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Aim The way in which environmental conditions determine the distribution and abundance of species is a crucial topic in ecology, biogeography and conservation. It is especially important to understand the nature of this relationship regarding threatened species. The ability to forecast local densities over the geographic range of species provides a way to link population ecology and biogeography. Particularly, our aim was to test whether predictions derived from species distribution modelling data provide useful information on spatial patterns of abundance and, consequently, may act as surrogates for local density estimates. Location Andalusia (southern Spain). Methods Logistic regression and the favourability function were applied as modelling tools to presence-absence data to compare the predicted results with current abundance. This approach is useful in the identification of local priorities for the target species whenever broad-scale surveys need to be performed. Results The model included variables related to topography, vegetation and spatial location to explain the presence of Bonelli's eagle. A positive relationship was found between both probability and favourability and the density of this species, but a triangular fit only with favourability, suggesting that the physiological and ecological requirements of this species in the study area are better reflected in the favourability model. Main conclusions We suggest that favourability models derived from presence-absence data provide insights into abundance data and valuable information on carrying capacity over large scales. To minimize costs, maximize output and optimize results, priority could be given to detecting the presence of the species, instead of investing resources aimed at estimating abundance, which is more expensive. [ABSTRACT FROM AUTHOR]

Aim Nowadays, large amounts of species distribution data and software for implementing different species distribution modelling methods are freely available through the internet. As a result, methodological works that analyse the relative performance of modelling techniques, as well as those that study which species characteristics affect their performance, are necessary. We discuss three important topics that must be kept in mind when modelling species distributions, namely (i) the distinction between potential and realized distribution, (ii) the effect of the relative occurrence area of the species on the results of the evaluation of model performance, and (iii) the general inaccuracy of the predictions of the realized distribution provided by species distribution modelling methods. Location Unspecific. Methods Using some recent papers as a basis, we illustrate the three issues mentioned above and discuss the negative implications of neglecting them. Results Considering a potential-realized distribution gradient, different modelling methods may be arranged along this gradient according to their ability to model any concept. Complex techniques may be more suitable to model the realized distribution than simple ones, which may be more appropriate to estimate the potential distribution. Comparisons among techniques must consider this scenario. The relative occurrence area of the species conditions the results of the evaluation scores, implying that models of rare species will unavoidably yield higher discrimination values. Moreover, discrimination values that are usually reported in the literature may imply considerable over or underestimations of the distribution of the species. Main conclusions It is extremely important to establish a solid conceptual and methodological framework on which the emergent field of species distribution modelling can stand and develop. [ABSTRACT FROM AUTHOR]

Different distribution maps can be obtained for the same species if localities where species are present are mapped at different times. We analysed the accumulation of information over time for a group of dung beetle species in the Iberian Peninsula. To do this, we used all available information about the distribution of the group as well as data on selected species to examine if the process of discovery of species distribution has occurred in a climatically or spatially structured fashion. Our results show the existence of a well-defined pattern of temporal growth in distributional information; due to this, the date of capture of each specimen can be explained by the environmental and spatial variables associated to the collection sites. We hypothesize that such temporal biases could be the rule rather than the exception in most distributional data. These biases could affect the weighting of environmental factors that influence species distributions, as well as the accuracy of predictive distribution models. Systematic surveys should be a priority for the description of species geographical ranges in order to make robust predictions about the consequences of habitat and climate change for their persistence and conservation. [ABSTRACT FROM AUTHOR]