1. Harvest-release decisions in recreational fisheries
- Author
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Kaemingk, Mark A., Hurley, Keith L., Chizinski, Christopher J., and Pope, Kevin L.
- Subjects
Fishery conservation -- Laws, regulations and rules ,Machine learning -- Laws, regulations and rules ,Data mining -- Laws, regulations and rules ,Algorithms -- Laws, regulations and rules ,Government regulation ,Data warehousing/data mining ,Algorithm ,Earth sciences - 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. La plupart des reglements relatifs a la peche visent a controler les prises des pecheurs a la ligne. Une comprehension de base de ce qui determine reellement la decision d'un pecheur de conserver ou de relacher un poisson peche manque toutefois. Nous avons utilise XGBoost, un logarithme d'apprentissage automatique, pour elaborer un modele predictif de decisions de pecheurs de conserver ou relacher un poisson en tirant parti d'un vaste ensemble de donnees de peche sportive (24 plans d'eau, 9 annees, 193 523 poissons). Nous avons ete en mesure de predire avec succes le resultat (conserver ou relacher) pour 99 % des poissons peches dans l'ensemble de donnees d'entrainement et 96 % des poissons peches dans l'ensemble de donnees experimental. Les predictions inexactes etaient pour la plupart de poissons conserves qui avaient en fait ete relaches. La longueur du poisson est l'aspect examine le plus important pour la prediction de la conservation par les pecheurs. D'autres aspects importants pour predire la conservation ou le lacher comprennent le nombre de specimens de la meme espece peches, l'emplacement geographique de la residence du pecheur, la distance parcourue et le temps passe a pecher. L'algorithme XGBoost est arrive a predire efficacement les decisions de conserver ou de relacher et a fait ressortir des relations cachees et complexes dont les methodes d'analyse classiques ne tiennent souvent pas compte. La reconnaissance et la prise en consideration de ces facteurs complexes associes aux pecheurs et aux poissons sont d'importance cle pour la conservation et la gestion des ressources halieutiques. [Traduit par la Redaction], Introduction Once a fish is caught by an angler, will it be harvested or released? Currently, we lack a basic understanding of this very important decision process. Fish harvest by [...]
- Published
- 2020
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