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Cross-Scale Assessment of Potential Habitat Shifts in a Rapidly Changing Climate

Published online by Cambridge University Press:  20 January 2017

Catherine S. Jarnevich*
U.S. Geological Survey Fort Collins Science Center, 2150 Center Ave Building C Fort Collins, CO 80526
Tracy R. Holcombe
U.S. Geological Survey Fort Collins Science Center, 2150 Center Ave Building C Fort Collins, CO 80526
Elizabeth M. Bella
U.S. Fish and Wildlife Service Kenai National Wildlife Refuge, 1 Ski Hill Road, PO Box 2139, Soldotna, AK 99669
Matthew L. Carlson
UAA Alaska Natural Heritage Program & Biological Sciences Department, 707 A Street, Anchorage, AK 99501
Gino Graziano
UAF Cooperative Extension Service, 1675 C Street, Suite 100, Anchorage, AK 99501
Melinda Lamb
U.S. Forest Service Region 10 Forest Health Protection, 11175 Auke Lake Way. Juneau, AK 99801
Steven S. Seefeldt
UAF Cooperative Extension Service Tanana District, 724 27th Ave., Suite 2 and 3, P.O. Box 758155, Fairbanks, AK 99775-8155
Jeffery Morisette
U.S. Geological Survey, DOI North Central Climate Science Center, Colorado State University, Natural Resource and Ecology Lab, Fort Collins, CO 80523
Corresponding author's E-mail:


We assessed the ability of climatic, environmental, and anthropogenic variables to predict areas of high-risk for plant invasion and consider the relative importance and contribution of these predictor variables by considering two spatial scales in a region of rapidly changing climate. We created predictive distribution models, using Maxent, for three highly invasive plant species (Canada thistle, white sweetclover, and reed canarygrass) in Alaska at both a regional scale and a local scale. Regional scale models encompassed southern coastal Alaska and were developed from topographic and climatic data at a 2 km (1.2 mi) spatial resolution. Models were applied to future climate (2030). Local scale models were spatially nested within the regional area; these models incorporated physiographic and anthropogenic variables at a 30 m (98.4 ft) resolution. Regional and local models performed well (AUC values > 0.7), with the exception of one species at each spatial scale. Regional models predict an increase in area of suitable habitat for all species by 2030 with a general shift to higher elevation areas; however, the distribution of each species was driven by different climate and topographical variables. In contrast local models indicate that distance to right-of-ways and elevation are associated with habitat suitability for all three species at this spatial level. Combining results from regional models, capturing long-term distribution, and local models, capturing near-term establishment and distribution, offers a new and effective tool for highlighting at-risk areas and provides insight on how variables acting at different scales contribute to suitability predictions. The combinations also provides easy comparison, highlighting agreement between the two scales, where long-term distribution factors predict suitability while near-term do not and vice versa.

