Competing Harvesting Strategies in a Simulated Population Under Uncertainty
We present a case study of the use of simulation modelling to develop and test strategies for managing populations under uncertainty. Strategies that meet a stock conservation criterion under a base case scenario are subjected to a set of robustness trials, including biased and highly variable abundance estimates and poaching.
Strategy performance is assessed with respect to a conservation criterion, the revenues achieved and their variability. Strategies that harvest heavily, even when the population is apparently very large, perform badly in the robustness trials. Setting a threshold below which harvesting does not take place, and above which all individuals are harvested, does not provide effective protection against over-harvesting. Strategies that rely on population growth rates rather than estimates of population size are more robust to biased estimates. The strategies that are most robust to uncertainty are simple, involving harvesting a relatively small proportion of the population each year. The simulation modelling approach to exploring harvesting strategies is suggested as a useful tool for the assessment of the performance of competing strategies under uncertainty.