-reconstruction using a MCMC approach is not free of errors. Namely, the algorithm
used by PEDIGREE 2.2 tends to split large full-sib families into subgroups when using
allelic frequencies estimated in the sample instead of true population allelic frequencies
(Smith et al. 2001; Butler et al. 2004). This type of error will have the same consequence
as considering only full-sibs, i.e. the split families are assumed to be unrelated when in fact
they are, which may artificially decrease phenotypic variance between families and thus
produces conservative heritability estimates (Wilson et al. 2003b). However, our use of a
higher weight while reconstructing Pedigree 2 specifically aimed to mitigate this tendency
but did not result in coalescence of large full-sib groups. We thus conclude that splitting
large full-sib families into subgroups was not of concern in this study. Moreover,
quantitative genetic estimates were generally in good agreement between pedigrees using a
77 weight of 1 or 5. Genotyping errors are also likely to influence sib-reconstruction (Butler
et al. 2004) and these errors are not taken into account in subsequent quantitative genetic
analyses under the animal model. Null alleles have been detected in this population
(Thériault et al. 2007a). Such mistyping of heterozygotes as homozygotes is more likely to
cause families to be split rather than incorrect families to be formed, which should cause a
downward bias in quantitative genetic parameter estimates (Thomas and Hill 2000).
Simulation analyses performed by Butler et al. (2004) have shown that the reconstruction
algorithm such as that implemented in PEDIGREE 2.2 is relatively robust to such
problems. It appears that when information from several other loci is available, the
resulting offspring genotypes are still full-sib compatible, and are, in the majority of cases
(75%), consistent with Mendelian rules (Butler et al. 2004).
Two studies have compared estimates of quantitative genetic parameters obtained using a
sib-reconstructed pedigree to ideal values obtained with a « true-pedigree » resolved from
parentage analysis: both concluded that the results were reliable and close to ideal values,
although parameters were generally underestimated (Thomas et al. 2002; Wilson et al.
2003b). In particular, Wilson et al. (2003b) used an aquaculture population of rainbow
trout (Oncorhynchus mykiss) to compare heritability and genetic correlation estimates
between a pedigree obtained by sibship-reconstructions (using the same MCMC approach
as in the present study) and a pedigree built using a parentage analysis based on an
exclusion approach. Their population contained a high number of half-sib relationships due
to factorial crossing in the parental generation, but these relationships were not taken into
account while performing sibship-reconstructions (as in our study). The authors concluded
that the underestimation of quantitative genetic parameters from sib-reconstructed
pedigrees, relative to a “true-pedigree”, is mainly explained by the complex structure of the
true pedigree. The true pedigree consisted of a high number of half-sibling relationships,
which caused an inaccurate partitioning of full-sibships and reduced the recognition of
relatedness between families (see Wilson et al. 2003b). Furthermore, the authors reported
difficulties in obtaining meaningful estimates of quantitative genetic parameters, especially
78 for genetic correlations, when using subsets of their dataset and thus smaller sample sizes
(Wilson et al. 2003b).
Although our analyses suggest no major problems of assuming only full-sibs relationships
(e.g. no underestimation of estimated parameters as observed by Wilson et al. 2003b), the
methods we used to reach such a conclusion may not completely account for the
complexity of the half-sib structure. Furthermore, we do not know the extent of inaccurate
partitioning of full-sibs owing to a high number of half-sibs in our system and to the
presence of null alleles at certain loci. Resolving the half-sib structure in such a system
where the mating pattern is complex and where full-sib family sizes seem small (at least
when using 1+ and 2+ juveniles for sib-reconstruction) would require parental assignment
and thus a sampling of spawners as complete as possible. Because sib-reconstruction was
the only alternative, increasing juvenile sample size would have improved the power of
analyses in our case. Arguably however, such constraints are likely to apply to most
studies performed in similar systems involving natural populations. Finally, as adoption of
a particular life-history tactic can occur at two different ages in our study system, it would
be pertinent in future studies to measure age-specific values of heritability and genetic
correlation. Indeed, age-specific variation in the amount of genetic variance has been
shown to occur under natural conditions in morphological and life-history traits (see Perry
et al. 2004; Charmantier et al. 2006). As such, different age classes are different with
respect to their evolutionary potential or response to selection. Moreover, estimates over
many years would also shed light on the temporal stability of response to selection, as
genetic parameters are expected to change depending on environmental conditions
(Hoffmann and Merilä 1999; Charmantier and Garant 2005).
To conclude, this study represents a contribution towards the acquisition of estimates of
quantitative genetic parameters under wild conditions, and more than being one of the few
(see also Wilson et al. 2003a) to demonstrate the usefulness of sibship-reconstruction in
79 nature in the absence of parental information, it also provides for the first time heritability
estimates of anadromy and residency in salmonids in an entirely natural set-up.
3.7 Acknowledgments We acknowledge A. Boivin, S. Bordeleau, M. Foy-Guitard, S.-P. Gingras, Fannie Martin,
François Martin, A. Ménard, G. Morinville, L. Papillon, L. V. St-Hilaire Gravel, and R.
Saint-Laurent for field assistance and laboratory work. The authors would also like to thank
C. M. Herbinger for assistance with the software PEDIGREE 2.2 and M. B. Morrissey for
assistance with the software PEDANTIX. Funding of this project was provided to J.J.D.
and L.B. by NSERC of Canada (Strategic Grant and Collaborative Special Projects), the
Fondation de la Faune du Québec, the Government of Québec (FAPAQ), the Government
of Canada (Economic development) and the financial partners of AquaSalmo R&D. This
study is a contribution to the program of CIRSA (Centre interuniversitaire de recherche sur
le saumon Atlantique) and Québec-Ocean. V.T. and D.G. were financially supported by
funding from NSERC and FQRNT.
80
3.8 Tables Table 3-1. Number of alleles (N), observed (Ho) and expected (He) heterozygosity at each locus. * indicates significant departure from Hardy-Weinberg equilibrium. SfoB52, SfoC113, SfoC129, SfoC28, SfoC88, SfoC115, SfoD100, SfoD75, T. L. King, US Geological Survey, unpublished; SCO204, SCO216, SCO218 from DeHaan and Ardren (2005); Sfo262Lav, Sfo266Lav from Perry et al. (2005b).
Locus N Ho He SfoB52* 12 0.68 0.74SfoC113 12 0.79 0.75SfoC129 7 0.73 0.71SfoC28 10 0.52 0.54SfoC88 6 0.60 0.61SfoC115* 23 0.55 0.63SfoD100* 10 0.76 0.81SfoD75* 13 0.75 0.80SCO204* 13 0.76 0.80SCO216 18 0.88 0.89SCO218 14 0.81 0.80Sfo262Lav* 19 0.85 0.82Sfo266Lav 30 0.87 0.87
81
Table 3-2. Sample size (N), trait means with their standard deviation (SD), estimates of residual (VR), additive (VA) and phenotypic (VP) variance components and heritability (h2) with their standard error (SE) for life-history tactic (anadromy/residency: Tactic), body size (fork length: FL) and six morphological traits (body depth: DEP, maximum body width: WID, peduncle depth: PED, caudal fin height: CAUD, pectoral fin length: PECT and pelvic fin length: PELV, all transformed following PCA, see methods) in brook charr. Pedigree 1 is the best one obtained with a weight of 1, Pedigree 2 is the best one obtained with a weight of 5, and Pedigree 3 is the same as Pedigree 1, but where half-sib relationships were added (see methods). h2 of the life-history tactic is given transformed to the liability scale (see methods). P-values are those obtained from likelihood ratio tests to assess the significance of the additive genetic component.
Traits N Mean (SD) VR (SE) VA (SE) VP (SE) h2 (SE) P
Pedigree 1 Tactic 349 1.47 (0.50) 0.15 (0.03) 0.08 (0.03) 0.24 (0.02) 0.56 (0.18) 0.0003FL (mm) 349 88.95 (14.55) 42.33 (8.51) 42.73 (12.47) 85.06 (7.18) 0.50 (0.12) < 0.0001DEP 215 -0.15 (0.80) 0.26 (0.06) 0.17 (0.08) 0.43 (0.04) 0.40 (0.16) 0.0069WID 215 0.06 (0.59) 0.26 (0.05) 0.04 (0.05) 0.30 (0.03) 0.13 (0.18) 0.6351PED 215 -0.04 (0.49) 1.52 (0.30) 0.24 (0.26) 1.77 (0.17) 0.14 (0.15) 0.7574CAUD 215 0.56 (1.42) 0.19 (0.04) 0.05 (0.04) 0.24 (0.02) 0.22 (0.15) 0.1024PECT 215 -0.19 (0.57) 0.20 (0.04) 0.01 (0.03) 0.21 (0.02) 0.06 (0.13) 0.7216PELV 215 -0.07 (0.50) 0.23 (0.05) 0.09 (0.05) 0.32 (0.03) 0.28 (0.15) 0.0386
Pedigree 2 Tactic 351 1.48 (0.50) 0.11 (0.02) 0.05 (0.02) 0.16 (0.01) 0.52 (0.19) 0.0002FL (mm) 351 88.99 (14.63) 48.68 (9.65) 38.78 (12.16) 87.48 (7.25) 0.44 (0.12) < 0.0001DEP 216 -0.15 (0.79) 0.20 (0.06) 0.22 (0.08) 0.41 (0.04) 0.53 (0.16) 0.0003WID 216 0.03 (0.58) 0.26 (0.05) 0.03 (0.05) 0.30 (0.03) 0.11 (0.16) 0.5868PED 216 -0.02 (0.50) 1.29 (0.28) 0.55 (0.31) 1.84 (0.19) 0.30 (0.16) 0.0445CAUD 216 0.55 (1.45) 0.22 (0.04) 0.04 (0.04) 0.26 (0.03) 0.15 (0.14) 0.2465PECT 216 -0.06 (0.51) 0.22 (0.04) 0.03 (0.04) 0.25 (0.02) 0.13 (0.14) 0.3126PELV 216 -0.09 (0.55) 0.22 (0.05) 0.08 (0.05) 0.30 (0.02) 0.27 (0.15) 0.0463
Pedigree 3 Tactic 349 1.47 (0.50) 0.15 (0.03) 0.08 (0.03) 0.24 (0.01) 0.55 (0.19) 0.0005FL (mm) 349 88.95 (14.55) 43.21 (9.83) 42.65 (13.00) 85.86 (7.34) 0.50 (0.13) <0.0001DEP 215 -0.146 (0.80) 0.26 (0.07) 0.17 (0.08) 0.43 (0.04) 0.39 (0.16) 0.0105WID 215 0.055 (0.59) 0.28 (0.05) 0.02 (0.05) 0.30 (0.03) 0.06 (0.16) 0.8024PED 215 -0.037 (0.49) 1.55 (0.28) 0.22 (0.25) 1.77 (0.17) 0.12 (0.14) 0.3753CAUD 215 0.560 (1.42) 0.19 (0.04) 0.06 (0.04) 0.24 (0.02) 0.23 (0.15) 0.0797PECT 215 -0.109 (0.57) 0.22 (0.04) 0.03 (0.04) 0.25 (0.02) 0.12 (0.14) 0.3820PELV 215 -0.069 (0.50) 0.26 (0.05) 0.07 (0.05) 0.33 (0.03) 0.22 (0.15) 0.1160
82
Table 3-3. Power analysis for a range of simulated heritabilities (h2) for a continuous and a binomial trait. The mean heritability estimate and the mean standard error of 20 simulations are presented. Simulations were ran in order to mimic the actual data. Heritabilities estimated with a sample size of 349 individuals refer to body size and tactic, while a sample size of 215 relates to morphological traits. The heritability of tactic is given transformed to the liability scale for comparison purposes (see methods). Power is assessed by dividing the number of simulations that gave a significant additive variance estimate over a total number of 20 simulations.
N = 349 individuals N = 215 individuals Binomial trait Continuous trait Continuous trait h2 simulated h2 estimated
mean (SE) Power h2 estimated
mean (SE) Power h2 estimated
mean (SE) Power
0.20 0.28 (0.15) 0.35 0.25 (0.11) 0.75 0.24 (0.14) 0.39 0.30 0.44 (0.14) 0.55 0.32 (0.11) 0.90 0.33 (0.15) 0.60 0.40 0.37 (0.18) 0.60 0.41 (0.11) 0.95 0.44 (0.16) 0.85 0.50 0.47 (0.17) 0.90 0.50 (0.12) 1.00 0.50 (0.16) 0.90
83
Table 3-4. Genetic correlations with their standard errors between life-history tactic (anadromy/residency) and body size (FL) and the six morphological traits for the 3 pedigrees used (as in Table 2). Life-history tactic was coded as 0 for anadromous and 1 for resident. P-values are those obtained from likelihood ratio tests to assess the significance of the covariance genetic component.
Traits Pedigree 1 P Pedigree 2 P Pedigree 3 P FL -0.52 (0.22) 0.0333 -0.20 (0.24) 0.4401 -0.61 (0.23) 0.0188DEP 0.11 (0.32) 0.8149 -0.0007 (0.0002) 1.0000 0.13 (0.33) 0.7876WID 0.55 (0.74) 0.3762 0.37 (0.51) 0.4708 1.09 (2.45) 0.2506PED -0.003 (0.55) 0.6634 0.21 (0.31) 0.5508 -0.06( 0.66) 1.0000CAUD -0.50 (0.54) 0.4393 0.07 (0.47) 1.0000 -0.54 (0.54) 0.3877PECT -0.82 (0.31) 0.0655 -0.70 (0.25) 0.0477 -0.60 (0.32) 0.1526PELV 0.16 (0.35) 1.0000 0.35 (0.30) 0.3192 0.24 (0.37) 0.5565
84
Table 3-5 Mean heritability (h2) and genetic correlation (rG) estimates as well as mean standard error (SE) obtained over 20 simulations using a full-sib pedigree along with simulated phenotype data from a half-sib pedigree. Phenotype data were simulated according to h2 and rG similar to those estimated in the present study.
Simulated Estimated mean (SE)
h2 binomial 0.50 0.48 (0.17) h2 continuous 0.50 0.51 (0.12) rG binomial - continuous 0.50 0.52 (0.23)
85
3.9 Figures
0
10
20
30
40
2 4 6 8 10 12Family size
Num
ber o
f fam
ilies
Figure 3-1. Distribution of family size for Pedigree 1 and 3 (black bars) and Pedigree 2 (white bars). These families are those that were kept in the final analyses, i.e. the ones that were deemed significant by the randomization procedures (see methods).
86
Figure 3-S1. Location of the study area, sampling traps and electro-fishing sites on Morin Creek, a tributary of the Sainte-Marguerite River, Quebec, Canada.
Chapitre 4. The impact of fishing-induced mortality on the evolution of alternative life-history tactics in brook charr
88
4.1 Résumé Bien que des données récentes aient démontré des changements évolutifs dans des traits
d’histoire de vie tels la croissance, l’âge et la taille à maturité pour des population de
poissons exploitées, on ne connaît pas encore l’influence que pourrait avoir la pêche sur
l’évolution des tactiques alternatives de vie chez les espèces migratrices. Nous avons
construit un modèle pour prédire l’effet de la pêche sur l’évolution de l’anadromie et la
résidence chez une population exploitée d’omble de fontaine, Salvelinus fontinalis. Notre
modèle permet à la fois des changements phénotypiques plastiques ainsi que génétiques
(additifs) dans l’âge et la taille à la migration en employant des normes de réaction de
migration. À l’aide de ce modèle, nous prédisons qu’une pêche sportive centrée
uniquement sur les individus anadromes sur une période de 100 ans cause une évolution
dans les normes de réaction de migration, résultant en une diminution de la probabilité de
migrer avec une augmentation du taux d’exploitation. De plus, nous démontrons que des
changements dans les taux de mortalité naturels en eau douce peuvent grandement
influencer l’ampleur et la vitesse des changements évolutifs. Les changements évolutifs
dans le comportement migrateur engendrés par la pêche modifient l’abondance de la
population ainsi que le résultat de la reproduction et devraient être pris en considération si
une gestion efficace et durable des stocks de salmonidés est souhaitée.
89
4.2 Abstract Although contemporary trends indicative of evolutionary change have been detected in the
life-history traits of exploited populations such as growth and maturation schedule, it is not
known to what extent fishing influences the evolution of alternative life-history tactics in
migratory species such as salmonids. Here, we build a model to predict the evolution of
anadromy and residency in an exploited population of brook charr, Salvelinus fontinalis.
Our model allows for both phenotypic plasticity and additive genetic change in the age and
size at migration by including migration reaction norms. Using this model, we predict that
fishing of anadromous individuals over the course of 100 years causes evolution in the
migration reaction norm, resulting in a decrease in average probabilities of migration with
increasing harvest rate. Moreover, we showed that diffrences in natural mortalities in
freshwater can greatly influence the magnitude and rate of evolutionary change. The
fishing-induced changes in migration predicted by our model alter population abundances
and reproductive output and should be accounted for in the sustainable management of
salmonids.
90
4.3 Introduction Fishing is now acknowledged as a potential evolutionary force, described as a “massive
uncontrolled experiment in evolutionary selection” (Stokes and Law 2000). Whenever
individuals with certain characteristics are more likely to survive harvest than others,
fishing can induce evolutionary changes in life-history traits (Law 2000; Haugen and
Vøllestad 2001; Conover and Munch 2002; Barot et al. 2004; Olsen et al. 2004; Reznick
and Ghalambor 2005). That fishing can generate substantial selection differentials on
phenotypic traits that are influenced by additive genetic variation is beyond doubt. Yet, the
rate of these changes and their consequences for stock viability, stability, yield, and
recovery are less clear (Law 2000; Hutchings and Fraser 2008). Survivors of the harvesting
process are likely to be genotypes with traits that confer relatively high fitness under
fishing selection, but may be less than optimal with respect to natural selection (Conover
2000; Carlson et al. 2007). This may lead to slow recovery when fishing mortality is
relaxed. Moreover, because cessation of fishing does not automatically produce equal
selection pressures in the opposite direction, paying off this “Darwinian debt” (Cookson
2004) may take a long time (Conover 2000; Law 2000).
Salmonids are well known for their diversity of life-history forms, with alternative mating
tactics such as early maturing jacks in coho salmon, Oncorhynchus kisutch (Gross 1985),
precocious parr in Atlantic Salmon, Salmo salar (Hutchings and Myers 1988), or various
benthic and pelagic morphs in Artic charr, Salvelinus alpinus and brook charr, Salvelinus
fontinalis (Skúlason et al. 1996; Proulx and Magnan 2004). A common feature of many
systems is the presence of both anadromous (sea-run) and resident males and females found
in sympatry, where resident fish complete their entire life cycle without migrating to sea
(Jonsson and Jonsson 1993). Accumulating evidence suggests that these two forms may
occur as alternative tactics within a single breeding population (Nordeng 1983; Morita et al.
2000; Olsson and Greenberg 2004; Thériault et al. 2007a). Individuals adopt a particular
tactic if a certain threshold is exceeded or not, and various components of the energetic
state of individuals (growth, lipid deposition, and metabolic rate) have been implicated in
91 this process (Thorpe 1986; Bohlin et al. 1990; Thorpe et al. 1998; Hutchings and Myers
1994; Forseth et al. 1999; Morinville and Rasmussen 2003). Although influenced by
environmental conditions (e.g. Olsson et al. 2006), the adoption of alternative tactics in
salmonids involves significant additive genetic variation, which has been demonstrated
both in the laboratory (Silverstein and Hershberger 1992; Heath et al. 1994; Wild et al.
1994) and in the field (Garant et al. 2003; Thériault et al. 2007b). Moreover, whether an
individual migrates or not will have critical consequences for its growth, survival,
maturation, and reproduction. Survival is elevated in freshwater, but growth rates are
reduced and resident individuals attain a smaller size at maturation (Gross 1987). As
reproductive success is linked to body size in females (Fleming 1996; Morita and
Takashima 1998; Thériault et al. 2007a), resident females experience decreased
reproductive success relative to the bigger anadromous females. Reproductive success of
males seems to be less affected by smaller size, as resident males employ alternative
reproductive tactics, such as sneaking, to get access to mating opportunities (Hutchings and
Myers 1988; Fleming 1996).
