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...
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ftdatacite:10.48550/arxiv.1807.07996 2023-05-15T16:33:26+02:00 Variance propagation for density surface models Bravington, Mark V Miller, David L Hedley, Sharon L 2018 https://dx.doi.org/10.48550/arxiv.1807.07996 https://arxiv.org/abs/1807.07996 unknown arXiv https://dx.doi.org/10.1007/s13253-021-00438-2 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Methodology stat.ME FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2018 ftdatacite https://doi.org/10.48550/arxiv.1807.07996 https://doi.org/10.1007/s13253-021-00438-2 2022-04-01T09:34:13Z 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. : 38 pages (incl. supp. mat.), 5 figures Text Harbour porpoise DataCite Metadata Store (German National Library of Science and Technology) Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Methodology stat.ME FOS Computer and information sciences |
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Methodology stat.ME FOS Computer and information sciences Bravington, Mark V Miller, David L Hedley, Sharon L Variance propagation for density surface models |
topic_facet |
Methodology stat.ME FOS Computer and information sciences |
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. : 38 pages (incl. supp. mat.), 5 figures |
format |
Text |
author |
Bravington, Mark V Miller, David L Hedley, Sharon L |
author_facet |
Bravington, Mark V Miller, David L Hedley, Sharon L |
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 |
publisher |
arXiv |
publishDate |
2018 |
url |
https://dx.doi.org/10.48550/arxiv.1807.07996 https://arxiv.org/abs/1807.07996 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Gam |
geographic_facet |
Gam |
genre |
Harbour porpoise |
genre_facet |
Harbour porpoise |
op_relation |
https://dx.doi.org/10.1007/s13253-021-00438-2 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1807.07996 https://doi.org/10.1007/s13253-021-00438-2 |
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1766023124320518144 |