Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals
So far the remoteness of the Arctic has limited the number of biological surveys in the region and thus, estimates about distributions and abundances of Arctic marine mammals (AMMs) are missing in many areas. A better knowledge about distributions of AMMs would improve the assessment of their sensit...
Published in: | Proceedings of the 5th European Congress of Conservation Biology |
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Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Open Science Centre, University of Jyväskylä
2018
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Subjects: | |
Online Access: | https://doi.org/10.17011/conference/eccb2018/107515 http://urn.fi/ |
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ftjyvaeskylaenun:oai:jyx.jyu.fi:123456789/61953 |
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Open Polar |
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JYX - Jyväskylä University Digital Archive |
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ftjyvaeskylaenun |
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English |
description |
So far the remoteness of the Arctic has limited the number of biological surveys in the region and thus, estimates about distributions and abundances of Arctic marine mammals (AMMs) are missing in many areas. A better knowledge about distributions of AMMs would improve the assessment of their sensitivities to the impacts of climate change and increasing human actions. We present how this data shortage can be tackled by combining several (heterogeneous) data sets within a single spatiotemporal Poisson point process framework. We demonstrate our approach with a study on distributions of polar bears, walruses and ringed seals in the Kara Sea. We combined species observations from multiple studies which had differing survey methods. Based on the data we estimated how species respond to the habitat covariates and created a hindcast of species relative densities in the study area. Our data set was mostly based on survey cruises where researchers had made species sightings with varying effort. The novelty of our modelling methodology is in taking into account the survey bias and spatiotemporal autocorrelation, which come as downsides of utilizing an extensive but poorly controlled data (Fithian et al. 2015). We built a hierarchical Bayesian framework, which allowed us to model observations as a Poisson point process and to formulate an additive regression model for the species density process (Warton & Shepherd 2010). In the additive model we assigned fixed effects for covariates and random effects for survey specific observation bias and spatiotemporal autocorrelation. According to our results, the (relative) density of polar bears was mostly explained by the relative density of seals. As apex predators polar bears are dependent on prey abundance, which has not been considered in earlier estimates of polar bears’ habitat suitability. Hence, the response of seals to shrinking ice cover may be an important feature for the future of the polar bear distribution. Seal density was highest in areas with ice cover around 70 % and walrus density was highest relatively near coastal regions (shallow water areas), which support that they are both dependent on access to prey. Moreover, there was strong variation assigned to both random effects. The spatiotemporal effect explained variation caused by unmeasured environmental covariates and possibly by spatiotemporally structured survey bias. Our model structure could treat the heterogenic sampling protocols, which came with the cost of predicting only the relative densities instead of absolute ones. Anyhow, this did not have an effect on the estimates of species’ habitat characteristics. The novel methods in SDM field proved their efficiency in our study and created quantitative knowledge and new understanding about the Arctic ecosystem. Fithian, et al. (2015). Methods in ecology and evolution, 6, 424-438. Warton & Shepherd (2010). The Annals of Applied Statistics, 4, 1383-1402. peerReviewed |
format |
Article in Journal/Newspaper |
author |
Mäkinen, Jussi Vanhatalo, Jarno |
spellingShingle |
Mäkinen, Jussi Vanhatalo, Jarno Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals |
author_facet |
Mäkinen, Jussi Vanhatalo, Jarno |
author_sort |
Mäkinen, Jussi |
title |
Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals |
title_short |
Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals |
title_full |
Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals |
title_fullStr |
Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals |
title_full_unstemmed |
Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals |
title_sort |
hierarchical bayesian models reveal the habitat characteristics of arctic marine mammals |
publisher |
Open Science Centre, University of Jyväskylä |
publishDate |
2018 |
url |
https://doi.org/10.17011/conference/eccb2018/107515 http://urn.fi/ |
geographic |
Arctic Kara Sea |
geographic_facet |
