Latent trajectory models for spatio‐temporal dynamics in Alaskan ecosystems

• Published in: Biometrics Vol. 79; no. 4; pp. 3664 - 3675
• Main Authors: Lu, Xinyi, Hooten, Mevin B., Raiho, Ann M., Swanson, David K., Roland, Carl A., Stehn, Sarah E.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.12.2023
• Subjects: Bayes Theorem; Bayesian; Bayesian analysis; Climate Change; data augmentation; ecological succession; Ecosystem; Ecosystems; Ecotypes; Land cover; Mathematical models; Remote sensing; Spatial analysis; state‐space models; Transition probabilities; state-space models; data augmentation; ecological succession; Bayesian; climate change
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/biom.13832

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.