global patterns and predictors of marine biodiversity across taxa
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Global patterns and predic-tors
of marine biodiversity across taxaDerek P. Tittensor1, Camilo Mora1, Walter Jetz2, Heike K.
Lotze1, Daniel Ricard1, Edward Vanden Berghe3 & Boris Worm1
1: Department of Biology, Dalhousie University, 1355 Oxford Street, Halifax B3H 4J1, Canada.
2: Department of Ecology and Evolutionary Biology, Yale University, 165 Prospect Street,
New Haven, Connecticut 06520-8106, USA. 3Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey 08901-8521, USA.
2010.09.14정다금
Global patterns of species richness and their structuring forces
Ecology, evolution, conservation
Examine:-Global patterns(2-D) and predictors of species richness across 13 major species groups (zooplankton to marine mammals)
* Coastal species: Western pacific
* Oceanic groups: mid-latitudinal in all oceans
* Spatial regression analyses: - Sea surface temperature - habitat availability and historical factors
Important: Temperature or kinetic energy, human impacts
Patterns
Predictors
DP Tittensor et al. Nature 000, 1-4 (2010) doi:10.1038/nature09329
Patterns of species richness for Coastal taxa.
PRIMARILY
COASTAL
DP Tittensor et al. Nature 000, 1-4 (2010) doi:10.1038/nature09329
Patterns of species richness for individual taxa.
DP Tittensor et al. Nature 000, 1-4 (2010) doi:10.1038/nature09329
Patterns of species richness for individual taxa.
PRIMARILY
OCEANIC
DP Tittensor et al. Nature 000, 1-4 (2010) doi:10.1038/nature09329
Global species richness and hotspots across taxa.
0~1 normalizedB-hotspots: Philippins, Japan, China, Indonesia, Australia, India and SriLanka, South Africa, and the Caribbean and southeast USAC-coastal species: Southeast AsiaD-oceanic diversity: ~30’ North or South
SLM ResultsNumber: z-values*: significance levels
13 taxa
6 Hypothesis
Multivariate spatial linear models (SLMs) 6 hypothesis
1)The kinetic energy or temperature hypothe-sis:
Higher temperature
-> increased metabolic rates
-> promote higher rates of speciation
2)‘Productivity-richness’ hypothesis:
Extinction or Niche specialist
- Better discrimination than on land
3) The stress hypothesis:
Negative relationship of richness with environ-mental stress
( Quantifying the extent of oxygen depletion)
4)The Climate stability hypothesis
Higher diversity in more environmentally stable regions
Test: using a measure of temporal variance in sea surface temperature (SST)
5) The availability of important habitat feature:
Influence positively both abundance and richness• coastline length for coastal species • Frontal systems for oceanic species (SST slopes)
6) Evolutionary history among ocean basins
‘Oceans~’
SST: * only predictor of species richness identified as sta-
tistically significant across all species groups in the SLMs
* support to kinetic energy or temperature hypothe-sis
(higher metabolic rates or relaxed thermal con-straints promote diversity)
* supported by minimal-adequate generalized-linear models (GLMs)
SST is the BEST
(3)Historical Geo-
graphicfactors
Not sup-ported(2)Habitat
Temperature or kinetic energy has consistent and dominant role in structuring broad-scale marine diversity patterns, particularly for ectothermic species, with habitat(2) area and historical factors(3) important for coastal taxa, and support for other factors varying by taxon
1) Endothermic groups ( cetaceans and pinnipeds) showed stronger positive relationships with primary productivity than SST ( 5.5***, 12.1*** vs. -10.0***, 6.6***)
1)
DP Tittensor et al. Nature 000, 1-4 (2010) doi:10.1038/nature09329
Diversity, SST and human impact overlap.
SST and species richness was generally positive (a-c)(except pinnipeds , selective advantage in cold waters)
Coastal groups; increase monotonically with temperature
Oceanic groups: asymptotic with SST
Total Div. Coastal Div.
Oceanic Div.Large human impacts (statistically significant): coastal areas of East Asia, Europe, North America and Caribbean
Total s.r ( r = 0.19 , P<0.01)Normalized richnessAll: r = 0.35Cs: r = 0.15Os: r = 0.43 p <0.01 all cases
Limitation
-Limited taxa-Large gap: deep-sea diversity-Microbes or viruses-Limited marine invertebrate data- Analyze only a subset of mechanisms that may shape biodiversity
Founding!: 2 distinct patterns of global marine biodiversity
*** Coastal habitat taxa vs. Open ocean taxa
* Temperature => kinetic energy=> Diversity (species richness) over evo & eco
* Habitat
Limiting the extent of ocean warmingMitigating multiple human impacts
Methods
Data collecting: - www.iobis.org and expert
Analysis: GLMs and SLMs, Dep-indep. Variables -> log-transformed to linearize and normalize dataExcluding: zero diversity, <10% ocean areaMaximum likelihood spatial autoregressive (SAR) model Akaike Information Criterion
Thank you
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