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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Bibliographic Details
Main Authors: Fieberg, John, Alexander, Michael, Tse, Scarlett, St. Clair, Katie
Format: Dataset
Language:English
Published: Dryad 2013
Subjects:
Online Access:https://dx.doi.org/10.5061/dryad.f8669
https://datadryad.org/stash/dataset/doi:10.5061/dryad.f8669
id ftdatacite:10.5061/dryad.f8669
record_format openpolar
spelling ftdatacite:10.5061/dryad.f8669 2024-01-28T09:58:12+01:00 Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach ... Fieberg, John Alexander, Michael Tse, Scarlett St. Clair, Katie 2013 https://dx.doi.org/10.5061/dryad.f8669 https://datadryad.org/stash/dataset/doi:10.5061/dryad.f8669 en eng Dryad https://dx.doi.org/10.1111/2041-210x.12078 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 sightability data 2006-2007 Alces alces Aerial survey marked animals Dataset dataset 2013 ftdatacite https://doi.org/10.5061/dryad.f866910.1111/2041-210x.12078 2024-01-04T15:12:18Z 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 ... : Sightability models and dataData and JAGS models associated with the following paper published in Methods in Ecology and Evolution: Fieberg, J., Alexander, M., Tse, S,, and K. St. Clair. 2013. Abundance estimation with sightability data: a Bayesian data augmentation approach. Methods in Ecology and Evolution.Fieberg et al sightability data and models.zip ... Dataset Alces alces alce DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic sightability data
2006-2007
Alces alces
Aerial survey
marked animals
spellingShingle sightability data
2006-2007
Alces alces
Aerial survey
marked animals
Fieberg, John
Alexander, Michael
Tse, Scarlett
St. Clair, Katie
Data from: Abundance estimation with sightability data: a Bayesian data augmentation approach ...
topic_facet sightability data
2006-2007
Alces alces
Aerial survey
marked animals
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 ... : Sightability models and dataData and JAGS models associated with the following paper published in Methods in Ecology and Evolution: Fieberg, J., Alexander, M., Tse, S,, and K. St. Clair. 2013. Abundance estimation with sightability data: a Bayesian data augmentation approach. Methods in Ecology and Evolution.Fieberg et al sightability data and models.zip ...
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 ...
publisher Dryad
publishDate 2013
url https://dx.doi.org/10.5061/dryad.f8669
https://datadryad.org/stash/dataset/doi:10.5061/dryad.f8669
genre Alces alces
alce
genre_facet Alces alces
alce
op_relation https://dx.doi.org/10.1111/2041-210x.12078
op_rights Creative Commons Zero v1.0 Universal
https://creativecommons.org/publicdomain/zero/1.0/legalcode
cc0-1.0
op_doi https://doi.org/10.5061/dryad.f866910.1111/2041-210x.12078
_version_ 1789324860043821056