Stochastic approach to determine spatial patterns of lizard community on a desert island

One of the principal sources of error in identifying spatial arrangements is autocorrelation, since nearby points in space tend to have more similar values than would be expected by random change. When a Markovian approach is used, spatial arrangements can be measured as a transition probability between occupied and empty spaces in samples that are spatially dependent. We applied a model that incorporates first-order Markov chains to analyse spatial arrangement of numerical dominance, richness, and abundance on a lizard community at different spatial and temporal scales. We hypothesized that if a spatial dependence on abundance and richness exists in a diurnal desert community, then the Markov chains can predict the spatial arrangement. We found that each pair of values was dependent only on its immediate predecessor segment. In this sense, we found intergeneric differences at temporal and spatial scales of recurrence estimates. Also, in desert scrub, species show higher spatial aggregation and had lower species richness than at the island level; the inverse pattern occurred on rocky hillsides. At the species level, Uta stansburiana is the most abundant species in desert scrub, while Sauromalus slevini is the most abundant species on rocky hillsides. This report attempts to understand, using Markovian spatial models, the effect of nearby samples on local abundance and richness on different scales and over several seasons.