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Climate Analyses to Assess Risks from Invasive Forest Insects: Simple Matching to Advanced Models

  • Forest Entomology (E Brockerhoff, Section Editor)
  • Published:
Current Forestry Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

The number of invasive alien insects that adversely affect trees and forests continues to increase as do associated ecological, economic, and sociological impacts. Prevention strategies remain the most cost-effective approach to address the issue, but risk management decisions, particularly those affecting international trade, must be supported by scientifically credible pest risk assessments. Pest risk assessments typically include an evaluation of the suitability of the climate for pest establishment within an area of concern. A number of species distribution models have been developed to support those efforts, and these models vary in complexity from simple climate matching to mechanistic models. This review discusses the rationale for species distribution models and describes some common and influential approaches.

Recent Findings

Species distribution models that use distributional records and environmental covariates are routinely applied when ecological information about a species of concern is limited, an all-too common situation for pest risk assessors. However, fundamental assumptions of the models may not always hold.

Summary

A structured literature review suggests that many common species distribution models are not regularly applied to alien insects that may threaten trees and forests. For ten high-impact alien insect species that are invading North America, MaxEnt and CLIMEX were applied more often than other modeling approaches. Some impediments to model development and publication exist. More applications of species distribution models to forest insects are needed in the peer-reviewed literature to ensure the credibility of pest risk maps for regulatory decision making, to deepen understanding of the factors that dictate species’ distributions, and to better characterize uncertainties associated with these models.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Lovett GM, Weiss M, Liebhold AM, Holmes TP, Leung B, Lambert KF, et al. Nonnative forest insects and pathogens in the United States: impacts and policy options. Ecol Appl. 2016;26(5):1437–55. doi:10.1890/15-1176.

    Article  Google Scholar 

  2. Pimentel D, Lach L, Zuniga R, Morrison D. Environmental and economic costs of nonindigenous species in the United States. Bioscience. 2000;50(1):53–65. doi:10.1641/0006-3568(2000)050[0053:eaecon]2.3.co;2.

    Article  Google Scholar 

  3. Colautti RI, Bailey SA, van Overdijk CDA, Amundsen K, MacIsaac HJ. Characterised and projected costs of nonindigenous species in Canada. Biol Invasions. 2006;8(1):45–59. doi:10.1007/s10530-005-0236-y.

    Article  Google Scholar 

  4. Holmes TP, Aukema JE, Von Holle B, Liebhold A, Sills E. Economic impacts of invasive species in forests past, present, and future. Ann NY AcadSci. 2009;1162:18–38. doi:10.1111/j.1749-6632.2009.04446.x.

  5. • Aukema JE, Leung B, Kovacs K, Chivers C, Britton KO, Englin J, et al. Economic impacts of non-native forest insects in the continental United States. PLoS One. 2011;6(9):e24587. The authors estimated damages caused by different guilds of forests pests to different sectors of the US economy.

    Article  CAS  Google Scholar 

  6. Hulme PE. Trade, transport and trouble: managing invasive species pathways in an era of globalization. J Appl Ecol. 2009;46:10–8.

    Article  Google Scholar 

  7. Kahn RP. Exclusion as a plant-disease control strategy. Annu Rev Phytopathol. 1991;29:219–46. doi:10.1146/annurev.py.29.090191.001251.

    Article  Google Scholar 

  8. Leung B, Lodge DM, Finnoff D, Shogren JF, Lewis MA, Lamberti G. An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. Proc R Soc Lond B Biol Sci. 2002;269(1508):2407–13.

    Article  Google Scholar 

  9. Leung B, Springborn MR, Turner JA, Brockerhoff EG. Pathway-level risk analysis: the net present value of an invasive species policy in the US. Front Ecol Environ. 2014;12(5):273–9.

    Article  Google Scholar 

  10. Keller RP, Lodge DM, Finnoff DC. Risk assessment for invasive species produces net bioeconomic benefits. Proc Natl Acad Sci U S A. 2007;104(1):203–7. doi:10.1073/pnas.0605787104.

    Article  CAS  Google Scholar 

  11. Yoe C. Risk analysis: decision making under uncertainty. Boca Raton: CRC Press; 2012.

    Google Scholar 

  12. Parker IM, Simberloff D, Lonsdale WM, Goodell K, Wonham M, Kareiva PM, et al. Impact: toward a framework for understanding the ecological effects of invaders. Biol Invasions. 1999;1:3–19.