Owing to its size-selectivity and temporally variable nature (Ricker 1995; Quinn et al.
2007), commercial fishing in salmonids has been shown to impact several life-history traits
such as growth, age and size at maturation, and run timing. However, despite wide
commercial and recreational interests in salmonids, direct evidence of evolutionary change
caused by salmonid fisheries is still mostly circumstantial (Myers et al. 1986; Waples et al.
2007). A fishery that targets only the migrant part of a population will inevitably be
selective with respect to life-history tactics such as anadromy and residency. However, the
consequences of such differential fishery-induced selection on the evolution of alternative
life-history tactics have, to our knowledge, never been rigorously investigated.
Here we used a novel modeling approach in order to predict the consequences of fishery-
induced mortality on the evolution of anadromy and residency. The model (hereafter
termed “eco-genetic”) incorporates both ecological and quantitative genetic processes,
92 providing a mechanistically rich framework in which to predict the rate of evolutionary
change on ecological timescales (Dunlop et al. 2007, 2008). In particular, our modeling
approach enables distinguishing between the plastic and evolutionary responses to fishing.
In the wild, salmonids show phenotypic plasticity in the age and size at migration. To
account for such plasticity in the process of migration, we adopted a reaction norm
approach. Reaction norms in the narrow sense describe how a single genotype is translated
into different phenotypes depending on environmental conditions (Stearns 1992), while
empirical estimations of reaction norms must typically rely in a broader definition
operating at the population level (Sarkar and Fuller 2003). Alternative tactics in salmonids
have previously been described by reaction norms, based on the idea that the adoption of a
particular tactic is governed by thresholds in growth rate (Myers and Hutchings 1986;
Thorpe 1986; Bohlin et al. 1990; Hazel et al. 1990). Here we extend this approach and
consider the probability for the adoption of a particular migration tactic (anadromy or
residency) as a function of size-at-age, where size-at-age integrates all environmental
factors affecting growth. Such an approach has previously been used to model the
evolution of maturation reaction norms (Ernande et al. 2004; Dunlop et al. 2007) and to
tease apart phenotypically plastic responses from possible genetic changes in the age and
size at maturation (Heino et al. 2002; Grift et al. 2003; Barot et al. 2004, 2005; Olsen et al.
2004, 2005; Dunlop et al. 2005; Dieckmann and Heino 2007). Our study represents an
extension of the maturation reaction norm approach so as to account for phenotypic
plasticity in another fundamental life-history transition in the study of exploited
populations.
We used data from a well-studied brook charr population in Québec, Canada, to
parameterize our model. Recreational fishermen in the region are increasingly exploiting
the sea-run components of this species as a result of the decline in Atlantic salmon stocks.
Yet, anadromous populations of brook charr are not rigorously managed in many systems.
Here we examine the impact of various exploitation rates on the evolution of migration
93 reaction norms, as well as on ecological and demographic characteristics of the population.
We chose to model dynamics over a 100-year time horizon as this timeframe is commonly
viewed as a manageable window from a conservation standpoint (Frankham et al. 2002).
The main purpose of our study is to address the following two questions (1) Does fishing
induce evolutionary changes in the conditional migration strategy? (2) In a population with
an evolving migration strategy, what are the effects of fishing on fecundity, abundance, and
yield from the fishery? We also explored whether differences in freshwater mortality rates
counteract or exacerbate the impact of fishing in saltwater on the evolution of anadromy
and residency.
4.4 Material and Methods We constructed an individual-based eco-genetic model similar to that developed by Dunlop
et al. (2007, 2008) to evaluate the effects of selective fishing mortality on the evolution of
anadromy and residency within a conditional strategy framework. The model was built to
reflect the life history of a sympatric population of brook charr in which anadromous and
resident migration tactics coexist (Figure 4-1) inhabiting a small tributary of the Ste-
Marguerite River (Québec, Canada), named Morin Creek. The behaviour and life history of
brook charr in this system are well studied (Morinville and Rasmussen 2003, 2006, 2007;
Thériault and Dodson 2003; Lenormand et al. 2004; Thériault et al. 2007a and b) and ample
data from the years 1998-2004 were available to parameterize the model (Figure 4-2, Table
4-1). The model follows evolution of the migration reaction norm, a quantitative trait that
is passed on at the individual-level from parents to offspring. We assumed a closed
population such that no new genetic variance was introduced by immigration. Model
simulations were run for a total duration of 100 years in discrete, one-year time steps. Each
year, individuals may experience the processes of migration to and from saltwater, growth,
maturation, reproduction, and mortality (Figure 4-1).
94 4.4.1 Migration The migration reaction norm was represented by a logistic function, describing the
probability p of migrating as a function of age a and size L,
0 1 2 3logit( )p c c L c a c La= + + + , (1)
where logit( ) log [ (1 )]ep p p= − . This form was chosen because it fits the empirical
relationship and allows the probability of migrating as a function of size to change its slope
with age (Figure 4-2a). Each individual is thus characterized by the four evolving
parameters c0, c1, c2, and c3 describing its probabilistic migration reaction norm (PMigRN),
which, together with its age and length, determines its probability of migrating in a given
year. An individual may migrate at either age 1 or 2 years only: if a fish did not migrate by
age 2, we assumed that it would be a freshwater resident for all its life. This understanding
is corroborated by field observations on this system (Thériault and Dodson 2003).
4.4.2 Somatic growth Individuals grow according to the growth model introduced by Lester et al. (2004).
Newborns in the model are given a random size at emergence, in accordance with the
empirical mean and standard deviation estimated from back-calculations of the 1998, 1999,
and 2000 year classes of fish captured in Morin creek (Thériault 2001; Thériault and
Dodson 2003). Prior to maturation, individuals grow the annual phenotypic growth
increment ig determined by the environment in which they reside during that year
(freshwater, i f= , or saltwater, i s= ). The maximal growth capacity is expressed in
saltwater, whereas individuals living in freshwater grow slower due to the poorer growing
environment they experience. The environment-specific growth rates fg and sg were
empirically derived from immature individuals of the Ste-Marguerite River system and
Morin Creek (Lenormand 2003; Thériault and Dodson 2003, Table 4-1).
Immediately following maturation, a proportion of energy is devoted to reproductive
tissues, so that the length L at age a+1 is given by
95
13 ( )
3a aL L gGSI+ = +
+,
(4)
where GSI is the gonado-somatic index (gonad mass divided by somatic mass) estimated
for anadromous females. The gonado-somatic index is parsimoniously assumed to be
similar and constant for all mature individuals in the population. Growth rates were
assumed to be density-independent, both in saltwater and in freshwater, to keep predictions
simple and motivated by the following two reasons. First, in view of the high productivity
of marine habitats and the small population sizes modeled here, the density dependence of
growth rates in the sea must be expected to be weak. Second, results gathered from a creek
adjacent and similar to Morin Creek failed to detect any density-dependence in freshwater
growth for brook charr of age 0 and older (Centre Interuniversitaire de Recherche sur le
Saumon Atlantique, CIRSA, unpublished data). When we tested the effects of our model
assumption and added density-dependent freshwater growth, there was little impact on the
probability to migrate at age 1, but the probability to migrate at age 2 did evolve to be
higher when the strength of the density-dependence was increased (Supplementary
Information).
4.4.3 Maturation In any given year, an immature individual has a probability to mature during the upcoming
year that is based on its environment (freshwater or saltwater) and on its size and age. This
probability is given by a probabilistic maturation reaction norm (PMRN, Figure 4-2b,
Heino et al. 2002; Dieckmann and Heino 2007). To keep the model simple and to focus on
the evolution of migration, maturation tendency was not considered as an evolving trait in
our model.
4.4.4 Reproduction There is no sex-structure in our model and reproduction occurs annually in freshwater
between random pairs of mature individuals. The largest individual in the reproductive pair
is chosen to be the mother, so as to account for frequently observed mating between
anadromous females (bigger) and resident males (smaller) and the apparent absence of the
reverse (that is, big anadromous males with small resident females, Thériault et al. 2007a).
96 The number of eggs produced by a reproductive pair is estimated from the body length L of
the mother in the pair according to an empirically derived relationship between fecundity
and body size (Figure 4-2c),
F = (H1L)H2 , (5)
with constants H1 and H2. The number of new individuals recruiting to the population at
age 1 is determined from a Ricker-type stock-recruitment function (Figure 4-2d),
R = rSe−bS , (6)
where S is the number of adults, and r and b are constants. As the necessary data to derive
such stock-recruitment functions specifically for our system were not available, they were
instead obtained from corresponding constants estimated for brown trout (Elliott 1993), a
species with life-history characteristics very similar to brook charr. We adjusted these
constants so as to obtain realistic estimates of recruitment and spawner abundance in our
system (Table 4-1).
4.4.5 Inheritance and expression Genotype determination. Inheritance of the PMigRN was described by the infinitesimal
model of quantitative genetics (Cavalli-Sforza and Feldman 1976). We assumed that
phenotypic plasticity for migration is heritable by modeling genetically based reaction
norms that are passed from parents to offspring (e.g. Brommer et al. 2005; Nussey et al.
2005; Dunlop et al. 2007, 2008). The four parameters that describe the PMigRN were thus
considered as evolving traits. The genetic trait value of an offspring was drawn at random
from a normal distribution with a mean given by the mid-parental genetic trait value and a
variance that equaled half the genetic variance in the initial population. Modeling offspring
variance in this way assumes equal variances of maternal and paternal traits and that the
segregation and recombination of genes during reproduction introduce a constant amount of
variation to the population (Roughgarden 1979).
97 Phenotype determination. The phenotypically expressed values of an individual’s four
PMigRN traits were drawn randomly in each year from a normal distribution with a mean
given by the individual’s genetic trait value and a variance that equaled the assumed
environmental variance. The latter was calculated based on an assumed heritability h2 (see
section on initial population structure below) for the PMigRN traits and on an assumed
genetic coefficient of variation. Based on the definition of heritability, h2 = VA/VP with VP
= VA + VE (VA = additive genetic variance; VP = phenotypic variance; VE = environmental
variance), it is possible to calculate VE from h2 and VA. While the environmental variance
for each trait was kept constant in the model, the corresponding values of VA, and thus of
h2, were free to evolve.
