Cooperative Research Units
Education, Research And Technical Assistance For Managing Our Natural Resources
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Advancing Adaptive Management of Harvested Animals with R

American black bear with GPS collar.

Duration

April 2015 - June 2019

Narrative

Wildlife biologists are typically tasked with maintaining viable wildlife populations through time. In its simplest form, a population can gain numbers through the processes of birth or immigration, and can lose numbers through death or emigration. In more complex forms, these numbers are shaped by a myriad of factors which may or may not interact with each other.

Management of some species includes a regulated harvest season, where hunters are permitted to harvest animals according to set of regulations and thereby contribute to the population's total deaths. When managing a harvested species, a critical challenge is predicting how the wild population will respond to both the harvest and to other environmental factors. If the harvest is too great, the population may decline. And if the harvest is too little, the population may increase to the point of disrupting ecosystem structure and function. Thus, managing the harvest is delicate balancing act. This challenge can be daunting because is very difficult to estimate the vital rates that shape a population's trajectory (birth, survival, and harvest rates).

Most state agencies collect data on the age, sex, and number of harvested animals on an annual basis. Fortunately, the data associated with the harvested (dead) animals can provide a glimpse into the status of the living population, making these tasks more tractable. As Gove et al. (2002) suggest, "Intriguingly nested within the age-at-harvest data is information on age and sex composition, survival, and fecundity rates." Because of this, and because of the tremendous effort spent in collecting harvest information across agencies and through time, harvest datasets are among the richest datasets in North America for analyzing the population size, trend, and health of wildlife populations.

Managing game species, however, entails much, much more than producing a population estimate. Agencies are called to implement their harvest programs within an adaptive management (AM) framework. The process generally involves identifying the natural resource management problem, setting management objectives, identifying potential alternative management actions, estimating the likely consequences of each alternative, and then weighing each alternative and identifying trade-offs among alternatives. "Adaptive management" is the application of such approaches when a decision problem is iterative, such that new information can be incorporated into decision making.

While a significant amount has been written about the process of adaptive management, in practice it can be difficult to understand how to implement an adaptive management program that seamlessly integrates data collection, models, decision making, analysis, and outputs.

To aid this process, we are developing a suite of R packages called AMHarvest, AMPop, and AMModels that includes a variety of functions for implementing an adaptive management program for harvested species using the open source modeling platform, R (R Core Team, 2013). R can be downloaded from the Comprehensive R Network website.

 

Current Staff

Federal Staff: 102

Masters Students: 247

Phd Students: 163

Post Docs: 55

University Staff: 266

5 Year Summary

Students graduated: 722

Scientific Publications: 1960

Presentations: 4355

 

Personnel

Funding Agencies

  • Maine Department of Fish and Wildlife

Links

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