title: Individual-scale variation, species-scale differences: inference needed to understand diversity.
abstract: As ecological data are usually analysed at a scale different from the one at which the process of interest operates, interpretations can be confusing and controversial. For example, hypothesised differences between species do not operate at the species level, but concern individuals responding to environmental variation, including competition with neighbours. Aggregated data from many individuals subject to spatio-temporal variation are used to produce species-level averages, which marginalise away the relevant (process-level) scale. Paradoxically, the higher the dimensionality, the more ways there are to differ, yet the more species appear the same. The aggregate becomes increasingly irrelevant and misleading. Standard analyses can make species look the same, reverse species rankings along niche axes, make the surprising prediction that a species decreases in abundance when a competitor is removed from a model, or simply preclude parameter estimation. Aggregation explains why niche differences hidden at the species level become apparent upon disaggregation to the individual level, why models suggest that individual-level variation has a minor impact on diversity when disaggregation shows it to be important, and why literature-based synthesis can be unfruitful. We show how to identify when aggregation is the problem, where it has caused controversy, and propose three ways to address it.
authors: Nicholas School of the Environment, Duke University, Durham, NC 27708, USA. jimclark@duke.edu
authors: James S
authors: JS
authors: Clark
authors: David M
authors: DM
authors: Bell
authors: Michelle H
authors: MH
authors: Hersh
authors: Matthew C
authors: MC
authors: Kwit
authors: Emily
authors: E
authors: Moran
authors: Carl
authors: C
authors: Salk
authors: Anne
authors: A
authors: Stine
authors: Denis
authors: D
authors: Valle
authors: Kai
authors: K
authors: Zhu
mesh_terms: D000818:Animals; D044822:Biodiversity; D019295:Computational Biology / Q000379:methods*; D004463:Ecology / Q000379:methods*; D008954:Models, Biological