4.4.6 Natural mortality Default natural mortalities. Age-specific annual mortality probabilities were estimated
from Morin Creek data for resident individuals, and from a larger mark-recapture
experiment in the whole Ste-Marguerite River system for anadromous individuals
(Lenormand 2003). Immature (mimm) and mature (mmat) mortality probabilities were applied
annually to resident and anadromous individuals (Table 4-1). For anadromous fish, the
mortality probability of an immature individual varied depending on whether it was the first
or second year the individual spent in saltwater (mimm 1st ana and mimm 2nd ana respectively,
Table 4-1).
Alternative natural mortalities. In addition to the default values representing “normal”
freshwater mortality probabilities, we simulated “poor” freshwater survival conditions and
“good” freshwater survival conditions (Table 4-1), while keeping natural saltwater
mortalities unchanged.
4.4.7 Fishing-induced mortality We applied fishing mortality to anadromous individuals only, as they are the only targets of
the recreational fishery. Annual harvest probabilities for these fish were derived based on
the observed sizes of fish caught and on data quantifying overall annual exploitation rates
98 (Lenormand 2003 and CIRSA, unpublished data, Figure 4-2e). Medium-sized anadromous
fish (with lengths between 200 mm and 350 mm) are most likely to be caught, because they
are abundant and, during the upstream migration of immature anadromous brook charr in
early fall (Lenormand et al. 2004), concentrated in the river’s estuary, where their
exploitation is little regulated. Smaller brook charr (with lengths between 110 mm and 200
mm) are not attractive to fishermen, whereas the bigger, mostly mature charr (with lengths
larger than 350 mm) are under spatial and temporal regulations that prevent high fishing
pressures on these larger fish. Size-selective fishing mortality is applied to individuals
regardless of their maturation status. We varied the maximal harvest probabilities in the
selectivity curves of anadromous fish between 0 and 1 in increments of 0.05.
4.4.8 Initial population structure and parameterization The initial population in the model consisted of 5000 age-1 individuals with initial lengths
following a normal distribution with mean and standard deviation estimated for the 1998-
1999-2000 year classes of fish captured in Morin creek (Thériault and Dodson 2003). A
logistic regression was applied to age-specific length distributions of immature and mature
fish to estimate the population’s PMRN (Heino et al. 2002; Dieckmann and Heino 2007).
For anadromous fish, we used data gathered from the whole Ste-Marguerite River system
(pooled from 1998 to 2001), whereas for resident fish, we used data gathered from Morin
creek (pooled from 1998 to 2002). A linear regression of lengths at ages 2 and 3 (the two
age classes for which sufficient data were available) at which the probability to mature in
the next year was 50% was used to estimate the slope and intercept of a linear PMRN
(Figure 4-2a). We then used the 1% and 99% maturation probability percentiles of 3-year-
old individuals to determine the PMRN width.
The initial population-level PMigRN was estimated using data on size and age at migration
from Morin Creek. Data on fish of ages 1 and 2 were analyzed for the years 1998-1999-
2000, as migration occurs almost exclusively at these two ages (Thériault and Dodson
2003). All individuals from the initial population were assigned genetic values for the four
evolving traits c0, c1, c2, and c3 following a normal distribution with means given by the
99 trait values implied by the population-level PMigRN (Figure 4-2b) and standard deviations
given by the assumed genetic coefficient of variation.
The initial heritability of each trait describing the probabilistic migration reaction norm was
assumed as 0.5. We do not know the actual value of heritability of plasticity for anadromy
and residency in this system, but genetic variation and heritability have been demonstrated
for plasticity in general (Scheiner 1993; Nussey et al. 2005) and have been assumed for
migration in salmonids in particular (Hazel et al. 1990; Hutchings and Myers 1994; see also
the review by Hutchings 2004a). After initialization, heritabilities, genetic variances, and
genetic covariances were free to evolve, and can thus be regarded as emerging properties of
the model. Even though heritabilities directly scale the speed of evolution, so that we must
expect slower or faster changes in reaction norms if we assume lower or higher
heritabilities, the nature of predicted evolutionary changes remains unchanged as
heritabilities are jointly increased or decreased (see, e.g., Dunlop et al. 2007).
4.5 Results
4.5.1 Impact of different exploitation rates over 100 years Increasing harvest probability causes a shift in the migration reaction norms for both age 1+
and age 2+ individuals where, for an individual of the same size, the probability of
migration is lowered as harvest probability increases (Figure 4-3). This translates into an
overall probability of migration that is decreasing with increasing harvest probability
(Figure 4-3). The absolute number of fish that migrate decreases as harvest probability
increases, and this trend is more pronounced for age 1 individuals than for age 2 (Figure 4-
3). Mean age at migration increases with harvest probability (Figure 4-3) primarily
reflecting the fact that the proportion of fish migrating at 2 years of age goes up as harvest
probability increases. Mean age at maturation did not change for residents, but decreased
for anadromous individuals (Figure 4-3). Mean individual fecundity, highly dependent on
size, decreased with maximum harvest probability for anadromous fish, but showed no
variation for resident fish (Figure 4-3). Overall abundance of the population shows little
100 change with increasing maximum harvest probability, because the number of fish in
freshwater increased while the number in saltwater decreased to almost zero (Figure 4-3).
Heritability found in the migration reaction norm varied though time, and did not show a
significant increase or decrease, either at low or high harvest probabilities (Figure S1).
4.5.2 Impact of different natural mortality rates Survival conditions in freshwater influenced the evolution of the migration reaction norm.
After 100 years of fishing, low survival in freshwater associated with poor conditions lead
to a migration reaction norm associated with a higher probability of migrating for a given
size than under normal freshwater survival. In contrast, good survival conditions in
freshwater lead to a lower probability of migrating for a given size than seen under normal
conditions (Figure 4-4). Poor survival in freshwater thus offsets the effect of fishing by
increasing the probability of migrating and the number of migrants, while good survival in
freshwater had the opposite effect (Figure 4-4). Population abundance in function of
maximum harvest probability is higher, and relatively constant, in poor than normal
freshwater survival conditions, while under good conditions important variations are seen
(Figure 4-4). The cumulative catch shows similar dome-shaped relationships for the three
survival conditions in freshwater, but peaked at higher values and harvest rates as survival
conditions worsened in freshwater (Figure 4-4).
4.6 Discussion By using a modeling approach, we explored the impact of recreational fishing on the
evolution of anadromy and residency of a small population of brook charr. Following a
hundred years of fishing of anadromous individuals, we predicted evolution in the
migration reaction norm, which translated into a decrease in average probabilities of
migration with increasing harvest rate. This change was accompanied by an increase in the
proportion of fish migrating at age 2, resulting in a higher mean age at migration. These
findings suggest that selective harvesting of anadromous fish would result in an increased
tendency for residency as well as an increased advantage of staying longer in freshwater
and delayed migration. Shifts in the maturation reaction norm towards younger age and
smaller size at maturation in commercially important marine species have been strongly
101 suggested to result from heavy fishing pressure that selected against genotypes
predisposing fish to mature later and larger (American plaice Hippoglossoides platessoides,
Barot et al. 2005; North Sea plaice Pleuronectes platessa, Grift et al. 2003; Atlantic cod
Gadus morhua Barot et al. 2004; Olsen et al. 2004, 2005). Here we show that shifts in the
reaction norm of another ontogenetic process, i.e. migration, an important life-history
characteristic in salmonids, are also expected under fishing-induced mortality.
By acting on heritable traits, selective harvesting by humans can unintentionally select
against those which they desire the most: bigger individuals and increase in harvestable
biomass (Coltman 2008; Hutchings and Fraser 2008). For instance, trophy hunting of
bighorn sheep over a period of 30 years has generated an undesired evolutionary response
in weight and horn length, both of these traits having decreased in association with a
decline in their breeding values (Coltman et al. 2003). Here we show that high harvest
rates on anadromous fish reduce the probability of migration, and thus ultimately lead to a
reduced number of fish in saltwater and thus less fish available to the recreational fishery.
Changes are also seen in age at maturation for the anadromous part of the population but
must not be interpreted as genetic changes as the maturation tendency was not allowed to
evolve in the model. These changes rather reflect the fact that at high harvest rates, only
the smallest, youngest anadromous fish are escaping the harvest process (according to the
harvest probability curves, Figure 4-2e). The removal of large fish by the recreational
fishery also causes a decline in the mean individual fecundity of anadromous fish at high
harvest probabilities.
Genetic changes induced by fisheries, as with any other selective force, are potentially
reversible, given that sufficient heritable genetic variation remains and that selection
differentials in the opposite direction are generated once fishing is relaxed or stopped (Law
2000). According to the body of work on commercially exploited marine species, it
appears that reversibility is a difficult and slow process (Barot et al. 2004; Reznick and
Ghalambor 2005; Swain et al. 2007). Although we have not explored the extent of trait
102 reversal following the cessation of fishing, our results suggest that heritable additive
genetic variance in the migration reaction norm is preserved even at high harvest rates. It
has been theoretically demonstrated elsewhere that a large fraction of the genetic variation
underlying threshold traits is maintained even under strong directional selection, because
variation remains “hidden” by virtue of the threshold nature of the trait (Roff 1994). It
follows from the preservation of genetic variation that threshold traits may be quasi-
immune to extinction, and that giving the return of favorable conditions, a lost alternative
tactic could be restored provided that the mechanisms for producing it have not degenerated
during a period of disuse (West-Eberhard 2003). In fact, cases of non-anadromous
salmonid fish stocks that have kept their capacity to migrate or that have given rise to
anadromous ones have been documented (Staurnes et al. 1992; Pascual et al. 2001;
Thrower et al. 2004).
