Reconciling multiple data sources to improve accuracy of large-scale prediction of forest disease incidence

Ecological Applications

Volumn 21, Issue 4, 2011, Pages 1173-1188

  Hanks, Ephraim M ^a   Hooten, Mevin B ^b   Baker, Fred A ^c  

^a Department of Environmental and Radiological Health Sciences   (United States)

^b Utah State University ^*  (United States)

^c UTAH STATE UNIVERSITY   (United States)

Author keywords

Arceuthobium pusillum; Bayesian hierarchical models; Black spruce; Disease monitoring; Dwarf mistletoe; Multiple data sources; Northern Minnesota, USA; Picea mariana; Spatial autocorrelation

Indexed keywords

ACCURACY ASSESSMENT; AUTOCORRELATION; BAYESIAN ANALYSIS; CONIFEROUS TREE; DATA SET; DISEASE PREVALENCE; ECOLOGICAL APPROACH; FOREST ECOSYSTEM; FOREST INVENTORY; HERB; SPATIAL ANALYSIS; STAND DYNAMICS;

ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; FACTUAL DATABASE; HOST PARASITE INTERACTION; PARASITOLOGY; PHYSIOLOGY; SPRUCE; TREE; UNITED STATES; VISCACEAE;

BAYES THEOREM; DATABASES, FACTUAL; HOST-PARASITE INTERACTIONS; MINNESOTA; MODELS, BIOLOGICAL; PICEA; TREES; VISCACEAE;

MINNESOTA; UNITED STATES;

ARCEUTHOBIUM PUSILLUM; PICEA; PICEA MARIANA;

EID: 79958850090     PISSN: 10510761     EISSN: None     Source Type: Journal    
DOI: 10.1890/09-1549.1     Document Type: Article

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