What does it mean to “manage” bears?
“Bear management usually refers to the process of monitoring populations and the movements, behaviour, and habitat choice of bears in order to make informed decisions about issues that affect bears.” 1

What are some tools of the trade?
Managing bears also means managing bear habitat, and the developments and activities of people in it. Tools of the trade include:

• habitat restoration: implementing prescribed burn or let burn policies to restore high quality habitats, managing for habitat effectiveness targets, etc.
• involving “public” in land-use planning
• establishing guidelines for industry: timber harvest guidelines, policies for industrial camps and employees etc.
• hunting management: setting bear harvest quotas, disallowing hunting in populations that cannot sustain it, etc.
• human recreation management:  seasonal/temporary trail closures, re-routing trails to avoid prime bear habitats, use quotas, concentrating human-use on established trails, closing/decommissioning roads that provide access into bear habitat etc.
• education: educating residents, visitors and workers in areas occupied by bears; target groups such as hunters, back-country recreationists and etc. 
• bear-human conflict management: educating people, food and garbage-management, aversive conditioning, translocation, etc.

How can science influence management and policy?
Herrero et al summarized key factors that contributed to the successful integration of research conducted by the Eastern Slopes Grizzly Bear Project into bear management and policy in Banff National Park. These factors include:

• employing a multi-stakeholder and interagency approach to research
• solid public understanding of the issue was established before discussing solutions
• key decision makers were directly involved in developing the Banff National Park management plan; they understood the issues and were able to contribute solutions
• the scientific community provided specific targets and goals in a way that could be incorporated into policy
• scientists were persistent and provided information on a timely basis
  (Herrero et al 1998)

With regard to the last point, the authors emphasized that scientists who hope to influence policy and management must be actively involved in the decision making process:  

“Scientists cannot be passive players, hoping that by publishing research results they will influence decisions, or by writing the occasional letter, or by talking to managers through the media, or pounding the table at public meetings. Scientists need to adapt their behaviour to the process of information gathering or decision making that is being used.” 2
 

Science and adaptive management
“Active adaptive management proposes application of different management tactics in time and space, essentially as experiments, to develop a better understanding of the behaviour of the system.” 3

In recent years, much effort has been directed towards the construction of scientific models that attempt to simulate some aspect(s) of ecosystem function. While such models are constrained by limitations in data and by the enormous complexity of natural systems, they can be a valuable tool when applied in the context of adaptive management.

Boyce explains this point with respect to Population Viability Analysis modeling (PVA). PVA attempts to model the fluctuations in an animal population that result from human-induced or non human-induced changes in their environment over time. Boyce states that “simulation models [such as PVA] can be used to generate hypotheses of how we expect the system to respond to perturbations or management manipulations”, and that monitoring of the consequences of these manipulations “provides data to validate and/or refine” the model. 4

In other words, managers and scientists can use models to predict the response of a natural system or population to a proposed management action. Once they have actually applied this management action, they can monitor the system’s or population’s actual response and compare it to the response that was predicted by the model. If they are the same, the model is validated; if they are different, the information can be used to refine and improve the model for future applications.

Selected examples

• Human-use management at Lake O’Hara, Yoho National Park
• Establishing ecological goals in Banff National Park
• Grizzly bear harvest management in British Columbia