Differences in predictions of marine species distribution models based on expert maps and opportunistic occurrences.

Abstract

Species distribution models (SDMs) are important tools for assessing biodiversity change. These models require high-quality occurrence data, which are not always available. Therefore, it is increasingly important to determine how data choice affects predictions of species' ranges. Opportunistic occurrence records and expert maps are both widely used sources of species data for SDMs. However, it is unclear how SDMs based on these data differ in performance, particularly for the marine realm. We built SDMs for 233 marine fish species from 2 families with these 2 occurrence data types and compared their performances and potential distribution predictions. Opportunistic occurrences were sourced from field surveys in the South China Sea and online repositories and expert maps from the International Union for Conservation of Nature Red List database. We used generalized linear models to explore drivers of differences in prediction between the 2 model types. When projecting to distinct regions with no occurrence data, models calibrated using opportunistic occurrences performed better than those using expert maps, indicating better transferability to new environments. Differences in marine predictor values between the 2 data types accounted for the dissimilarity in model predictions, likely because expert maps included large areas with unsuitable environmental conditions. Dissimilarity levels among fish families differed, suggesting a taxonomic bias in biodiversity data between data sources. Our findings highlight the sensitivity of species distribution predictions to the choice of distributional data. Although expert maps have an important role in biodiversity modeling, we suggest researchers assess the accuracy of these maps and reduce commission errors based on knowledge of target species.