Hierarchical dynamic modeling of outbreaks of mountain pine beetle using partial differential equations

Environmetrics

Volumn 21, Issue 7-8, 2010, Pages 801-816

  Zheng, Yanbing ^a   Aukema, Brian H ^b,c,d  

^a UNIVERSITY OF KENTUCKY   (United States)

^b LAURENTIAN FORESTRY CENTRE   (Canada)

^c UNIVERSITY OF NORTHERN BRITISH COLUMBIA   (Canada)

^d UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Dendroctonus ponderosae; Generalized linear mixed model; Mountain pine beetle; Partial differential equation; Spatial temporal process

Indexed keywords

ADVECTION-DIFFUSION EQUATION; ALGORITHM; BAYESIAN ANALYSIS; BEETLE; CONIFEROUS TREE; ENVIRONMENTAL FACTOR; HIERARCHICAL SYSTEM; LINEARITY; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; POPULATION OUTBREAK; SPATIOTEMPORAL ANALYSIS;

BRITISH COLUMBIA; CANADA;

COLEOPTERA; DENDROCTONUS PONDEROSAE; PINUS MUGO;

EID: 78650652746     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.1058     Document Type: Article

References (30)

1. Aukema BH, Carroll AL, Zheng Y, Zhu J, Raffa KF, Moore D, Stahl KK, Taylor SH. 2008. Movement of outbreak populations of mountain pine beetle: influences of spatiotemporal patterns and climate. Ecography 31: 348-358.
2. Banerjee S, Carlin BP, Gelfand AE. 2004. Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC: Boca Raton, Florida.
3. Banks HT, Kareiva PM, Zia L. 1988. Analyzing field studies of insect dispersal using two-dimensional transport equations. Environmental Entomology 17: 815-820.
4. Berliner LM. 1996. Hierarchical Bayesian time series models. In Maximum Entropy and Bayesian Methods, Hanson KM, Silver RN (eds). Kluwer: Dordrecht; 15-22.
5. Campbell EM, Alfaro RI, Hawkes B. 2007. Spatial distribution of mountain pine beetle outbreaks in relation to climate and stand characteristics: a dendroecological analysis. Journal of Integrative Plant Biology 49: 168-178.
6. Clark JS. 2007. Models for Ecological Data: An Introduction. Princeton University Press: New Jersey.
7. Cressie N, Huang HC. 1999. Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association 94: 1330-1340.
8. de la Giroday HM. 2009. Spatial associations between infestations of mountain pine beetle and landscape features in the Peace River region of British Columbia. M.Sc. Thesis, Natural Resources and Environmental Studies, University of Northern British Columbia, Prince George, British Columbia, Canada. 120 pp.
9. Diggle PJ, Tawn JA, Moyeed RA. 1998. Model-based geostatistics. Applied Statistics 47: 453-473.
10. Gneiting T. 2002. Nonseparable, stationary covariance functions for space-time data. Journal of the American Statistical Association 97: 590-600.
11. Haberman R. 1987. Elementary Applied Partial Differential Equations (2nd edn). Prentice-Hall: New Jersey.
12. Holmes EE, Lewis MA, Banks JE, Veit RR. 1994. Partial differential equations in ecology: spatial interactions and population dynamics. Ecology 75: 17-29.
13. Hooten MB, Wikle CK. 2007. Invasions, epidemics, and binary data in a cellular world. Proceedings of the American Statistical Association [CD-ROM]. American Statistical Association: Alexandria, VA; 3999-4010.
14. Hooten MB, Wikle CK. 2008. A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove. Environmental and Ecological Statistics 15: 59-70.
15. Jackson PL, Straussfogel D, Lindgren BS, Mitchell S, Murphy B. 2008. Radar observation and aerial capture of mountain pine beetle, Dendroctonus ponderosae Hopk. (Coleoptera: Scolytidae) in flight above the forest canopy. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 38: 2313-2327.
16. Kurz WA, Dymond CC, Stinson G, Rampley GJ, Neilson ET, Carroll A, Ebata T, Safranyik L. 2008. Mountain pine beetle and forest carbon feedback to climate change. Nature 452: 987-990.
17. McCullagh P, Nelder JA. 1989. Generalized Linear Models (2nd edn). Chapman & Hall: Boca Raton.
18. Okubo A, Levin SA. 2001. Diffusion and Ecological Problems: Modern Perspectives. Spring-Veleg: New York.
19. Powell JA, Jenkins JL, Logan JA, Bentz BJ. 2000. Seasonal temperature alone can synchronize life cycles. Bulletin of Mathematical Biology 62: 977-998.
20. Raffa KF, Aukema BH, Bentz BJ, Carroll AL, Hicke JA, Turner MG, Romme WH. 2008. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. BioScience 58: 501-517.
21. Royle JA, Dorazio RM. 2008. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities. Elsevier: London.
22. Safranyik L, Linton DA, Silversides R, Mcmullen LH. 1992. Dispersal of released mountain pine beetles under the canopy of a mature lodgepole pine stand. Journal of Applied Entomology-Zeitschrift Fur Angewandte Entomologie 113: 441-450.
23. Safranyik L, Shrimpton DM, Whitney HS. 1975. An interpretation of the interaction between lodgepole pine, the mountain pine beetle and its associated blue stain fungi in Western Canada. In Management of Lodgepole Pine Ecosystems Symposium Proceedings, Baumgartner DM (ed.). Washington State University Cooperative Extension Service: Pullman, Washington; 406-428.
24. Stahl K, Moore RD, McKendry IG. 2006. Climatology of winter cold spells in relation to mountain pine beetle mortality in British Columbia, Canada. Climate Research 32: 13-23.
25. Wikle CK. 2003. Hierarchical Bayesian models for predicting the spread of eological processes. Ecology 84: 1382-1394.
26. Wikle CK, Hooten MB. 2006. Hierarchical Bayesian spatio-temporal models for population spread. In Applications of Computational Statistics in the Environmental Sciences: Hierarchical Bayes and MCMC Methods, Clark JS, Gelfand AE (eds). Oxford University Press: Oxford.
27. Wikle CK, Milliff RF, Nychka D, Berliner LM. 2001. Spatial-temporal hierarchical Bayesian modeling. Journal of the American Statistical Association 96: 382-397.
28. Wygant ND. 1940. Effects of low temperature on the black hills beetle (Dendroctonus ponderosae Hopk.). In Summary of Ph.D. Thesis Submitted to New York School of Forestry, on File at USDA Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado; 57, 16 Figs.
29. Zhu J, Huang HC, Wu J. 2005. Modeling spatial-temporal binary data using Markov Random Model. Journal of Agricultural, Biological, and Environmental Statistics 10: 212-225.
30. Zhu J, Zheng Y, Carroll AL, Aukema BH. 2008. Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood. Journal of Agricultural, Biological, and Environmental Statistics 13: 84-98.