A probabilistic model for analysing species co-occurrence

• Published in: Global ecology and biogeography Vol. 22; no. 2; pp. 252 - 260
• Main Author: Veech, Joseph A.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.02.2013; Blackwell Publishing; Blackwell; Wiley Subscription Services, Inc
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biogeography; Biological and medical sciences; combinatorics; community assembly; Datasets; distribution; Ecological modeling; False positive errors; Fundamental and applied biological sciences. Psychology; General aspects; MACROECOLOGICAL METHODS; Matrices; Modeling; nestedness; null model; Population ecology; Probabilistic modeling; probability; Random allocation; Species; Studies; Synecology; probability; Biogeography; Ecology; distribution; Hypothesis test; null model; Nested subset; Statistical model; nestedness; Probabilistic model; Community; Species richness; community assembly; combinatorics
• ISSN: 1466-822X; 1466-8238; 1466-822X
• DOI: 10.1111/j.1466-8238.2012.00789.x

Aim: To develop a new probabilistic model that can be used to test for statistically significant pair-wise patterns of species co-occurrence. The model gives the probability that two species would co-occur at a frequency less than (or greater than) the observed frequency if the two species were distributed independently of one another among a set of sites. The model can be used to classify species associations as negative, positive or random. Innovation: Historically, the analysis of species co-occurrence has involved the use of data randomization. An observed species presence-absence matrix is compared with randomized matrices to determine if the observed matrix has structure, either an excess or deficit of species positively or negatively associated with each other. The computer algorithms used to randomize matrices can sometimes produce Type I and Type II errors (when the randomization algorithm produces a biased set of all possible matrices) due to the randomization process itself. The probabilistic model does not rely on any data randomization, hence it has a very low Type I error rate and is powerful having a low Type II error rate. Main conclusions: When applied to 10 different data sets the probabilistic model revealed significant positive and negative species associations in most of the data sets. Compared with previous analyses the model tended to find fewer significant associations; this may indicate a generally low rate of Type I error in the model. The model is easy to implement and requires no special software. The model could potentially transform the way that ecologists test for species co-occurrence in a wide range of ecological studies.