Multivariate Bayesian clustering using covariate‐informed components with application to boreal vegetation sensitivity

Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation respo...

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Bibliographic Details
Published in:Biometrics 2022-12, Vol.78 (4), p.1427-1440
Main Authors: Scharf, Henry R., Raiho, Ann M., Pugh, Sierra, Roland, Carl A., Swanson, David K., Stehn, Sarah E., Hooten, Mevin B.
Format: Article
Language:English
Subjects:
Alaska
Alaska national parks
Bayes Theorem
Bayesian analysis
Climate Change
Clustering
clustering regression coefficients
community ecology
Ecosystem
joint distribution models
Labels
Mathematical models
Multivariate analysis
Plants
Robustness
Sensitivity
Smoothness
Statistical models
Vegetation
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Summary:Climate change is impacting both the distribution and abundance of vegetation, especially in far northern latitudes. The effects of climate change are different for every plant assemblage and vary heterogeneously in both space and time. Small changes in climate could result in large vegetation responses in sensitive assemblages but weak responses in robust assemblages. But, patterns and mechanisms of sensitivity and robustness are not yet well understood, largely due to a lack of long‐term measurements of climate and vegetation. Fortunately, observations are sometimes available across a broad spatial extent. We develop a novel statistical model for a multivariate response based on unknown cluster‐specific effects and covariances, where cluster labels correspond to sensitivity and robustness. Our approach utilizes a prototype model for cluster membership that offers flexibility while enforcing smoothness in cluster probabilities across sites with similar characteristics. We demonstrate our approach with an application to vegetation abundance in Alaska, USA, in which we leverage the broad spatial extent of the study area as a proxy for unrecorded historical observations. In the context of the application, our approach yields interpretable site‐level cluster labels associated with assemblage‐level sensitivity and robustness without requiring strong a priori assumptions about the drivers of climate sensitivity.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.13507