Multimodel inference and adaptive management

Abstract

a b s t r a c tEcology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study's inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

Framework for assessing the impact of human activities on the environment: the impact of forest harvesting and petroleum drilling on habitat of moose (Alces alces) and marten (Martens americana)

“…This undesirable conclusion reinforces the need for multiple methods, as advocated in the multi-model structure ( Fig. 1) and supported by Rehme et al (2011).…”

Forecasting cattle depredation risk by recolonizing gray wolves

“…Adaptive management allows researchers and managers to develop and improve management practices through an iterative learning process (Rehme et al 2011). Washington Department of Fish and Wildlife has successfully achieved wildlife management objectives for multiple species using this approach (Beausoleil et al 2013, Maletzke et al 2016.…”

Adaptive management for a turbulent future

“…Adaptive management is an ideal method to resolve uncertainty when research results in weak inference (Rehme et al, 2011). The recent development of an adaptive management technical guide by the U.S. Department of Interior (Williams et al, 2009) is an indication of the growing movement in natural resource management toward taking a more proactive role in management decisions.…”