Arctic Kara Sea |
genre |
Arctic Arctic marine mammals Arctic Climate change Kara Sea walrus* |
genre_facet |
Arctic Arctic marine mammals Arctic Climate change Kara Sea walrus* |
op_relation |
https://peerageofscience.org/conference/eccb2018/107515/ ECCB2018: 5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland Mäkinen, J. and Vanhatalo, J. (2018). Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals. 5th European Congress of Conservation Biology. doi:10.17011/conference/eccb2018/107515 doi:10.17011/conference/eccb2018/107515 http://urn.fi/ |
op_rights |
CC BY 4.0 © the Authors, 2018 openAccess http://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.17011/conference/eccb2018/107515 |
container_title |
Proceedings of the 5th European Congress of Conservation Biology |
_version_ |
1766298526211375104 |
spelling |
ftjyvaeskylaenun:oai:jyx.jyu.fi:123456789/61953 2023-05-15T14:26:03+02:00 Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals Mäkinen, Jussi Vanhatalo, Jarno 2018 text/html fulltext https://doi.org/10.17011/conference/eccb2018/107515 http://urn.fi/ eng eng Open Science Centre, University of Jyväskylä https://peerageofscience.org/conference/eccb2018/107515/ ECCB2018: 5th European Congress of Conservation Biology. 12th - 15th of June 2018, Jyväskylä, Finland Mäkinen, J. and Vanhatalo, J. (2018). Hierarchical Bayesian models reveal the habitat characteristics of Arctic marine mammals. 5th European Congress of Conservation Biology. doi:10.17011/conference/eccb2018/107515 doi:10.17011/conference/eccb2018/107515 http://urn.fi/ CC BY 4.0 © the Authors, 2018 openAccess http://creativecommons.org/licenses/by/4.0/ CC-BY Article http://purl.org/eprint/type/ConferenceItem conference paper not in proceedings publishedVersion conferenceObject 2018 ftjyvaeskylaenun https://doi.org/10.17011/conference/eccb2018/107515 2021-09-23T20:19:45Z So far the remoteness of the Arctic has limited the number of biological surveys in the region and thus, estimates about distributions and abundances of Arctic marine mammals (AMMs) are missing in many areas. A better knowledge about distributions of AMMs would improve the assessment of their sensitivities to the impacts of climate change and increasing human actions. We present how this data shortage can be tackled by combining several (heterogeneous) data sets within a single spatiotemporal Poisson point process framework. We demonstrate our approach with a study on distributions of polar bears, walruses and ringed seals in the Kara Sea. We combined species observations from multiple studies which had differing survey methods. Based on the data we estimated how species respond to the habitat covariates and created a hindcast of species relative densities in the study area. Our data set was mostly based on survey cruises where researchers had made species sightings with varying effort. The novelty of our modelling methodology is in taking into account the survey bias and spatiotemporal autocorrelation, which come as downsides of utilizing an extensive but poorly controlled data (Fithian et al. 2015). We built a hierarchical Bayesian framework, which allowed us to model observations as a Poisson point process and to formulate an additive regression model for the species density process (Warton & Shepherd 2010). In the additive model we assigned fixed effects for covariates and random effects for survey specific observation bias and spatiotemporal autocorrelation. According to our results, the (relative) density of polar bears was mostly explained by the relative density of seals. As apex predators polar bears are dependent on prey abundance, which has not been considered in earlier estimates of polar bears’ habitat suitability. Hence, the response of seals to shrinking ice cover may be an important feature for the future of the polar bear distribution. Seal density was highest in areas with ice cover around 70 % and walrus density was highest relatively near coastal regions (shallow water areas), which support that they are both dependent on access to prey. Moreover, there was strong variation assigned to both random effects. The spatiotemporal effect explained variation caused by unmeasured environmental covariates and possibly by spatiotemporally structured survey bias. Our model structure could treat the heterogenic sampling protocols, which came with the cost of predicting only the relative densities instead of absolute ones. Anyhow, this did not have an effect on the estimates of species’ habitat characteristics. The novel methods in SDM field proved their efficiency in our study and created quantitative knowledge and new understanding about the Arctic ecosystem. Fithian, et al. (2015). Methods in ecology and evolution, 6, 424-438. Warton & Shepherd (2010). The Annals of Applied Statistics, 4, 1383-1402. peerReviewed Article in Journal/Newspaper Arctic Arctic marine mammals Arctic Climate change Kara Sea walrus* JYX - Jyväskylä University Digital Archive Arctic Kara Sea Proceedings of the 5th European Congress of Conservation Biology |