Predicting regional densities from bird occurrence data: Validation and effects of species traits in a Macaronesian Island

© 2015 John Wiley & Sons Ltd. Aim: Quantifying species abundances is costly, especially when many species are involved. To overcome this problem, several studies have predicted local abundances (at the sample unit level) from species occurrence distribution models (SODMs), with differences in predictive performance among studies. Surprisingly, the ability of SODM to predict regional abundances of an entire area of interest has never been tested, despite the fact that it is an essential parameter for species conservation and management. We tested whether local and regional abundances of 21 terrestrial bird species could be predicted from SODMs in an exhaustively surveyed island, and examined the variation explained by species-specific traits. Location: La Palma Island, Canary Islands. Methods: We firstly assessed two types of algorithms representing the two main families of SODMs. We built models using presence/absence (boosted classification trees) and presence/background (MaxEnt) data as a function of relevant environmental predictors and tested their ability to predict the observed local abundances. The predicted probabilities of occurrence (Pi) were translated into animal numbers (n′) using the revisited equation ni′ = -ln(1-Pi), and we obtained regional abundances (for the whole island). Results: Predictive ability of presence/absence models was superior than that of MaxEnt. At the regional level, the observed average densities of all species were highly predictable from occurrence probabilities (R2 = 93.5%), without overall overestimation or underestimation. Interspecific variation in the accuracy of predicted regional density was largely explained (R2 = 73%), with habitat breath and variation in local abundance being the traits of greatest importance. Main conclusions: Despite uncertainties associated with local predictions and the idiosyncrasies of each species, our procedures enabled us to predict regional abundances in an unbiased way. Our approach provides