Andersen, M. C., Adams, H., Hope, B. & Powell, M. (2004a). Risk assessment for invasive species. Risk Analysis, 24(4), 787–793.CrossRefGoogle ScholarPubMed

Andersen, M. C., Adams, H., Hope, B. & Powell, M. (2004b). Risk analysis for invasive species: General framework and research needs. Risk Analysis, 24(4), 893–900.Google Scholar

Arrow, K. J. (1971). Essays in the theory of risk bearing. Chicago: Markham Publishing.Google Scholar

Baker, R., Cannon, R., Bartlett, P. & Barker, I. (2005). Novel strategies for assessing and managing the risks posed by invasive alien species to global crop production and biodiversity. Annals of Applied Biology, 146(2), 177–191.Google Scholar

BenDor, T. K., Metcalf, S. S., Fontenot, L. E., Sangunett, B. & Hannon, B. (2006). Modelling the spread of the emerald ash borer. Ecological Modelling, 197(1–2), 221–236.Google Scholar

Black, W. R. (1972). Interregional commodity flows: Some experiments with the gravity model. Journal of Regional Science, 12(1), 107–118.Google Scholar

Borchert, D., Fowler, G. & Jackson, L. (2007). Organism pest risk analysis: Risks to the conterminous United States associated with the woodwasp, Sirex noctilio Fabricius, and the symbiotic fungus, Amylostereum areolatum (Fries: Fries) Boidin. Raleigh, NC: USDA-APHIS-PPQ-CPHST-PERAL.Google Scholar

Bossenbroek, J. M., Kraft, C. E. & Nekola, J. C. (2001). Prediction of long-distance dispersal using gravity models: Zebra mussel invasion of inland lakes. Ecological Applications, 11(6), 1778–1788.CrossRefGoogle Scholar

Brockerhoff, E. G., Bain, J., Kimberley, M. & Knížek, M. (2006). Interception frequency of exotic bark and ambrosia beetles (Coleoptera: Scolytinae) and relationship with establishment in New Zealand and worldwide. Canadian Journal of Forest Research, 36, 289–298.Google Scholar

Carnegie, A. J., Matsuki, M., Haugen, D. A., et al. (2006). Predicting the potential distribution of Sirex noctilio (Hymenoptera: Siricidae), a significant exotic pest of Pinus plantations. Annals of Forest Science, 63(2), 119–128.Google Scholar

Caulfield, J. P. (1988). A stochastic efficiency approach for determining the economic rotation of a forest stand. Forest Science, 34(2), 441–457.Google Scholar

Chapman, D. S., Dytham, C. & Oxford, G. S. (2007). Modelling population redistribution in a leaf beetle: An evaluation of alternative dispersal functions. Journal of Animal Ecology, 76(1), 36–44.CrossRefGoogle Scholar

Clark, J. S., Fastie, C., Hurtt, G., et al. (1998). Reid’s paradox of rapid plant migration: Dispersal theory and interpretation of paleoecological records. BioScience, 48(1), 13–24.Google Scholar

Colautti, R. I., Bailey, S. A., van Overdijk, C. D. A., Amundsen, K. & MacIsaac, H. J. (2006). Characterised and projected costs of nonindigenous species in Canada. Biological Invasions, 8(1), 45–59.Google Scholar

Condeso, T. E. & Meentemeyer, R.K. (2007). Effects of landscape heterogeneity on the emerging forest disease sudden oak death. Journal of Ecology, 95(2), 364–375.Google Scholar

Corley, J. C., Villacide, J. M. & Bruzzone, O. A. (2007). Spatial dynamics of a Sirex noctilio woodwasp population within a pine plantation in Patagonia, Argentina. Entomologia Experimentalis et Applicata, 125(3), 231–236.Google Scholar

Costello, C. & McAusland, C. (2003). Protectionism, trade, and measures of damage from exotic species introductions. American Journal of Agricultural Economics, 85(4), 964–975.Google Scholar

Costello, C., Springborn, M., McAusland, C. & Solow, A. (2007). Unintended biological invasions: Does risk vary by trading partner? Journal of Environmental Economics and Management, 54(3), 262–276.Google Scholar