    Article  Google Scholar 

  13. Orr RL, Cohen SD, Griffin RL. Generic nonindigenous pest risk assessment process for estimating pest risk associated with the introduction of nonindigenous organisms. U.S. Department of Agriculture, Animal and Plant Health Insepction Service: Riverdale; 1993.

    Google Scholar 

  14. Allen JC, Foltz JL, Dixon WN, Liebhold AM, Colbert JJ, Régnière J, et al. Will the gypsy moth become a pest in Florida? Fla Entomol. 1993;76(1):102–13. doi:10.2307/3496018.

  15. North American Forest Commission. Exotic Forest Pest Information System. US Department of Agriculture, Forest Service, Ft. Collins, CO. 2004. foresthealth.fs.usda.gov/exfor/. Accessed 7 Oct 2016.

  16. Baker R, Eyre D, Brunel S, Dupin M, Reynaud P, Jarosik V. Mapping endangered areas for pest risk analysis. In: Venette RC, editor. Pest risk modelling and mapping for invasive alien species. Wallingford: CABI; 2015. pp. 18–34.

  17. FAO. Glossary of phytosanitary terms. International Standards for Phytosanitary Measures No. 5. Food and Agriculture Organization of the United Nations, Rome. 2016. https://www.ippc.int/en/publications/glossary-phytosanitary-terms/. Accessed 6 Oct 2016.

  18. Barzman M, Lamichhane JR, Booij K, Boonekamp P, Desneux N, Huber L, et al. Research and development priorities in the face of climate change and rapidly evolving pests. Sustainable Agriculture Reviews. 2015;17:1–27. doi:10.1007/978-3-319-16742-8_1.

    Article  Google Scholar 

  19. Venette RC, Kriticos DJ, Magarey R, Koch F, Baker RHA, Worner S, et al. Pest risk maps for invasive alien species: a roadmap for improvement. Bioscience. 2010;60:349–62.

    Article  Google Scholar 

  20. Pulliam HR. On the relationship between niche and distribution. Ecol Lett. 2000;3(4):349–61. doi:10.1046/j.1461-0248.2000.00143.x.

    Article  Google Scholar 

  21. Soberón J, Peterson AT. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inform. 2005;2:1–10.

    Article  Google Scholar 

  22. Peterson AT, Soberon J, Pearson RG, Anderson RP, Martinez-Meyer E, Nakamura M et al. Ecological niches and geographic distributions. Monographs in Population Biology 49. Princeton University Press; 2011.

  23. Baker RHA, Sansford CE, Jarvis CH, Cannon RJC, MacLeod A, Walters KFA. The role of climatic mapping in predicting the potential geographical distribution of non-indigenous pests under current and future climates. Agric Ecosyst Environ. 2000;82(1–3):57–71. doi:10.1016/s0167-8809(00)00216-4.

    Article  Google Scholar 

  24. Bradie J, Pietrobon A, Leung B. Beyond species-specific assessments: an analysis and validation of environmental distance metrics for non-indigenous species risk assessment. Biol Invasions. 2015;17(12):3455–65. doi:10.1007/s10530-015-0970-8.

    Article  Google Scholar 

  25. • Elith J, Kearney M, Phillips S. The art of modelling range-shifting species. Methods Ecol Evol. 2010;1(4):330–42. doi:10.1111/j.2041-210X.2010.00036.x. This paper highlighted the challenges of extrapolating inductive species distribution models from non-equilibrium conditions or into novel climatic spaces and offered a practical approach to address these issues.

    Article  Google Scholar 

  26. Guillera-Arroita G, Lahoz-Monfort JJ, Elith J, Gordon A, Kujala H, Lentini PE, et al. Is my species distribution model fit for purpose? Matching data and models to applications. Glob Ecol Biogeogr. 2015;24(3):276–92. doi:10.1111/geb.12268.

    Article  Google Scholar 

  27. Webber BL, Yates CJ, Le Maitre DC, Scott JK, Kriticos DJ, Ota N, et al. Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models. Divers Distrib. 2011;17(5):978–1000. doi:10.1111/j.1472-4642.2011.00811.x.

    Article  Google Scholar 

  28. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F. World map of the Köppen-Geiger climate classification updated. Meteorol Z. 2006;15(3):259–63. doi:10.1127/0941-2948/2006/0130.