Research Article
Copyright © Weed Science Society of America 

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Literature Cited

[ACIA] Arctic Climate Impact Assessment (2005) Arctic Climate Impact Assessment - Scientific Report. New York Cambridge University Press. 1042 pGoogle Scholar
[AKEPIC] Alaska Exotic Plant Information Clearinghouse (2010) Alaska Natural Heritage Program. Accessed 10 May, 2010Google Scholar
Amor, RL, Harris, RV (1974) Distribution and seed production of Cirsium arvense (L.) Scop, in Victoria, Australia. Weed Res 14:317323 CrossRefGoogle Scholar
Araujo, MB, Guisan, A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:16771688 CrossRefGoogle Scholar
Araújo, MB, New, M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:4247 CrossRefGoogle ScholarPubMed
Araújo, MB, Peterson, AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:15271539 CrossRefGoogle ScholarPubMed
Ashcroft, MB, French, KO, Chisholm, LA (2011) An evaluation of environmental factors affecting species distributions. Ecol Model 222:524531 CrossRefGoogle Scholar
Bakker, D (1960) A comparative life-history study of Cirsium arvense (L.) Scop. and Tussilago farfara (L.), the most troublesome weeds in the newly reclaimed polders of the former Zuiderzee. Pages 205222 in Harper, JL, ed. The Biology of Weeds, Symposium No. 1, British Ecology Society. Oxford, England Blackwell Google Scholar
Beaumont, LJ, Hughes, L, Pitman, AJ (2008) Why is the choice of future climate scenarios for species distribution modelling important? Ecol Lett 11:11351146 CrossRefGoogle ScholarPubMed
Bella, EM (2009) Invasive Plant Species Response to Climate Change in Alaska: Bioclimatic models of current and predicted future ranges: HDR Alaska, Inc. Accessed November 1, 2011Google Scholar
Bella, EM (2011) Invasion prediction on Alaska trails: distribution, habitat, and trail use. Invasive Plant Sci Manage 4:296305 CrossRefGoogle Scholar
Berg, EE, Anderson, RS (2006) Fire history of white and Lutz spruce forests on the Kenai Peninsula, Alaska, over the last two millennia as determined from soil charcoal. For Ecol Manag 227:275283 CrossRefGoogle Scholar
Carlson, ML, Cortes-Burns, H (2013) Rare vascular plant distributions in Alaska: evaluating patterns of habitat suitability. Pages 118 in Proceedings of the Conserving Plant Biodiversity in a Changing World: A View from Northwestern North America. Seattle, WA University of Washington Botanic Gardens Google Scholar
Carlson, ML, Lapina, IV, Shephard, M, Conn, JS, Densmore, R, Spencer, P, Heys, J, Riley, J, Nielsen, J (2008) Invasiveness ranking system for non-native plants of Alaska: USDA Forest Service, Gen. Tech. Rep. R10, R10-TP-143.218 Google Scholar
Carlson, ML, Shepherd, MA (2007) Is the spread of non-native plants in Alaska accelerating? Pages 111127 in Harrington, TB, Reichard, SH, eds. Meeting the Challenge: Invasive Plants in Pacific Northwest Ecosystems. Portland, OR U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station Technical Report PNW-GTR-694Google Scholar
Chapin, FS, Hoel, M, Carpenter, SR, Lubchenco, J, Walker, B, Callaghan, TV, Folke, C, Levin, SA, Mäler, K-Gr, Nilsson, C, Barrett, S, Berkes, F, Crépin, A-S, Danell, K, Rosswall, T, Starrett, D, Xepapadeas, A, Zimov, SA (2006) Building resilience and adaptation to manage Arctic change. AMBIO: J Hum Envir 35:198202 CrossRefGoogle ScholarPubMed
Chapin, FS III, Trainor, SF, Huntington, O, Lovecraft, AL, Zavaleta, E, Natcher, DC, McGuire, AD, Nelson, JA, Ray, LCM, Fresco, N, Huntington, H, Rupp, TS, DeWilde, L, Naylor, RL (2008) Increasing wildfire in Alaska's boreal forest: pathways to potential solutions of a wicked problem. Bioscience 58:531540 CrossRefGoogle Scholar
Conn, J, Gronquist, R, Mueller, M (2003) Invasive plants in Alaska: assessment of research priorities. Agroborealis 35:1318 Google Scholar
Crall, AW, Jarnevich, CS, Panke, B, Young, N, Renz, M, Morisette, J (2013) Using habitat suitability models to target invasive plant species surveys. Ecol Appl 23:6072 CrossRefGoogle ScholarPubMed
Elith, J, Graham, CH, Anderson, RP, Dudik, M, Ferrier, S, Guisan, A, Hijmans, RJ, Huettmann, F, Leathwick, JR, Lehmann, A, Li, J, Lohmann, LG, Loiselle, BA, Manion, G, Moritz, C, Nakamura, M, Nakazawa, Y, Overton, JM, Peterson, AT, Phillips, SJ, Richardson, K, Scachetti-Pereira, R, Schapire, RE, Soberon, J, Williams, S, Wisz, MS, Zimmermann, NE (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129151 CrossRefGoogle Scholar
Elith, J, Kearney, M, Phillips, S (2010) The art of modelling range-shifting species. Meth Ecol Evol 1:330342 CrossRefGoogle Scholar
Evangelista, P, Kumar, S, Stohlgren, TJ, Jarnevich, CS, Crall, AW, Norman, JB III, Barnett, D (2008) Modelling invasion for a habitat generalist and a specialist plant species. Divers Distrib 14:808817 CrossRefGoogle Scholar
Fielding, AH, Bell, JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:3849 CrossRefGoogle Scholar
Franklin, J (1995) Predictive vegetation mapping: Geographic modelling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:474499 CrossRefGoogle Scholar
