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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creator | Fieberg, John Alexander, Michael Tse, Scarlett St. Clair, Katie |
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 the
ability to incorporate prior information and perform exact inference with
small samples.More importantly,themodel-based approach provides additional
flexibility when designing and analyzing multi-year surveys
(e.g.,rotational sampling designs could be used to focus sampling effort
in important areas,and random effects could be used to share information
across years). |
doi_str_mv | 10.5061/dryad.f8669 |
format | Dataset |
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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 the
ability to incorporate prior information and perform exact inference with
small samples.More importantly,themodel-based approach provides additional
flexibility when designing and analyzing multi-year surveys
(e.g.,rotational sampling designs could be used to focus sampling effort
in important areas,and random effects could be used to share information
across years).</description><identifier>DOI: 10.5061/dryad.f8669</identifier><language>eng</language><publisher>Dryad</publisher><subject>2007 ; Aerial survey ; Alces alces ; marked animals ; sightability data</subject><creationdate>2013</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,1894</link.rule.ids><linktorsrc>$$Uhttps://commons.datacite.org/doi.org/10.5061/dryad.f8669$$EView_record_in_DataCite.org$$FView_record_in_$$GDataCite.org$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Fieberg, John</creatorcontrib><creatorcontrib>Alexander, Michael</creatorcontrib><creatorcontrib>Tse, Scarlett</creatorcontrib><creatorcontrib>St. Clair, Katie</creatorcontrib><title>Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach</title><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 the
ability to incorporate prior information and perform exact inference with
small samples.More importantly,themodel-based approach provides additional
flexibility when designing and analyzing multi-year surveys
(e.g.,rotational sampling designs could be used to focus sampling effort
in important areas,and random effects could be used to share information
across years).</description><subject>2007</subject><subject>Aerial survey</subject><subject>Alces alces</subject><subject>marked animals</subject><subject>sightability data</subject><fulltext>true</fulltext><rsrctype>dataset</rsrctype><creationdate>2013</creationdate><recordtype>dataset</recordtype><sourceid>PQ8</sourceid><recordid>eNqVjs0KgkAUhWfTIqpVL3D3kSmhlLt-6QHaxnB1Rr2go8xciXn71HqBVgcO5-N8QqyjMIjDJNop61EFxSFJjnPxuiIjFLZtUjhlvVFocg3aMTXI1Bp4E1fgqKwYM6qJPaiBSAHhjF47QjMVgH3ZaMNfCLvOtphXSzErsHZ69cuF2Nxvz8tjOyI5sZadHY6sl1EoRzk5yclJbv_f-gNklkmq</recordid><startdate>20130701</startdate><enddate>20130701</enddate><creator>Fieberg, John</creator><creator>Alexander, Michael</creator><creator>Tse, Scarlett</creator><creator>St. Clair, Katie</creator><general>Dryad</general><scope>DYCCY</scope><scope>PQ8</scope></search><sort><creationdate>20130701</creationdate><title>Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach</title><author>Fieberg, John ; Alexander, Michael ; Tse, Scarlett ; St. Clair, Katie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-datacite_primary_10_5061_dryad_f86693</frbrgroupid><rsrctype>datasets</rsrctype><prefilter>datasets</prefilter><language>eng</language><creationdate>2013</creationdate><topic>2007</topic><topic>Aerial survey</topic><topic>Alces alces</topic><topic>marked animals</topic><topic>sightability data</topic><toplevel>online_resources</toplevel><creatorcontrib>Fieberg, John</creatorcontrib><creatorcontrib>Alexander, Michael</creatorcontrib><creatorcontrib>Tse, Scarlett</creatorcontrib><creatorcontrib>St. Clair, Katie</creatorcontrib><collection>DataCite (Open Access)</collection><collection>DataCite</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fieberg, John</au><au>Alexander, Michael</au><au>Tse, Scarlett</au><au>St. Clair, Katie</au><format>book</format><genre>unknown</genre><ristype>DATA</ristype><title>Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach</title><date>2013-07-01</date><risdate>2013</risdate><abstract>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 the
ability to incorporate prior information and perform exact inference with
small samples.More importantly,themodel-based approach provides additional
flexibility when designing and analyzing multi-year surveys
(e.g.,rotational sampling designs could be used to focus sampling effort
in important areas,and random effects could be used to share information
across years).</abstract><pub>Dryad</pub><doi>10.5061/dryad.f8669</doi><oa>free_for_read</oa></addata></record> |
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subjects | 2007 Aerial survey Alces alces marked animals sightability data |
title | Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach |
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