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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 ( Max Ent) data as a function of relevant environmental predictors and tested their ability to predict the observed local abundances. The predicted probabilities of occurrence ( P i) were translated into animal numbers ( n′) using the revisited equation n i ′ = − ln(1−P i ), 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 a cost-effective tool when a large number of species are involved. Furthermore, the influence of species-specific traits on the prediction accuracy provides insights into sampling designs for focal species. [ABSTRACT FROM AUTHOR]

We identify autoecological traits of bird species that influence the accuracy of predictive models of species distribution based on census data obtained from stratified sampling. These models would serve as a complementary approach to the development of regional bird atlases. We model the winter bird abundance of 64 terrestrial bird species in 77 census plots in Central Spain (Madrid province), using regression tree analyses. The predicted distribution of species density derived from statistical models (birds/10 ha) was compared with the published relative abundances depicted by a very accurate regional atlas of wintering birds (birds observed per 10 h). Statistical models explained an average of 41.7% of the original deviance observed in the local bird distribution (range 19.6–79.3%). Significant associations between observed relative abundances (atlas data) and predicted average densities in 1×1 km squares within 10×10 km UTMs were attained for 44 out of 64 species. Interspecific discrepancies between predicted and observed distribution maps decreased with between-year constancy in regional bird distribution and the degree of ecological specialization of species. Therefore, statistical modeling using census localities allowed us to depict geographical variations in bird abundance that were similar to those in the quantitative atlas maps. Nevertheless, bird distributions derived from statistical models are less reproducible in some species than in others, depending on their autoecological traits. [ABSTRACT FROM AUTHOR]

We analyzed the variation in island bird communities of urban environments related to habitat characteristics, using regression/classification tree analyses. Data from field censuses in cities/towns representing the urban heterogeneity of the whole island were obtained in Tenerife (Canary Islands, Spain). Urban bird abundance in Tenerife was negatively affected by altitude and the cover of dry Euphorbia shrubs and positively influenced by the height and cover of the tree layer. Species richness was negatively associated with building cover and positively related to tree height and altitude. Data from field censuses in Tenerife were compared with that of similar urban environments in the mainland (Madrid Province, central Spain). Species diversity was higher in the urban sample of Tenerife than in that of Madrid and the proportion of bird species from the regional pool ‘captured’ by urban environments was higher in the island than in the mainland. Nine native species and three alien species were more abundant in island urban environments than in the continent (densities, at least, 100% higher), the converse occurring for only five species. At a biogeographic scale, we conclude that the avifauna of Tenerife Island is more prone to occupy a new artificial environment than the mainland bird fauna of central Spain. [ABSTRACT FROM AUTHOR]

Abstract: Identification of species-specific traits that make a species a better surrogate of biodiversity is a need in order to implement successful conservation programmes in the face of limited data and resources. This study analyzes the relationship between the abundance of different surrogate species and species richness for terrestrial native avifauna of autochthonous steppe and semiarid environments in Fuerteventura Island (Spain) at different spatial grains, and explores which species-specific ecological traits (body mass, ecological density, habitat breadth, coverage of urban and agricultural environments) and conservation features (endemicity, conservation status) make a species more efficient as a surrogate. Results indicate that abundance of those surrogate species which are typically targeted by local conservation managers (according to their rarity and increase public awareness) proves to be a poor predictor of three different measures of species richness of the native terrestrial avifauna of Fuerteventura at all spatial resolutions. Nonetheless, some species were found to perform better than others according to partial least squares regression analyses applied to relate species-specific ecological traits and conservation features with correlation coefficients between abundance of each bird species and total bird richness. The best surrogates for global bird species richness are those smaller birds of medium–high abundances, broad habitat preferences, less threatened status, and with a high degree of endemicity. No scale-dependency was observed in the surrogacy power of species. Conservation planners in island scenarios should use a selection of bird species with these characteristics to identify conservation target areas in order to maximize the efficiency of surrogacy approaches. [Copyright &y& Elsevier]

Abstract: Although there is a wide range of empirical models applied to predict the distribution and abundance of organisms, we lack an understanding of which ecological characteristics of the species being predicted affect the accuracy of those models. However, if we knew the effect of specific traits on modelling results, we could both improve the sampling design for particular species and properly judge model performance. In this study, we first model spatial variation in winter bird density in a large region (Central Spain) applying regression trees to 64 species. Then we associate model accuracy to characteristics of species describing their habitat selection, environmental specialization, maximum densities in the study region, gregariousness, detectability and body size. Predictive power of models covaried with model characteristics (i.e., sample size) and autoecological traits of species, with 48% of interspecific variability being explained by two partial least regression components. There are species-specific characteristics constraining abundance forecasting that are rooted in the natural history of organisms. Controlling for the positive effect of prevalence, the better predicted species had high environmental specialization and reached higher maximum densities. We also detected a measurable positive effect of species detectability. Thus, generalist species and those locally scarce and inconspicuous are unlikely to be modelled with great accuracy. Our results suggest that the limitations caused by those species-specific traits associated with survey work (e.g., conspicuousness, gregariousness or maximum ecological densities) will be difficult to circumvent by either statistical approaches or increasing sampling effort while recording biodiversity in extensive programs. [Copyright &y& Elsevier]