Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach
1.Steinhorst&Samuel(1989)showedhowlogistic-regressionmodels,fit to detection data collected from radiocollaredanimals,can be used to estimate and adjust forvisibility bias in wildlife population surveys.Population abundance is estimated using a modified Horvitz Thompson(mHT) estimator in which c...
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fttriple:oai:gotriple.eu:50|dedup_wf_001::41682c0d10153f7e82d78b55d389188b 2023-05-15T13:13:39+02:00 Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach Fieberg, John Alexander, Michael Tse, Scarlett St. Clair, Katie 2013-01-01 https://doi.org/10.5061/dryad.f8669 undefined unknown http://dx.doi.org/10.5061/dryad.f8669 https://dx.doi.org/10.5061/dryad.f8669 lic_creative-commons oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:83802 oai:easy.dans.knaw.nl:easy-dataset:83802 10.5061/dryad.f8669 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 re3data_____::r3d100000044 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 Life sciences medicine and health care abundance aerial survey marked animals sightability data wildlife Minnesota 2006-2007 Alces alces stat envir Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2013 fttriple https://doi.org/10.5061/dryad.f8669 2023-01-22T16:51:09Z 1.Steinhorst&Samuel(1989)showedhowlogistic-regressionmodels,fit to detection data collected from radiocollaredanimals,can be used to estimate and adjust forvisibility bias in wildlife population surveys.Population abundance is estimated using a modified Horvitz Thompson(mHT) estimator in which counts of observed animal groups are divided by their estimated inclusion probabilities (determinedbyplot level sampling probabilities and detection probabilities estimated from radiocollaredindividuals).The sampling distribution of the mHT estimator is typically right skewed,and statistica linference relies on asymptotic theory that may not b eappropriate with small samples.2.We develop an alternative, Bayesian model based approach which we apply to data collected from moose (Alce salces) in Minnesota. We model detection probabilities as a function of visual obstruction, informed by data from 124 sightability trials involving radiocollared moose.These sightability data,along with counts of moose from a stratified random sample of aerial plots,are used to estimate moose abundance in2006 and 2007 and the log rate of change between the two years. 3.Unlike traditional design-based estimators,model based estimators require assumptions regarding stratum specific distributions of the detection covariates,the number of animal groups per plot,and the number of animals per animal group.We demonstrate numerical and graphical methods for assessing the validity of these assumption and compare two different models for the distribution of the number of animal groups per plot,a beta-binomial model and a logistic-t -model 4.Estimates of the log-rate of change (95%CI) between 2006 and 2007 were-0.21(-0.53,0.12),-0.24(-0.64,0.16),and-0.25(-0.64,0.15) for the beta-binomialmodel,logistic-t-model,and mHT estimator,respectively.Plots of posterior-predictive distributions and goodness-of-fit measures both suggest the beta-binomial model provides a better fit to the data. 5.The Bayesian frame work offers many inferential advantages,including ... Dataset Alces alces alce Unknown |
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Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
unknown |
topic |
Life sciences medicine and health care abundance aerial survey marked animals sightability data wildlife Minnesota 2006-2007 Alces alces stat envir |
spellingShingle |
Life sciences medicine and health care abundance aerial survey marked animals sightability data wildlife Minnesota 2006-2007 Alces alces stat envir Fieberg, John Alexander, Michael Tse, Scarlett St. Clair, Katie Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
topic_facet |
Life sciences medicine and health care abundance aerial survey marked animals sightability data wildlife Minnesota 2006-2007 Alces alces stat envir |
description |
1.Steinhorst&Samuel(1989)showedhowlogistic-regressionmodels,fit to detection data collected from radiocollaredanimals,can be used to estimate and adjust forvisibility bias in wildlife population surveys.Population abundance is estimated using a modified Horvitz Thompson(mHT) estimator in which counts of observed animal groups are divided by their estimated inclusion probabilities (determinedbyplot level sampling probabilities and detection probabilities estimated from radiocollaredindividuals).The sampling distribution of the mHT estimator is typically right skewed,and statistica linference relies on asymptotic theory that may not b eappropriate with small samples.2.We develop an alternative, Bayesian model based approach which we apply to data collected from moose (Alce salces) in Minnesota. We model detection probabilities as a function of visual obstruction, informed by data from 124 sightability trials involving radiocollared moose.These sightability data,along with counts of moose from a stratified random sample of aerial plots,are used to estimate moose abundance in2006 and 2007 and the log rate of change between the two years. 3.Unlike traditional design-based estimators,model based estimators require assumptions regarding stratum specific distributions of the detection covariates,the number of animal groups per plot,and the number of animals per animal group.We demonstrate numerical and graphical methods for assessing the validity of these assumption and compare two different models for the distribution of the number of animal groups per plot,a beta-binomial model and a logistic-t -model 4.Estimates of the log-rate of change (95%CI) between 2006 and 2007 were-0.21(-0.53,0.12),-0.24(-0.64,0.16),and-0.25(-0.64,0.15) for the beta-binomialmodel,logistic-t-model,and mHT estimator,respectively.Plots of posterior-predictive distributions and goodness-of-fit measures both suggest the beta-binomial model provides a better fit to the data. 5.The Bayesian frame work offers many inferential advantages,including ... |
format |
Dataset |
author |
Fieberg, John Alexander, Michael Tse, Scarlett St. Clair, Katie |
author_facet |
Fieberg, John Alexander, Michael Tse, Scarlett St. Clair, Katie |
author_sort |
Fieberg, John |
title |
Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
title_short |
Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
title_full |
Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
title_fullStr |
Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
title_full_unstemmed |
Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
title_sort |
data from: abundance estimation with sightability data: a bayesian data augmentation approach |
publishDate |
2013 |
url |
https://doi.org/10.5061/dryad.f8669 |
genre |
Alces alces alce |
genre_facet |
Alces alces alce |
op_source |
oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:83802 oai:easy.dans.knaw.nl:easy-dataset:83802 10.5061/dryad.f8669 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 re3data_____::r3d100000044 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 |
op_relation |
http://dx.doi.org/10.5061/dryad.f8669 https://dx.doi.org/10.5061/dryad.f8669 |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.5061/dryad.f8669 |
_version_ |
1766259671761420288 |