Title: Partitioning Abiotic and Biotic Contributions to Community Variation
Advisor: Dr. Gail Rosen
Date: Wednesday, December 11, 2013
Time: 3:00 p.m.
Location: ECE Conference Room 302, 3rd Floor, Bossone Research Enterprise Center
It is well known that both environmental factors and species interactions structure ecological communities. To study community composition responses to environmental gradients, ordination and regression techniques are typically employed; however, for studying species interactions, methods primarily rely on analyzing patterns of presence/absence. Each of these types of analyses are carried out independently because there is a lack of unified statistical methods for simultaneous analysis of biotic and abiotic factors influencing community composition. This thesis presents a unified method that enables the removal of environmentally explained variation from species responses so that apparent species interactions are not masked or augmented by the abiotic responses, thus partitioning the abiotic/biotic factors.
To achieve a unified method, first, species responses to environmental gradients are removed via a multivariate regression procedure. Second, the residual responses, void of environmentally explained variation, are tested for species interactions using a null model. Third, communities identified with significant interactions are summarized by the average pairwise covariation among the member species. The method can be used to test hypotheses about species interactions when environmental gradients are present and it may be used to calculate percentages of variation explained due to abiotic, biotic and unexplained factors. Via a sensitivity analysis, I demonstrate that sufficient detection (~95%) and false positive rates (~5%) can be achieved under particular site-species ratios, number of environmental gradients, and covariation-to-noise ratios. My method can guarantee a sufficient false positive rate (~5%) for communities with >60 samples, up to 500 species and influenced by up to four environmental gradients.