Multi‐species duck harvesting using dynamic programming and multi‐criteria decision analysis

Abstract

1. Multiple species are often exposed to a common hunting season, but harvest and population objectives may not be fully achieved if harvest potential varies among species and/or species abundances are not correlated through time. Our goal was to develop an approach for setting a common hunting season that would recognize heterogeneity in species productivity and would select annual hunting seasons conditioned on the status of individual species.2. We first used stochastic dynamic programming to generate optimal, state-dependent harvest strategies for 18 candidate regulatory scenarios. We simulated the performance of these strategies, and then used multi-criteria decision analysis to identify preferred regulatory scenarios for duck hunting seasons in the Atlantic Flyway of the U.S.3. Generally, estimates of annual population size were not correlated among species.Mallards had the highest estimated intrinsic rate of growth, green-winged teal, wood ducks, and ring-necked ducks had intermediate values, and goldeneyes were the least productive. Estimated carrying capacity was highest for mallards and lowest for green-winged teal. 4. Managers had greatest interest in maximizing season length (33%) and aggregate duck abundance (28%), and less interest in maximizing aggregate harvest (19%) and the number of years between a change in hunting season regulations (19%).Several regulatory scenarios provided acceptable trade-offs among these objectives. Synthesis and applications.Separate hunting seasons for various species of game may be untenable, either due to the added cost and regulatory complexity, or because selective harvesting of stocks may be difficult due to problems in species identification. Rather than averaging species-specific productivities, or basing hunting seasons on the least (or most) productive species, we describe an approach in which productivity and annual population status are considered 1448 | Journal of Applied Ecology JOHNSON et al.

Heuristics for the sustainable harvest of wildlife in stochastic social-ecological systems

“…This is particularly important to consider in the context of terrestrial wildlife harvest, where there is seemingly a widespread tendency for the objective of maximizing yields to be included. It persists even in cases where extensive stakeholder and manager engagement do not indicate maximum yields as a universally valued objective, and even while recognising the strong trade-off between population stability and harvest goals (Johnson et al, 1997(Johnson et al, , 2019. In all of our simulated species the critical thresholds for management were often well below theoretical maximum sustainable yield levels (Supporting Information S1).…”

Environmental models in waterfowl management under uncertainty

“…For example, in the multi-species duck harvesting decision study described in Johnson et al (2019) a statespace approach with a discrete logistic model was used for modeling annual population growth (post-harvest). Then based on time series of population size and harvest rate (harvest size), hierarchical Bayesian model was used to estimate model parameters (carrying capacity, population growth rate and process error).…”

“…At the end, the projects following this methodology tend to build less complex environmental models, that solely focus on decision problem at hand. The North American Waterfowl Management Plan is a prime example of this direction in research (Nichols et al, 1995;Johnson and Williams, 1999;Johnson and Case, 2000;Johnson et al, 2019). • articulating critical uncertainty • designing and implementing an appropriate monitoring system • updating the predictive models based on ongoing monitoring information • adapting future decisions based on the new understanding of how the system responds to management.…”

“…• Annual hunting season length. Prolonged hunting season allows greater flexibility in achieving target harvest values (Johnson et al, 2019). • Frequency of changes in regulation.…”

“…Frequent changes in the hunting regulations may increase confusion among hunters and discourage them from participating in the harvest. Stable regulatory environment contributes to predictability and increases chances of achieving population targets (Johnson et al, 2019). • Risk to air traffic safety.…”