Although the effects of life history traits on population density have been investigated widely, how spatial environmental variation influences population density for a large range of organisms and at a broad spatial scale is poorly known. Filling this knowledge gap is crucial for global species management and conservation planning and to understand the potential impact of environmental changes on multiple species.
Alice Pezzarossa will be in Jyväskylä (Finland).
Fine‐scale knowledge of how anthropogenic effects may alter habitat selection by wolves (Canis lupus) is important to inform conservation management, especially where wolf populations are expanding into more populated areas or where human activity and development are increasingly encroaching on formerly pristine environments.
Asexual taxa often have larger ranges than their sexual progenitors, particularly in areas affected by Pleistocene glaciations.
Species distribution models (SDMs) are often calibrated using presence-only datasets plagued with environmental sampling bias, which leads to a decrease of model accuracy. In order to compensate for this bias, it has been suggested that background data (or pseudoabsences) should represent the area that has been sampled. However, spatially-explicit knowledge of sampling effort is rarely available. In multi-species studies, sampling effort has been inferred following the target-group (TG) approach, where aggregated occurrence of TG species informs the selection of background data. However, little is known about the species- specific response to this type of bias correction.