    Article  Google Scholar 

  29. Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, et al. Terrestrial ecoregions of the worlds: a new map of life on Earth. Bioscience. 2001;51(11):933–8. doi:10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2.

    Article  Google Scholar 

  30. Gallagher RV, Beaumont LJ, Hughes L, Leishman MR. Evidence for climatic niche and biome shifts between native and novel ranges in plant species introduced to Australia. J Ecol. 2010;98(4):790–9. doi:10.1111/j.1365-2745.2010.01677.x.

    Article  Google Scholar 

  31. Brunel S, Branquart E, Fried G, van Valkenburg J, Brundu G, Starfinger U, et al. The EPPO prioritization process for invasive alien plants. Bulletin OEPP. 2010;40(3):407–22.

    Article  Google Scholar 

  32. Venette RC. The challenge of modelling and mapping the future distribution and impact of invasive alien species. In: Venette RC, editor. Pest risk modelling and mapping for invasive alien species. Wallingford: CAB International; 2015. p. 1–17.

    Google Scholar 

  33. Daly C, Widrlechner MP, Halbleib MD, Smith JI, Gibson WP. Development of a new USDA plant hardiness zone map for the United States. J Appl Meteorol Climatol. 2012;51(2):242–64. doi:10.1175/2010jamc2536.1.

    Article  Google Scholar 

  34. Elkinton JS, Boettner GH, Sermac M, Gwiazdowski R, Hunkins RR, Callahan J, et al. Survey for winter moth (Lepidoptera: Geometridae) in northeastern North America with pheromone-baited traps and hybridization with the native bruce spanworm (Lepidoptera: Geometridae). Ann Entomol Soc Am. 2010;103(2):135–45. doi:10.1603/an09118.

    Article  CAS  Google Scholar 

  35. Venette RC, Ragsdale DW. Assessing the invasion by soybean aphid (Homoptera : Aphididae): where will it end? Ann Entomol Soc Am. 2004;97(2):219–26. doi:10.1603/0013-8746(2004)097[0219:atibsa]2.0.co;2.

    Article  Google Scholar 

  36. Davis EE, French S, Venette RC. Mini risk assessment, metalic beetle: Agrilus biguttatus Fabricius. U.S. Department of Agriculture, Animal and Plant Health Inspection Service: St. Paul; 2005.

    Google Scholar 

  37. Davis EE, French S, Venette RC. Mini risk assessment, ambrosia beetle: Platypus quercivorus Murayama. U.S. Department of Agriculture, Animal and Plant Health Inspection Service: St. Paul; 2005.

    Google Scholar 

  38. •• Sutherst RW, Maywald GF. A computerized system for matching climates in ecology. Agric Ecosyst Environ. 1985;13(3–4):281–99. doi:10.1016/0167-8809(85)90016-7. This paper introduced the rationale and equations for CLIMEX, a commonly used process-oriented species distribution model.

    Article  Google Scholar 

  39. Sutherst RW, Maywald GF, Kriticos DJ. CLIMEX version 3: user’s guide. South Yarra: Hearne Scientific Software; 2007.

    Google Scholar 

  40. Sutherst RW. Prediction of species geographical ranges. J Biogeogr. 2003;30(6):805–16. doi:10.1046/j.1365-2699.2003.00861.x.

    Article  Google Scholar 

  41. Kriticos DJ. Regional climate-matching to estimate current and future sources of biosecurity threats. Biol Invasions. 2012;14(8):1533–44. doi:10.1007/s10530-011-0033-8.

    Article  Google Scholar 

  42. Fowler G, Takeuchi Y. Mapping, climate and geographic information for risk analysis. In: Plant pest risk analysis: concepts and application. 2012, p. 151–63.

  43. Gould J. Proposed release of three parasitoids for the biological control of the emerald ash borer (Agrilus planipennis) in the continental United States: Environmental assessment. U.S. Department of Agriculture, Marketing and Regulatory Programs, Animal and Plant Health Inspection Service, Riverdale, MD. 2007. http://www.nrs.fs.fed.us/disturbance/invasive_species/eab/local-resources/downloads/07-060-1%20EA.pdf.

  44. Peacock L, Worner S. Using analogous climates and global insect distribution data to identify potential sources of new invasive insect pests in New Zealand. New Zealand Journal of Zoology. 2006;33:141–5.

