Integrating multiple datasets with species distribution models to inform conservation of the poorly-recorded Chinese seahorses

Abstract

Modeling and mapping species distributions are vital to biodiversity conservation, but challenging for data-limited species whose localities are poorly recorded. Here we examine the utility of three datasets and species distribution models in conservation of seahorses (Hippocampus spp.), a genus of poorly-recorded marine fishes. We collated occurrences from field data of species sightings (SS), peer-reviewed literature (PRL), and fishers local ecological knowledge (LEK) for five seahorse species in China. We modelled seahorse distributions using different combinations of these datasets. We first compared model performance and predictions between PRL and LEK, and then evaluated the impact of adding LEK and/or PRL to SS. Our results indicated that LEK provided higher-resolution maps than PRL and tended to generate slightly better models. There is more predictive consistency between LEK and PRL on presence-probability maps than on presence/absence maps. Adding LEK and/or PRL to SS improved model performance across species. Our study suggests that integrating LEK (and PRL) and limited SS with species distribution models can usefully inform conservation for poorly-recorded species.