Our study also aimed at assessing how varying natural survival conditions in freshwater
could influence evolution of migration. The unpredictability of evolution has been
demonstrated on the scale of decades in Darwin’s finches, where selection on body size and
beak shape was changing direction in time (Grant and Grant 2002). Fluctuating selection on
body size has been hypothesized as a factor favoring threshold variation and thus ultimately
maintaining alternative male life-cycles in Atlantic salmon (Aubin-Horth et al. 2005). The
results obtained here emphasize the idea that natural variation in the environment can act to
maintain phenotypic and genetic variability (see also Thrower et al. 2004). Shifts in
migration reaction norms owing to selective harvesting were either impeded or exacerbated
depending on if survival in freshwater was low or high, respectively. In the face of high
temporal variability, such as that occurring in northern temperate salmonid rivers,
predictions about rates and magnitude of life-history evolution caused by fishing would
certainly become difficult over the long-term (e.g. Grant and Grant 2002).
We wanted to focus on fishing-induced changes in the migration reaction norm, which has
received little, if any, attention in the published literature, and in order to keep predictions
103 simple, we did not allow for the evolution of other life-history traits, such as growth, the
maturation reaction norm, or reproductive investment. These traits are most likely to evolve
but usually only maturation age or size, or the maturation reaction norm have been allowed
to evolve in other models (Ernande et al. 2004; Dunlop et al. 2007). However, adding
growth as an evolving trait did not significantly alter the predictions about migration
(Figure S2). The inclusion of the PMigRN as an evolving trait generates a substantial level
of complexity; adding additional variables would unduly complicate the model, at least at
this stage of investigation. Future extensions of the model could include other such
additional evolving traits. The effect of correlated response to selection would also merit
further investigation. Fishing-induced selection on one trait could generate a response in
other genetically correlated traits: this could impede or increase the rate of evolutionary
change, depending on the sign and magnitude of the correlation (Lynch 1999; Walsh et al.
2006; Hutchings and Fraser 2008). Smolting, maturation and growth have been shown to
be genetically correlated to varying degrees in rainbow trout (Oncorhynchus mykiss) and
the dynamic interactions of these traits with season-specific growth rates have been
hypothesized to be a key factor maintaining genetic variation in smolting, despite complete
selection against the phenotypic expression of migration (Thrower et al. 2004). Genes
associated with smolting are thus conserved in the population, through selection for, or
against, other genetically correlated traits. Here migrating at age 1 was positively
genetically correlated with size at age 1 (Thériault et al. 2007b). One could expect that
such a correlation would exacerbate the effect of fishing: selecting against anadromous
individuals migrating at age 1 might select for smaller size at age 1, associated with a
higher probability of staying resident. However, a smaller size at age 1 could also translate
into a smaller size at age 2, which is in turn associated with a higher probability of
migrating. The effect of genetic correlation among traits related to the life-history form
adopted in the face of fishing-induced selection remains to be rigorously explored.
Despite their commercial and recreational interests, the effects of fishing on salmonids
(beyond the immediate consequences for abundance) have rarely been demonstrated.
Genetic responses resulting from the effect of commercial fisheries have been proposed to
104 explain a decrease in size of Pacific salmon (Ricker 1995), as well as change in age and
size at maturation in the European grayling (Thymallus thymallus) (Haugen and Vøllestad
2001), lake whitefish Coregonus clupeaformis (Handford et al. 1977) and Atlantic salmon
(Bielak and Power 1986). However, most of the arguments presented in these previous
studies were largely circumstantial. By illustrating the impact of a sport-fishery on the
evolution of life-history tactics in salmonids, along with its associated ecological and
demographic consequences, this study contributes to the emerging view that evolution can
be rapid enough to be an integral part of ecological interactions (Hendry and Kinnison
1999; Reznick and Ghalambor 2005). The high harvest probabilities that were associated
with the most significant demographic and evolutionary changes in this study are more
often associated with commercial salmonid fisheries (e.g. Atlantic salmon, Dempson et al.
2001) than with recreational fisheries (e.g. this system, mean harvest probability = 0.3,
CIRSA, unpublished data,); however, the latter has still the potential to contribute to
fisheries declines (Cooke and Cowx 2006). It is our hope that modeling approaches such as
the one used in this study will be increasingly integrated by managers and policy-makers as
a complement to traditional fisheries management approaches based on population
dynamics alone.
4.7 Acknowledgments We thank the CIRSA (Centre Interuniversitaire de Recherche sur la Saumon Atlantique)
where data on the Ste-Marguerite River system were collected. The authors would also like
to thank C. Jørgensen and J.A. Hutchings for helpful comments on earlier drafts of this
manuscript. Funding of this project was provided to J.J.D. and L.B. by NSERC of Canada
(Strategic Grant and Collaborative Special Projects), the Fondation de la Faune du Québec,
the Government of Québec (FAPAQ), the Government of Canada (Economic development)
and the financial partners of AquaSalmo R&D. This study is a contribution to the program
of CIRSA and Québec-Océan. V.T. was financially supported by funding from the Natural
Sciences and Engineering Research Council of Canada (NSERC) and the Fonds québécois
de recherches sur la nature et les technologies (FQRNT). E.D. gratefully acknowledges
financial support provided by the Research Council of Norway.
105
4.8 Table Table 4-1. Model parameters and their values. Symbol Description Equation Source Value
__ Initial mean body size (mm) __ 1 82.98
__ Initial standard deviation of body size (mm) __ 1 13.68
__ Mean PMRN slope of resident morph (mm/year) __ 2 -32.41
__ Mean PMRN intercept of resident morph (mm) __ 2 259.72
__ PMRN width of resident morph (mm) __ 2 114.53
__ Mean PMRN slope of anadromous morph (mm/year)
__ 2 -177.58
__ Mean PMRN intercept of anadromous morph (mm)
__ 2 843.38
__ PMRN width of anadromous morph (mm) __ 2 532.9
__ Mean emergence size (mm) __ 1 31.71
__ Standard deviation of emergence size (mm) __ 1 5.52
h2 Initial heritability of evolving PMigRN traits __ __ 0.5
CV Coefficient of variation for all traits __ __ 0.08
c0 Evolving PMigRN trait 1 1 -12.36
c1 Evolving PMigRN trait 1 1 0.11
c2 Evolving PMigRN trait 1 1 9.69
c3 Evolving PMigRN trait 1 1 -0.08
gs Mean growth rate in saltwater (mm/year) 3 2 95.53
gf Mean growth rate in freshwater (mm/year) 3 1 35.35
GSI Mean gonado-somatic index 4 2 0.147
H1 Constant in fecundity function 5 2 0.04
H2 Constant in fecundity function 5 2 2.86
r Constant in stock-recruitment function 6 3 25.0
b Constant in stock-recruitment function 6 3 0.0027
mimm res Immature natural mortality probability for resident morph under default conditions
__ 2 0.60
mmat res Mature natural mortality probability for resident morph under default conditions
__ 2 0.88
mimm 1st ana Immature 1st year natural mortality probability for anadromous morph under default conditions
__ 2 0.80
mimm 2nd ana Immature 2nd year mortality probability for anadromous morph under default conditions
__ 2 0.60
106 mmat ana Mature natural mortality probability for
anadromous morph under default conditions __ 2 0.55
mimm res poor Immature natural mortality probability for resident morph under poor conditions
__ 2 0.80
mmat res poor Mature natural mortality probability for resident morph under poor conditions
__ 2 0.95
mimm res good Immature natural mortality probability for resident morph under good conditions
__ 2 0.20
mmat res good Mature natural mortality probability for resident morph under good conditions
__ 2 0.70
PMRN = probabilistic maturation reaction norm; PMigRN= probabilistic migration reaction norm. Sources : (1) Morin Creek data reproduced from Thériault (2001) and Thériault and Dodson (2003), (2) Ste-Marguerite River data for anadromous fish and Morin Creek data for resident fish reproduced from Lenormand (2003), (3) modified from Elliott (1993).
107
4.9 Figures
Figure 4-1. Schematic of the life-cycle of brook charr showing the sequence of events in the eco-genetic model.
108
Figure 4-2. Empirically derived functions used in the model. (a) Probabilistic migration reaction norms estimated for Morin creek (reproduced from Thériault and Dodson 2003); (b) Probabilistic maturation reaction norms estimated for anadromous and resident individuals showing the 50% (midpoint) 1% and 99% probability curves; (c) Relationship between fecundity and body size for anadromous (open circles) and resident individuals (closed circles) reproduced from Lenormand (2003); (d) Stock-recruitment relationship derived from Elliott (1993); (e) Harvest probabilities curves according to different maximal harvest probabilities increasing from 0.05 to 1 in increments of 0.05.
109
0 100 200 300 400Body size (mm)
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Figure 4-3. Model results after 100 years of fishing-induced mortality, according to different maximum harvest probabilities. The top two panels show the age-specific migration reaction norms (line thickness increases with increasing maximum harvest probabilities between 0 and 1). Results are averaged for 30 independent model runs.
110
0 100 200 300 400Body size (mm)
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h ab
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0.5 max harvest probability
0.5 max harvest probability
Figure 4-4. Model results after 100 years of fishing-induced mortality, according to varying survival conditions in freshwater (poor, normal and good, see methods). The maximum harvest probability was 0.5. Results are averaged for 30 independent model runs.
111 4.10 Supplemental material
4.10.1 Heritability through time
0 20 40 60 80 100Time (years)
0.485
0.49
0.495
0.5
0.505
Her
itabi
lity
in c
1
Max harv prob = 0.1Max harv prob = 0.8
0 20 40 60 80 100Time (years)
0.485
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0.505
Her
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0
0 20 40 60 80 100Time (years)
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erita
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y in
c3
0 20 40 60 80 100Time (years)
0.485
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Her
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2
Figure S1. Heritability through time according to different maximum harvest probability
values for the different evolving coefficients describing the probabilistic migration reaction
norm (co, c1, c2, c3 – see mtehods). All results represent averages over 30 independent
model runs.
.
112
4.10.2 Comparison of results with growth included as an evolving trait The genetic growth rate was the intrinsic capacity for growth or the fastest an individual
could possibly grow, and was based on the mean growth rate in saltwater (95.53 mm/year)
with an assumed heritabiliy value of 0.5. Individuals living in freshwater grow slower due
to the poorer growing environment that they experienced, and therefore, the phenotypic
growth rate for these individuals, g, was defined as
g = (Gg + VE ) − Rf (Gg + VE )[ ] (S1)
where Gg is the genetic growth rate, VE the environmental variance and Rf the proportional
reduction in freshwater,
Rf =(gs − gf )
gs
(S2)
where gs and gf are the mean growth rate in salt and freshwater respectively, empirically
derived from immature individuals of the Ste-Marguerite River system and Morin Creek
(Table 1).
