Date of this Version
Adaptive management is becoming an increasingly popular management-decision tool within the scientific community. The application of adaptive management is appropriate for complicated natural-resources management problems high in uncertainty. Two primary schools of thought have developed that may yield varying levels of success as they primarily differ in stakeholder involvement and model complexity. I evaluated peer-reviewed literature that incorporated adaptive management to identify components of successful adaptive management plans and to make comparisons between the two schools of thought. Identifying the elements of successful adaptive management is advantageous to natural-resources managers, such as those managing the Platte River, Nebraska. The Platte River is a complicated ecosystem where management decisions affect endangered and threatened species such as the Interior Least Tern (Sternula antillarum athalassos) and Piping Plover (Charadius melodus). Because high uncertainty is associated with these species’ responses to habitat restoration and other resource uses and management efforts differ between the lower Platte River (LPR) and the central Platte River (CPR), I developed quantitative applications for each section. For terns and plovers on the LPR, I developed a population model that estimates population characteristics for on-channel and off-channel habitat. Model results suggest that population sizes respond similarly for short-term simulations, but differ for long-term scenarios. The ability of this quantitative model to adapt to new information makes it ideal for projecting management implications within an adaptive management context. As the CPR is further along in the adaptive management process, I developed a multi-model analysis of 10 models based on simulated data to simplify hypotheses and prioritize management needs. Model results suggest that in not accounting for overdispersion in the data leads to a greater probability of concluding a false relationship when the parameter effect sizes are close to 0. Utilizing statistical models to evaluate management consequences through an iterative decision-making process allows for continuous model improvements based directly on monitoring data. The process of evaluating effects of ecological factors is helpful in setting and prioritizing objectives and implementing actions for adaptively managing complicated ecosystems.