global patterns and predictors of marine biodiversity across taxa
DESCRIPTION
Global patterns and predictors of marine biodiversity across taxa. Derek 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. - PowerPoint PPT PresentationTRANSCRIPT
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