Developing political-ecological theory: The need for Many-Task Computing

Abstract

Models of political-ecological systems can inform policies for managing ecosystems that contain endangered species. One way to increase the credibility of these models is to subject them to a rigorous suite of data-based statistical assessments. Doing so involves statistically estimating the models parameters, computing confidence intervals for these parameters, determining the models prediction error rate, and assessing its sensitivity to parameter misspecification. Here, these statistical algorithms along with a method for constructing politically feasible policies from a statistically fitted model, are coded as JavaSpaces programs that run as compute jobs on either supercomputers or a collection of in-house workstations. Several new algorithms for implementing such jobs in distributed computing environments are described. This downloadable code is used to compute each jobs output for the management challenge of conserving the East African cheetah (Acinonyx jubatus). This case study shows that the proposed suite of statistical tools can be run on a supercomputer to establish the credibility of a managerially-relevant model of a political-ecological system that contains one or more endangered species. This demonstration means that the new standard of credibility that any political-ecological model needs to meet before being used to inform ecosystem management decisions, is the one given herein.