Efficient algorithms for sampling feasible sets of macroecological patterns
- Published
- Accepted
- Subject Areas
- Computational Biology, Ecology, Computational Science
- Keywords
- combinatorics, macroecology, feasible set, species abundance distribution, constraints, sampling algorithms, integer partitions, species spatial abundance distribution
- Copyright
- © 2013 Locey et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Cite this article
- 2013. Efficient algorithms for sampling feasible sets of macroecological patterns. PeerJ PrePrints 1:e78v1 https://doi.org/10.7287/peerj.preprints.78v1
Abstract
Ecological variables such as species richness (S) and total abundance (N) can strongly influence the forms of macroecological patterns. For example, the majority of variation in the species abundance distribution (SAD) can often be explained by the majority of possible forms having the same N and S, i.e. the feasible set. The feasible set reveals how variables such as N and S determine observable variation and whether empirical patterns are exceptional to the majority of possible forms. However, this approach has currently only been applied to the SAD using relatively inefficient random sampling algorithms. We extend the use of the feasible set approach by developing new algorithms to efficiently generate random samples of the feasible set for the SAD and the intraspecific spatial abundance distribution (SSAD). These algorithms are often several orders of magnitude faster than a previous method, which greatly increases the size and diversity of communities that can be examined.