Competitive exclusion, or species aggregation?

Abstract

There is a long-standing dispute over whether the analysis of species co-occurrence data, typically on islands in an archipelago, can disclose the forces at work in structuring a community. Here we present and utilise three "scores" S, C and T. S gives the mean number of islands shared by a species pair in the presence/absence data under study. The scores C and T are based on the way that a pair of species occurs on a pair of islands. When each species occurs on a different island, this adds to the "checkerboard score" C; if they occupy the same island, this increases the "togetherness score" T.In judging whether observed values of S, C and T are compatible with a null hypothesis assuming no species interaction, we follow Connor and Simberloff (1979) in generating a "control group" of (constrained) simulated incidence patterns.Presence/absence matrices can have paradoxical features, in combining a high mutual exclusion by species (checkerboardedness) with a degree of species aggregation that is also high. We show that this is in fact inevitable - that, given the usual contraints, C and T can differ only by a constant. This means that extreme checkerboardedness can be produced by forces making for species aggregation, just as well as by those making for avoidance.If we restrict our attention to a subset of species, the constraints are less rigid and the S, C and T scores are somewhat freer to vary. We consider the confamilial subsets in the Vanuatu archipelago as likely candidates for revealing any competition forces at work. Calculating the actual S, C and T scores for these subsets, we compare them with the corresponding scores in a sample of simulated colonization patterns.The actual species-distributions differ significantly from what we would expect if the colonization choices of different species were uncorrelated (save for some biological constraints). The confamilial species of the real world share more islands, and occur in a pattern less checkerboarded, and more aggregated, than their simulation counterparts. This suggests that competition pressures, if they exist, are overcome by countervailing factors.The method used is applicable in other ways, and to a wider class of problems, in analysing the forces behind community structure.

Conflicts between traditional pastoralism and conservation of Himalayan ibex (Capra sibirica) in the Trans‐Himalayan mountains

“…Although all aspects of spatial auto-correlation can not be incorporated in randomisation algorithms (Koenig, 1999), the nature of the association between species (positive or negative trends in co-occurrence) is expected to be appropriately represented. This nullmodel approach is adapted from Diamond (1975) and Connor & Simberloff (1983) and has been widely debated among ecologists (for reviews, see Connor & Simberloff, 1984;Weins, 1989;Stone & Roberts, 1992;Gotelli & Graves, 1996;Gotelli, 2000). Spatial patterns can also be analyzed using alternative indices (niche-overlap indices: Albrecht & Gotelli, 2001).…”

Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks

“…The simplest possible measure of co-occurrence and hence similarity in host preferences (Roberts and Stone 1990;Stone and Roberts 1992).…”

Subtidal macrozoobenthos communities from northern Chile during and post El Niño 1997–1998

“…After calculating C-scores, EcoSim calculated the probability for the observed C-score to be higher than expected by chance. In competitively structured communities, the observed C-score are significantly higher than the C-score expected by chance (Stone and Roberts 1992). Given that competitive interactions are more likely to occur between species with similar resource requirements, we also performed co-occurrence analyses separately for the five most common feeding groups detected in both sampling periods: carnivores, filter feeders, interface feeders, carnivore/surface deposit feeders (Opportunistic type 1), omnivore/carnivore (Opportunistic type 2).…”