Biography

My broad research interest lies in finding practical (often interdisciplinary) solutions to natural resource use issues. I consider fisheries to be a 'perfect problem' involving natural, social, economic, and political systems. These interests are a product of the problem-solving focus of my undergraduate training and subsequent time I have spent living and working in fishing communities around the world.

PhD Research

My PhD research will develop tools to assess and manage depleted fish populations for recovery. I will use the lantern fishery in the Central Philippines as a case study, focusing specifically on the seahorse component of the catch. This fishery exemplifies many of the challenges encountered in small-scale, multi-species fisheries. As in many of these fisheries, despite reports of significant declines in catches from fishermen, there has been no quantitative assessment of the extinction risk or an evaluation of the effects of even simple policies on the species' recovery. The need for assessment of Hippocampus comes, the tiger tail seahorse (the dominant seahorse species caught) is highlighted by international recognition of vulnerability (IUCN Redlist as Vulnerable and Appendix II of CITES) and its (at least historical) economic importance to fishermen.

As an important first step toward understanding recovery options for this depleted species, I will integrate local knowledge, available and new biological data, to understand historical trends and current status of seahorse populations and the fishery. This analysis will allow me to identify influential factors driving fish and/or fishery dynamics that will then become priority for data gathering.

All analyses will be incorporated into a modeling framework that will both compare data sources, and the effects of combining them, on management suggestions for recovery. When data are scarce it would greatly benefit decision makers to understand the relative costs and benefits of data type, quantity, and quality in terms of how data use will impact species assessments and ultimately the ranking of recovery policies. The ultimate goal of this modeling exercise is to combine interdisciplinary data sources (local knowledge, ecological data, and fisheries data) to minimize uncertainty in the ranking of management actions for the recovery of H. comes in the context of the lantern fishery.