Dawson, M. (2002). Plant quarantine: A tool for preventing the introduction and spread of alien species harmful to plants. In Claudi, R., Nantel, P. & Muckle-Jeffs (eds.), E. Alien invaders in Canada’s waters, wetlands and Forests (pp. 243–51). . Ottawa, Canada: Canadian Forest Service.Google Scholar

de Groot, P., Nystrom, K. & Scarr, T. (2006). Discovery of Sirex noctilio (Hymenoptera: Siricidae) in Ontario, Canada. Great Lakes Entomologist, 39(1–2), 49–53.Google Scholar

de Jong, G., Gunn, H. F. & Walker, W. (2004). National and international freight transportation models: An overview and ideas for further developments. Transport Reviews, 24(1), 103–124.Google Scholar

de Vos, C. J., Saatkamp, H. W., Nielen, M. & Huirne, R. B. (2004). Scenario tree modeling to analyze the probability of classical swine fever virus introduction into Member States of the European Union. Risk Analysis, 24(1), 237–253.Google Scholar

Demeritt, D., Cloke, H., Pappenberger, F., et al. (2007). Ensemble predictions and perceptions of risks, uncertainty, and error in flood forecasting. Environmental Hazards, 7(2), 115–127.Google Scholar

Dixon, G. E. (2002). Essential FVS: A user’s guide to the forest vegetation simulator. Internal Report. Fort Collins, CO: USDA Forest Service, Forest Management Service Center. Available from www.fs.fed.us/fmsc/ftp/fvs/docs/gtr/EssentialFVS.pdfGoogle Scholar

Durrett, R. & Levin, S. (1994). The importance of being discrete (and spatial). Theoretical Population Biology, 46(3), 363–394.Google Scholar

Elith, J., Burgman, M. A. & Regan, H. M. (2002). Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecological Modelling, 157(2), 313–329.Google Scholar

Elith, J. & Graham, C. G. (2009). Do they? How do they? WHY do they differ? On finding reasons for differing performance of species distributions models. Ecography, 32(1), 66–77.CrossRefGoogle Scholar

Elith, J. & Leathwick, J. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics, 40(1), 677–697.Google Scholar

Elith, J., Graham, C. H., Anderson, R. P., et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129–151.CrossRefGoogle Scholar

Elith, J., Kearney, M. & Phillips, S. (2010). The art of modelling range-shifting species. Methods in Ecology and Evolution, 1(4), 330–342.Google Scholar

FAO-IPPC. (2006). Guidelines for regulating wood packaging material in international trade. ISPM No.15. In International standards for phytosanitary measures (pp. 174–183). Rome, Italy: Food and Agriculture Organisation of the United Nations–Intergovernmental Panel on Climate Change.Google Scholar

FHTET (US Department of Agriculture, Forest Health Technology Enterprise Team). (2007a). Commodities likely associated with Sirex noctilio. Available from www.fs.fed.us/foresthealth/technology/pdfs/assoc_commodities_sirex.pdfGoogle Scholar

FHTET (US Department of Agriculture, Forest Health Technology Enterprise Team). (2007b). Invasive pest risk maps: Sirex woodwasp – Sirex noctilio. Available from www.fs.fed.us/foresthealth/technology/invasives_sirexnoctilio_riskmaps.shtmlGoogle Scholar

FHTET (US Department of Agriculture, Forest Health Technology Enterprise Team). (2010). Invasive pest risk maps: Oak splendor beetle – Agrilus biguttatus (Fabricus). Available from www.fs.fed.us/foresthealth/technology/invasives_agrilusbiguttatus_riskmaps.shtmlGoogle Scholar

Fuentes, M. A. & Kuperman, M. N. (1999). Cellular automata and epidemiological models with spatial dependence. Physica A, 267(3–4), 471–486.Google Scholar

Gerber, H. U. & Pafumi, G. (1998). Utility functions: From risk theory to finance. North American Actuarial Journal, 2(3), 74–100.Google Scholar

Gibson, G. J. & Austin, E. J. (1996). Fitting and testing spatio-temporal stochastic models with application in plant epidemiology. Plant Pathology, 45(2), 172–184.Google Scholar

Gillis, M. D. (2001). Canada’s national forest inventory, responding to current information needs. Environmental Monitoring and Assessment, 67(1–2), 121–129.Google Scholar

Götze, U., Northcott, D. & Schuster, P. (2008). Investment appraisal: Methods and models. Berlin, Germany: Springer-Verlag.Google Scholar

