NDLI: A Spatio-Temporal Model for Mountain Pine Beetle Damage

Content Provider	Springer Nature Link
Author	Kaufeld, Kimberly A. Heaton, Matthew J. Sain, Stephan R.
Copyright Year	2014
Abstract	Forest composition in the western region of the United States has seen a dramatic change over the past few years due to an increase in mountain pine beetle damage. In order to mitigate the pine beetle epidemic, statistical modeling is needed to predict both the occurrence and the extent of pine beetle damage. Using data on the front range mountains in Colorado between the years 2001–2010 from the National Forest Service, we develop a zero-augmented spatio-temporal beta regression model to predict both the occurrence of pine beetle damage (a binary outcome) and, given damage occurred, the percent of the region infected. Temporal evolution of the pine beetle damage is captured using a dynamic linear model where both the probability and extent of damage depend on the amount of damage incurred in neighboring regions in the previous time period. A sparse conditional autoregressive model is used to capture any spatial information not modeled by spatially varying covariates. We find that the occurrence and extent of pine beetle damage are positively associated with slope and damage in previous time periods.
Starting Page	437
Ending Page	450
Page Count	14
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	19
Issue Number	4
e-ISSN	15372693
Language	English
Publisher	Springer US
Publisher Date	2014-12-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Zero-augmented Beta regression Stick-breaking Dimension reduction Statistics for Life Sciences, Medicine, Health Sciences Agriculture Environmental Monitoring/Analysis Biostatistics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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