Using cumulative human-impact models to reveal global threat patterns for seahorses.

Area of Science:

• Marine Biology
• Conservation Science
• Ecology

Background:

• Assessing threats to marine organisms is crucial but hindered by limited data, especially for data-poor species like seahorses (Hippocampus spp.).
• Understanding cumulative human impact (CHI) is vital for effective conservation strategies.

Purpose of the Study:

• To assess and map the cumulative human impact (CHI) on 42 seahorse species.
• To evaluate the predictive power of CHI indices for seahorse conservation status.
• To identify key anthropogenic stressors and geographic hotspots impacting seahorses.

Main Methods:

• Developed linear-additive models using expert knowledge and spatial data to calculate CHI for 12 stressors.
• Employed random forest (RF) models to predict conservation status based on CHI indices.
• Compared species-level CHI models with existing ecosystem-level models.

Main Results:

• Threatened seahorse species exhibited significantly higher CHI values than non-threatened species.
• High-accuracy RF models (87% and 96%) predicted 5 of 17 data-deficient species as threatened.
• Demersal fishing with high bycatch and pollution were identified as the primary predictors of threat category.
• Major threat epicenters were identified in China, Southeast Asia, and Europe.
• Species-level CHI models proved more effective than ecosystem-level models for analyzing focal species threats.

Conclusions:

• CHI modeling provides a robust method for assessing and mapping threats to data-poor marine species.
• Maps of CHI can guide global seahorse conservation efforts.
• Demersal fishing and pollution require urgent attention in seahorse conservation planning, particularly in identified threat epicenters.