Evaluating the Efficacy of Adaptive Management Approaches: Is There a Formula For Success?

Within the field of natural-resources management, the application of adaptive management is appropriate for complex problems high in uncertainty. Adaptive management is becoming an increasingly popular management-decision tool within the scientific community and has developed into two primary schools of thought: the Resilience-Experimentalist School (with high emphasis on stakeholder involvement, resilience, and highly complex models) and the Decision-Theoretic School (which results in relatively simple models through emphasizing stakeholder involvement for identifying management objectives). Because of these differences, adaptive management plans implemented under each of these schools may yield varying levels of success. We evaluated peer-reviewed literature focused on incorporation of adaptive management to identify components of successful adaptive management plans. Our evaluation included adaptive management elements such as stakeholder involvement, definitions of management objectives and actions, use and complexity of predictive models, and the sequence in which these elements were applied. We also defined a scale of degrees of success to make comparisons between the two adaptive management schools of thought. Our results include the relationship between the adaptive management process documented in the reviewed literature and our defined continuum of successful outcomes. Our data suggest an increase in the number of published articles with substantive discussion of adaptive management from 2000 to 2009 at a mean rate of annual change of 0.92 (r2 = 0.56). Additionally, our examination of data for temporal patterns related to each school resulted in an increase in acknowledgement of the Decision-Theoretic School of thought at a mean annual rate of change of 0.02 (r2 = 0.6679) and a stable acknowledgement for the Resilience-Experimentalist School of thought (r2 = 0.0042; slope = 0.0013). Identifying the elements of successful adaptive management will be advantageous to natural-resources managers considering adaptive management as a decision tool

Spatial Distribution of Black Bear Incident Reports in Michigan

<div><p>Interactions between humans and carnivores have existed for centuries due to competition for food and space. American black bears are increasing in abundance and populations are expanding geographically in many portions of its range, including areas that are also increasing in human density, often resulting in associated increases in human-bear conflict (hereafter, bear incidents). We used public reports of bear incidents in Michigan, USA, from 2003–2011 to assess the relative contributions of ecological and anthropogenic variables in explaining the spatial distribution of bear incidents and estimated the potential risk of bear incidents. We used weighted Normalized Difference Vegetation Index mean as an index of primary productivity, region (i.e., Upper Peninsula or Lower Peninsula), primary and secondary road densities, and percentage land cover type within 6.5-km² circular buffers around bear incidents and random points. We developed 22 <i>a priori</i> models and used generalized linear models and Akaike’s Information Criterion (AIC) to rank models. The global model was the best compromise between model complexity and model fit (<i>w</i> = 0.99), with a ΔAIC 8.99 units from the second best performing model. We found that as deciduous forest cover increased, the probability of bear incident occurrence increased. Among the measured anthropogenic variables, cultivated crops and primary roads were the most important in our AIC-best model and were both positively related to the probability of bear incident occurrence. The spatial distribution of relative bear incident risk varied markedly throughout Michigan. Forest cover fragmented with agriculture and other anthropogenic activities presents an environment that likely facilitates bear incidents. Our map can help wildlife managers identify areas of bear incident occurrence, which in turn can be used to help develop strategies aimed at reducing incidents. Researchers and wildlife managers can use similar mapping techniques to assess locations of specific conflict types or to address human impacts on endangered species.</p></div

Relative distribution of the probability of black bear incident report occurrence in Michigan, USA.

<p>Based on black bear incident reports collected by Michigan Department of Natural Resources during 2003–2011. Solid gray areas were excluded from analysis as they contained no black bear incident reports and are outside the black bear population range.</p

Summary of model selection results.

<p>Summary of model selection results.</p

Best model parameter coefficients.

<p>Best model parameter coefficients.</p

Densities of black bear incident reports in Michigan.

<p>Density of black bear incident reports received by Michigan Department of Natural Resources during 2003–2011 for the Upper Peninsula (solid line) and Lower Peninsula (dashed line) regions of the study area with (A) the average annual black bear incident report density and (B) average monthly black bear incident report density.</p

<i>A priori</i> model set.

<p><i>A priori</i> model set.</p

Locations of black bear incident reports in Michigan.

<p>Locations at the section level of publically reported black bear incidents (black dots) received by Michigan Department of Natural Resources, Michigan, USA, 2003–2011. Gray areas were excluded from analyses as they contained no black bear incident reports and are outside the black bear range.</p