Haack, R. A. (2006). Exotic bark- and wood-boring Coleoptera in the United States: Recent establishments and interceptions. Canadian Journal of Forest Research, 36(2), 269–288.Google Scholar

Hadar, J. & Russell, W. R. (1969). Rules for ordering uncertain prospects. American Economic Review, 59(1), 25–34.Google Scholar

Hamlet, A. F. & Lettenmaier, D. P. (2005). Production of temporally consistent gridded precipitation and temperature fields for the continental United States. Journal of Hydrometeorology, 6(3), 330–336.Google Scholar

Hastings, A. (1996). Models of spatial spread: Is the theory complete? Ecology, 77(6), 1675–1679.Google Scholar

Haugen, D. A. (2006). Sirex noctilio, Exotic Forest Pest Information System for North America. North American Forest Commission, Available from http://spfnic.fs.fed.us/exfor/data/pestreports.cfm?pestidval=33&langdisplay=englishGoogle Scholar

Haugen, D. A. & Hoebeke, E. R. (2005). Pest Alert: Sirex woodwasp – Sirex noctilio F. (Hymenoptera: Siricidae). USDA Forest Service NA-PR-07-05. Available from http://na.fs.fed.us/spfo/pubs/pest_al/sirex_woodwasp/sirex_woodwasp.htmGoogle Scholar

Henderson-Sellers, B. & Henderson-Sellers, A. (1996). Sensitivity evaluation of environmental models using fractional factorial experimentation. Ecological Modelling, 86(2–3), 291–295.Google Scholar

Herborg, L.-M., Jerde, C. L., Lodge, D. M., Ruiz, G. M. & MacIsaac, H. J. (2007). Predicting invasion risk using measures of introduction effort and environmental niche models. Ecological Applications, 17(3), 663–674.Google Scholar

Higgins, S. I., & Richardson, D. M. (1999). Predicting plant migration rates in a changing world: The role of long-distance dispersal. American Naturalist, 153(5), 464–475.CrossRefGoogle Scholar

Higgins, S. I., Richardson, D. M. & Cowling, R. M. (2001). Validation of a spatial simulation model of a spreading alien plant population. Journal of Applied Ecology 38(3), 571–584.CrossRefGoogle Scholar

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978.Google Scholar

Hildebrandt, P. & Knoke, T. (2011). Investment decisions under uncertainty – A methodological review on forest science studies. Forest Policy and Economics, 13(1), 1–15.Google Scholar

Holmes, E. E. (1993). Are diffusion models too simple? A comparison with telegraph models of invasion. American Naturalist, 142(5), 779–795.Google Scholar

Hurley, B. P., Slippers, B. & Wingfield, M. J. (2007). A comparison of the control results for the alien invasive woodwasp, Sirex noctilio, in the southern hemisphere. Agricultural and Forest Entomology, 9(3), 159–171.Google Scholar

Jarvis, C. H. & Baker, R. H. A. (2001). Risk assessment for non-indigenous pests: I. Mapping the outputs of phenology models to assess the likelihood of establishment. Diversity and Distributions, 7(5), 223–236.CrossRefGoogle Scholar

Jules, E. S., Kauffman, M. J., Ritts, W. D. & Carroll, A. L. (2002). Spread of an invasive pathogen over a variable landscape: A nonnative root rot on Port Orford cedar. Ecology, 83(11), 3167–3181.CrossRefGoogle Scholar

Kaplan, S. & Garrick, B. J. (1981). On the quantitative definition of risk. Risk Analysis, 1(1), 11–27.Google Scholar

Keeney, R. L. & Raiffa, H. (1976). Decisions with multiple objectives. New York: John Wiley & Sons.Google Scholar

Koch, F. H., Yemshanov, D., Colunga-Garcia, M., Magarey, R.D. & Smith, W. D. (2011). Potential establishment of alien-invasive forest insect species in the United States: Where and how many? Biological Invasions, 13(4) 969–985.CrossRefGoogle Scholar

Koch, F. H., Yemshanov, D., McKenney, D. W. & Smith, W. D. (2009). Evaluating critical uncertainty thresholds in a spatial model of forest pest invasion risk. Risk Analysis, 29(9), 1227–1241.Google Scholar

Kot, M., Lewis, M. A., & van den Driessche, P. (1996). Dispersal data and the spread of invading organisms. Ecology, 77(7), 2027–2042.Google Scholar

