Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability
Optimal harvesting strategies for ungulate populations are estimated using stochastic dynamic programming. In the context used here, optimal strategy refers to a sequential decision rule that is optimal with respect to maximizing expected long term returns from ungulate populations. The effects of f...
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ndltd-UBC-oai-circle.library.ubc.ca-2429-219052018-01-05T17:41:21Z Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability Stocker, Max Optimal harvesting strategies for ungulate populations are estimated using stochastic dynamic programming. In the context used here, optimal strategy refers to a sequential decision rule that is optimal with respect to maximizing expected long term returns from ungulate populations. The effects of fluctuating environmental conditions and uncertainty about population parameters were considered. Three case examples were selected for this study to represent classes of real ungulate systems. In effect these cases represent three fragmentary views of the basic food-ungulate-predation food chain. Models incorporating functional information with regard to fecundity, survival, resource utilization, and predation were formulated as stochastic dynamic programming models and optimal harvesting strategies were derived numerically using a digital computer. The strategies are expressed as isopleth diagrams relating state variables and harvest rates. The optimal harvest strategies were generally found to be insensitive to environmental fluctuations. On the other hand, it was found that assumptions regarding biological processes have to be carefully investigated for their effect on the functional form of the optimal harvesting strategies. Though only simple objective functions were considered, indications are that optimal harvesting strategies are sensitive to assumptions regarding the management goals. The response of the model populations to harvesting, and the returns obtained from applying optimal harvesting strategies as well as alternative strategies were explored through simulation. Though the functional form of the optimal strategies is robust with regard to the uncertainties considered in this investigation, the returns obtained from applying optimal strategies are very sensitive to these uncertainties. The effects of decreased productivity resulting from varying the stochastic population variables affected harvesting returns in all cases. The most interesting results from this study emerged from value of information experiments, investigating returns from collapsing the original information systems to yield simplified harvesting strategies. Essentially two types of results were obtained. Applying simplified harvesting strategies either had a negative effect or no effect on returns obtained over long term management periods. The best simplified strategies were based on ungulate population density information. For practical ungulate management this implies that efforts should be directed towards collecting ungulate density information, while extrinsic factors need not be regularly monitored. It is concluded that for ungulate populations harvested in a fluctuating environment, the optimal harvesting decision in any given year must be based on the state of the system in that year. In general, given the inherent unpredictabilities of the real world, it is indefensible to use non-feedback control policies, such as fixed harvest rates or quota systems. Science, Faculty of Zoology, Department of Graduate 2010-03-15T18:13:46Z 2010-03-15T18:13:46Z 1979 Text Thesis/Dissertation http://hdl.handle.net/2429/21905 eng For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
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English |
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Optimal harvesting strategies for ungulate populations are estimated using stochastic dynamic programming. In the context used here, optimal strategy refers to a sequential decision rule that is optimal with respect to maximizing expected long term returns from ungulate populations. The effects of fluctuating environmental conditions and uncertainty about population parameters were considered.
Three case examples were selected for this study to represent classes of real ungulate systems. In effect these cases represent three fragmentary views of the basic food-ungulate-predation food chain. Models incorporating functional information with regard to fecundity, survival, resource utilization, and predation were formulated as stochastic dynamic programming models and optimal harvesting strategies were derived numerically using a digital computer. The strategies are expressed as isopleth diagrams relating state variables and harvest rates.
The optimal harvest strategies were generally found to be insensitive to environmental fluctuations. On the other hand, it was found that assumptions regarding biological processes have to be carefully investigated for their effect on the functional form of the optimal harvesting strategies. Though only simple objective functions were considered, indications are that optimal harvesting strategies are sensitive to assumptions regarding the management goals. The response of the model populations to harvesting, and the returns obtained from applying optimal harvesting strategies as well as alternative strategies were explored through simulation. Though the functional form of the optimal strategies is robust with regard to the uncertainties considered in this investigation, the returns obtained from applying optimal strategies are very sensitive to these uncertainties. The effects of decreased productivity resulting from varying the stochastic population variables affected harvesting returns in all cases.
The most interesting results from this study emerged from value of information experiments, investigating returns from collapsing the original information systems to yield simplified harvesting strategies. Essentially two types of results were obtained. Applying simplified harvesting strategies either had a negative effect or no effect on returns obtained over long term management periods. The best simplified strategies were based on ungulate population density information. For practical ungulate management this implies that efforts should be directed towards collecting ungulate density information, while extrinsic factors need not be regularly monitored.
It is concluded that for ungulate populations harvested in a fluctuating environment, the optimal harvesting decision in any given year must be based on the state of the system in that year. In general, given the inherent unpredictabilities of the real world, it is indefensible to use non-feedback control policies, such as fixed harvest rates or quota systems. === Science, Faculty of === Zoology, Department of === Graduate |
author |
Stocker, Max |
spellingShingle |
Stocker, Max Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
author_facet |
Stocker, Max |
author_sort |
Stocker, Max |
title |
Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
title_short |
Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
title_full |
Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
title_fullStr |
Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
title_full_unstemmed |
Optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
title_sort |
optimal harvest strategies for ungulate populations in relation to population parameters and environmental variability |
publishDate |
2010 |
url |
http://hdl.handle.net/2429/21905 |
work_keys_str_mv |
AT stockermax optimalharveststrategiesforungulatepopulationsinrelationtopopulationparametersandenvironmentalvariability |
_version_ |
1718591851135500288 |