Code and data for: Modeling the interaction between salmon management and consumption by coastal brown bears ...
Harvest management policy for species with strong trophic connections can reverberate through food webs and cause unintended consequences such as altering the abundance of a harvested species' predators or prey. Pacific salmon ( Oncorhynchus spp.), a key food for many predators and an economica...
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
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Zenodo
2023
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| Online Access: | https://dx.doi.org/10.5281/zenodo.7187003 https://zenodo.org/record/7187003 |
| Summary: | Harvest management policy for species with strong trophic connections can reverberate through food webs and cause unintended consequences such as altering the abundance of a harvested species' predators or prey. Pacific salmon ( Oncorhynchus spp.), a key food for many predators and an economically valuable harvested species, is generally managed for maximum sustained harvests without explicit consideration for the freshwater and terrestrial food webs which they support. The density of brown bear ( Ursus arctos ) populations in Alaska, USA is correlated with the amount of salmon they can access and consume, so it seems likely their populations are inadvertently affected by salmon management. We simulated the effect of salmon management policy on brown bears by customizing a general bear-salmon model using empirical data from three watersheds in southwest Kodiak, Alaska. Our goal was to quantify the effect of current salmon management policy (i.e., escapement goals and early/late run allocations) on salmon ... : "Bear_Fxns_to_Source_Kodiak.R" has all of the novel functions and the empirical datasets used in the simulations. It is not run directly, but is sourced from the "Kodiak_Model.R" file before setting the desired parameters and running the similation in parrallel using functions from the "snowfall" package. Funding provided by: U.S. Fish and Wildlife Service Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000202 Award Number: ... |
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