Latent trajectory models for spatio-temporal dynamics in Alaskan ecosystems.
Biometrics
; 79(4): 3664-3675, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-36715694
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Mudança Climática
/
Ecossistema
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos