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Harbour porpoise habitat preferences: robust spatio-temporal inferences from opportunistic data

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Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data. / Isojunno, Saana; Matthiopoulos, Jason; Evans, Peter G H.

In: Marine Ecology Progress Series, Vol. 448, 23.02.2012, p. 155-170.

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Isojunno, S, Matthiopoulos, J & Evans, PGH 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

APA

Isojunno, S., Matthiopoulos, J., & Evans, P. G. H. (2012). Harbour porpoise habitat preferences: robust spatio-temporal inferences from opportunistic data. Marine Ecology Progress Series, 448, 155-170. https://doi.org/10.3354/meps09415

Vancouver

Isojunno S, Matthiopoulos J, Evans PGH. Harbour porpoise habitat preferences: robust spatio-temporal inferences from opportunistic data. Marine Ecology Progress Series. 2012 Feb 23;448:155-170. https://doi.org/10.3354/meps09415

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Isojunno, Saana ; Matthiopoulos, Jason ; Evans, Peter G H. / Harbour porpoise habitat preferences : robust spatio-temporal inferences from opportunistic data. In: Marine Ecology Progress Series. 2012 ; Vol. 448. pp. 155-170.

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@article{bb617f9e789c41ab9f1f1734281e8782,
title = "Harbour porpoise habitat preferences: robust spatio-temporal inferences from opportunistic data",
abstract = "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.",
keywords = "Generalized additive models, Habitat model, Wales, Model selection, Tidal environments, Phocoena phocoena, Non-linear interactions, Multi-model inference",
author = "Saana Isojunno and Jason Matthiopoulos and Evans, {Peter G H}",
year = "2012",
month = "2",
day = "23",
doi = "10.3354/meps09415",
language = "English",
volume = "448",
pages = "155--170",
journal = "Marine Ecology Progress Series",
issn = "0171-8630",
publisher = "Inter-Research",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Harbour porpoise habitat preferences

T2 - robust spatio-temporal inferences from opportunistic data

AU - Isojunno, Saana

AU - Matthiopoulos, Jason

AU - Evans, Peter G H

PY - 2012/2/23

Y1 - 2012/2/23

N2 - 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.

AB - 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.

KW - Generalized additive models

KW - Habitat model

KW - Wales

KW - Model selection

KW - Tidal environments

KW - Phocoena phocoena

KW - Non-linear interactions

KW - Multi-model inference

U2 - 10.3354/meps09415

DO - 10.3354/meps09415

M3 - Article

VL - 448

SP - 155

EP - 170

JO - Marine Ecology Progress Series

JF - Marine Ecology Progress Series

SN - 0171-8630

ER -

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