Treffer: Using a sequential sampling algorithm to apply the niche-neutral model to species occurrence patterns

AimSpecies occurrence patterns are typically analysed using data-randomisation approaches, which reveal when observed patterns deviate from random expectation, but give little insight why. Mechanistic models, such as neutral models, have been proposed as an alternative null model, but they are computationally expensive. Here, we use an efficient method to simulate such models and to explore likely mechanisms governing the occurrence patterns of birds on islands.LocationRiau archipelago, Indonesia.TaxonBirds.MethodsWe used species richness and island area data to fit a niche–neutral model, where species obey neutral dynamics within non-overlapping discrete niches. We used a sequential sampling algorithm that can efficiently generate randomised presence– absence matrices under the niche-neutral model and used mismatches to identify which mechanisms were potentially important to occurrence patterns.ResultsBirds were more segregated and less nested than expected from both data randomisation and the niche–neutral model. The niche–neutral model reproduced the mean relationship between island size and species richness, but it could not produce sufficient variability to account for the richness data. The model was brought into closer agreement with the data by allowing niche diversity to vary across islands (increased segregation) and allowing the per-capita immigration rate to vary across islands (decreased nestedness).Main conclusionWhile the species-area relationship could be explained by a model with constant per-capita immigration rates and number of niches across islands, interisland heterogeneity was needed to explain higher-order metrics of nestedness and cooccurrence patterns. The sequential sampling algorithm allowed us to explore different scenarios efficiently, and our approach may be useful for identifying the mechanisms structuring occurrence patterns in other systems.Correspondencenadiah@nadiah.org