Adaptive Harvest Management

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Black lab with drake mallardMost waterfowl hunters have heard the term Adaptive Harvest Management, or the initials AHM, but few probably know what it is or how it works.

The annual process of setting duck-hunting regulations in the United States is based on a system of resource monitoring, data analyses, and rule making. Each year, monitoring activities such as aerial surveys and hunter questionnaires provide information on harvest levels, population size, and habitat conditions. Data collected from these monitoring programs are analyzed each year, and proposals for duck-hunting regulations are developed by the Flyway Councils, States, and the U.S. Fish & Wildlife Service (USFWS). After extensive public review, the USFWS announces a regulatory framework within which States can set their hunting seasons.

In 1995, the USFWS adopted the concept of adaptive resource management for regulating duck harvests in the United States. The adaptive approach explicitly recognizes that managers cannot know with certainty the effect of a particular set of hunting regulations, and provides a framework for making objective decisions in the face of that uncertainty. Key to the adaptive approach is an ability to predict the effects of regulations -- the better those predictions are, the more effective future regulatory decisions will be. Thus, adaptive management relies on a repetitive cycle of population and habitat monitoring, modeling and prediction, and decision making to clarify the relationships among hunting regulations, harvests, and waterfowl abundance.

In regulating waterfowl harvests, managers face four primary sources of uncertainty:

  1. variation in the environment - changing weather conditions, as well as changes in other key features of waterfowl habitat; an example is the annual change in the number of ponds in the Prairie Pothole Region, where water conditions influence duck reproductive success;
  2. limited management control - the ability of managers to control harvest only within limits; the harvest resulting from a particular set of hunting regulations cannot be predicted with certainty because of variation in weather conditions, timing of migration, hunter effort, and other factors;
  3. sampling error - key population attributes (e.g., population size, reproductive rate, harvest) must be estimated through application of statistical sampling designs, and estimates are computed with known error related to the process of sampling; and
  4. biological uncertainty - an incomplete understanding of biological processes that cause waterfowl populations to increase or decrease; an example is the long-standing debate about whether harvest is additive to other sources of mortality or whether populations compensate for hunting losses through reduced natural mortality. Biological uncertainty increases contentiousness in the decision-making process (because different biological hypotheses can get great implications to selecting the most appropriate harvest strategy) and decreases the extent to which managers can meet long-term conservation goals.

Adaptive Harvest Management (AHM) was developed to be an open and systematic process (management objectives and ground rules clearly defined) for making sound, objective decisions even given all the sources of uncertainty just described. The key components of AHM include:

  1. a limited number of regulatory alternatives, which describe Flyway-specific season lengths, bag limits, and framework dates (earliest opening, last closing dates). These have been established as the familiar Liberal, Moderate, and Restrictive packages in each Flyway;
  2. a set of population models describing various hypotheses about the effects of harvest and the environment on waterfowl abundance;
  3. a measure of reliability (probability or "weight") for each population model that provides an indication of how well this model has predicted population change in the past, in relation to other models in the model set; and
  4. an explicitly defined management objective against which the outcome of regulatory strategies can be evaluated.

These components are used in a mathematical procedure called optimization (used in many fields and not unique to wildlife management) to specify the best possible regulatory strategy given current biological understanding (expressed through alternative models and associated weights), the current population size and habitat condition (from monitoring programs), the expressed goal of management, and the regulatory options available. A regulatory strategy specifies the best regulatory alternative (package) for each possible combination of breeding population size and amount of available habitat. The setting of annual hunting regulations then occurs as a repetitive or iterative process, where:

  1. each year, an optimal regulatory alternative (package) is identified based on population size and habitat conditions, and on current model weights;
  2. after the regulatory decision is made, each competing biological model is used to predict the following year's breeding population size;
  3. when monitoring data become available the following year, each model's weight is increased to the extent that observed population size agrees with the predictions of that model, and decreased to the extent that they disagree; and
  4. the new model weights are used to start another iteration of the process.

By iteratively updating model weights and using these new weights in making sound regulatory choices, the process can help identify which model is most appropriate to describe the dynamics of the managed population, that is, which model best predicts the effects of harvest and environmental conditions. The process is optimal in that it specifies the best possible decision given a management objective, the current understanding of important biological processes, present population size and habitat conditions, and the regulatory packages at managers' disposal. It is adaptive in the sense that the harvest strategy "evolves" as managers learn through the comparison of observed population sizes and those predicted by the competing biological models.

More detailed information on the process can be found in the USFWS annual AHM reports published since 1999 and in other scientific reports and journal articles published on the topic.

Download the 2014 report
View previous reports.