    Article  Google Scholar 

  45. Elith J. Predicting distributions of invasive species. School of Biosciences, University of Melbourne, Australia. 2016. https://arxiv.org/ftp/arxiv/papers/1312/1312.0851.pdf. Accessed 6 Oct 2016.

  46. Booth TH, Nix HA, Busby JR, Hutchinson MF. BIOCLIM: the first species distribution modelling package, its early applications and relevance to most current MAXENT studies. Divers Distrib. 2014;20(1):1–9. doi:10.1111/ddi.12144.

    Article  Google Scholar 

  47. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25:1965–78.

    Article  Google Scholar 

  48. Mendoza MG, Salinas-Moreno Y, Olivo-Martinez A, Zuniga G. Factors influencing the geographical distribution of Dendroctonus rhizophagus (Coleoptera: Curculionidae: Scolytinae) in the Sierra Madre Occidental, Mexico. Environ Entomol. 2011;40(3):549–59. doi:10.1603/en10059.

    Article  Google Scholar 

  49. de Queiroz DL, Majer J, Burckhardt D, Zanetti R, Fernandez JIR, de Queiroz EC, et al. Predicting the geographical distribution of Glycaspis brimblecombei (Hemiptera: Psylloidea) in Brazil. Aust J Entomol. 2013;52(1):20–30. doi:10.1111/aen.12001.

    Article  Google Scholar 

  50. •• Farber O, Kadmon R. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol Model. 2003;160(1–2):115–30. doi:10.1016/s0304-3800(02)00327-7. The authors presented justification for the use of Mahalanobis distances for inductive species distribution models and tested the concept with 192 woody plant species in Israel.

    Article  CAS  Google Scholar 

  51. Kadmon R, Farber O, Danin A. A systematic analysis of factors affecting the performance of climatic envelope models. Ecol Appl. 2003;13(3):853–67. doi:10.1890/1051-0761(2003)013[0853:asaofa]2.0.co;2.

    Article  Google Scholar 

  52. Simpson KLS, Jackson GE, Grace J. The response of aphids to plant water stress–the case of Myzus persicae and Brassica oleracea var. capitata. Entomol Exp Appl. 2012;142(3):191–202. doi:10.1111/j.1570-7458.2011.01216.x.

  53. Carpenter G, Gillison AN, Winter J. DOMAIN–a flexible modeling procedure for mapping potential distributions of plants and animals. Biodivers Conserv. 1993;2(6):667–80. doi:10.1007/bf00051966.

    Article  Google Scholar 

  54. Shatz AJ, Rogan J, Sangermano F, Ogneva-Himmelberger Y, Chen H. Characterizing the potential distribution of the invasive Asian longhorned beetle (Anoplophora glabripennis) in Worcester County, Massachusetts. Appl Geogr. 2013;45:259–68. doi:10.1016/j.apgeog.2013.10.002.

    Article  Google Scholar 

  55. •• Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model. 2006;190(3–4):231–59. doi:10.1016/j.ecolmodel.2005.03.026. The authors introduced a machine-learning approach, MaxEnt, for inductive species distribution modeling and suggested the utility of the method when only presence records are available.

    Article  Google Scholar 

  56. Phillips SJ, Dudik M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography. 2008;31(2):161–75. doi:10.1111/j.0906-7590.2008.5203.x.

    Article  Google Scholar 

  57. Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Divers Distrib. 2011;17(1):43–57. doi:10.1111/j.1472-4642.2010.00725.x.

    Article  Google Scholar 

  58. Merow C, Smith MJ, Silander JA. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography. 2013;36(10):1058–69. doi:10.1111/j.1600-0587.2013.07872.x.

    Article  Google Scholar 

  59. Sobek-Swant S, Kluza DA, Cuddington K, Lyons DB. Potential distribution of emerald ash borer: what can we learn from ecological niche models using Maxent and GARP? For Ecol Manag. 2012;281:23–31. doi:10.1016/j.foreco.2012.06.017.

    Article  Google Scholar 

  60. Venette RC, Coleman TW, Seybold SJ. Assessing the risks posed by goldspotted oak borer to California and beyond. In: Standiford RB, Kliejunas J, editors. 7th California Oak Symposium: Managing Oak Woodlands in a Dynamic World. Visalia: USDA Forest Service, Pacific Southwest Research Station; 2015. p. 317–29.

