Extending density surface models to include multiple and double-observer survey data
Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected vi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-09-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/12113.pdf |
| _version_ | 1797420325616484352 |
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| author | David L. Miller David Fifield Ewan Wakefield Douglas B. Sigourney |
| author_facet | David L. Miller David Fifield Ewan Wakefield Douglas B. Sigourney |
| author_sort | David L. Miller |
| collection | DOAJ |
| description | Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains. |
| first_indexed | 2024-03-09T06:59:53Z |
| format | Article |
| id | doaj.art-3a239ddd1c584b9099dd3eae52caee6a |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:59:53Z |
| publishDate | 2021-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-3a239ddd1c584b9099dd3eae52caee6a2023-12-03T09:52:24ZengPeerJ Inc.PeerJ2167-83592021-09-019e1211310.7717/peerj.12113Extending density surface models to include multiple and double-observer survey dataDavid L. Miller0David Fifield1Ewan Wakefield2Douglas B. Sigourney3Centre for Research into Ecological and Environmental Modelling and School of Mathematics and Statistics, University of St Andrews, St Andrews, ScotlandWildlife Research Division, Science and Technology Branch, Environment and Climate Change Canada, Mount Pearl, NL, CanadaInstitute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow, ScotlandIntegrated Statistics, Woods Hole, MA, United States of AmericaSpatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.https://peerj.com/articles/12113.pdfDensity surface modelDistance samplingGeneralized additive modelSpatial modellingVariance propagationAbundance estimation |
| spellingShingle | David L. Miller David Fifield Ewan Wakefield Douglas B. Sigourney Extending density surface models to include multiple and double-observer survey data PeerJ Density surface model Distance sampling Generalized additive model Spatial modelling Variance propagation Abundance estimation |
| title | Extending density surface models to include multiple and double-observer survey data |
| title_full | Extending density surface models to include multiple and double-observer survey data |
| title_fullStr | Extending density surface models to include multiple and double-observer survey data |
| title_full_unstemmed | Extending density surface models to include multiple and double-observer survey data |
| title_short | Extending density surface models to include multiple and double-observer survey data |
| title_sort | extending density surface models to include multiple and double observer survey data |
| topic | Density surface model Distance sampling Generalized additive model Spatial modelling Variance propagation Abundance estimation |
| url | https://peerj.com/articles/12113.pdf |
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