Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales
Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at surve...
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ftdatacite:10.48550/arxiv.1604.06013 2023-05-15T15:45:09+02:00 Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales Yuan, Y. Bachl, F. E. Lindgren, F. Brochers, D. L. Illian, J. B. Buckland, S. T. Rue, H. Gerrodette, T. 2016 https://dx.doi.org/10.48550/arxiv.1604.06013 https://arxiv.org/abs/1604.06013 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Methodology stat.ME FOS Computer and information sciences Preprint Article article CreativeWork 2016 ftdatacite https://doi.org/10.48550/arxiv.1604.06013 2022-04-01T11:47:27Z Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consitent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates. : 33 pages 19 figures Report Blue whale DataCite Metadata Store (German National Library of Science and Technology) Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) Pacific |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Methodology stat.ME FOS Computer and information sciences |
spellingShingle |
Methodology stat.ME FOS Computer and information sciences Yuan, Y. Bachl, F. E. Lindgren, F. Brochers, D. L. Illian, J. B. Buckland, S. T. Rue, H. Gerrodette, T. Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
topic_facet |
Methodology stat.ME FOS Computer and information sciences |
description |
Distance sampling is a widely used method for estimating wildlife population abundance. The fact that conventional distance sampling methods are partly design-based constrains the spatial resolution at which animal density can be estimated using these methods. Estimates are usually obtained at survey stratum level. For an endangered species such as the blue whale, it is desirable to estimate density and abundance at a finer spatial scale than stratum. Temporal variation in the spatial structure is also important. We formulate the process generating distance sampling data as a thinned spatial point process and propose model-based inference using a spatial log-Gaussian Cox process. The method adopts a flexible stochastic partial differential equation (SPDE) approach to model spatial structure in density that is not accounted for by explanatory variables, and integrated nested Laplace approximation (INLA) for Bayesian inference. It allows simultaneous fitting of detection and density models and permits prediction of density at an arbitrarily fine scale. We estimate blue whale density in the Eastern Tropical Pacific Ocean from thirteen shipboard surveys conducted over 22 years. We find that higher blue whale density is associated with colder sea surface temperatures in space, and although there is some positive association between density and mean annual temperature, our estimates are consitent with no trend in density across years. Our analysis also indicates that there is substantial spatially structured variation in density that is not explained by available covariates. : 33 pages 19 figures |
format |
Report |
author |
Yuan, Y. Bachl, F. E. Lindgren, F. Brochers, D. L. Illian, J. B. Buckland, S. T. Rue, H. Gerrodette, T. |
author_facet |
Yuan, Y. Bachl, F. E. Lindgren, F. Brochers, D. L. Illian, J. B. Buckland, S. T. Rue, H. Gerrodette, T. |
author_sort |
Yuan, Y. |
title |
Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
title_short |
Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
title_full |
Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
title_fullStr |
Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
title_full_unstemmed |
Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
title_sort |
point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales |
publisher |
arXiv |
publishDate |
2016 |
url |
https://dx.doi.org/10.48550/arxiv.1604.06013 https://arxiv.org/abs/1604.06013 |
long_lat |
ENVELOPE(141.467,141.467,-66.782,-66.782) |
geographic |
Laplace Pacific |
geographic_facet |
Laplace Pacific |
genre |
Blue whale |
genre_facet |
Blue whale |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1604.06013 |
_version_ |
1766379499257069568 |