    Google Scholar 

  61. López-Martínez V, Sánchez-Martínez G, Jiménez-García D, Pérez-De la O NB, Coleman TW. Environmental suitability for Agrilus auroguttatus (Coleoptera: Bupresitdae) in Mexico using MaxEnt and database records of four Quercus (Fagaceae) species. Agric For Entomol. 2016; doi:10.1111/afe.12174.

  62. Jarnevich CS, Young N. Using the MAXENT program for species distribution modelling to assess invasion risk. In: Venette RC, editor. Pest risk modelling and mapping for invasive alien species. Wallingford: CABI; 2015. pp. 65–81.

  63. Barve N, Barve V, Jimenez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, et al. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model. 2011;222(11):1810–9. doi:10.1016/j.ecolmodel.2011.02.011.

    Article  Google Scholar 

  64. VanDerWal J, Shoo LP, Graham C, William SE. Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Model. 2009;220(4):589–94. doi:10.1016/j.ecolmodel.2008.11.010.

    Article  Google Scholar 

  65. Phillips SJ. Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography. 2008;31(2):272–8. doi:10.1111/j.0906-7590.2008.5378.x.

    Article  Google Scholar 

  66. •• Stolar J, Nielsen SE. Accounting for spatially biased sampling effort in presence-only species distribution modelling. Divers Distrib. 2015;21(5):595–608. doi:10.1111/ddi.12279. These authors developed logistic regression models to estimate the probability that a site would be sampled and used these models to adjust the selection of background points in MaxEnt models.

    Article  Google Scholar 

  67. • Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carre G, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36(1):27–46. doi:10.1111/j.1600-0587.2012.07348.x. The authors demonstrated the value of efforts to avoid multicollinearity among potential environmental covariates in inductive species distribution models.

    Article  Google Scholar 

  68. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. 2006;29(2):129–51. doi:10.1111/j.2006.0906-7590.04596.x.

    Article  Google Scholar 

  69. Booth GD, Niccolucci MJ, Schuster EG. Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. United States Department of Agriculture, Forest Service, Intermountain Research Station: Ogden; 1994.

    Google Scholar 

  70. • Dennis RLH, Thomas CD. Bias in butterfly distribution maps: the influence of hot spots and recorder’s home range. J Insect Conserv. 2000;4(2):73–7. doi:10.1023/a:1009690919835. The authors identified sources of spatial bias among presence records for a set of British butterfly species.

    Article  Google Scholar 

  71. Troia MJ, McManamay RA. Filling in the GAPS: evaluating completeness and coverage of open-access biodiversity databases in the United States. Ecol Evol. 2016;6(14):4654–69. doi:10.1002/ece3.2225.

    Article  Google Scholar 

  72. Fourcade Y, Engler JO, Rodder D, Secondi J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS One. 2014;9(5) doi:10.1371/journal.pone.0097122.

  73. Lahoz-Monfort JJ, Guillera-Arroita G, Wintle BA. Imperfect detection impacts the performance of species distribution models. Glob Ecol Biogeogr. 2014;23(4):504–15. doi:10.1111/geb.12138.

    Article  Google Scholar 

  74. Mercader RJ, Siegert NW, McCullough DG. Estimating the influence of population density and dispersal behavior on the ability to detect and monitor Agrilus planipennis (Coleoptera: Buprestidae) populations. J Econ Entomol. 2012;105(1):272–81. doi:10.1603/ec11172.

    Article  CAS  Google Scholar 

  75. Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick J, et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl. 2009;19(1):181–97. doi:10.1890/07-2153.1.

    Article  Google Scholar 

  76. Mesgaran MB, Cousens RD, Webber BL. Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Divers Distrib. 2014;20(10):1147–59. doi:10.1111/ddi.12209.

    Article  Google Scholar 

  77. Lee CE. Evolutionary genetics of invasive species. Trends Ecol Evol. 2002;17(8):386–91. doi:10.1016/s0169-5347(02)02554-5.

    Article  Google Scholar 

  78. Morey AC, Venette RC, Hutchison WD. Could natural selection change the geographic range limits of light brown apple moth (Lepidoptera, Tortricidae) in North America? NeoBiota. 2013;18:151–6.

    Article  Google Scholar 

  79. Kriticos DJ, Ota N, Hutchison WD, Beddow J, Walsh T, Tay WT, et al. The potential distribution of invading Helicoverpa armigera in North America: is it just a matter of time? PLoS One. 2015;10(3):24. doi:10.1371/journal.pone.0119618.

    Article  CAS  Google Scholar 

  80. Stephens AEA, Kriticos DJ, Leriche A. The current and future potential geographical distribution of the oriental fruit fly, Bactrocera dorsalis (Diptera : Tephritidae). Bull Entomol Res. 2007;97(4):369–78. doi:10.1017/s0007485307005044.

    Article  CAS  Google Scholar 

  81. Saavedra MC, Avila GA, Withers TM, Holwell GI. The potential global distribution of the bronze bug Thaumastocoris peregrinus Carpintero and Dellape (Hemiptera: Thaumastocoridae). Agric For Entomol. 2015;17(4):375–88. doi:10.1111/afe.12117.

    Article  Google Scholar 

  82. Olfert O, Weiss RM, Kriticos D. Application of general circulation models to assess the potential impact of climate change on potential distribution and relative abundance of Melanoplus sanguinipes (Fabricius) (Orthoptera: Acrididae) in North America. Psyche (Cambridge). 2011;2011:1–9.

    Article  Google Scholar 

  83. van Klinken RD, Lawson BE, Zalucki MP. Predicting invasions in Australia by a neotropical shrub under climate change: the challenge of novel climates and parameter estimation. Glob Ecol Biogeogr. 2009;18(6):688–700. doi:10.1111/j.1466-8238.2009.00483.x.

    Article  Google Scholar 

  84. Matsuki M, Kay M, Serin J, Floyd R, Scott JK. Potential risk of accidental introduction of Asian gypsy moth (Lymantria dispar) to Australasia: effects of climatic conditions and suitability of native plants. Agric For Entomol. 2001;3(4):305–20. doi:10.1046/j.1461-9555.2001.00119.x.

    Article  Google Scholar 

  85. MacLeod A, Evans HF, Baker RHA. An analysis of pest risk from an Asian longhorn beetle (Anoplophora glabripennis) to hardwood trees in the European community. Crop Prot. 2002;21(8):635–45. doi:10.1016/s0261-2194(02)00016-9.

    Article  Google Scholar 

  86. Johnson PC, Mason DP, Radke SL, Tracewski KT. Gypsy moth, Lymantria dispar (L) (Lepidoptera, Lymantriidae), egg eclosion: degree-day accumulation. Environ Entomol. 1983;12(3):929–32.

    Article  Google Scholar 

  87. Gray DR, Logan JA, Ravlin FW, Carlson JA. Toward a model of gypsy moth egg phenology: using respiration rates of individual eggs to determine temperature time requirements of prediapause development. Environ Entomol. 1991;20(6):1645–52.

    Article  Google Scholar 

  88. Gray DR, Ravlin FW, Braine JA. Diapause in the gypsy moth: a model of inhibition and development. J Insect Physiol. 2001;47(2):173–84. doi:10.1016/s0022-1910(00)00103-7.

    Article  CAS  Google Scholar 

  89. Gray DR, Ravlin FW, Régnière J, Logan JA. Further advances toward a model of gypsy moth (Lymantria dispar (L)) egg phenology: respiration rates and thermal responsiveness during diapause, and age-dependent developmental rates in postdiapause. J Insect Physiol. 1995;41(3):247–56. doi:10.1016/0022-1910(94)00102-m.

  90. Sawyer AJ, Tauber MJ, Tauber CA, Ruberson JR. Gypsy moth (Lepidoptera, Lymantriidae) egg development: a simulation analysis of laboratory and field data. Ecol Model. 1993;66(1–2):121–55. doi:10.1016/0304-3800(93)90043-r.

    Article  Google Scholar 

  91. Logan JA, Casagrande RA, Liebhold AM. Modeling environment for simulation of gypsy moth (Lepidoptera, Lymantriidae) larval phenology. Environ Entomol. 1991;20(6):1516–25.

    Article  Google Scholar 

  92. Casagrande RA, Logan PA, Wallner WE. Phenological model for gypsy moth, Lymantria dispar (Lepidoptera, Lymantriidae), larvae and pupae. Environ Entomol. 1987;16(2):556–62.

    Article  Google Scholar 

  93. Régnière J, Sharov A. Simulating temperature-dependent ecological processes at the sub-continental scale: male gypsy moth flight phenology as an example. Int J Biometeorol. 1999;42(3):146–52. doi:10.1007/s004840050098.

