Trends in Ecology & Evolution
ReviewMoving beyond assumptions to understand abundance distributions across the ranges of species
Section snippets
The importance of abundance
Geographical patterns of the abundance of species underlie some of the most fundamental issues in ecology, including the causes of species range limits, gene flow within populations, population dynamics and explanations for macroecological patterns such as the species-area relationship. Patterns of species abundance have also been used to develop hypotheses about applied issues such as how species will respond to climate change, how to identify probable locations of pest outbreaks and where to
Assumptions about abundance
A widespread biogeographical assumption is that species are most abundant in the center of their range and decline in abundance toward the range edges. This assumption, which we call the ‘abundant-center hypothesis’, has a long history in theoretical and applied ecology. It has even been called a ‘general rule’ of biogeography [1] and ‘a common feature shared by all species’ [2].
The abundant-center assumption is more than an observational curiosity, because it has also been used extensively in
Moving beyond assumptions
Recognizing both that species distributions do not always exhibit an assumed distribution, and that actual abundance distributions provide limited opportunities for inference, suggests that adding additional layers of data on a geographical scale (including basic population demographics, biophysical variables and genetic population structure) will be necessary to narrow the range of viable hypotheses to explain range boundaries and population distributions. Caughley et al. [20], for example,
Using data to test hypotheses
The growing list of studies like those above provides examples of key factors to address when developing more realistic hypotheses related to species distributions. The multitude of factors points to the need to layer multiple elements of range-scaled data sequentially in an ordered progression to rule out large classes of mechanistic hypotheses. These additional layers of data can be viewed as filters through which a clearer signal can be delivered from noisy abundance data. This new round of
The need for more large-scale descriptive ecology
After decades of advances in experimental ecology, observational approaches to hypothesis testing are not at the forefront of the field and are often considered ‘less rigorous’ than experimental approaches [51]. When addressing larger-scale problems, however, we are still ‘observation limited’. Moreover, as the studies of spatial variability reviewed here suggest, findings from even the most carefully controlled experiments conducted at a given site with a species range might not be relevant to
Acknowledgements
This paper benefited greatly from the support of the Andrew W. Mellon Foundation and the David and Lucile Packard Foundation. This is contribution number 216 from PISCO, the Partnership for Interdisciplinary Studies of Coastal Oceans funded primarily by the Gordon and Betty Moore Foundation and David and Lucile Packard Foundation.
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