Incorporating spatial autocorrelation and association in the statistical null model test of co-occurrence

Lagat, Vitalis Kimutai (2017-03)

Thesis (MSc)--Stellenbosch University, 2017.


ENGLISH ABSTRACT: To avoid conflicts and optimally exploit environmental resources, species will partition available habitats, forming co-occurrence patterns. Such datasets are often described as a species-by-site matrix. Null models based on permutations with constraints on row or column sums have been used in this regard, with the Chessboard score (C-score) a common metric for detecting significant signals of association or dissociation, from which the type of biotic interactions can be inferred. However, such a permutation test often ignore the spatial autocorrelation of species distributions which could lead to counterintuitive results in the null model test. Consequently, tests should account for the spatial autocorrelation of each species. Another important concept that is ignored in the classic permutation test is the matching of environmental heterogeneity and species' habitat preference. To tease apart the role of environmental heterogeneity from biotic interactions, the permutation test should also be allowed to reserve the association between species. This project thus designs a permutation null model test that can progressively include the spatial autocorrelation of species and the association between species so that the role of aggregation and environmental heterogeneity can be further examined. A R package has been designed to implement both classic (spatially implicit) null model tests of co-occurrence and newly designed approaches for the permutation test with constraints on species autocorrelation and association. Though both the classic and the newly designed null models lead to the same inference regarding inter-specific competition as a factor structuring ecological communities, the latter is more reliable because it does not violate any of the assumptions of the test. Keywords: Null model; interspecific competition; spatial autocorrelation; species association; species co-occurrence; null hypothesis; species-by-site matrix; permutation test; checkerboard distribution.

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