Lindsay Aylesworth/Project Seahorse

Lindsay Aylesworth/Project Seahorse

Researcher Lindsay Aylesworth (@l_aylesworth) figured out how to accurately determine the size and distribution of seahorse populations where little to no information exists. 

How can you tell, for example, if cryptic species — well-camouflaged, low-density species such as seahorses — are present in, or absent from, a given area? How does the way you generate such data affect the management and conservation of threatened species?

Lindsay's work will help Thailand, the world’s largest exporter of seahorses, to ensure that their wild seahorse trade is sustainable.

Project details

Lindsay's work takes her to locations all over the Andaman Sea and the Gulf of Thailand

Lindsay's work takes her to locations all over the Andaman Sea and the Gulf of Thailand

For many of the world’s species, both on land and in the ocean, we do not have enough data on where they live, why they live there and the size of their populations. My research focuses on how to make smart conservation decisions about data-poor species (a species where local information on occurrence and distribution is lacking). With human impacts on the world’s ecosystems, there is an urgent need to identify conservation actions that do not require additional research. I worked on seahorses in Thailand as a way to determine what type of data, how much data, and which methods are best for prioritizing conservation action for data-poor species.

What type of data?

Do fishers know best when it comes to identifying areas with data-poor fishes? The global conservation crisis demands that managers marshal all available datasets for conservation of depleted species, yet the level of trust placed in local knowledge remains uncertain. My research compared four methods for inferring species distributions: the use of (i) fisher interviews; (ii) government research trawls, (iii) scientific diving surveys, and (iv) citizen science contributions. We found that fisher knowledge provided more information on our data-poor fish genus at larger spatial scales, with less effort, and for a cheaper price than all other datasets. One drawback was that fishers were unable to provide data down to the species level. People embarking on conservation endeavors for data-poor species may wish to begin with fisher interviews and use these to inform the application of government research, scientific diving, or citizen science programs

How much data?

How can you determine the presence or absence of species at a site? As a marine ecologist, you would probably don your mask and snorkel, perhaps a scuba tank, and start searching in previously documented habitats.  But what happens when you fail to observe your species of interest? How can you distinguish among the various reasons why you did not see it – wrong habitat, inefficient method, lack of experience, or human extraction? I compared different search protocols to identify the effort needed to detect at least one individual of a cryptic and data-poor fish genus in sandy soft bottom habitats. 

Which methods are best?

New methods to assess data-poor fisheries are increasing in use, but are these new methods better than typical stock assessments where data are simulated based on best available information? My research compared the Productivity-Susceptibility Analysis (PSA) approach and typical fisheries stock assessment techniques to determine for which seahorse species is the most vulnerable to fishing pressure.

The Thailand Context

Thailand is the world’s largest exporters of seahorses, and is home to seven of the 14 seahorse species found in Southeast Asia. Seahorses are internationally traded and regulated under the Convention on International Trade in Endangered Species (CITES). Therefore any country exporting seahorses may be asked to demonstrate their trade is sustainable. Thailand is the first country asked to undergo this process, but is also a country where there is insufficient data on local species. My research supports Thailand to evaluate the sustainability of their seahorse trade.


Blog posts

Images from the field

My publications 

Aylesworth, L., Loh, T. L., Rongrongmuang, W., & Vincent, A. C. J. (2017). Seahorses (Hippocampus spp.) as a case study for locating cryptic and data‐poor marine fishes for conservation. Animal Conservation.

Aylesworth, L., Phoonsawat, R., Suvanachai, P., & Vincent, A. C. (2016). Generating spatial data for marine conservation and management. Biodiversity and Conservation, 1-17.

Aylesworth, L. (2016) Developing conservation action for data-poor species using seahorses as a case study. (Doctoral dissertation, The University of British Columbia, Vancouver, Canada).

Aylesworth, L., Lawson, J. M., Laksanawimol, P., Ferber, P., & Loh, T. L. (2016). New records of the Japanese seahorse Hippocampus mohnikei in Southeast Asia lead to updates in range, habitat and threats. Journal of Fish Biology. 88: 1620–1630.

Further reading

MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Bailey, L.L. & Hines, J.E. (2006) Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Academic Press.

Thornton, T.F. & Scheer, A.M. (2012) Collaborative engagement of local and traditional knowledge and science in marine environments: a review. Ecology And Society, 17, 8.

Katsanevakis, S., Weber, A., Pipitone, C., Leopold, M., Cronin, M., Scheidat, M., Doyle, T.K., Buhl-Mortensen, L., Buhl-Mortensen, P., D’Anna, G., de Boois, I., Dalpadado, P., Damalas, D., Fiorentino, F., Garofalo, G., Giacalone, V.M., Hawley, K.L., Issaris, Y., Jansen, J., Knight, C.M., Knittweis, L., Kröncke, I., Mirto, S., Muxika, I., Reiss, H., Skjoldal, H.R. & Vöge, S. (2012) Monitoring marine populations and communities: Methods dealing with imperfect detectability. Aquatic Biology, 16, 31–52.

Johannes, R.E. (2000) Ignore fishers’ knowledge and miss the boat. Fish and Fisheries, 1, 257–271.

Vincent, A.C.J., Sadovy de Mitcheson, Y.J., Fowler, S.L. & Lieberman, S. (2013) The role of CITES in the conservation of marine fishes subject to international trade. Fish and Fisheries, 1–30.

O’Donnell, K.P., Molloy, P.P. & Vincent, A.C.J. (2012) Comparing fisher interviews, logbooks, and catch landings estimates of extraction rates in a small-scale fishery. Coastal Management, 40, 594–611.

Neis, B., Schneider, D.C., Felt, L., Haedrich, R.L., Fischer, J. & Hutchings, J.A. (1999) Fisheries assessment: what can be learned from interviewing resource users? Canadian Journal of Fisheries and Aquatic Sciences, 56, 1949–1963.

Bottrill, M.C., Joseph, L.N., Carwardine, J., Bode, M., Cook, C., Game, E.T., Grantham, H., Kark, S., Linke, S., McDonald-Madden, E., Pressey, R.L., Walker, S., Wilson, K. a & Possingham, H.P. (2008) Is conservation triage just smart decision making? Trends in ecology & evolution, 23, 649–54.

Hall, G.B., Moore, A., Knight, P. & Hankey, N. (2009) The extraction and utilization of local and scientific geospatial knowledge within the Bluff oyster fishery, New Zealand. Journal of Environmental Management, 90, 2055–2070.

Dickinson, J.L., Zuckerberg, B. & Bonter, D.N. (2010) Citizen science as an ecological research tool: challenges and benefits. Annual Review of Ecology, Evolution, and Systematics, 41, 149–172.

Tear, T.H., Kareiva, P., Angermeier, P.L., Czech, B., Kautz, R., Landon, L., Mehlman, D., Ruckelshaus, M., Scott, J.M. & Wilhere, G. (2005) How Much Is Enough ? The Recurrent Problem of Setting Measurable Objectives in Conservation. BioScience, 55, 835–849.

Greig, L.A., Marmorek, D.R., Murray, C. & Robinson, D.C.E. (2013) Insight into Enabling Adaptive Management. Ecology and Society, 18, 1–11.