Kovacs, K. F., Haight, R. G., McCullough, D. G., et al. (2010). Cost of potential emerald ash borer damage in U.S. communities, 2009–2019. Ecological Economics, 69(3), 569–578.CrossRefGoogle Scholar

LeSage, J. P. & Kelley Pace, R. (2008). Spatial econometric modeling of origin-destination flows. Journal of Regional Science, 48(5), 941–967.Google Scholar

LeSage, J. P. & Polasek, W. (2006). Incorporating transportation network structure in spatial econometric models of commodity flows. Economics Series 188. Vienna, Austria: Institute for Advanced Studies.Google Scholar

Levine, J. M. & D’Antonio, C.M. (2003). Forecasting biological invasions with increasing international trade. Conservation Biology, 17(1), 322–326.Google Scholar

Levy, H. (1992). Stochastic dominance and expected utility: Survey and analysis. Management Science, 38(4), 555–593.CrossRefGoogle Scholar

Levy, H. (1998). Stochastic dominance: Investment decision making under uncertainty. Boston: Kluwer Academic.Google Scholar

Levy, H. & Markowitz, H. M. (1979). Approximating expected utility by a function of mean and variance. American Economic Review, 69(3), 308–317.Google Scholar

Lowe, T., Cieszewski, C. J., Zasada, M. & Zawadzki, J. (2005). Distributing FIA information onto segmented Landsat Thematic Mapper images stratified with industrial ground data. In McRoberts, R. E., Reams, G. A., Van Deusen, P. C., McWilliams, W. H. & Cieszewski, C. J. (eds.), Proceedings of the Fourth Annual Forest Inventory and Analysis Symposium(pp. 111–116). St. Paul, MN: US Department of Agriculture, Forest Service, North Central Research Station.Google Scholar

Magarey, R. D., Borchert, D. M., Engle, J. S., et al. (2011). Risk maps for targeting exotic plant pest detection programs in the United States. EPPO Bulletin, 41(1), 1–11.CrossRefGoogle Scholar

Magarey, R. D., Borchert, D. M., Fowler, G. L., et al. (2007). NAPPFAST: An internet system for the weather-based mapping of plant pathogens. Plant Disease, 91(4), 336–345.Google Scholar

Magarey, R. D., Colunga-Garcia, M. & Fieselmann, D. A. (2009). Plant biosecurity in the United States: Roles, responsibilities, and information needs. Bioscience, 59(10), 875–884.Google Scholar

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.Google Scholar

McKenney, D., Papadopol, P., Lawrence, K., Campbell, K. & Hutchinson, M. (2007). Customized spatial climate models for Canada. Canadian Forest Service. Technical Note No. 108.Google Scholar

McRoberts, R. E., Holden, G. R., Nelson, M. D., Liknes, G. C. & Gormanson, D. D. (2005). Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service. Canadian Journal of Forest Research, 35(12), 2968–2980.Google Scholar

Meentemeyer, R., Rizzo, D., Mark, W. & Lotz, E. (2004). Mapping the risk of establishment and spread of sudden oak death in California. Forest Ecology and Management, 200(1–3), 195–214.Google Scholar

Melbourne, B. A. & Hastings, A. (2009). Highly variable spread rates in replicated biological invasions: Fundamental limits to predictability. Science, 325(5947), 1536–1539.Google Scholar

Morgan, M. G. & Dowlatabadi, H. (1996). Learning from integrated assessment of climate change. Climatic Change, 34(3–4), 337–368.Google Scholar

Morgan, M. G. & Henrion, M. (1990). Uncertainty: A guide to dealing with uncertainty in quantitative risk and policy analysis. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Muirhead, J. R., Leung, B., van Overdijk, C., et al. (2006). Modelling local and long-distance dispersal of invasive emerald ash borer Agrilus planipennis (Coleoptera) in North America. Diversity and Distributions, 12(1), 71–79.Google Scholar

Muirhead, J. R. & MacIsaac, H. J. (2005). Development of inland lakes as hubs in an invasion network. Journal of Applied Ecology, 42(1), 80–90.CrossRefGoogle Scholar

Nathan, R. (2003). Seeking the secrets of dispersal. Trends in Ecology and Evolution, 18(6), 275–276.Google Scholar

Nathan, R. (2005). Long-distance dispersal research: Building a network of yellow brick roads. Diversity and Distributions, 11(2), 125–130.Google Scholar

