Global shark species richness is more constrained by energy than evolutionary history
16 April 2022
Global shark species richness is more constrained by energy than evolutionary history
Emmaline R. Sheahan, Gavin J.P. Naylor, Daniel J. McGlinn
ABSTRACT:
Aim To 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.
Location Global scale and two ecological realms: the Tropical Atlantic and the Central Indo-Pacific.
Time Period Past 100 years
Major Taxa Studied We 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.
Methods We 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.
Results The 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 β 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 Conclusions The 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.
bioRxiv, DOI: 10.1101/2022.04.15.488537