    Article  Google Scholar 

  94. Régnière J, Nealis V. Modelling seasonality of gypsy moth, Lymantria dispar (Lepidoptera : Lymantriidae), to evaluate probability of its persistence in novel environments. Can Entomol. 2002;134(6):805–24.

    Article  Google Scholar 

  95. • Gray DR. The gypsy moth life stage model: landscape-wide estimates of gypsy moth establishment using a multi-generational phenology model. Ecol Model. 2004;176(1–2):155–71. doi:10.1016/j.ecolmodel.2003.11.010. The author generated a mechanistic model for Lymantria dispar development and applied it to identify new areas in North America that might be climatically suitable for this pest to establish.

    Article  Google Scholar 

  96. Régnière J, Bentz B. Modeling cold tolerance in the mountain pine beetle, Dendroctonus ponderosae. J Insect Physiol. 2007;53(6):559–72. doi:10.1016/j.jinphys.2007.02.007.

    Article  CAS  Google Scholar 

  97. Bentz BJ, Mullins DE. Ecology of mountain pine beetle (Coleoptera : Scolytidae) cold hardening in the intermountain west. Environ Entomol. 1999;28(4):577–87.

    Article  Google Scholar 

  98. Logan JA, Régnière J, Powell JA. Assessing the impacts of global warming on forest pest dynamics. Front Ecol Environ. 2003;1(3):130–7. doi:10.2307/3867985.

    Article  Google Scholar 

  99. Magarey RD, Fowler GA, Borchert DM, Sutton TB, Colunga-Garcia M, Simpson JA. NAPPFAST: an internet system for the weather-based mapping of plant pathogens. Plant Dis. 2007;91:336–45.

  100. Magarey RD, Borchert DM, Fowler GA, Hong SC. The NCSU/APHIS Plant Pest Forecasting System (NAPPFAST). In: Venette RC, editor. Pest risk modelling and mapping for invasive alien species. Wallingford: CABI; 2015. pp. 82–96.

  101. Gray DR. Hitchhikers on trade routes: a phenology model estimates the probabilities of gypsy moth introduction and establishment. Ecol Appl. 2010;20(8):2300–9. doi:10.1890/09-1540.1.

    Article  Google Scholar 

  102. Gevrey M, Worner SP. Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method. J Econ Entomol. 2006;99(3):979–86.

    Article  Google Scholar 

  103. Lippitt CD, Rogan J, Toledano J, Saylgermano F, Eastmana JR, Mastro V, et al. Incorporating anthropogenic variables into a species distribution model to map gypsy moth risk. Ecol Model. 2008;210(3):339–50. doi:10.1016/j.ecolmodel.2007.08.005.

    Article  Google Scholar 

  104. Gottschalk KW, Colbert JJ, Feicht DL. Tree mortality risk of oak due to gypsy moth. Eur J Forest Pathol. 1998;28(2):121–32.

    Article  Google Scholar 

  105. Vanhanen H, Veleli TO, Paivinen S, Kellomaki S, Niemela P. Climate change and range shifts in two insect defoliators: gypsy moth and nun moth—a model study. Silva Fenn. 2007;41(4):621–38. doi:10.14214/sf.469.

    Article  Google Scholar 

  106. Cocco A, Cossu AQ, Erre P, Nieddu G, Luciano P. Spatial analysis of gypsy moth populations in Sardinia using geostatistical and climate models. Agric For Entomol. 2010;12(4):417–26. doi:10.1111/j.1461-9563.2010.00488.x.

    Article  Google Scholar 

  107. Peterson AT, Williams R, Chen G. Modeled global invasive potential of Asian gypsy moths, Lymantria dispar. Entomol Exp Appl. 2007;125(1):39–44. doi:10.1111/j.1570-7458.2007.00603.x.

    Article  Google Scholar 

  108. Anderson T, Dragicevic S. An agent-based modeling approach to represent infestation dynamics of the emerald ash borer beetle. Ecological Informatics. 2015;30:97–109. doi:10.1016/j.ecoinf.2015.09.003.

    Article  Google Scholar 

  109. Anderson T, Dragicevic S. A geosimulation approach for data scarce environments: modeling dynamics of forest insect infestation across different landscapes. Isprs International Journal of Geo-Information. 2016;5(2) doi:10.3390/ijgi5020009.