We included a trade-off between growth capacity and survival. Without such a cost, the
model would always favor higher growth capacities, and thus growth rate would evolve
indefinitely and be unrealistically high. With this trade-off, the probability of mortality Mg
increased with increasing growth capacity Gg,
max
gg
GM
g=
(S3)
where gmax is the maximum growth rate at which the probability of survival drops to 0
(gmax = 140 mm/year). Owing to this additional mortality, we adjusted the natural mortality
probabilities accordingly so that the total natural mortality probability was the same as the
simulations without evolving growth.
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0 100 200 300 400Body size (mm)
0
0.2
0.4
0.6
0.8
1P
roba
bilit
y of
m
igra
ting
at a
ge 1
0 100 200 300 400Body size (mm)
0
0.2
0.4
0.6
0.8
1
Pro
babi
lity
of
mig
ratin
g at
age
2
0 0.2 0.4 0.6 0.8 1Maximum harvest probability
0
0.2
0.4
0.6
0.8
Pro
babi
lity
of m
igra
ting
0 0.2 0.4 0.6 0.8 1Maximum harvest probability
0
400
800
1200
1600
Num
ber m
igra
ting
Figure S2. Sensitivity of migration results to allowing growth capacity to evolve. Top
panels give the probability of migrating when growth capacity is an evolving trait. Bottom
panels show the probability and numbers migrating for age 1 when growth is an evolving
trait (triangles), for age 2 when growth is an evolving trait (plus signs), for age 1 when
growth is not an evolving trait (solid lines with no symbols), and for age 2 when growth is
not an evolving trait (dashed lines with no symbols).
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4.10.3 The effect of density-dependent growth We tested the effect of including density-dependent growth in freshwater on model results.
To implement density-dependent growth, we assumed a reduction in somatic growth as
biomass increased, owing to density-dependent resource limitation:
1P
d D
ggBb
=+
where gd is the density-dependent growth rate in freshwater, gP is the phenotypic growth
rate in freshwater (i.e., the growth rate in freshwater before density-dependent growth is
taken into account), b is the freshwater population biomass, and B and D are constants
(Dunlop et al. 2007).
As we increase the strength of density-dependence (by increasing B or D), there is little
effect on the probability to migrate at age 1. However, the probability to migrate at age 2
increases as the strength of density-dependence increases. Therefore, severe density-
dependence in growth is expected to offset the fishing-induced tendency to become a
resident, but most notably for age 2 individuals.
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Figure S3. The effect of density-dependent growth in freshwater on the probability to
migrate to saltwater. As B or D is increased, the severity of density-dependent growth is
increased. Fishing occurred for 100 years at a maximum harvest probability of 0.5.
Results are averaged over 30 independent model runs.
Chapitre 5. Conclusion générale « Pour tirer le meilleur parti des connaissances
acquises, pour en extraire toute la richesse, il importe de ne pas s'y habituer trop vite, de se laisser le temps
de la surprise et de l'étonnement.» Hubert Reeves
5.1 Rappel des principaux résultats Cette étude voulait apporter un peu plus de lumière sur le déterminisme génétique de
l’anadromie et la résidence chez l’omble de fontaine et s’inscrit dans un contexte général
plus large qui vise la compréhension de l’évolution des tactiques alternatives de vie. Les
résultats de cette étude répondent sans contredit à cet objectif. Tout d’abord, nous avons
mis en évidence qu’une reproduction est fréquente entre anadromes et résidents dans notre
système, et que le mélange semble être assuré par les mâles résidents qui, en adoptant
vraisemblablement une tactique de reproduction furtive en présence de plus grosses
femelles, fécondent autant les œufs de femelles anadromes que de femelles résidentes. Un
examen plus poussé du système de reproduction nous a aussi permis de conclure en un plus
grand succès reproducteur pour les femelles anadromes, lié en partie à leur plus grande
taille. Par contre, la plus grande taille des mâles anadromes ne semble pas se refléter dans
un meilleur succès reproducteur : les mâles résidents obtiennent autant de paternités que les
plus gros mâles anadromes. Cette analyse détaillée du système de reproduction a aussi
révélé que tout type de croisement, qu’il soit entre résidents, anadromes, ou bien entre
résident et anadrome, peut produire des jeunes des deux formes. Ces résultats sont les
premiers à examiner en milieu naturel le système de reproduction entre anadromes et
résidents où des mâles et femelles sont retrouvés sous les deux formes. Ils appuient
l’hypothèse d’un certain déterminisme environnemental dans l’adoption d’une forme ou
l’autre. De plus, ces résultats viennent réfuter certaines croyances populaires, qui veulent
qu’anadromes et résidents fassent partie de deux populations distinctes, justifiant une
gestion différente et séparée. Du moins, le ruisseau Morin au Saguenay ne fait plus partie
de cette croyance…
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Un des principaux résultats de cette thèse consiste en la démonstration d’une d’héritabilité
significative pour une tactique alternative en milieu naturel. Avec le raffinement des
méthodes de reconstruction de pedigree et des modèles statistiques employés en génétique
quantitative, nous avons pu estimer l’héritabilité de l’anadromie et la résidence, de même
que de la taille, ainsi que la corrélation génétique entre ces deux traits. Dans le contexte où
la tactique adoptée est un trait-seuil, les valeurs ici estimées (0.52-0.56) représentent
l’héritabilité du ou des traits sous-jacents (« liability trait »). La valeur de ce trait sous-
jacent, conjointement avec la position du seuil, va déterminer quelle forme sera adoptée.
Nos résultats démontrent que la valeur du trait sous-jacent se transmet de génération en
génération et donc soulignent l’influence des facteurs génétiques dans l’adoption de
l’anadromie et la résidence. Dans le ruisseau Morin, sous les conditions où notre étude a
été menée, il y a toutefois la moitié de la variation phénotypique dans la tactique qui n’est
pas expliquée par la variation génétique, ce qui laisse place aux conditions
environnementales. Nos résultats suggèrent de plus que la corrélation phénotypique entre
la taille et la tactique préalablement observée (Thériault et Dodson 2003) est en partie
déterminée par une corrélation génétique significative. L’évolution de la tactique et de la
taille se ferait donc conjointement : une sélection sur un ou l’autre de ces traits aura
vraisemblablement un effet sur l’autre.
Finalement, grâce à un exercice de modélisation, nous avons démontré quels pourraient être
les effets génétiques et démographiques potentiels de la pêche sportive sur l’anadromie et la
résidence. La plasticité dans l’adoption d’une tactique ou l’autre est incluse dans le modèle
à travers une approche de norme de réaction, où la probabilité de migrer en fonction de
l’âge et la taille a une base héritable (i.e. nous assumons qu’il existe une variation génétique
dans les normes de réaction). Une pêche sportive ciblant les anadromes sur une période de
100 ans a comme effet de déplacer les normes de réaction, de sorte que pour une même
taille, les individus ont en moyenne une moins grande probabilité de migrer à mesure que
l’intensité de la pêche augmente. La résidence est donc favorisée et la proportion
d’anadromes dans la population diminue. À de fortes intensités de pêche, le nombre de
poissons anadromes disponibles pour les pêcheurs sportifs diminue également. Ces
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changements s’accompagnent d’une légère baisse dans l’âge et la taille à maturité. Par
contre, il semble qu’un taux maximal de récolte autour de 30% soit une cible intéressante
d’un point de vue de gestion, lequel, tout en maximisant le nombre de poissons pêchés, a
peu de conséquences sur la probabilité de migrer.
5.2 Contributions Cette thèse contribue à l’avancement des connaissances à trois niveaux. Tout d’abord, au
niveau théorique, ce travail approfondit nos connaissances sur les comportements de
reproduction et les déterminants du succès reproducteur, sur l’évolution des cycles vitaux
et sur la genèse et la dynamique de la diversité phénotypique. Ensuite, au niveau
méthodologique. Tout au long de cette thèse, de nouvelles approches et de nouveaux
outils ont été mis à profit. La réassignation parentale en milieu ouvert, naturel, représente
un défi de taille et cette étude est un excellent exemple des limites et des possibilités d’une
telle entreprise. L’estimation de l’héritabilité en nature fait ses preuves depuis les dernières
années, mais cette thèse fait un pas de plus en estimant des paramètres de génétique
quantitative basés sur des pedigrees obtenus grâce à des marqueurs moléculaires, en ne
considérant qu’une génération, avec seul les liens plein-frères (voir aussi Wilson et al.
2003a). L’approche « éco-génétique » de modélisation ici développée est le dernier cri en
ce qui concerne l’inclusion des facteurs génétiques, environnementaux et démographiques
lorsque l’on s’intéresse à l’effet de la mortalité sélective (Dunlop et al. 2007). Cette thèse
sert donc de bases méthodologiques solides et stimulera (ou du moins ne découragera pas!),
je l’espère, les études de génétique quantitative en milieu naturel. Elles représentent un
défi, certe, mais les bénéfices de pouvoir appliquer ces résultats à ce qui se passe vraiment
dehors, loin de nos bassins, cages et éprouvettes, en valent sans contredit le coup!
Finalement, au niveau pratique. L’omble de fontaine est une espèce d’intérêt sportif
grandissant au Québec, surtout pour l’exploitation des dites « truites de mer ». Avant que
le CIRSA n’entreprenne son programme de recherche, peu était connu sur l’omble
anadrome. Cette thèse apporte des nouvelles connaissances et pourrait éclairer les
différents acteurs de la gestion des salmonidés au Québec, et pourquoi pas, ailleurs en
Amérique. De plus, les conclusions de cette thèse peuvent être mises à contribution dans
le domaine de l’aquaculture où l’héritabilité est un paramètre important des programmes de
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croisements. En étudiant l’écologie et l’évolution d’une espèce exploitée, ce travail tente
de comprendre dans le but de pouvoir prédire. Prédire comment les perturbations
anthropogéniques, biologiques et physiques de l’environnement vont influencer l’histoire
de vie, les taux d’accroissement et la persistance des populations. Cette capacité de
prédiction représente en quelque sorte le but pratique de l’écologie et de l’évolution, et
cette thèse contribue à l’atteinte de cet objectif. Un ensemble de données empiriques a été
exploité ici, créant un cadre réaliste permettant d’émettre des prédictions quant au devenir
d’une population de salmonidés exploitée par la pêche sportive. Cette approche est une
façon élégante de jumeler théorie et apllication, et d’utiliser toute l’information disponible
sur le cycle de vie d’une espèce, des paramètres écologiques et démographiques à
l’architecture génétique.
