Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data
Statistical habitat modelling is often flagged as a cost-effective decision tool for species management. However, data that can produce predictions with the desired precision are difficult to collect, especially for species with spatially extensive and dynamic distributions. Data from platforms of o...
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Online Access: | https://hdl.handle.net/10023/16207 https://doi.org/10.3354/meps09415 |
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ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/16207 2024-04-28T08:22:58+00:00 Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data Isojunno, Saana Matthiopoulos, Jason Evans, Peter G H University of St Andrews. School of Biology University of St Andrews. Marine Alliance for Science & Technology Scotland University of St Andrews. Scottish Oceans Institute University of St Andrews. Centre for Research into Ecological & Environmental Modelling 2018-10-12T15:30:09Z 16 4393533 application/pdf https://hdl.handle.net/10023/16207 https://doi.org/10.3354/meps09415 eng eng Marine Ecology Progress Series 251763465 bb617f9e-789c-41ab-9f1f-1734281e8782 Isojunno , S , Matthiopoulos , J & Evans , P G H 2012 , ' Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data ' , Marine Ecology Progress Series , vol. 448 , pp. 155-170 . https://doi.org/10.3354/meps09415 0171-8630 Bibtex: urn:6d164c4e758084cf525aef94b857071e ORCID: /0000-0002-2212-2135/work/39714953 https://hdl.handle.net/10023/16207 doi:10.3354/meps09415 Generalized additive models Habitat model Wales Model selection Tidal environments Phocoena phocoena Non-linear interactions Multi-model inference GC Oceanography HA Statistics GC HA Journal article 2018 ftstandrewserep https://doi.org/10.3354/meps09415 2024-04-03T14:07:22Z Statistical habitat modelling is often flagged as a cost-effective decision tool for species management. However, data that can produce predictions with the desired precision are difficult to collect, especially for species with spatially extensive and dynamic distributions. Data from platforms of opportunity could be used to complement or help design dedicated surveys, but robust inference from such data is challenging. Furthermore, regression models using static covariates may not be sufficient for animals whose habitat preferences change dynamically with season, environmental conditions or foraging strategy. More flexible models introduce difficulties in selecting parsimonious models. We implemented a robust model-averaging framework to dynamically predict harbour porpoise Phocoena phocoena occurrence in a strongly tidal and topographically complex site in southwest Wales using data from a temporally intensive platform of opportunity. Spatial and temporal environmental variables were allowed to interact in a generalized additive model (GAM). We used information criteria to examine an extensive set of 3003 models and average predictions from the best 33. In the best model, 3 main effects and 2 tensorproduct interactions explained 46% of the deviance. Model-averaged predictions indicated that harbour porpoises avoided or selected steeper slopes depending on the tidal flow conditions; when the tide started to ebb, occurrence was predicted to increase 3-fold at steeper slopes. Peer reviewed Article in Journal/Newspaper Harbour porpoise Phocoena phocoena University of St Andrews: Digital Research Repository Marine Ecology Progress Series 448 155 170 |
institution |
Open Polar |
collection |
University of St Andrews: Digital Research Repository |
op_collection_id |
ftstandrewserep |
language |
English |
topic |
Generalized additive models Habitat model Wales Model selection Tidal environments Phocoena phocoena Non-linear interactions Multi-model inference GC Oceanography HA Statistics GC HA |
spellingShingle |
Generalized additive models Habitat model Wales Model selection Tidal environments Phocoena phocoena Non-linear interactions Multi-model inference GC Oceanography HA Statistics GC HA Isojunno, Saana Matthiopoulos, Jason Evans, Peter G H Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
topic_facet |
Generalized additive models Habitat model Wales Model selection Tidal environments Phocoena phocoena Non-linear interactions Multi-model inference GC Oceanography HA Statistics GC HA |
description |
Statistical habitat modelling is often flagged as a cost-effective decision tool for species management. However, data that can produce predictions with the desired precision are difficult to collect, especially for species with spatially extensive and dynamic distributions. Data from platforms of opportunity could be used to complement or help design dedicated surveys, but robust inference from such data is challenging. Furthermore, regression models using static covariates may not be sufficient for animals whose habitat preferences change dynamically with season, environmental conditions or foraging strategy. More flexible models introduce difficulties in selecting parsimonious models. We implemented a robust model-averaging framework to dynamically predict harbour porpoise Phocoena phocoena occurrence in a strongly tidal and topographically complex site in southwest Wales using data from a temporally intensive platform of opportunity. Spatial and temporal environmental variables were allowed to interact in a generalized additive model (GAM). We used information criteria to examine an extensive set of 3003 models and average predictions from the best 33. In the best model, 3 main effects and 2 tensorproduct interactions explained 46% of the deviance. Model-averaged predictions indicated that harbour porpoises avoided or selected steeper slopes depending on the tidal flow conditions; when the tide started to ebb, occurrence was predicted to increase 3-fold at steeper slopes. Peer reviewed |
author2 |
University of St Andrews. School of Biology University of St Andrews. Marine Alliance for Science & Technology Scotland University of St Andrews. Scottish Oceans Institute University of St Andrews. Centre for Research into Ecological & Environmental Modelling |
format |
Article in Journal/Newspaper |
author |
Isojunno, Saana Matthiopoulos, Jason Evans, Peter G H |
author_facet |
Isojunno, Saana Matthiopoulos, Jason Evans, Peter G H |
author_sort |
Isojunno, Saana |
title |
Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
title_short |
Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
title_full |
Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
title_fullStr |
Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
title_full_unstemmed |
Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
title_sort |
harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data |
publishDate |
2018 |
url |
https://hdl.handle.net/10023/16207 https://doi.org/10.3354/meps09415 |
genre |
Harbour porpoise Phocoena phocoena |
genre_facet |
Harbour porpoise Phocoena phocoena |
op_relation |
Marine Ecology Progress Series 251763465 bb617f9e-789c-41ab-9f1f-1734281e8782 Isojunno , S , Matthiopoulos , J & Evans , P G H 2012 , ' Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data ' , Marine Ecology Progress Series , vol. 448 , pp. 155-170 . https://doi.org/10.3354/meps09415 0171-8630 Bibtex: urn:6d164c4e758084cf525aef94b857071e ORCID: /0000-0002-2212-2135/work/39714953 https://hdl.handle.net/10023/16207 doi:10.3354/meps09415 |
op_doi |
https://doi.org/10.3354/meps09415 |
container_title |
Marine Ecology Progress Series |
container_volume |
448 |
container_start_page |
155 |
op_container_end_page |
170 |
_version_ |
1797584236641779712 |