Evaluating Mallard Adaptive Management Models With Time Series

Abstract

Wildlife practitioners concerned with midcontinent mallard (Anas platyrhynchos) management in the United States have instituted a system of adaptive harvest management (AHM) as an objective format for setting harvest regulations. Under the AHM paradigm, predictions from a set of models that reflect key uncertainties about processes underlying population dynamics are used in coordination with optimization software to determine an optimal set of harvest decisions. Managers use comparisons of the predictive abilities of these models to gauge the relative truth of different hypotheses about density‐dependent recruitment and survival, with better‐predicting models giving more weight to the determination of harvest regulations. We tested the effectiveness of this strategy by examining convergence rates of “predictor” models when the true model for population dynamics was known a priori. We generated time series for cases when the a priori model was 1 of the predictor models as well as for several cases when the a priori model was not in the model set. We further examined the addition of different levels of uncertainty into the variance structure of predictor models, reflecting different levels of confidence about estimated parameters. We showed that in certain situations, the model‐selection process favors a predictor model that incorporates the hypotheses of additive harvest mortality and weakly density‐dependent recruitment, even when the model is not used to generate data. Higher levels of predictor model variance led to decreased rates of convergence to the model that generated the data, but model weight trajectories were in general more stable. We suggest that predictive models should incorporate all sources of uncertainty about estimated parameters, that the variance structure should be similar for all predictor models, and that models with different functional forms for population dynamics should be considered for inclusion in predictor model sets. All of these suggestions should help lower the probability of erroneous learning in mallard AHM and adaptive management in general.

Black Brant Harvest, Density Dependence, and Survival: A Record of Population Dynamics

“…This confounding between population size and harvest mortality tends to favor models of additive harvest mortality when examining the relationship between harvest and survival because survival rates decline at high harvest rates (Sedinger and Rexstad 1994), which are associated with high population sizes. Conn and Kendall (2004) demonstrated this phenomenon occurred in the North American adaptive waterfowl management program (Johnson et al 1997); simulations of population dynamics and model selection favored models of additive harvest mortality even when populations were regulated entirely by density-dependent processes. Despite the potential bias favoring additive harvest models, most early studies of the relationship between harvest and survival in duck populations failed to detect evidence for additive harvest mortality (Anderson and Burnham 1976, Nichols and Hines 1983, Burnham and Anderson 1984, Trost 1987, but see Conroy and Krementz 1990).…”

Individual heterogeneity in black brant survival and recruitment with implications for harvest dynamics

“…Harvest management of most waterfowl, particularly in North America, is currently guided by the assumption that harvest mortality is at least partially additive (Johnson et al. ; Conn and Kendall ), and if compensation occurs, it is primarily through density dependence in survival probability and reproduction (Anderson and Burnham ; Nichols et al. ).…”

“…Harvest management of most waterfowl, particularly in North America, is currently guided by the assumption that harvest mortality is at least partially additive (Johnson et al 1993 ; Conn and Kendall 2004 ), and if compensation occurs, it is primarily through density dependence in survival probability and reproduction (Anderson and Burnham 1976 ; Nichols et al 1995 ). Harvest mortality may be compensated through density-dependent increases in survival or reproduction postharvest, such that harvest mortality may have no effect on overall survival or growth rate of the population.…”

Adaptive management of animal populations with significant unknowns and uncertainties: a case study

“…Perhaps though, the expectations of identifying ecological hypotheses with correct dynamics should be tempered, based on the ease with which model weight can accrue with incorrect models, even in the presence of the correct model (this study; Conn and Kendall 2004); this can happen when models have different variance structures (e.g., some models' predictions are highly precise compared to others) or when the observational process isn't corrected for and masks the true population trajectory. It is satisfying that the ARM learning process correctly identified the Generating Model with 100% weight, but only when the population size and age-structure was annually observed without error.…”