Using cumulative-human-impact models to reveal global threat patterns for seahorses
Article impact statement: : Modeling and mapping human impacts can reveal conservation status and threat patterns for data‐poor marine species.
Understanding the threats acting upon marine organisms and their conservation status is vital but challenging given a paucity of data. Here we provide a global‐scale study of cumulative human impact (CHI) and conservation status of seahorses (Hippocampus spp.) – a genus of rare and data‐poor marine fishes. We built linear‐additive models to assess and map the CHI of 12 anthropogenic stressors on 42 seahorse species, based on expert knowledge and spatial datasets. We examined the utility of the estimated impact indices (impact of each stressor and CHI) in predicting conservation status for the species using random forest (RF) models.
The results indicated that the CHIs for ‘threatened’ species were significantly higher than CHIs for ‘non‐threatened’ species listed on the Red List of the International Union for Conservation of Nature (IUCN). We derived high‐accuracy RF models (87% and 96%) and predicted that five of the 17 Data Deficient species were ‘threatened’. The demersal fishing practices (with high bycatch) and pollution were the most important predictors. Major threat epicenters were found in China, Southeast Asia, and Europe. The new knowledge and maps may guide global conservation efforts for seahorses.
Our study indicates that modeling and mapping human impacts can reveal threat patterns and conservation status for data‐poor species. We highlight the need to build species‐level models in exploring threat patterns for focal species.
Zhang, X. & A.C.J. Vincent (2019). Using cumulative‐human‐impact models to reveal global threat patterns for seahorses. Conservation Biology 33(6):1380-1391. https://doi.org/10.1111/cobi.13325