Which bird traits most affect the goodness-of-fit of species distribution models?

Species distribution models (SDMs) are numerical tools that combine species occurrence (or abundance) data with environmental variables, to predict the species' distribution spatially. SDMs are increasingly used for purposes of conservation planning and management of ecosystems. The model performance can be measured as the goodness-of-fit (GOF), which describes how well it fits (e.g., the discrepancy between the statistical model and the data observed). However, there is still a need for a deeper understanding of the ecological characteristics of the modelled species which can affect the accuracy of those models. Here, we compared the goodness-of-fit of SDMs, considering several ecological characteristics of 56 bird species: Most frequently used environment, body mass, home-range, species specialization index (SSI), diet specialization and detectability. All SDMs were performed on the same dataset, and the relative frequency of each species was also incorporated to account for occurrence heterogeneity. GOF of SDMs was not significantly correlated with species' frequency, home-range, body mass, degree of detectability or level of diet specialization. Overall, the birds with more accurate SDMs (GOF) were species of grasslands and the GOF was positively associated with SSI, indicating that more habitatspecialized species are better predictable. Our findings suggest that is important to focus not only on statistical issues potentially related to model performance but also on ecological characteristics of single species because can improve the performance of modellistic procedures, increasing their predictive power.

Automated summary

Species distribution models (SDMs) are numerical tools used for predicting species' spatial distribution. This study found that ecological characteristics, such as habitat specialization, play a role in improving the accuracy of SDMs.