Kevin L. Pope, PhD

Management frequently creates system conditions that poorly mimic the conditions of a desirable self-organizing regime. Such management is ubiquitous across complex systems of people and nature and will likely intensify as these systems face rapid change. However, it is highly uncertain whether the costs (unintended consequences, including negative side effects) of management but also...

Recreational angler surveys typically collect information on how anglers access a fishery. Yet, it is unclear how this information is useful for fisheries management and conservation. The objective of this study was to compare behavior (e.g., party size, time fished, and numbers of fish released and harvested) of bank and boat anglers, representing two angler-access types. Bank and boat...

Ring-necked pheasant (Phasianus colchicus; i.e., pheasant) hunting participation is declining across North America, reflecting a larger downward trend in American hunting participation and threatening benefits to grassland conservation and rural economies. To stabilize and expand the pheasant hunting population, we must first identify factors that influence pheasant hunter participation...

Aging infrastructure is prevalent throughout the world but water control management structures, specifically dams are of growing concern. Dams and their corresponding reservoirs have inherent, but separate, lifespans. The proportion of dams around the world that continue operation beyond their intended lifespans is growing at an alarming rate. Society will not only have to navigate the...

Recreational fisheries are complex adaptive systems that are inherently difficult to manage due to a heterogeneous user group (consumptive vs. non-consumptive) that utilize patchily distributed resources on the landscape (lakes, rivers, coastlines). There is a need to identify which system components can effectively predict and be used to manage nonlinear and cross-scale dynamics within...

Most fishery regulations aim to control angler harvest. Yet, we lack a basic understanding of what actually determines the angler’s decision to harvest or release fish caught. We used XGBoost, a machine learning algorithm, to develop a predictive angler harvest–release model by taking advantage of an extensive recreational fishery data set (24 water bodies, 9 years, and 193 523 fish). We...