Harvest–release decisions in recreational fisheries

Abstract

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.

Fisheries Volume 45 Number 5 May 2020

“…This is a crucial task because many of the world ' s most pressing environmental challengesthose that threaten ecosystems and human systems alike (e.g., climate change, biodiversity loss, air and water pollution, food and nutrition insecurity, water scarcity)-are social-ecological by origin and structure, demanding integrative solutions rooted in human-environmental inquiry. Fortunately, the interconnectedness of humans and nature that so fascinated Leopold, Carson, and countless others represents an expanding research area-coupled human and natural systems (CHANS)-with promising potential to improve ecosystem integrity and human health and wellbeing (Hulina et al 2017 ;López-Hoffman et al 2017a ;Kaemingk et al 2020 ).…”

“…We also illustrated benefi ts of management-focused CHANS research for dozens of symposium attendees throughout the 1-day event. For example, studying fi sheries as CHANS has facilitated creative ways to understand these systems, including social-ecological catchments (Martin et al 2015 ;Kaemingk et al 2020 ): spatial networks of anglers and waterbodies that offer unique fi sheries management insights, such as where to hold public meetings and where to focus stocking and habitat rehabilitation activities. Likewise, proposed research in Central African fl ooded forests intends to build on past studies (Poulsen et al 2008 ;Eaton et al 2009 ;Eaton 2010 ;Shirley et al 2019 ) by applying CHANS methods to better understand complex predator-prey and food web interactions among resource-dependent humans, an apex predator (African dwarf crocodile Osteolaemus tetraspis ), and a diverse forest fi shery as human populations grow and climate change intensifi es.…”

“…Using CHANS research to characterize resource-user complexity and thereby enhance fi sheries and wildlife management was another focal area of the AFS-TWS symposium. For example, recent work has revealed a linkage between an angler ' s fi sh harvest propensity and their residence (Kaemingk et al 2020 ), making it important to measure how the spatial distribution of anglers that use a particular waterbody (e.g., size and shape) changes within and across networks of different waterbodies. Fisheries management agencies are currently discussing how these networks-social-ecological catchments-can be used to evaluate the effectiveness of generalized versus area-specifi c management actions (e.g., harvest regulations), predict the spread of invasive species, and potentially recruit new anglers.…”

A Social–Ecological Odyssey in Fisheries and Wildlife Management

“…; Kaemingk et al. ): spatial networks of anglers and waterbodies that offer unique fisheries management insights, such as where to hold public meetings and where to focus stocking and habitat rehabilitation activities. Likewise, proposed research in Central African flooded forests intends to build on past studies (Poulsen et al.…”

“…For example, recent work has revealed a linkage between an angler's fish harvest propensity and their residence (Kaemingk et al. ), making it important to measure how the spatial distribution of anglers that use a particular waterbody (e.g., size and shape) changes within and across networks of different waterbodies. Fisheries management agencies are currently discussing how these networks—social–ecological catchments—can be used to evaluate the effectiveness of generalized versus area‐specific management actions (e.g., harvest regulations), predict the spread of invasive species, and potentially recruit new anglers.…”