A NOVEL METHOD OF FITTING SPATIO-TEMPORAL MODELS TO DATA, WITH APPLICATIONS TO THE DYNAMICS OF MOUNTAIN PINE BEETLES
We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of lodgepole pine (Pinus contorta Douglas). Beetle attack dynamics are modeled using response functions and beetle movement using dispersal kernels. This model...
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| Published in | Natural resource modeling Vol. 21; no. 4; pp. 489 - 524 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Malden, USA
Blackwell Publishing Inc
2008
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| Subjects | |
| Online Access | Get full text |
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| Abstract | We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of lodgepole pine (Pinus contorta Douglas). Beetle attack dynamics are modeled using response functions and beetle movement using dispersal kernels. This modeling technique yields four model candidates. These models allow discrimination between four broad possibilities at the landscape scale: whether or not beetles are subject to an Allee effect at the landscape scale and whether or not host selection is random or directed. We fit the models with aerial damage survey data to the Sawtooth National Recreation Area using estimating functions, which allows for more rapid and complete parameter determination. We then introduce a novel model selection procedure based on facial recognition technology to compliment traditional nonspatial selection metrics. Together with these we are able to select a best model and draw inferences regarding the behavior of the beetle in outbreak conditions. |
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| AbstractList | We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of lodgepole pine (Pinus contorta Douglas). Beetle attack dynamics are modeled using response functions and beetle movement using dispersal kernels. This modeling technique yields four model candidates. These models allow discrimination between four broad possibilities at the landscape scale: whether or not beetles are subject to an Allee effect at the landscape scale and whether or not host selection is random or directed. We fit the models with aerial damage survey data to the Sawtooth National Recreation Area using estimating functions, which allows for more rapid and complete parameter determination. We then introduce a novel model selection procedure based on facial recognition technology to compliment traditional nonspatial selection metrics. Together with these we are able to select a best model and draw inferences regarding the behavior of the beetle in outbreak conditions. Abstract We develop a modular landscape model for the mountain pine beetle ( Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of lodgepole pine ( Pinus contorta Douglas). Beetle attack dynamics are modeled using response functions and beetle movement using dispersal kernels. This modeling technique yields four model candidates. These models allow discrimination between four broad possibilities at the landscape scale: whether or not beetles are subject to an Allee effect at the landscape scale and whether or not host selection is random or directed. We fit the models with aerial damage survey data to the Sawtooth National Recreation Area using estimating functions, which allows for more rapid and complete parameter determination. We then introduce a novel model selection procedure based on facial recognition technology to compliment traditional nonspatial selection metrics. Together with these we are able to select a best model and draw inferences regarding the behavior of the beetle in outbreak conditions. |
| Author | HEAVILIN, JUSTIN POWELL, JAMES |
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| Cites_doi | 10.1145/954339.954342 10.2307/2986328 10.1007/BF02515505 10.4039/Ent1231083-5 10.1093/ae/47.3.160 10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2 10.1007/BF02510942 10.4039/Ent1021229-10 10.1006/jtbi.1996.0064 10.2307/1940552 10.1093/forestscience/39.3.528 10.2307/2265698 10.1890/0012-9658(1998)079[1489:SAOPDI]2.0.CO;2 10.1006/bulm.2000.0192 10.2307/1942586 10.1111/j.0269-8463.2005.00935.x 10.1093/forestscience/37.5.1390 10.1016/j.tpb.2004.10.001 10.1890/02-0535 10.1109/34.598228 10.1139/b67-116 10.1016/j.foreco.2006.10.011 10.1093/ee/28.6.924 10.2307/3565345 10.1007/0-306-47596-0_2 10.1139/x88-105 10.1111/j.1439-0418.1992.tb00687.x 10.5962/bhl.title.68709 10.4039/Ent94531-5 10.1139/x05-241 10.1890/1540-9295(2003)001[0130:ATIOGW]2.0.CO;2 10.1162/jocn.1991.3.1.71 10.1016/S0304-3800(00)00240-4 10.1111/j.1439-0418.1989.tb00484.x |