  110. BenDor TK, Metcalf SS. The spatial dynamics of invasive species spread. Syst Dyn Rev. 2006;22(1):27–50. doi:10.1002/sdr.328.

    Article  Google Scholar 

  111. Liang L, Fei SL. Divergence of the potential invasion range of emerald ash borer and its host distribution in North America under climate change. Clim Chang. 2014;122(4):735–46. doi:10.1007/s10584-013-1024-9.

    Article  Google Scholar 

  112. Shatz AJ, Rogan J, Sangermano F, Miller J, Elmes A. Modeling the risk of spread and establishment for Asian longhorned beetle (Anoplophora glabripennis) in Massachusetts from 2008-2009. Geocarto Int. 2016;31(8):813–31. doi:10.1080/10106049.2015.1086901.

    Article  Google Scholar 

  113. Park YS, Rabinovich J, Lek S. Sensitivity analysis and stability patterns of two-species pest models using artificial neural networks. Ecol Model. 2007;204(3–4):427–38. doi:10.1016/j.ecolmodel.2007.01.021.

    Article  Google Scholar 

  114. Carnegie AJ, Matsuki M, Haugen DA, Hurley BP, Ahumada R, Klasmer P, et al. Predicting the potential distribution of Sirex noctilio (Hymenoptera : Siricidae), a significant exotic pest of Pinus plantations. Ann For Sci. 2006;63(2):119–28. doi:10.1051/forest:2005104.

    Article  Google Scholar 

  115. Yemshanov D, Koch FH, McKenney DW, Downing MC, Sapio F. Mapping invasive species risks with stochastic models: a cross-border United States-Canada application for Sirex noctilio Fabricius. Risk Anal. 2009;29:868–84.

    Article  Google Scholar 

  116. Jones C, Song CH, Moody A. Where’s woolly? An integrative use of remote sensing to improve predictions of the spatial distribution of an invasive forest pest the hemlock woolly adelgid. For Ecol Manag. 2015;358:222–9. doi:10.1016/j.foreco.2015.09.013.

    Article  Google Scholar 

  117. Liang L, Clark JT, Kong N, Rieske LK, Fei SL. Spatial analysis facilitates invasive species risk assessment. For Ecol Manag. 2014;315:22–9. doi:10.1016/j.foreco.2013.12.019.

    Article  Google Scholar 

  118. Koch FH, Cheshire HM, Devine HA. Landscape-scale prediction of hemlock woolly adelgid, Adelges tsugae (Homoptera : Adelgidae), infestation in the southern Appalachian Mountains. Environ Entomol. 2006;35(5):1313–23. doi:10.1603/0046-225x(2006)35[1313:lpohwa]2.0.co;2.

    Article  Google Scholar 

  119. Venette RC. Pest risk modelling and mapping for invasive alien species. Wallingford: CAB International; 2015.

    Book  Google Scholar 

  120. Magarey RD, Colunga-Garcia M, Fieselmann DA. Plant biosecurity in the United States: roles, responsibilities, and information needs. Bioscience. 2009;59(10):875–84. doi:10.1525/bio.2009.59.10.9.

    Article  Google Scholar 

  121. Regan HM, Colyvan M, Burgman MA. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol Appl. 2002;12(2):618–28. doi:10.2307/3060967.

    Article  Google Scholar 

  122. •• Randin CF, Dirnbock T, Dullinger S, Zimmermann NE, Zappa M, Guisan A. Are niche-based species distribution models transferable in space? J Biogeogr. 2006;33(10):1689–703. doi:10.1111/j.1365-2699.2006.01466.x. The authors studied the transferability of general additive models and generalized linear models for the distribution of 54 plant species in Switzerland and Australia and found that more than half of the models developed for one country performed no better than random when applied to the other country.

    Article  Google Scholar 

  123. Koch FH, Yemshanov D, McKenney DW, Smith WD. Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk. Risk Anal. 2009;29(9):1227–41.

    Article  Google Scholar 

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  1. Northern Research Station, USDA Forest Service, 1561 Lindig Street, St. Paul, MN, 55108-6125, USA

    Robert C. Venette

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Venette, R.C. Climate Analyses to Assess Risks from Invasive Forest Insects: Simple Matching to Advanced Models. Curr Forestry Rep 3, 255–268 (2017). https://doi.org/10.1007/s40725-017-0061-4

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