Species traits and the form of individual species-energy relationships.
Environmental energy availability explains much of the spatial variation in species richness at regional scales. While numerous mechanisms that may drive such total species-energy relationships have been identified, knowledge of their relative contributions is scant. Here, we adopt a novel approach to identify these drivers that exploits the composite nature of species richness, i.e. its summation from individual species distributions. We construct individual species-energy relationships (ISERs) for each species in the British breeding avifauna using both solar (temperature) and productive energy metrics (normalized difference vegetation index) as measures of environmental energy availability. We use the slopes of these relationships and the resultant change in deviance, relative to a null model, as measures of their strength and use them as response variables in multiple regressions that use ecological traits as predictors. The commonest species exhibit the strongest ISERs, which is counter to the prediction derived from the more individuals hypothesis. There is no evidence that predatory species have stronger ISERs, which is incompatible with the suggestion that high levels of energy availability increase the length of the food chain allowing larger numbers of predators to exist. We find some evidence that species with narrow niche breadths have stronger ISERs, thus providing one of the few pieces of supportive evidence that high-energy availability promotes species richness by increasing the occurrence of specialist species that use a narrow range of resources.
Evans
Karl L
KL
Biodiversity & Macroecology Group, Department of Animal & Plant Sciences, University of Sheffield, UK. karl.evans@sheffield.ac.uk
Jackson
Sarah F
SF
Greenwood
Jeremy J D
JJ
Gaston
Kevin J
KJ
D000818:Animals; D044822:Biodiversity; D001717:Birds / Q000378:metabolism* / Q000502:physiology; D001947:Breeding; D004734:Energy Metabolism; D008954:Models, Biological; D012044:Regression Analysis; D013696:Temperature