Geology topics
Mark A Kaemingk
Publications and source records attributed to Mark A Kaemingk.
Spatial and temporal behavioral differences between angler-access types
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 anglers were surveyed across 29 Nebraska waterbodies from April through October, 2007–2017. We documented behavioral differences between bank and boat anglers that varied as a function of waterbody size and season. Patterns of party size, time fished, and numbers of fish released and harvested for bank and boat anglers differed across extra small, small, medium, and large waterbodies and across spring, summer, and fall. How anglers choose to access a fishery appears to be a source of heterogeneity within angler populations. Accounting for these spatial and temporal behavioral differences between angler-access types will be important for designing and implementing management regulations. We predict that angler-access types may respond uniquely to different management actions (e.g., size and bag limits, access maintenance, and cleanliness of amenities) that could lead to local and regional changes within and across fisheries (e.g., shift the composition of angler-access types). Continued collection and assessment of angler-access information is warranted and should lead to improved management and conservation of recreational fisheries.
Harvest–release decisions in recreational fisheries
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 were able to successfully predict the harvest–release outcome for 99% of fish caught in the training data set and 96% of fish caught in the test data set. Unsuccessful predictions were mostly attributed to predicting harvest of fish that were released. Fish length was the most essential feature examined for predicting angler harvest. Other important predictive harvest–release features included the number of individuals of the same species caught, geographic location of an angler’s residence, distance traveled, and time spent fishing. The XGBoost algorithm was able to effectively predict the harvest–release decision and revealed hidden and intricate relationships that are often unaccounted for with classical analysis techniques. Exposing and accounting for these angler–fish intricacies is critical for fisheries conservation and management.