Modelling ocean temperatures from bio-probes under preferential sampling
In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes) which are often used to study the animals’ behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), a...
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ftculeuclid:oai:CULeuclid:euclid.aoas/1560758425 2023-05-15T16:05:44+02:00 Modelling ocean temperatures from bio-probes under preferential sampling Dinsdale, Daniel Salibian-Barrera, Matias 2019-06 application/pdf https://projecteuclid.org/euclid.aoas/1560758425 https://doi.org/10.1214/18-AOAS1217 en eng The Institute of Mathematical Statistics 1932-6157 1941-7330 https://projecteuclid.org/euclid.aoas/1560758425 Ann. Appl. Stat. 13, no. 2 (2019), 713-745 doi:10.1214/18-AOAS1217 Copyright 2019 Institute of Mathematical Statistics Animal movement models bio-probes Laplace approximation preferential sampling template model builder Text 2019 ftculeuclid https://doi.org/10.1214/18-AOAS1217 2020-01-19T01:08:45Z In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes) which are often used to study the animals’ behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), and measurements are taken from sensors mounted on elephant seals in the southern Indian Ocean. We show that standard geostatistical models may not be reliable for this type of data, due to the possibility that the regions visited by the animals may depend on the SST. This phenomenon is know in the literature as preferential sampling, and, if ignored, it may affect the resulting spatial predictions and parameter estimates. Research on this topic has been mostly restricted to stationary sampling locations such as monitoring sites. The main contribution of this manuscript is to extend this methodology to observations obtained by devices that move through the region of interest, as is the case with the tagged seals. More specifically, we propose a flexible framework for inference on preferentially sampled fields where the process that generates the sampling locations is stochastic and moving over time through a two-dimensional space. Our simulation studies confirm that predictions obtained from the preferential sampling model are more reliable when this phenomenon is present, and they compare very well to the standard ones when there is no preferential sampling. Finally, we note that the conclusions of our analysis of the SST data can change considerably when we incorporate preferential sampling in the model. Text Elephant Seals Project Euclid (Cornell University Library) Indian Laplace ENVELOPE(141.467,141.467,-66.782,-66.782) The Annals of Applied Statistics 13 2 |
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Project Euclid (Cornell University Library) |
op_collection_id |
ftculeuclid |
language |
English |
topic |
Animal movement models bio-probes Laplace approximation preferential sampling template model builder |
spellingShingle |
Animal movement models bio-probes Laplace approximation preferential sampling template model builder Dinsdale, Daniel Salibian-Barrera, Matias Modelling ocean temperatures from bio-probes under preferential sampling |
topic_facet |
Animal movement models bio-probes Laplace approximation preferential sampling template model builder |
description |
In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes) which are often used to study the animals’ behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), and measurements are taken from sensors mounted on elephant seals in the southern Indian Ocean. We show that standard geostatistical models may not be reliable for this type of data, due to the possibility that the regions visited by the animals may depend on the SST. This phenomenon is know in the literature as preferential sampling, and, if ignored, it may affect the resulting spatial predictions and parameter estimates. Research on this topic has been mostly restricted to stationary sampling locations such as monitoring sites. The main contribution of this manuscript is to extend this methodology to observations obtained by devices that move through the region of interest, as is the case with the tagged seals. More specifically, we propose a flexible framework for inference on preferentially sampled fields where the process that generates the sampling locations is stochastic and moving over time through a two-dimensional space. Our simulation studies confirm that predictions obtained from the preferential sampling model are more reliable when this phenomenon is present, and they compare very well to the standard ones when there is no preferential sampling. Finally, we note that the conclusions of our analysis of the SST data can change considerably when we incorporate preferential sampling in the model. |
format |
Text |
author |
Dinsdale, Daniel Salibian-Barrera, Matias |
author_facet |
Dinsdale, Daniel Salibian-Barrera, Matias |
author_sort |
Dinsdale, Daniel |
title |
Modelling ocean temperatures from bio-probes under preferential sampling |
title_short |
Modelling ocean temperatures from bio-probes under preferential sampling |
title_full |
Modelling ocean temperatures from bio-probes under preferential sampling |
title_fullStr |
Modelling ocean temperatures from bio-probes under preferential sampling |
title_full_unstemmed |
Modelling ocean temperatures from bio-probes under preferential sampling |
title_sort |
modelling ocean temperatures from bio-probes under preferential sampling |
publisher |
The Institute of Mathematical Statistics |
publishDate |
2019 |
url |
https://projecteuclid.org/euclid.aoas/1560758425 https://doi.org/10.1214/18-AOAS1217 |
long_lat |
ENVELOPE(141.467,141.467,-66.782,-66.782) |
geographic |
Indian Laplace |
geographic_facet |
Indian Laplace |
genre |
Elephant Seals |
genre_facet |
Elephant Seals |
op_relation |
1932-6157 1941-7330 https://projecteuclid.org/euclid.aoas/1560758425 Ann. Appl. Stat. 13, no. 2 (2019), 713-745 doi:10.1214/18-AOAS1217 |
op_rights |
Copyright 2019 Institute of Mathematical Statistics |
op_doi |
https://doi.org/10.1214/18-AOAS1217 |
container_title |
The Annals of Applied Statistics |
container_volume |
13 |
container_issue |
2 |
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1766401632227033088 |