Efficient algorithms for sampling feasible sets of abundance distributions
- Published
- Accepted
- Subject Areas
- Biodiversity, Computational Biology, Ecology, Mathematical Biology, Computational Science
- Keywords
- combinatorics, macroecology, feasible set, species abundance distribution, constraints, sampling algorithms, integer partitions, species spatial abundance distribution
- Copyright
- © 2014 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
- 2014. Efficient algorithms for sampling feasible sets of abundance distributions. PeerJ PrePrints 2:e78v2 https://doi.org/10.7287/peerj.preprints.78v2
Abstract
Ecological variables such as species richness (S) and total abundance (N) can strongly influence ecological patterns. For example, the general form of the species abundance distribution (SAD) can often be explained by the majority of possible forms having the same N and S, i.e. the SAD feasible set. The feasible set reveals how variables determine observable variation, whether empirical patterns are exceptional to the majority of possible forms, and provides a constraint-based explanation for the ubiquity of hollow-curve SADs in nature. However, use of the feasible set has been limited to inefficient sampling algorithms that prevent large ecological communities and ecologically realistic combinations of N and S from being examined. This is the primary hindrance to using this otherwise novel perspective and theoretical framework. We developed efficient computational algorithms to generate random samples of the feasible set for the SAD and similar discrete distributions of abundance, including those that allow for zero-values, e.g., absences. We provide Python and R based implementations of our algorithms and tools for testing and using them. Our algorithms are often several orders of magnitude faster than a long-standing and recently used approach. This greatly increases the size and diversity of communities that can be examined with the feasible set approach and thus advances progress using constraint-based approaches to decipher ecological patterns.
Author Comment
We have worked to clarify each section, to simplify the algorithms, and to explain the meaningfulness and interpretation of the overall approach.