Neubert, M. G., & Caswell, H. (2000). Demography and dispersal: Calculation and sensitivity analysis of invasion speed for structured populations. Ecology, 81(6), 1613–1628.Google Scholar

Okubo, A. & Levin, S.A. (2002). Diffusion and ecological problems: Modern perspectives. New York: Springer Science+Business Media.Google Scholar

O’Sullivan, P. & Ralston, B. (1974). Forecasting intercity commodity transport in the U.S.A. Regional Studies, 8(2), 191–195.Google Scholar

Phillips, S. J. & Elith, J. (2010). POC-plots: Calibrating species distribution models using presence-only data. Ecology, 91(8), 2476–2484.CrossRefGoogle ScholarPubMed

Piel, F., Gilbert, M., De Cannière, C. & Grégoire, J.-C. (2008). Coniferous round wood imports from Russia and Baltic countries to Belgium: A pathway analysis for assessing risks of exotic pest introductions. Diversity and Distributions, 14(2), 318–328.Google Scholar

Pitt, J. P. W., Worner, S. P. & Suarez, A.V. (2009). Predicting Argentine ant spread over the heterogeneous landscape using a spatially explicit stochastic model. Ecological Applications, 19(5), 1176–1186.Google Scholar

Porojan, A. (2001). Trade flows and spatial effects: The gravity model revisited. Open Economies Review, 12(3), 265–280.Google Scholar

Rafoss, T. (2003). Spatial stochastic simulation offers potential as a quantitative method for pest risk analysis. Risk Analysis, 23(4), 651–661.Google Scholar

Reams, G. A., Smith, W. D., Hansen, M. H., et al. (2005). The Forest Inventory and Analysis sampling frame. In Bechtold, W. A. & Patterson, P. L. (pp. 11–26). The enhanced forest inventory and analysis program – National sampling design and estimation Procedures. Asheville, NC: USDA Forest Service, Southern Research Station.Google Scholar

Refsgaard, J. C., van der Sluijs, J. P., Højberg, A. L. & Vanrolleghem, P. A. (2007). Uncertainty in the environmental modelling process – A framework and guidance. Environmental Modelling and Software, 22(11), 1543–1556.Google Scholar

Regan, H. M., Colyvan, M. & Burgman, M. A. (2002). A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecological Applications, 12(2), 618–628.Google Scholar

Reichert, P. & Borsuk, M. E. (2005). Does high forecast uncertainty preclude effective decision support? Environmental Modelling and Software, 20(8), 991–1001.Google Scholar

Royama, T. (1992). Analytical population dynamics. London: Chapman and Hall.Google Scholar

Sharov, A. A. & Colbert, J. J. (1996). A model for testing hypotheses of gypsy moth Lymantria dispar L., population dynamics. Ecological Modelling, 84(1–3), 31–51.Google Scholar

Sharov, A. A. & Liebhold, A. M. (1998). Model of slowing the spread of gypsy moth (Lepidoptera: Lymantriidae) with a barrier zone. Ecological Applications, 8(4), 1170–1179.Google Scholar

Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. Journal of Finance, 14(3), 425–442.Google Scholar

Shigesada, N. & Kawasaki, K. (1997). Biological invasions: Theory and practice. Oxford: Oxford University Press.Google Scholar

Shigesada, N., Kawasaki, K. & Takeda, Y. (1995). Modeling stratified diffusion in biological invasions. American Naturalist, 146(2), 229–251.Google Scholar

Stewart, T. J. (1992). A critical survey on the status of multiple criteria decision making theory and practice. Omega, 20(5–6), 569–586.Google Scholar

Swartzman, G. L. & Kaluzny, S. P. (1987). Ecological simulation primer. New York: Macmillan USA.Google Scholar

Tobin, P. C., Liebhold, A. M. & Roberts, E. A. (2007). Comparison of methods for estimating the spread of a non-indigenous species. Journal of Biogeography, 34(2), 305–312.Google Scholar

USDA FS (USDA Forest Service). (2007). Forest inventory and analysis database: Database description and users guide version 3.0. USDA Forest Service, Forest Inventory and Analysis Program. Available from www.fia.fs.fed.us/tools-data/docs/pdfs/FIADB_user%20guide%203-0_P3_6_01_07.pdfGoogle Scholar

van den Bosch, F., Hengeveld, R. & Metz, J. A. J. (1992). Analysing the velocity of animal range expansion. Journal of Biogeography, 19(2), 135–150.Google Scholar

