PhD student Xiong Zhang (@Harry01301) is mapping Chinese seahorse populations and the threats they face, both from overfishing and habitat loss.

China is the world’s largest importer and consumer of dried seahorses, thanks mainly to demand for the animals as ingredients in traditional medicine. Little is known, however, about wild seahorse populations within the country’s own maritime borders.

 Xiong's work will identify conservation priorities, informing fisheries policy and the creation of new marine protected areas, which are still relatively rare in China. 

Project details

Click on the map hotspots for more information.

Setting priorities to protect habitats of species in situ (i.e. conservation prioritization) has become a critical strategy in biodiversity conservation, as resources are usually limited. Marine ecosystems are facing increasing threats from land- and ocean-based human pressures. However, we are lacking a system to prioritize marine species. Only a few initiatives have been conducted for marine animals, such as coastal fishes, sea turtles, and sharks. Little research focused on rare and threatened (R&T) marine fish. 

Seahorses are charismatic representatives of R&T marine fishes and have served as flagship species for many conservation initiatives. Of the 41 seahorse species, 1 is Endangered, 10 are Vulnerable and 20 are “Data Deficient” on the IUCN Red List. Over the past few decades, scientific research and citizen science has begun to enrich databases about seahorse distribution, habitat use, and human pressures. 

My PhD research will explore the use of these resources to advance conservation prioritization of rare/threatened marine fishes, with a specific focus on seahorse species.

How to set conservation priorities?

Two types of maps are critical when conducting conservation prioritization. One is the distribution map of species under concern – usually threatened or impacted species by human activities. Species distribution models are commonly used to generate this map based on species-presence data and environmental maps. The other map is the cumulative human impacts upon the concerned species. Cumulative impacts can be qualified and mapped through methods derived from decision science and spatial analysis techniques. 

What are the data sources?

Given seahorses are generally understudied in published literature, I will derive much distribution data from fishers’ knowledge, divers’ observations, and museum collections. This work will be done through field surveys, lab-based data mining, emailing, etc. I will use data from our own Citizen Science initiative –, which attracts global divers to report their sightings of seahorses. To obtain data related to threats, I will collect open-access threat data online and filter them to fit seahorses. These threats include trawling strength, water pollution, and many other habitat-related activities. 

What are my expected outputs?

I will generate “hotspot” maps of seahorse species - both at the global scale and for a specific region (i.e. China’s seas).

China Context

China has a large marine territory (~ 3.3 million km²) that supports diverse marine species and 1/5 of the global fisheries production. 22,629 species belonging to 46 phyla have been recorded so far. Along with many other marine species, seahorses are highly valued and broadly used by Chinese people. China has exploited seahorses and used them in its traditional medicine for more than 2,000 years. Now China is considered to be the largest consumer of dried seahorses - with an annual demand of 500 t. However, we still know little about the species composition and distribution ranges of these valuable species in China’s seas because Chinese researchers mainly focus on seahorse aquaculture. It is noted that China’s seahorse populations are declining due to over-exploitation and habitat loss. But we lack formal data to elucidate the situation.

Blog posts

Images from the field


My publications 

Zhang, X., & Vincent, A. C. (2017). Integrating multiple datasets with species distribution models to inform conservation of the poorly-recorded Chinese seahorses. Biological Conservation, 211, 161-171.


Further reading

Crain, C. M., K. Kroeker, and B. S. Halpern. 2008. Interactive and cumulative effects of multiple human stressors in marine systems. Ecology letters 11:1304-1315.

Davidson, A. D., A. G. Boyer, H. Kim, S. Pompa-Mansilla, M. J. Hamilton, D. P. Costa, G. Ceballos, and J. H. Brown. 2012. Drivers and hot spots of extinction risk in marine mammals. Proceedings of the National Academy of Sciences 109:3395-3400.

Dickinson, J. L., B. Zuckerberg, and D. N. Bonter. 2010. Citizen science as an ecological research tool: challenges
and benefits. Annual review of ecology, evolution, and systematics 41:149-172.

Foster, S. and A. Vincent. 2004. Life history and ecology of seahorses: implications for conservation and management. Journal of Fish Biology 65:1-61.

Franklin, J. 2009. Mapping species distributions: spatial inference and prediction. Cambridge University Press.

Geselbracht, L., R. Torres, G. S. Cumming, D. Dorfman, M. Beck, and D. Shaw. 2009. Identification of a spatially efficient portfolio of priority conservation sites in marine and estuarine areas of Florida. Aquatic Conservation: Marine and Freshwater Ecosystems 19:408-420.

Halpern, B. S., S. Walbridge, K. A. Selkoe, C. V. Kappel, F. Micheli, C. D'Agrosa, J. F. Bruno, K. S. Casey, C. Ebert, and H. E. Fox. 2008. A global map of human impact on marine ecosystems. Science 319:948-952.

Liu, J. 2013. Status of marine biodiversity of the China Seas. PLOS ONE 8:e50719.

Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259.

Selig, E. R., W. R. Turner, S. Troëng, B. P. Wallace, B. S. Halpern, K. Kaschner, B. G. Lascelles, K. E. Carpenter, and R. A. Mittermeier. 2014. Global Priorities for Marine Biodiversity Conservation. PLOS ONE 9:e82898.

Shen, G. and M. Heino. 2014. An overview of marine fisheries management in China. Marine Policy 44:265-272.