Adaptive optimization and the harvest of biological populations

Adaptive management of renewable biotic resources accounts explicitly for uncertainties in system responses to management and recognizes the importance of reducing uncertainties while pursuing other management goals. An adaptive approach to the harvest of wildlife populations that are subject to (1)...

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Bibliographic Details
Published in:Mathematical biosciences 1996-08, Vol.136 (1), p.1-20
Main Author: Williams, Byron K.
Format: Article
Language:English
Subjects:
Animals
Animals, Wild
Biological and medical sciences
DINAMICA DE LA POBLACION
DYNAMIQUE DES POPULATIONS
FAUNE ET FLORE SAUVAGES
Fishes
FRECUENCIA DE LAS COSECHAS
FREQUENCE DE RECOLTE
GESTION
Human physiology applied to population studies and life conditions. Human ecophysiology
Medical sciences
METHODE D'OPTIMISATION
METODOS DE OPTIMIZACION
MODELE MATHEMATIQUE
MODELOS MATEMATICOS
Models, Biological
Nutritional survey. Food supply and nutritional requirement
Population Dynamics
VIDA SILVESTRE
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Summary:Adaptive management of renewable biotic resources accounts explicitly for uncertainties in system responses to management and recognizes the importance of reducing uncertainties while pursuing other management goals. An adaptive approach to the harvest of wildlife populations that are subject to (1) uncontrollable environmental variation, (2) uncertainties about the appropriate characterization of resource dynamics, (3) limitations on the controllability of harvest rates, and (4) uncertainties as to population status, expressed as sampling variation in the monitoring of populations and habitats, is described. Adaptive management is framed in terms of maximizing long-term harvest value against a background of various kinds and degrees of uncertainty, with an emphasis on structural uncertainty. By an appropriate extension of the “system state,” adaptive optimization can be defined in terms of Markov decision processes. Solution algorithms are described for systems that are subject to structural uncertainty and are either partially or completely observable. Adaptive optimization is illustrated with an example in waterfowl harvest management that incorporates uncertainty in the relationship between harvest rates and survivorship.
ISSN:0025-5564
1879-3134
DOI:10.1016/0025-5564(96)00021-1