Principal factors controlling biodiversity along an elevation gradient: Water, energy and their interaction. (9th April 2019)

• Record Type: Journal Article
• Authors: Vetaas, Ole R.; Paudel, Keshav P.; Christensen, Morten
• Is Part Of: Journal of biogeography. Volume 46:Number 8(2019:Aug.)
• Page Start: 1652
• Page End: 1663
• Publication Date: 2019-04-09
• Subjects: elevational gamma diversity -- elevation‐temperature -- gradient -- length of growing season -- more individuals hypothesis -- net primary production -- species richness -- water–energy dynamics; Biogeography -- Periodicals; 578.09
• DOI: 10.1111/jbi.13564
• Languages: English
• ISSNs: 0305-0270

Abstract

Abstract: Aim: Variation in diversity is a well‐documented spatial pattern in biogeography, but an overarching climate‐based theory of diversity is lacking. We evaluate two models of species richness related to species‐richness‐energy theory. One is based on net primary production (NPP) or the more individuals hypothesis (MiH), and the other is a model based on water–energy dynamics (WED). We use taxa from three kingdoms along an extensive elevation‐temperature gradient. Both WED and NPP‐MiH are based on thermal energy, but the question is whether energy operates through regulating production and chemical (potential) energy only (NPP‐MiH), or if kinetic energy as regulator of available liquid water is needed (WED). Location: Central Himalayas. Methods: The biodiversity, that is, elevational gamma diversity, of 12 taxa containing animals, plants, and fungi was estimated from range data along an elevation gradient. Generalized linear models were fitted to the species richness data, and the Akaike information criterion (AIC) and deviance explained were used to evaluate the NPP‐MiH and WED models. In addition, we tested the relationships with precipitation and length of growing season (LGS) and their interaction. Results: The peaks in richness of the taxa are dispersed along the entire Himalayan bioclimatic gradient from the subtropics to the alpine zone. WED performs best for all taxa along the entire gradient. In the non‐tropical zone, NPP‐MiH is best for reptiles, and NPP‐MiH and WED are equally good for mammals and amphibians. Including the length of the growing season in the WED model improves the AIC for eight taxa, and WED is superior for combined cross‐taxon biodiversity along the entire gradient, but WED and NPP‐MiH are equally good in the non‐tropical zone. Conclusion: Water–energy dynamics is able to predict peaks in species richness under different climate and primary production conditions; hence, WED is better and more general than NPP‐MiH. The interaction with precipitation and the length of the growing season, which also reflects primary production, improve the model for several organism groups. Hence, LGS may improve and unify future mechanistic first‐principle model of biodiversity.