Temporal transferability of species abundance models to study the changes of breeding bird species based on land cover changes

Abstract

AimSpecies distribution models have become important tools for studying changes in biodiversity. Most studies use these models to evaluate the impact of global changes on biodiversity. For that purpose, scenarios are used that are based on changes in land use and/or land cover, or on climatic changes. However, the temporal transferability of such models depends heavily on modeling methods, environmental predictors and the ecological traits of species. Here, we evaluate the power of modeling tools to predict changes in bird species abundances based on observed changes in land cover. LocationWallonia, Belgium MethodsTo assess this temporal transferability, this research makes use of two biological and two environmental datasets, both sampled with a 10-year interval. This allows us to compare the predictions of models for another period than the period used to fit the models, with actual values for species abundance. We also analyzed the impact of ecological traits on the temporal transferability. Generalized additive models were fitted for 75 breeding birds. While a lot of studies use occurrence data, we used abundance data for fitting models. Abundance data contains more information and should allow us to better capture abundance changes in bird populations. ResultsFor the majority of species studied, the results show a low temporal transferability. With a few exceptions, e.g., species with softwood habitats, predicted changes do not correspond to observed changes. For certain bird species, e.g., those on arable lands, we observed an increase in predicted abundances in the future, while these species actually decreased. Few ecological traits seem to significantly impact the models’ transferability. Main conclusionsOur findings show that it is difficult to predict abundance changes of bird species based only on land cover changes. It is necessary to add other predictors into the models, e.g., predictors of habitat quality or spatial configuration.