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| References_xml | – reference: J. Logan and J. Powell 2001], Ghost Forests, Global Warming and the Mountain Pine Beetle, Am. Entomologist, 160-173. – reference: R. Mitchell and H. Preisler 1991], Analysis of Spatial Patterns of Lodgepole Pine Attacked by Outbreak Populations of the Mountain Pine Beetle, Forest Sci. 37, 1390-1408. – reference: S. Lele, M. Taper, and S. Gage 1998], Statistical Analysis of Population Dynamics in Space and Time Using Estimating Functions, Ecology 79(5), 1489-1502. – reference: R. Whitehead and G. Russo 2005], "Beetle-Proofed" Lodgepole Pine Stands in Interior British Columbia have Less Damage from Mountain Pine Beetle. – reference: J. Logan, J. Régnière, and J. Powell 2003], Assessing the Impact of Global Warming on Forest Pest Dynamics, Front. Ecol. Environ. 1(3), 130-137. – reference: M. Turk and A. Pentland 1991], Eigenfaces for Recognition, J. Cogn. Neurosci. 3(1), 71-86. – reference: J. Logan and B. Bentz 1999], Model Analysis of Mountain Pine Beetle (Coleoptera: Scolytidae) Seasonality, Environ. Entomol. 28(6), 924-934. – reference: H. Preisler and K. Haiganough 1993], Colonization Patterns of the Mountain Pine Beetle in Thinned and Unthinned Lodgepole Stands, Forest Sci. 39, 528-545. – reference: A.A. Berryman, B. Dennis, K.F. Raffa, and N.C. Stenseth 1985], Evolution of Optimal Group Attack, with Particular Reference to Bark Beetles (Coleoptera: Scolyidae), Ecology 66(3), 898-903. – reference: C. Elkin and M. Reid 2005], Low Energy Reserves and Energy Allocation Decisions Affect Reproduction by Mountain Pine Beetles, Dendroctonus Ponderosae, Fundamental Ecol. 19, 102-109. – reference: B. Bentz 2006], Mountain Pine Beetle Population Sampling: Inferences from Lindgren Pheromone Traps and Tree Emergence Cages, Canadian J. Forest Res. 36, 351-360. – reference: C. Fettig, K. Klepzig, R. Billings, A. Munson, T. Nebeker, J. Negron, and J. Nowak 2007], The Effectiveness of Vegetation Management Practices for Prevention and Control of Bark Beetle Infestations in Coniferous Forests of the Western and Southern Unites States, Forest Ecol. Manage. 238, 24-53. – reference: W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld 2003], Face Recognition: A Literature Survey, ACM Comput. Surv. 35(4), 399-458. – reference: A.A. Berryman, K.F. Raffa, J.A. Millstein, and N.C. Stenseth 1989], Interaction Dynamics of Bark Beetle Aggregation and Conifer Defense Rates, Oikos 56, 256-263. – reference: Z. Biesinger, J. Powell, B. Bentz, and J. Logan 2000], Direct and Indirect Parametrization of a Localized Model for the Mountain Pine Beetle - Lodgepole Pine System, Ecological Modelling 129, 273-296. – reference: J.A. Powell and J.L. Jenkins 2000], Seasonal Temperature Alone can Synchronize Life Cycles, Bull. Math. Biol. 62, 977-998. – reference: R. Reid, H. Whitney, and J. Watson 1967], Reactions of Lodgepole Pine to Attack by Dendroctonus Ponderosae Hopkins and Blue Stain Fungi, Can. J. Bot. 45, 115-116. – reference: K.F. Raffa and A.A. Berryman 1983], TThe Role of Host Plant Resistance in the Colonization Behavior and Ecology of Bark Beetles (Coleoptera: Scolytidae), Ecol. Monogr. 53(1), 27-49. – reference: L. Safranyik, R. Silversides, L. McMullen, and D. Linton 1989], An Empirical Approach to Modeling Local Dispersal of the Mountain Pine Beetle (Dendroctonus ponderosae) in Relation to Sources of Attraction, Wind Direction, and Speed, J. Appl. Entomol. 108, 498-511. – reference: R. Reid 1962], Biology of the Mountain Pine Beetle, Dendroctonus Monticolae Hopkins, in the East Kootenay Region of British Columbia: I. Life Cycle, Brood Development, and Flight Periods, Can. Entomol. 5, 531-538. – reference: R.O. Duda, P.E. Hart, and D.G. Stork 2001], Pattern Classification, Ch. 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| Snippet | We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of lodgepole pine... Abstract We develop a modular landscape model for the mountain pine beetle ( Dendroctonus ponderosae Hopkins) infestation of a stage‐structured forest of... We develop a modular landscape model for the mountain pine beetle (Dendroctonus ponderosae Hopkins) infestation of a stage-structured forest of lodgepole pine... |
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| SubjectTerms | Dendroctonus ponderosae Estimating functions Integrodifference equation model Landscape model Mountain Pine Beetle Pinus contorta Principle Component Analysis (PCA) |
| Title | A NOVEL METHOD OF FITTING SPATIO-TEMPORAL MODELS TO DATA, WITH APPLICATIONS TO THE DYNAMICS OF MOUNTAIN PINE BEETLES |
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