5.3 La valeur et la signification de l’héritabilité En cherchant à comprendre les bases génétiques et environnementales de l’anadromie et la
résidence, on tente en fait de répondre à l’objectif central de la biologie évolutive :
expliquer la diversité. Les différents phénotypes peuvent être le produit de génotypes
différents, le résultat de différents environnements dans lesquels les individus se
développent, et, bien entendu, une combinaison de ces deux facteurs (Kruuk 2004). La
motivation derrière l’estimation des bases génétiques d’un trait repose en fait sur le désir de
vouloir prédire si un changement phénotypique permanent peut être causé par l’effet de la
sélection. Si certains phénotypes contribuent davantage à la génération suivante que
d’autres, et que ces différences phénotypiques sont associées à des différences génétiques
additives, une réponse évolutive est attendue. Un trait doit donc être héritable pour évoluer
sous l’effet de la sélection, dans le sens génétique du terme. La génétique quantitative pose
donc la question du « comment » (comment le phénotype est-il déterminé?), qui est
associée à la question du « pourquoi » (pourquoi observe-t-on tel ou tel phénotype?), posée
par l’étude de l’adaptation et de l’évolution (Lynch et Walsh 1998; Kruuk 2004).
La compréhension du potentiel adaptatif des espèces prend tout son sens dans le contexte
environnemental actuel. Réchauffement climatique et conservation de la biodiversité sont
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sur toutes les lèvres. Les humains sont d’ailleurs maintenant considérés comme la plus
grande force évolutive sur la planète (Palumbi 2001). Face aux perturbations anthropiques,
qui prennent la forme de dégradation et fragmentation de l’habitat, de surexploitation,
d’introduction d’espèces exotiques ou de modification du climat, les espèces doivent
répondre aux forces sélectives occasionnées par ces changements, sinon c’est l’extinction
qui les attend (Ashley et al. 2003; Pulido et Berthold 2004). Les populations s’adaptent
aux changements contemporains dans leur environnement de deux façons : soit par un
changement plastique (plasticité phénotypique), soit par des changements génétiques
(microévolution). La plasticité phénotypique est vue comme une façon de surmonter des
changements à plus court terme. Les réponses plastiques sont limitées par la gamme
d’environnements dans lesquels les réponses phénotypiques sont adaptatives : en dehors de
cette gamme, l’ajustement du phénotype peut être insuffisant (Pulido et Berthold 2004).
C’est pourquoi dans un environnement continuellement en mutation, les adaptations par
plasticité phénotypique seront tôt ou tard incapables de garder le rythme. Les réponses
évolutives sont associées à des changements à plus long terme et sont en principe la façon
dont les populations répondent à des changements continuels dans leur environnement, en
supposant la présence de variation génétique additive. La question est alors de savoir si les
réponses évolutives se feront assez rapidement, dans un monde où les changements causés
par les humains surgissent à une intensité et un taux sans précédent, pour assurer la
persistance des espèces (Hendry et Kinnison 1999; Berteaux et al. 2004).
5.4 Pour ou contre les réponses évolutives ? Une contradiction voit le jour lorsque l’on parle du potentiel évolutif adaptatif des espèces.
Une réponse évolutive rapide face aux perturbations anthropiques est parfois souhaitée,
alors qu’elle est parfois indésirée. Dans un contexte de conservation, nous souhaitons que
les espèces puissent s’adapter pour échapper à l’extinction, et alors nous voyons d’un bon
œil les réponses évolutives. Par exemple, l’ajustement de date de ponte vers une ponte
hâtive est souvent vu comme une réponse aux températures qui augmentent, de sorte que la
naissance des petits puisse coïncider avec les meilleures conditions de croissance (e.g. plus
de nourriture disponible). Alors que chez les oiseaux les évidences suggèrent que la ponte
hâtive est une réponse plastique au réchauffement climatique (Sheldon et al. 2003; Pulido
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et Berthold 2004; mais voir Nussey et al. 2005), une étude a démontré que l’avancement de
la date de mise-bas chez les écureuils rouges était en partie due à un changement génétique
(Réale et al. 2003). En à peine 10 ans, l’augmentation de température, traduite par une
augmentation de nourriture, a causé une réponse évolutive, menant à une meilleure
association phénotype-environnement. De telles réponses sont bénéfiques et apportent un
peu d’optimiste quant à la persistance des espèces face à d’importants changements
environnementaux.
Par contre, dans d’autres cas de telles réponses ne sont pas souhaitées. Nous ne désirons
pas voir des espèces nuisibles (du point de vue de l’homme bien entendu) s’adapter
rapidement aux antibiotiques et aux pesticides, comme l’histoire l’a démontré pour les
maladies bactériennes et les insectes ravageurs (Palumbi 2001). De même, nous ne
souhaitons pas que le rendement de nos récoltes diminue, ce qui est souvent le cas parce
que plusieurs exploitations, en choisissant les gros individus, favorisent les plus petits. Il
en résulte alors un changement génétique dans la population qui se traduit en une
diminution de la biomasse récoltable, changement qui peut prendre beaucoup plus de temps
à renverser qu’un changement plastique. Nous nous retrouvons donc à défavoriser ce que
nous désirons le plus : les plus gros individus, les meilleures récoltes (Conover et Munch
2002; Coltman et al. 2003; Hutchings 2004b; Hutchings et Fraser 2008). Les adaptations
aux nouveaux environnements sont donc toujours bénéfiques du point de vue des espèces,
leur permettant de survivre et de se reproduire, mais parfois néfastes du côté de l’homme.
5.5 La perte ou la cachette de l’anadromie? Dans le contexte d’un phénotype alternatif tel l’anadromie et la résidence, la question de la
fixation phénotypique revient toujours. Dans une population où seulement une des
tactiques est présente, on se demande si cela signifie la perte de la capacité d’exprimer
l’autre tactique, et donc, en d’autres mots, la perte de la plasticité. Dans le cas de l’omble
de fontaine, cette question est particulièrement intéressante : suite à la colonisation du Sud
vers le Nord dans l’Est de l’Amérique du Nord à partir d’un refuge glaciaire il y a plus de
10 000 ans (où les fondateurs auraient vraisemblablement été anadromes), on note que la
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forme migratrice a pratiquement disparu de l’aire de distribution Sud de l’espèce (Castric et
Bernatchez 2003). Plusieurs facteurs peuvent expliquer la fixation d’un phénotype
alternatif dans un contexte de trait seuil et nous éclairer sur la situation actuelle de l’omble
de fontaine.
Premièrement, une seule tactique peut être exprimée dans une population simplement à
travers une réponse plastique, parce que les conditions qui favorisent l’induction de cette
tactique sont toujours présentes. Donc, les conditions environnementales font en sorte que
le seuil est dépassé pour tous les individus. Dans ce cas particulier la norme de réaction ne
change pas, le phénotype observé n’est que le résultat du déplacement le long de cette
norme de réaction, et des changements génétiques ne sont donc pas impliqués (Fig. 5-1b).
Ceci s’illustre facilement lorsque les facteurs inducteurs sont évidents : par exemple, dans
un environnement sec, les feuilles de boutons d’or (Ranunculus sp.) n’exprimeront que la
forme terrestre (par opposition à la forme aquatique), comme cette forme est une réponse
facultative aux conditions d’humidité (Cook et Johnson 1968 dans West-Eberhard 2004).
Combien de temps un phénotype alternatif peut-il rester intact et être ré-exprimé au retour
des conditions favorables est encore peu investigué.
Ensuite, une fixation phénotypique peut être le résultat d’une réponse évolutive, qui
implique alors des changements génétiques. Face à une sélection directionnelle, favorisant
toujours le même phénotype, la fréquence de ce dernier augmentera dans la population, car
le seuil pour sa production changera. Ce changement est illustré graphiquement par le
déplacement des normes de réaction (Fig. 5-1c). La fixation du phénotype a lieu lorsque le
seuil est poussé si loin que malgré toute la gamme des variations environnementales
présentes, le même phénotype est toujours exprimé. Fait intéressant, la fixation du
phénotype peut se faire sans la fixation du génotype : une bonne portion de la variance
additive est alors conservée. À mesure que la fréquence d’une forme augmente dans la
population, l’intensité de la sélection diminue, car la variance phénotypique sur laquelle la
sélection peut agir diminue (i.e. la population approche de la fixation). La sélection
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directionnelle n’érode donc pas toute la variance génétique qui se cache parmi les individus
qui expriment le même phénotype avantagé (Roff 1994a). Par la nature même du trait seuil,
la variance additive associée au seuil et aux traits sous-jacents qui influencent l’adoption
d’une tactique se trouve non-exposée à la sélection, et peu être donc conservée. Pulido
(2007) stipule que ce bassin de « variation cryptique », protégé de l’action de la sélection
naturelle et donc difficilement éliminable, permet l’expression récurrente de la migration
chez la plupart des populations d’oiseaux qui semblent entièrement résidentes. De plus, la
conservation de la variance génétique pour des phénotypes alternatifs tels que la migration
et la résidence résiderait aussi dans le fait que ces phénotypes sont des traits complexes,
polygéniques, sujet à des interactions pléiotropiques antagonistes (Roff 1996). Certaines
contraintes imposées par des pressions de sélection de direction opposée sur des traits
génétiquement corrélés contribuent vraisemblablement à la quasi-immunité des phénotypes
alternatifs face à l’extinction (Staurnes et al. 1992; Pulido 2007). Ce scénario signifie donc
que des populations qui n’expriment qu’un phénotype et qui apparaissent donc « fixées »
ont le potentiel d’exprimer le phénotype alternatif si une sélection vers celui-ci se produit.
