Quantifying and Detecting Patterns of Association in Ecological Communities
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Date
Authors
Lavender, Mike
Keyword
community assembly , co-occurrence , species associations , community ecology , null model , scaled co-evenness , sCoE , pairwise associations , type I error , type II error
Abstract
Patterns of non-random species association (co-occurrence), which are common, are frequently used as an indicator of the processes responsible for shaping ecological communities. While a great deal of effort has gone into developing and evaluating the statistical methods used to detect non-random patterns of association, and the metrics used to quantify the patterns, questions remain about their performance. First, there has been no comparison or statistical evaluation of the tests of pairwise association (versus community-level assessments of association). Second, the metrics used to quantify association have not been evaluated independently of statistical tests. And third, evaluations of the community-level null models have focused exclusively on patterns of negative association and on matrices with log-normal species incidence distributions. As a result, it is unclear how well the null models perform when patterns of association are positive or when the underlying incidence distribution is not log-normal.
I conducted three sets of simulation studies to address these three questions, finding that 1) the choice of statistical method, whether pairwise or community-level null model, has a significant impact on the detection of association patterns; 2) the underlying species incidence distribution contributes significantly to the ability of the null models to detect non-random patterns in community-level matrices; and 3) using raw association metrics, and specifically the sCoE metric developed as part of this thesis, provides further insights into community structure allowing for comparisons of association strength over time or between communities.
By addressing these three questions, I not only provide clear guidance for improving the detection of non-random patterns in both community presence-absence matrices and between pairs of species but also provide an intuitive method for evaluating, comparing, and gaining new insights into species associations within ecological communities.