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.

Keywords

Aggregated data; functional traits; individual variation; marginal and joint distributions; matrix models; Simpson's paradox; species coexistence; species interactions

Published in

Ecology Letters
2011, Volume: 14, number: 12, pages: 1273-1287
Publisher: WILEY-BLACKWELL

UKÄ Subject classification

Ecology


Publication identifier

DOI: https://doi.org/10.1111/j.1461-0248.2011.01685.x

Permanent link to this page (URI)

https://res.slu.se/id/publ/91664