RT journal article
T1 Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases
A1 Bracho Estévanez, Claudio Acai
A1 Arenas-Castro, Salvador
A1 González Varo, Juan Pedro
A1 González-Moreno, Pablo
A2 Biología
K1 Bias correction
K1 Performance metrics
K1 Plant distribution
K1 Sampling bias
AB The proliferation of open repositories offering georeferenced occurrences on biodiversity has boosted the use of species distribution models (SDMs). However, the need of presence-only records from these repositories yields a substantial limitation due to sampling biases, which can introduce uncertainty and skew SDM predictions. Furthermore, most predictions rely only on non-spatial metrics such as the AUC and the TSS to evaluate model performance. These metrics may not adequately account for spatially biased predictions, whereas the use of spatially explicit metrics could be more informative. Here, the effectiveness of both non-spatial and spatially explicit metrics is evaluated in response to predictions affected by sampling biases. Using SDMs, the distribution of 31 fleshy-fruited plants was predicted as a case study with contrasting settings to generate pseudo-absences and sampling bias corrections. Then, the performance of predictions was assessed with two non-spatial and three alternative, spatially explicit metrics. Predictions were affected by substantial sampling biases, particularly from West to East. Significant discrepancies were found between non-spatial and spatially explicit metrics. The non-spatial metrics failed to detect predictions affected by sampling biases, often yielding higher scores for less reliable predictions. In contrast, spatially explicit metrics benefited the implementation of bias corrections. Moreover, the method to generate pseudo-absences was more influential in determining spatial differences between predictions than either the bias correction or the geographical characteristics of input occurrences. Overall, our findings reveal the utility of spatially explicit metrics as a complementary tool to evaluate SDMs affected by sampling biases.
PB Elsevier B.V.
SN 1574-9541
YR 2024
FD 2024
LK http://hdl.handle.net/10498/36092
UL http://hdl.handle.net/10498/36092
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026