Venette, R. C., Kriticos, D. J., Magarey, R., et al. (2010). Pest risk maps for invasive alien species: A roadmap for improvement. BioScience, 60, 349–362.Google Scholar

von Winterfeldt, D. & Edwards, W. (1986). Decision analysis and behavioral research. London: Cambridge University Press.Google Scholar

Waage, J. K., Fraser, R.W., Mumford, J. D., Cook, D. C. & Wilby, A. (2005). A new agenda for biosecurity. A Report for the Department for Food, Environment and Rural Affairs. London: Faculty of Life Sciences, Imperial College London.Google Scholar

Walker, W. E., Harramoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen, P. & Krayer von Krauss, M. P. (2003). Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support. Integrated Assessment, 4(1), 5–17.Google Scholar

Walley, P. (1991). Statistical reasoning with imprecise probabilities. London: Chapman and Hall.Google Scholar

Watkinson, A. R., Freckleton, R. P. & Dowling, P. M. (2000). Weed invasions of Australian farming systems: From ecology to economics. In Perrings, C., Williamson, M., & Dalmazzone, S. The economics of biological invasions (pp. 94–114). Cheltenham, UK: Edward Elgar.Google Scholar

Weinberger, H. F. (2002). On spreading speeds and travelling waves for growth and migration models in a periodic habitat. Journal of Mathematical Biology, 45(6), 511–548.Google Scholar

Westbrooks, R. (1998). Invasive plants, changing the landscape of America: Fact book. Washington, DC: Federal Interagency Committee for the Management of Noxious and Exotic Weeds.Google Scholar

Whitmore, G. A. (1970). Third degree stochastic dominance. American Economic Review, 60(3), 457–459.Google Scholar

Williamson, M. (1996). Biological invasions. London: Chapman and Hall.Google Scholar

Woodbury, P. B. (2003). Dos and don’ts of spatially explicit ecological risk assessments. Environmental Toxicology and Chemistry, 22(5), 977–982.Google Scholar

Worner, S. P. & Gevrey, M. (2006). Modelling global insect pest species assemblages to determine risk of invasion. Journal of Applied Ecology, 43(5), 858–867.Google Scholar

Woudenberg, S. W., Conkling, B. L., O’Connell, B. M., et al. (2010). The Forest Inventory and Analysis Database: Database description and Users Manual Version 4.0 for Phase 2. General Technical Report RMRS-GTR-245. Fort Collins, CO: United States Department of Agriculture, Forest Service, Rocky Mountain Research Station.Google Scholar

Yamamura, K. & Katsumata, H. (1999). Estimation of the probability of insect pest introduction through imported commodities. Researches on Population Ecology, 41(3), 275–282.Google Scholar

Yamamura, K., Katsumata, H. & Watanabe, T. (2001). Estimating invasion probabilities: A case study of fire blight disease and the importation of apple fruits. Biological Invasions, 3(4), 373–378.Google Scholar

Yemshanov, D., Koch, F.H., Ben-Haim, Y., et al. (2013). A new multicriteria risk mapping approach based on a multiattribute frontier concept. Risk Analysis, 33(9), 1694–1709.Google Scholar

Yemshanov, D., Koch, F., Ducey, M. & Koehler, K. (2012a). Trade-associated pathways of alien forest insect entries in Canada. Biological Invasions, 14(4), 797–812.Google Scholar

Yemshanov, D., Koch, F.H., Lyons, B., et al. (2012b). A dominance-based approach to map risks of ecological invasions in the presence of severe uncertainty. Diversity and Distributions, 18(1), 33–46.Google Scholar

Yemshanov, D., Koch, F. H., McKenney, D. W., Downing, M. C. & Sapio, F. (2009b). Mapping invasive species risks with stochastic models: A cross-border United States–Canada application for Sirex noctilio Fabricius. Risk Analysis, 29(6), 868–884.Google Scholar

Yemshanov, D., McKenney, D. W., de Groot, P., et al. (2009a). A bioeconomic approach to assess the impact of a nonnative invasive insect on timber supply and harvests: A case study with Sirex noctilio in eastern Canada. Canadian Journal of Forest Research, 39(1), 154–168.Google Scholar