Back

Global shark species richness is more constrained by energy than evolutionary history

Sheahan, E. R.; Naylor, G. J. P.; McGlinn, D. J.

2022-04-16 ecology
10.1101/2022.04.15.488537 bioRxiv
Show abstract

AimTo examine the support of two ecological diversity theories- The Ecological Limits Hypothesis (ELH) and the Niche Conservatism Hypothesis (NCH) - in explaining patterns of global shark diversity. LocationGlobal scale and two ecological realms: the Tropical Atlantic and the Central Indo-Pacific. Time PeriodPast 100 years Major Taxa StudiedWe examined 534 species of sharks and chimaeras, and we performed two subclade analyses on 272 species of ground sharks and 15 species of mackerel sharks. MethodsWe compared the species richness, mean root distance (MRD), and tree imbalance patterns to those simulated under the ELH and NCH with temperate and tropical centers of origin. We used sea temperature as a proxy for energy availability. We examined the importance of biogeographic history by comparing the model fits between two taxonomic groups, ground and mackerel sharks, and two geographic regions, the Tropical Atlantic realm and Central Indo-Pacific realm. ResultsThe ELH, temperate-origin model had the best fit to the global dataset and the sub-analyses on ground sharks, mackerel sharks, and the Tropical Atlantic. The NCH temperate-origin model provided the best fit for the Central Indo-Pacific. The {beta} metric of tree symmetry showed the best potential for differentiating between the ELH and NCH models, and the correlation coefficient for temperature vs MRD performed the best at differentiating between temperate and tropical origin of ancestors. Main ConclusionsThe global and subclade analyses indicate the ELH provides the best explanation for global scale shark diversity gradients even in clades with varying ecology. However, at the realm scale, biogeographic history has an impact on richness patterns. Comparing multiple metrics in relation to a simulation model provides a more rigorous comparison of these models than simple regression fits.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Global Ecology and Biogeography
41 papers in training set
Top 0.1%
23.1%
2
Ecography
50 papers in training set
Top 0.1%
23.1%
3
Journal of Biogeography
37 papers in training set
Top 0.1%
10.3%
50% of probability mass above
4
Global Change Biology
69 papers in training set
Top 0.4%
3.7%
5
PLOS ONE
4510 papers in training set
Top 38%
3.7%
6
Philosophical Transactions of the Royal Society B
51 papers in training set
Top 1%
3.7%
7
Ecological Modelling
24 papers in training set
Top 0.2%
2.7%
8
Proceedings of the Royal Society B: Biological Sciences
341 papers in training set
Top 3%
2.1%
9
Ecology Letters
121 papers in training set
Top 0.6%
2.1%
10
Biological Conservation
43 papers in training set
Top 0.4%
1.9%
11
Ecology and Evolution
232 papers in training set
Top 2%
1.8%
12
Scientific Reports
3102 papers in training set
Top 57%
1.7%
13
Frontiers in Ecology and Evolution
60 papers in training set
Top 2%
1.7%
14
Systematic Biology
121 papers in training set
Top 0.3%
1.5%
15
Ecological Informatics
29 papers in training set
Top 0.5%
1.3%
16
Diversity and Distributions
26 papers in training set
Top 0.3%
1.0%
17
PeerJ
261 papers in training set
Top 12%
0.9%
18
Methods in Ecology and Evolution
160 papers in training set
Top 2%
0.8%
19
Evolutionary Ecology
14 papers in training set
Top 0.3%
0.8%
20
Journal of Animal Ecology
63 papers in training set
Top 1%
0.7%
21
Nature Communications
4913 papers in training set
Top 65%
0.7%
22
BMC Ecology and Evolution
49 papers in training set
Top 2%
0.7%
23
The American Naturalist
114 papers in training set
Top 2%
0.5%
24
iScience
1063 papers in training set
Top 40%
0.5%
25
PLOS Computational Biology
1633 papers in training set
Top 28%
0.5%
26
Philosophical Transactions of the Royal Society B: Biological Sciences
53 papers in training set
Top 2%
0.5%