The OceanMatters Lab
Published Work
Here below highlights two primary papers we published in recent years. Find more at Xiong Zhang (researchgate.net)
Predicting distributions, habitat preferences, and associated conservation implications for a genus of rare fishes, seahorses (Hippocampus spp.)
Diversity and Distributions. 2018;1–13. DOI: 10.1111/ddi.12741
​Using species distribution models to determine global distributions of rare species and their species-habitat relationships is vital to biodiversity conservation, but often challenged by the shortage of data. My study provides guidance for identifying useful sources of species data and instrumental habitat variables to build robust species distribution models for rare marine fishes, using seahorses ( spp.) as the case study. My study demonstrated that using “proximity to macrohabitats” and integrating all datasets of species occurrences derived models with the highest accuracies among all dataset variations. This finding highlighted that using proper habitat variables is crucial to determine distributions and habitat preferences for rare and habitat-dependent marine fishes; collating and integrating quality-unknown occurrences (e.g. citizen science and museum collections) with quality research data is meaningful for building SDMs for rare species. I also encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.
Using cumulative human-impact models to reveal global threat patterns for seahorses
Conservation Biology, 2019. DOI: 10.1111/cobi.13325
Understanding cumulative human impact (CHI) on marine organisms is vital, given that they are threatened by multiple anthropogenic pressures. Here we provide a global-scale study on human impacts on, and conservation status of, a genus of rare marine fishes, seahorses (Hippocampus spp.). We developed species-level models to assess and map the cumulative impact of 12 anthropogenic stressors on 42 seahorse species, based on expert knowledge and spatial datasets. I then compared the CHI estimates between ‘threatened’ and ‘un-threatened’ species listed on the IUCN Red List. To predict conservation status for ‘Data Deficient’ species, I built random forest models based on the derived human impact indices from the CHI models. I mapped the CHI on seahorses and compared it with CHI on marine ecosystems. The results indicate that my CHI estimates for ‘threatened’ species are significantly higher than counterparts for ‘un-threatened’ species. The random forest models suggest that five of the 19 ‘Data Deficient’ species are ‘threatened’. The major stressors that determine conservation status are demersal fishing with high bycatch and (ocean and nutrient) pollutions. The threat epicenters with high CHIs on seahorses concentrate in the East and South China Seas, Southeast Asian waters, and European waters. I show that impacts on seahorses are more likely higher in shallower inshore waters compared with previous estimates on marine ecosystems, with only a medium correlation between them. My study highlights the importance of developing species-level CHI models to better estimate and map threats on specific organisms. I provide useful maps to guide threat management on seahorse species. My approaches might be useful to analyze threats and conservation status for other marine species, especially data-poor fishes.
