Ricerc@Sapienza

In biodiversity studies a species is often classified as original when it has few closely related species, a definition
that reflects its phylogenetic originality. More recently, studies have focussed on biological or functional traits
that reflect the role(s) that species play within communities and ecosystems. This has led many studies to an
alternative evaluation of species’ originality: its functional originality. Most indices of species' originality were

Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation.

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.