70(2):367–374.

Paper doi abstract bibtex

Paper doi abstract bibtex

Resource-selection probability functions and occupancy models are powerful methods of identifying areas within a landscape that are highly used by a species. One common design/analysis method for estimation of a resource-selection probability function is to classify a sample of units as used or unused and estimate the probability of use as a function of independent variables using, for example, logistic regression. This method requires that resource units are correctly classified as unused (i.e., the species is never undetected in a used unit), or that the probability of misclassification is the same for all units. In this paper, I explore these issues, illustrating how misclassifying units as unused may lead to incorrect conclusions about resource use. I also show how recently developed occupancy models can be utilized within the resource-selection context to improve conclusions by explicitly accounting for detection probability. These models require that multiple surveys be conducted at each of a sample of resource units within a relatively short timeframe, but given the growing evidence from simulation studies and field data, I recommend that such procedures should be incorporated into studies of resource use.

@article{mackenzieModelingProbabilityResource2006, title = {Modeling the Probability of Resource Use: The Effect of, and Dealing with, Detecting a Species Imperfectly}, author = {Mackenzie, Darryl I.}, date = {2006-04}, journaltitle = {Journal of Wildlife Management}, volume = {70}, pages = {367--374}, issn = {1937-2817}, doi = {10.2193/0022-541X(2006)70[367:MTPORU]2.0.CO;2}, url = {https://doi.org/10.2193/0022-541X(2006)70[367:MTPORU]2.0.CO;2}, abstract = {Resource-selection probability functions and occupancy models are powerful methods of identifying areas within a landscape that are highly used by a species. One common design/analysis method for estimation of a resource-selection probability function is to classify a sample of units as used or unused and estimate the probability of use as a function of independent variables using, for example, logistic regression. This method requires that resource units are correctly classified as unused (i.e., the species is never undetected in a used unit), or that the probability of misclassification is the same for all units. In this paper, I explore these issues, illustrating how misclassifying units as unused may lead to incorrect conclusions about resource use. I also show how recently developed occupancy models can be utilized within the resource-selection context to improve conclusions by explicitly accounting for detection probability. These models require that multiple surveys be conducted at each of a sample of resource units within a relatively short timeframe, but given the growing evidence from simulation studies and field data, I recommend that such procedures should be incorporated into studies of resource use.}, keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14258110,~to-add-doi-URL,bioclimatic-predictors,data-uncertainty,field-measurements,occupancy-vs-detection,uncertainty,uncertainty-propagation}, number = {2} }

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