Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems
Abstract The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sens...
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Oxford University Press (OUP)
2023
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croxfordunivpr:10.1111/biom.13832 2024-09-15T18:02:20+00:00 Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems Lu, Xinyi Hooten, Mevin B. Raiho, Ann M. Swanson, David K. Roland, Carl A. Stehn, Sarah E. National Science Foundation Division of Environmental Biology National Park Service National Park Service 2023 http://dx.doi.org/10.1111/biom.13832 https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13832 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/biom.13832 https://academic.oup.com/biometrics/article-pdf/79/4/3664/56502037/biometrics_79_4_3664.pdf en eng Oxford University Press (OUP) http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ Biometrics volume 79, issue 4, page 3664-3675 ISSN 0006-341X 1541-0420 journal-article 2023 croxfordunivpr https://doi.org/10.1111/biom.13832 2024-07-08T04:25:01Z Abstract The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio-temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi-scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya–Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios. Article in Journal/Newspaper Climate change Oxford University Press Biometrics |
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Open Polar |
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Oxford University Press |
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croxfordunivpr |
language |
English |
description |
Abstract The Alaskan landscape has undergone substantial changes in recent decades, most notably the expansion of shrubs and trees across the Arctic. We developed a Bayesian hierarchical model to quantify the impact of climate change on the structural transformation of ecosystems using remotely sensed imagery. We used latent trajectory processes to model dynamic state probabilities that evolve annually, from which we derived transition probabilities between ecotypes. Our latent trajectory model accommodates temporal irregularity in survey intervals and uses spatio-temporally heterogeneous climate drivers to infer rates of land cover transitions. We characterized multi-scale spatial correlation induced by plot and subplot arrangements in our study system. We also developed a Pólya–Gamma sampling strategy to improve computation. Our model facilitates inference on the response of ecosystems to shifts in the climate and can be used to predict future land cover transitions under various climate scenarios. |
author2 |
National Science Foundation Division of Environmental Biology National Park Service National Park Service |
format |
Article in Journal/Newspaper |
author |
Lu, Xinyi Hooten, Mevin B. Raiho, Ann M. Swanson, David K. Roland, Carl A. Stehn, Sarah E. |
spellingShingle |
Lu, Xinyi Hooten, Mevin B. Raiho, Ann M. Swanson, David K. Roland, Carl A. Stehn, Sarah E. Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems |
author_facet |
Lu, Xinyi Hooten, Mevin B. Raiho, Ann M. Swanson, David K. Roland, Carl A. Stehn, Sarah E. |
author_sort |
Lu, Xinyi |
title |
Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems |
title_short |
Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems |
title_full |
Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems |
title_fullStr |
Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems |
title_full_unstemmed |
Latent Trajectory Models for Spatio-Temporal Dynamics in Alaskan Ecosystems |
title_sort |
latent trajectory models for spatio-temporal dynamics in alaskan ecosystems |
publisher |
Oxford University Press (OUP) |
publishDate |
2023 |
url |
http://dx.doi.org/10.1111/biom.13832 https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13832 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/biom.13832 https://academic.oup.com/biometrics/article-pdf/79/4/3664/56502037/biometrics_79_4_3664.pdf |
genre |
Climate change |
genre_facet |
Climate change |
op_source |
Biometrics volume 79, issue 4, page 3664-3675 ISSN 0006-341X 1541-0420 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1111/biom.13832 |
container_title |
Biometrics |
_version_ |
1810439787117543424 |