Data from: cross-validation matters in species distribution models: a case study with goatfish species

In an era of ongoing biodiversity, it is critical to map biodiversity
patterns in space and time for better-informing conservation and
management. Species distribution models (SDMs) are widely applied in
various types of such biodiversity assessments. Cross-validation
represents a prevalent approach to assess the discrimination capacity of a
target SDM algorithm and determine its optimal parameters. Several
alternative cross-validation methods exist; however, the influence of
choosing a specific cross-validation method on SDM performance and
predictions remains unresolved. Here, we tested the performance of random
versus spatial cross-validation methods for SDM using goatfishes
(Actinopteri: Syngnathiformes: Mullidae) as a case study, which are
recognized as indicator species for coastal waters. Our results showed
that the random versus spatial cross-validation methods resulted in
different optimal model parameterizations in 57 out of 60 modeled species.
Significant difference existed in predictive performance between the
random and spatial cross-validation methods, and the two cross-validation
methods yielded different projected present-day spatial distribution and
future projection patterns of goatfishes under climate change exposure.
Despite the disparity in species distributions, both approaches
consistently suggested the Indo-Australian Archipelago as the hotspot of
goatfish species richness and also as the most vulnerable area to climate
change. Our findings highlight that the choice of cross-validation method
is an overlooked source of uncertainty in SDM studies. Meanwhile, the
consistency in richness predictions highlights the usefulness of SDMs in
marine conservation. These findings emphasize that we should pay special
attention to the selection of cross-validation methods in SDM studies.