Variance propagation for density surface models
Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilitie...
Published in: | Journal of Agricultural, Biological and Environmental Statistics |
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2021
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Online Access: | https://research-portal.st-andrews.ac.uk/en/researchoutput/variance-propagation-for-density-surface-models(b2dab681-8595-473f-821a-0f798448b04d).html https://doi.org/10.1007/s13253-021-00438-2 https://research-repository.st-andrews.ac.uk/bitstream/10023/21564/1/Bravington_2021_JABES_Variance_CC.pdf |
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ftunstandrewcris:oai:research-portal.st-andrews.ac.uk:publications/b2dab681-8595-473f-821a-0f798448b04d 2024-06-23T07:53:30+00:00 Variance propagation for density surface models Bravington, Mark V. Miller, David Lawrence Hedley, Sharon Louise 2021-02-23 application/pdf https://research-portal.st-andrews.ac.uk/en/researchoutput/variance-propagation-for-density-surface-models(b2dab681-8595-473f-821a-0f798448b04d).html https://doi.org/10.1007/s13253-021-00438-2 https://research-repository.st-andrews.ac.uk/bitstream/10023/21564/1/Bravington_2021_JABES_Variance_CC.pdf eng eng https://research-portal.st-andrews.ac.uk/en/researchoutput/variance-propagation-for-density-surface-models(b2dab681-8595-473f-821a-0f798448b04d).html info:eu-repo/semantics/openAccess Bravington , M V , Miller , D L & Hedley , S L 2021 , ' Variance propagation for density surface models ' , Journal of Agricultural, Biological and Environmental Statistics , vol. First Online . https://doi.org/10.1007/s13253-021-00438-2 Abundance estimation Distance sampling Generalized additive models Line transect sampling Point transect sampling Spatial modelling article 2021 ftunstandrewcris https://doi.org/10.1007/s13253-021-00438-2 2024-06-13T01:14:55Z Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilities—perhaps depending on covariates—are estimated based on details of individual encounters; next, local densities are estimated using a GAM, by fitting local encounter rates to location and/or spatially varying covariates while allowing for the estimated detectabilities. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as an extra random effect in the GAM stage. In effect, we refit an approximation to the detection function model at the same time as fitting the spatial model. This allows straightforward computation of the overall variance via exactly the same software already needed to fit the GAM. A further extension allows for spatial variation in group size, which can be an important covariate for detectability as well as directly affecting abundance. We illustrate these models using point transect survey data of Island Scrub-Jays on Santa Cruz Island, CA, and harbour porpoise from the SCANS-II line transect survey of European waters. Supplementary materials accompanying this paper appear on-line. Article in Journal/Newspaper Harbour porpoise University of St Andrews: Research Portal Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Journal of Agricultural, Biological and Environmental Statistics 26 2 306 323 |
institution |
Open Polar |
collection |
University of St Andrews: Research Portal |
op_collection_id |
ftunstandrewcris |
language |
English |
topic |
Abundance estimation Distance sampling Generalized additive models Line transect sampling Point transect sampling Spatial modelling |
spellingShingle |
Abundance estimation Distance sampling Generalized additive models Line transect sampling Point transect sampling Spatial modelling Bravington, Mark V. Miller, David Lawrence Hedley, Sharon Louise Variance propagation for density surface models |
topic_facet |
Abundance estimation Distance sampling Generalized additive models Line transect sampling Point transect sampling Spatial modelling |
description |
Spatially explicit estimates of population density, together with appropriate estimates of uncertainty, are required in many management contexts. Density surface models (DSMs) are a two-stage approach for estimating spatially varying density from distance sampling data. First, detection probabilities—perhaps depending on covariates—are estimated based on details of individual encounters; next, local densities are estimated using a GAM, by fitting local encounter rates to location and/or spatially varying covariates while allowing for the estimated detectabilities. One criticism of DSMs has been that uncertainty from the two stages is not usually propagated correctly into the final variance estimates. We show how to reformulate a DSM so that the uncertainty in detection probability from the distance sampling stage (regardless of its complexity) is captured as an extra random effect in the GAM stage. In effect, we refit an approximation to the detection function model at the same time as fitting the spatial model. This allows straightforward computation of the overall variance via exactly the same software already needed to fit the GAM. A further extension allows for spatial variation in group size, which can be an important covariate for detectability as well as directly affecting abundance. We illustrate these models using point transect survey data of Island Scrub-Jays on Santa Cruz Island, CA, and harbour porpoise from the SCANS-II line transect survey of European waters. Supplementary materials accompanying this paper appear on-line. |
format |
Article in Journal/Newspaper |
author |
Bravington, Mark V. Miller, David Lawrence Hedley, Sharon Louise |
author_facet |
Bravington, Mark V. Miller, David Lawrence Hedley, Sharon Louise |
author_sort |
Bravington, Mark V. |
title |
Variance propagation for density surface models |
title_short |
Variance propagation for density surface models |
title_full |
Variance propagation for density surface models |
title_fullStr |
Variance propagation for density surface models |
title_full_unstemmed |
Variance propagation for density surface models |
title_sort |
variance propagation for density surface models |
publishDate |
2021 |
url |
https://research-portal.st-andrews.ac.uk/en/researchoutput/variance-propagation-for-density-surface-models(b2dab681-8595-473f-821a-0f798448b04d).html https://doi.org/10.1007/s13253-021-00438-2 https://research-repository.st-andrews.ac.uk/bitstream/10023/21564/1/Bravington_2021_JABES_Variance_CC.pdf |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Gam |
geographic_facet |
Gam |
genre |
Harbour porpoise |
genre_facet |
Harbour porpoise |
op_source |
Bravington , M V , Miller , D L & Hedley , S L 2021 , ' Variance propagation for density surface models ' , Journal of Agricultural, Biological and Environmental Statistics , vol. First Online . https://doi.org/10.1007/s13253-021-00438-2 |
op_relation |
https://research-portal.st-andrews.ac.uk/en/researchoutput/variance-propagation-for-density-surface-models(b2dab681-8595-473f-821a-0f798448b04d).html |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1007/s13253-021-00438-2 |
container_title |
Journal of Agricultural, Biological and Environmental Statistics |
container_volume |
26 |
container_issue |
2 |
container_start_page |
306 |
op_container_end_page |
323 |
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1802645211205074944 |