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

Ecological spatial data often come from multiple sources, varying in extent and accuracy. We describe a general approach to reconciling such data sets through the use of the Bayesian hierarchical framework. This approach provides a way for the data sets to borrow strength from one another while allowing for inference on the underlying ecological process. We apply this approach to study the incidence of eastern spruce dwarf mistletoe (Arceuthobium pusillum) in Minnesota black spruce (Picea mariana). A Minnesota Department of Natural Resources operational inventory of black spruce stands in northern Minnesota found mistletoe in 11% of surveyed stands, while a small, specific-pest survey found mistletoe in 56% of the surveyed stands. We reconcile these two surveys within a Bayesian hierarchical framework and predict that 35-59% of black spruce stands in northern Minnesota are infested with dwarf mistletoe.

[1]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[2]  N. Reid,et al.  Contrasting research approaches to managing mistletoes in commercial forests and wooded pastures. , 2009 .

[3]  M. Salman,et al.  Animal disease surveillance and survey systems: methods and applications. , 2003 .

[4]  S. T. Buckland,et al.  An autologistic model for the spatial distribution of wildlife , 1996 .

[5]  J Andrew Royle,et al.  Generalized site occupancy models allowing for false positive and false negative errors. , 2006, Ecology.

[6]  Steven M. Teutsch,et al.  Comprar Principles and Practice of Public Health Surveillance | Lisa M. Lee | 9780195372922 | Oxford University Press , 2010 .

[7]  A. Tenenbein.  A Double Sampling Scheme for Estimating from Binomial Data with Misclassifications , 1970 .

[8]  P. Mielke,et al.  Spatial analysis of ponderosa pine trees infected with dwarf mistletoe , 1991 .

[9]  Mevin B. Hooten,et al.  Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model , 2003, Landscape Ecology.

[10]  Nicholas C. Collins,et al.  TREE REGRESSION ANALYSIS ON THE NESTING HABITAT OF SMALLMOUTH BASS , 1999 .

[11]  H. Akaike.  A new look at the statistical model identification , 1974 .

[12]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[13]  J. Andrew Royle.  N‐Mixture Models for Estimating Population Size from Spatially Replicated Counts , 2004, Biometrics.

[14]  S. Cherry,et al.  USE AND INTERPRETATION OF LOGISTIC REGRESSION IN HABITAT-SELECTION STUDIES , 2004 .

[15]  J. Nichols,et al.  ESTIMATING SITE OCCUPANCY, COLONIZATION, AND LOCAL EXTINCTION WHEN A SPECIES IS DETECTED IMPERFECTLY , 2003 .

[16]  Kiona Ogle,et al.  Hierarchical bayesian statistics: merging experimental and modeling approaches in ecology. , 2009, Ecological applications : a publication of the Ecological Society of America.

[17]  Mevin B. Hooten,et al.  Optimal spatio-temporal hybrid sampling designs for ecological monitoring , 2009 .

[18]  James S. Clark,et al.  Tree growth inference and prediction from diameter censuses and ring widths. , 2007, Ecological applications : a publication of the Ecological Society of America.

[19]  C. Gotway,et al.  Combining Incompatible Spatial Data , 2002 .

[20]  J. Andrew Royle,et al.  ESTIMATING ABUNDANCE FROM REPEATED PRESENCE–ABSENCE DATA OR POINT COUNTS , 2003 .

[21]  Jennifer A. Hoeting,et al.  An Improved Model for Spatially Correlated Binary Responses , 2000 .

[22]  Mark D. Semon,et al.  POSTUSE REVIEW: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1982 .

[23]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[24]  J. Nichols,et al.  Investigating species co-occurrence patterns when species are detected imperfectly , 2004 .

[25]  Wesley O. Johnson,et al.  Determining the infection status of a herd , 2003 .

[26]  H. Possingham,et al.  IMPROVING PRECISION AND REDUCING BIAS IN BIOLOGICAL SURVEYS: ESTIMATING FALSE‐NEGATIVE ERROR RATES , 2003 .

[27]  Carol A. Gotway,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[28]  W. G. Buist,et al.  Estimation of sensitivity and specificity of three conditionally dependent diagnostic tests in the absence of a gold standard , 2006 .

[29]  R. Swihart,et al.  Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models , 2004 .

[30]  Lisa M. Lee,et al.  Principles & Practice of Public Health Surveillance , 2010 .

[31]  B. Geils,et al.  Management strategies for dwarf mistletoe: silviculture. , 2002 .

[32]  L. Mark Berliner,et al.  Hierarchical Bayesian Time Series Models , 1996 .

[33]  S. Chib,et al.  Bayesian analysis of binary and polychotomous response data , 1993 .

[34]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[35]  Fred A. Baker,et al.  Dispersal of Arceuthobiumpusillum seeds , 1986 .

[36]  R. G. Davies,et al.  Methods to account for spatial autocorrelation in the analysis of species distributional data : a review , 2007 .

[37]  Catherine A Calder,et al.  Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. , 2009, Ecological applications : a publication of the Ecological Society of America.

[38]  F. G. Hawksworth,et al.  Damage , Effects , and Importance of Dwarf Mistletoes , 2002 .