Developing Nonlinear Methods for Understanding and Predicting Climate Impacts on FisheriesEPA Grant Number: FP917244
Title: Developing Nonlinear Methods for Understanding and Predicting Climate Impacts on Fisheries
Investigators: Deyle, Ethan Robert
Institution: University of California - San Diego
EPA Project Officer: Lee, Sonja
Project Period: September 1, 2010 through August 31, 2013
Project Amount: $111,000
RFA: STAR Graduate Fellowships (2010) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Global Change
Maximum sustainable yield puts species in close proximity to a tipping point and thus likely exposes species to the risk of being pushed to collapse by variations in the climate. At the same time, climate change is predicted to increase variability across a wide range of climate variables—including frequency of storms, ocean surface temperatures, and wind speeds—which means that as climate change intensifies, the risk of collapse to fished species also will increase. The goal of this project is to develop tools to predict the combined effects of fishing and climate on population dynamics and to integrate these tools in adaptive management schemes that can better protect fishing resources in the face of anthropogenic climate change.
Anthropogenic climate change has the potential to put further stress on fish populations exploited for fishing. Consequently, effective fishing policy for the future will need to account for the biological consequences of changing environments. The goal of this project is to develop predictive tools that integrate climate, biological, and human behavior variables which can be used in future fishery management.
This project will expand on the nonlinear forecasting techniques of simplex forecasting and state space reconstruction, which have shown great promise in improving forecasting in fisheries and other marine biological systems. Together, these techniques make forecasts out of patterns in previous observations of the variable of interest. The methods can be adapted to include information from physical variables as well (e.g., sea surface temperature). The techniques will be further augmented by tracking standard deviations, variance spectra, and auto-correlation of time series for signs of critical behavior. The first phase will test the power of these techniques for predicting collapse using time series kept by the Food and Agriculture Organization of the United Nations and a range of applicable physical time series. The second phase will simulate adaptive regulation to investigate if the climate forecasts can be utilized to reduce collapse risk.
By developing the ability to make forecasts of fishery dynamics that account for the effects of physical variables, this research will enable scenario exploration under various climate predictions to further understanding of climate effects on fished populations.
Potential to Further Environmental/Human Health Protection
Anthropogenic climate change and fishing behavior affect fish populations at two different time scales- climate change on the scale of decades and fishing patterns on the scale of years. Now, policy aims to minimize the risk of causing collapses and extinction. Though climate change mitigating policy is hopefully in the works, the effects of human activity up until the present have already locked in some amount of climate change over the next few decades, and these changes will impact marine populations. These techniques will facilitate management that can adjust fishing behavior to compensate for climate change and effectively manage these resources.