C’est peut-être ce qui explique l’apparition des formes anadromes suite à l’introduction de
souche résidente dans des rivières où elles étaient préalablement inexistantes (Pascual et al.
2001). Et c’est peut-être le cas pour l’omble de fontaine, où les populations du Sud
auraient conservé cette plasticité, mais n’exprimeraient qu’une seule tactique suite à une
sélection directionnelle favorisant les résidents. Les causes de cet avantage lié à la
résidence dans l’aire de distribution Sud de l’espèce ne sont pas claires. Qu’il y a-t-il de
différent dans le paysage adaptatif de ces populations : Meilleures conditions en eau douce?
Moins bonnes conditions en eau salée? Surexploitation des formes anadromes? Destruction
de l’habitat? Qui sait?...
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a
b
c
d
Figure 5-1. Représentation schématique de l’anadromie et la résidence en tant que trait seuil. (a) Un individu adopte la tactique qui lui procure le plus haut fitness selon la valeur du trait sous-jacent. L’intersection des fonctions de fitness représente le seuil, et la position de celui-ci changera si les fonctions de fitness se déplacent, influençant directement les proportions de chaque morphe dans la population (zone noire pour Anadrome, grise pour Résidents). Les cas où seule la tactique R est exprimée sont présentés en b, c et d. (b) Les conditions présentes (zone grise) font en sorte que le seuil est toujours dépassé. Les différents seuils représentent la variabilité génétique individuelle. (c) Les seuils sont déplacés sous l’effet de la sélection. (d) Assimilation génétique, menant à la canalisation du trait, où seule la résidence peut être exprimée, peu importe la valeur du trait sous-jacent.
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Finalement, malgré qu’une perte totale de la plasticité semble difficile, les changements
génétiques occasionnés par une sélection directionnelle poussés à l’extrême peuvent mener
ultimement à la canalisation d’un trait. C’est le phénomène d’assimilation génétique
(Pigliucci et Murren 2003; Pigliucci et al. 2006). L’assimilation génétique est le processus
par lequel un phénotype ne change plus de forme en réponse à un signal environnemental,
il est assimilé, canalisé. L’induction du phénotype alternatif n’est plus possible, la
plasticité est perdue. L’assimilation génétique entraîne une norme de réaction plate, où la
variance génétique est nulle (Fig. 5-1d). Parce que le même phénotype est continuellement
avantagé et que la plasticité a un coût (van Kleunen et Fischer 2005), elle sera
éventuellement perdue. Certains exemples, où l’induction d’un phénotype alternatif n’est
plus possible, suggère une assimilation génétique (plantes, Cook et Johnson 1968;
papillons, Shapiro 1976; oiseaux Pulido 2007). Certains auteurs (West-Eberhard 2003;
Pigliucci et al. 2006) prétendent que l’assimilation génétique a le potentiel d’expliquer
plusieurs processus évolutifs écologiques. Selon cette théorie, la plasticité phénotypique
serait en grande partie un processus développemental, inhérent, et permettrait l’apparition
de nouveaux traits quand une population fait face à un nouvel environnement. Ce nouveau
trait subit alors l’effet de la sélection et, lorsqu’il se trouve toujours avantagé, peut
éventuellement se traduire en une assimilation génétique (Price et al. 2003; Pigliucci et al.
2006). Serait-on en présence d’un exemple d’assimilation génétique « en cours » de la
forme résidente, suivant le gradient Sud-Nord de la colonisation des rivières par l’omble de
fontaine anadrome?
5.6 Perspectives «Quand on ne peut revenir en arrière, on ne doit se
préoccuper que de la meilleure façon d'aller de l'avant.»
Paulo Coehlo
Une thèse de doctorat soulève souvent plus de questions qu’elle ne trouve de réponses, et
c’est bien ainsi, car c’est le questionnement qui fait avancer nos connaissances. On dit
qu’estimer correctement son ignorance est une étape saine et nécessaire. Ce travail apporte
des éléments importants à notre compréhension de l’évolution des phénotypes alternatifs,
mais laisse la porte ouverte (encore grande) à de futures recherches.
126
Une piste intéressante à explorer réside dans la nature conditionnelle de l’anadromie et la
résidence. L’hypothèse veut qu’un individu adopte la tactique qui lui procure le plus haut
fitness, par rapport à son statut individuel (ex. taille, Gross 1996, Fig. 5-1a). Il y a donc peu
d’intérêt à comparer le fitness entre deux tactiques, car ce que nous observons est déjà le
résultat de ce qui est en théorie plus profitable au niveau de l’individu. Pour établir la
nature conditionnelle de la stratégie, la vraie question est alors de savoir quel aurait été le
fitness d’un individu qui adopte la tactique A s’il avait opté pour l’autre tactique. Il serait
alors intéressant d’évaluer, pour un même individu (i.e. un même génotype), les
conséquences d’adopter chacune des tactiques. Cet objectif n’est peut-être par contre pas
réalisable, car comment « forcer » un individu à adopter une tactique particulière, alors que
son mode de régulation est adapté pour exprimer l’autre tactique? La façon la plus
convaincante de mettre en évidence la nature condition-dépendante de la sélection dans le
contexte de la stratégie conditionnelle reste alors de démontrer que les fonctions de fitness
(fitness en fonction du statut de l’individu au moment de la « décision »), ne sont pas
linéaires (voir par exemple Hunt et Simmons 2001). La démonstration que les pentes
changent et que ce changement corresponde à un changement de phénotype viendrait
fortement suggérer que les fonctions de fitness se croisent, telle que stipulé par le modèle
de stratégie conditionnelle (Gross 1996). Dans un cas comme l’anadromie et la résidence,
la mise en relation du fitness avec le statut (ou la valeur du trait sous-jacent) n’a jamais été
réalisée, à ma connaissance. Cet objectif en est par contre un d’envergure, car il implique
de connaître à la fois la valeur du trait qui influence l’adoption d’une tactique pour chaque
individu, et le fitness du même individu, deux données qui sont difficilement mesurables, et
qui ne sont pas mesurées au même moment du stade de vie.
Il serait ensuite pertinent d’estimer la variance génétique additive (et par le fait même
l’héritabilité) présente dans le seuil, et non seulement à travers les traits sous-jacents
comme réalisé ici. Nous avons supposé que cette variance génétique dans le seuil était
présente pour les besoins du chapitre 4 basé sur des exemples existants dans la littérature,
surtout chez les insectes (Emlen 1996; Moczek et Nijhout 2003; Tomkins et Brown 2004).
127
La présence d’une telle variance est aussi suggérée chez les salmonidés (Hutchings et
Myers 1994; Aubin-Horth et al. 2005), mais, à ma connaissance, n’a jamais été
empiriquement démontrée. Une telle entreprise est possible en laboratoire et est
envisageable même en milieu naturel en comparant l’incidence des deux tactiques entre des
familles qui ont la même distribution du trait sous-jacent. En connaissant la valeur du seuil
(par exemple la taille où l’adoption d’une tactique se fait), on peut aussi estimer
directement l’héritabilité de cette valeur. Ce genre d’étude est présentement en cours dans
le laboratoire de J.J. Dodson. La démonstration d’une variance génétique importante
viendrait renforcer le bien-fondé de nos prédictions quant à l’évolution de l’anadromie et de
la résidence en tant que trait seuil.
Cette étude se voulait au départ une étude comparative entre plusieurs rivières où l’on
retrouve des individus anadromes et résidents en sympatrie. Est-ce que la dynamique
anadromes-résidents est la même entre les systèmes (ex. reproduction entre les formes) et si
non, quelles pourraient être les causes de ces différences? L’héritabilité de la tactique
diffèrent-t-elle entre systèmes? La notion d’héritabilité variable selon les conditions
environnementales, bonnes ou mauvaises, a été soulevée récemment (Hoffman et Merilä
1999; Charmantier et Garant 2005). Une comparaison spatiale des estimés d’héritabilité,
de même que temporelle, aurait contribué à l’avancement des connaissances dans ce champ
de recherche, ainsi qu’approfondi nos connaissances sur les réponses possibles des
populations de salmonidés face à des changements dans leur environnement, causés par des
facteurs anthropiques ou non. Il s’est avéré que ce projet de doctorat, dans son état actuel,
représentait un défi de taille au niveau pratique et méthodologique, et que son extension à
d’autres systèmes, et sur plusieurs années, était tout simplement impossible dans le seul
cadre de cette étude. Mais la voie reste ouverte…
Finalement, des extensions au modèle présenté dans le chapitre 4 ont le potentiel d’explorer
d’importantes et intéressantes questions. Premièrement, la question de mortalités variables
en eau douce, adjacente au concept de sélection fluctuante, pourrait jouer un rôle dans le
128
maintien de la variation phénotypique et génétique (Merilä et al. 2001; Grant et Grant 2002;
Aubin-Horth et al. 2005). Ensuite, ce modèle pourrait être utilisé pour prédire les
conséquences évolutives et démographiques des perturbations tel un barrage ou un ponceau
qui bloquent complètement l’accès à la montaison. Ces prédictions (ex. taille et âge à
maturité, fécondité) pourraient être comparées avec des observations faites sur des
populations naturelles qui ont subi de telles perturbations, ce qui validerait le modèle.
Enfin, avec un peu de gymnastique, peut-être serions-nous capable d’utiliser le même genre
de modèle pour prédire l’impact d’un réchauffement climatique? Une hypothèse, en partie
supportée par des données empiriques chez les oiseaux migrateurs, propose une
augmentation de la résidence en réponse à un réchauffement du climat (Pulido et Berthold
2004; Pulido 2007). Des augmentations dans le nombre de résidents, des diminutions dans
la distance de migration, des dates de départ du lieu de naissance repoussées, des dates
d’arrivée devancées ainsi que des changements dans la direction de la migration ont tous
été reliés au réchauffement climatique chez les oiseaux, réchauffement qui se traduirait en
des meilleures survies aux sites de reproduction (Fiedler 2003). Ces réponses reflètent en
partie la plasticité dans l’ajustement du comportement, mais seraient aussi le résultat de
changements évolutifs (i.e génétiques; Pulido et Berthold 2004). La résidence est-elle le
sort que nous réservons aux salmonidés qui expriment encore l’anadromie?
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