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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.

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Global patterns and predictors of marine biodiversity across taxa

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Global patterns and predictors of marine biodiversity across taxa

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.

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.

2010.09.14

정다금


Global patterns and predictors of marine biodiversity across taxa

Global patterns of species richness and their structuring forces

Ecology, evolution, conservation


Global patterns and predictors of marine biodiversity across taxa

  • 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

Patterns

Predictors

Important: Temperature or kinetic energy, human impacts


Global patterns and predictors of marine biodiversity across taxa

Patterns of species richness for Coastal taxa.

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DP Tittensoret al. Nature000, 1-4 (2010) doi:10.1038/nature09329


Global patterns and predictors of marine biodiversity across taxa

Patterns of species richness for individual taxa.

DP Tittensoret al. Nature000, 1-4 (2010) doi:10.1038/nature09329


Global patterns and predictors of marine biodiversity across taxa

Patterns of species richness for individual taxa.

P

R

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M

A

R

I

L

Y

O

C

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A

N

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DP Tittensoret al. Nature000, 1-4 (2010) doi:10.1038/nature09329


Global patterns and predictors of marine biodiversity across taxa

Global species richness and hotspots across taxa.

0~1 normalized

B-hotspots:

Philippins, Japan, China, Indonesia, Australia, India and SriLanka,

South Africa, and the Caribbean and southeast USA

C-coastal species: Southeast Asia

D-oceanic diversity: ~30’ North or South

DP Tittensoret al. Nature000, 1-4 (2010) doi:10.1038/nature09329


Global patterns and predictors of marine biodiversity across taxa

6 Hypothesis

13 taxa

SLM Results

Number: z-values

*: significance levels


Global patterns and predictors of marine biodiversity across taxa

Multivariate spatial linear models (SLMs)

6 hypothesis

The kinetic energy or temperature hypothesis:

Higher temperature

-> increased metabolic rates

-> promote higher rates of speciation

‘Productivity-richness’ hypothesis:

Extinction or Niche specialist

- Better discrimination than onland


Global patterns and predictors of marine biodiversity across taxa

3) The stress hypothesis:

Negative relationship of richness with environmental stress

( Quantifying the extent of oxygen depletion)

The Climate stability hypothesis

Higher diversity in more environmentally stable regions

Test: using a measure of temporal variance in sea surface temperature (SST)


Global patterns and predictors of marine biodiversity across taxa

  • 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~’


Global patterns and predictors of marine biodiversity across taxa

SST:

* only predictor of species richness identified as statistically significant across all species groups in the SLMs

* support to kinetic energy or temperature hypothesis

(higher metabolic rates or relaxed thermal constraints promote diversity)

* supported by minimal-adequate generalized-linear models (GLMs)


Global patterns and predictors of marine biodiversity across taxa

SST is the BEST

Not supported

(2)Habitat

1)

(3)Historical

Geographic

factors

1) Endothermic groups ( cetaceans and pinnipeds) showed stronger positive relationships

with primary productivity than SST ( 5.5***, 12.1*** vs. -10.0***, 6.6***)

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


Global patterns and predictors of marine biodiversity across taxa

Diversity, SST and human impact overlap.

Total Div.

Coastal Div.

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 s.r ( r = 0.19 , P<0.01)

Normalized richness

All: r = 0.35

Cs: r = 0.15

Os: r = 0.43 p <0.01 all cases

DP Tittensoret al. Nature000, 1-4 (2010) doi:10.1038/nature09329

Oceanic Div.

Large human impacts (statistically significant)

: coastal areas of East Asia, Europe, North America and Caribbean


Global patterns and predictors of marine biodiversity across taxa

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


Global patterns and predictors of marine biodiversity across taxa

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 warming

Mitigating multiple human impacts


Global patterns and predictors of marine biodiversity across taxa

Methods

Data collecting: - www.iobis.org and expert

Analysis:

GLMs and SLMs,

Dep-indep. Variables -> log-transformed to linearize and normalize data

Excluding: zero diversity, <10% ocean area

Maximum likelihood spatial autoregressive (SAR) model

Akaike Information Criterion


Global patterns and predictors of marine biodiversity across taxa

Thank you


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