Gallien, L, Douzet, R, Pratte, S, Zimmermann, NE, Thuiller, W (2012) Invasive species distribution models – How violating the equilibrium assumption can create new insights. Glob Ecol Biogeogr 21:11261136 CrossRefGoogle Scholar
Ge, Z-M, Kellomäki, S, Zhou, X, Peltola, H, Wang, K-Y, Martikainen, P (2012) Seasonal physiological responses and biomass growth in a bioenergy crop (Phalaris arundinacea L.) under elevated temperature and CO2, subjected to different water regimes in boreal conditions. Bioenerg. Res. 5:637648 CrossRefGoogle Scholar
Grinnell, J (1917) The niche relationships of the California thrasher. Auk 34:427433 CrossRefGoogle Scholar
Guisan, A, Thuiller, W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:9931009 CrossRefGoogle Scholar
Hijmans, RJ (2006) MkBCvars.AML version 2.3. Accessed May 17, 2010Google Scholar
Hoveland, CS, Foutch, HW, Buchanan, GA (1974) Response of Phalaris Genotypes and other cool-season grasses to temperature. 1. Agron J 66:686690 CrossRefGoogle Scholar
Jeschke, JM, Strayer, DL (2008) Usefulness of bioclimatic models for studying climate change and invasive species. Ann N Y Acad Sci 1134:124 CrossRefGoogle ScholarPubMed
Jimenez-Valverde, A, Lobo, JM, Hortal, J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14:885890 CrossRefGoogle Scholar
Jones, CC (2012) Challenges in predicting the future distributions of invasive plant species. For Ecol Manag 284:6977 CrossRefGoogle Scholar
Lassuy, D, Lewis, P (2013) Invasive Species: Human-Induced Arctic Biodiversity Assessment. Pages 560563 in Meltofte, H, ed. Arctic Biodiversity Assessment 2013: Status and Trends in Arctic Biodiversity. Akureyri, Iceland Conservation of Arctic Flora and Fauna Google Scholar
Lavergne, S, Molofsky, J (2004) Reed canary grass (Phalaris arundinacea) as a biological model in the study of plant invasions. Crit Rev Plant Sci 23:415429 CrossRefGoogle Scholar
Luoto, M, Virkkala, R, Heikkinen, RK (2007) The role of land cover in bioclimatic models depends on spatial resolution. Glob Ecol Biogeogr 16:3442 CrossRefGoogle Scholar
Moore, RJ (1975) The biology of Canadian weeds. 13. Cirsium arvense (L.) Scop. Can J Plant Sci 55:10331048 CrossRefGoogle Scholar
Morisette, JTCS, Holcombe, TR, Talbert, CB, Ignizio, D, Talbert, MK, Silva, C, Koop, D, Swanson, A, Young, NE (2013) VisTrails SAHM: visualization and workflow management for species habitat modeling. Ecography 36:129135 CrossRefGoogle Scholar
Moritz, RE, Bitz, CM, Steig, EJ (2002) Dynamics of recent climate change in the Arctic. Science 297:14971502 CrossRefGoogle ScholarPubMed
Nowacki, G, Brock, T (1995) Ecoregions and Subregions of Alaska, EcoMap Version 2.0 (map). USDA Forest Service, Alaska Region, Juneau, AK Google Scholar
Pearson, RG, Dawson, TP, Liu, C (2004) Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography 27:285298 CrossRefGoogle Scholar
Pearson, RG, Raxworthy, CJ, Nakamura, M, Peterson, AT (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34:102117 CrossRefGoogle Scholar
Peterson, N, Soberon, J, Pearson, RG, Anderson, RP, Martinez-Meyer, E, Nakamura, M, Araujo, MB (2011) Ecological Niches and Geographic Distributions. Princeton, New Jersey Princeton University Press. 328 pCrossRefGoogle Scholar
Phillips, SJ, Anderson, RP, Schapire, RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231259 CrossRefGoogle Scholar
Phillips, SJ, Dudik, M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161175 CrossRefGoogle Scholar
Phillips, SJ, Dudik, M, Elith, J, Graham, CH, Lehmann, A, Leathwick, J, Ferrier, S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181197 CrossRefGoogle ScholarPubMed
Reshetnikov, A, Ficetola, G (2011) Potential range of the invasive fish rotan (Perccottus glenii) in the Holarctic. Biol Invasions 13:29672980 CrossRefGoogle Scholar
Roura-Pascual, N, Brotons, L, Peterson, AT, Thuiller, W (2009) Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula. Biol Invasions 11:10171031 CrossRefGoogle Scholar
Sanderson, LA, Mclaughlin, JA, Antunes, PM (2012) The last great forest: a review of the status of invasive species in the North American boreal forest. Forestry DOI:10.1093/forestry/cps033CrossRefGoogle Scholar
Scheffer, M, Carpenter, S, Foley, JA, Folke, C, Walker, B (2001) Catastrophic shifts in ecosystems. Nature 413:591596 CrossRefGoogle ScholarPubMed
Serreze, MC (2010) Understanding recent climate change. Conserv Biol 24:1017 CrossRefGoogle ScholarPubMed
Serreze, MC. (2000) Observational evidence of recent change in the northern high-latitude environment. Clim Change 46:159207 CrossRefGoogle Scholar
[SNAP] Scenarios Network for Alaska Planning (2010) Alaska Climate Datasets. Accessed April 6, 2010Google Scholar
Soja, AJ, Tchebakova, NM, French, NHF, Flannigan, MD, Shugart, HH, Stocks, BJ, Sukhinin, AI, Parfenova, EI, Chapin, FS III, Stackhouse, PW Jr, (2007) Climate-induced boreal forest change: predictions versus current observations. Global and Planetary Change 56:274296 CrossRefGoogle Scholar
Stafford, JM, Wendler, G, Curtis, J (2000) Temperature and precipitation of Alaska: 50 year trend analysis. Theor Appl Clim 67:3344 CrossRefGoogle Scholar
Swets, JA (1988) Measuring the accuracy of diagnostic systems. Science 240:12851293 CrossRefGoogle ScholarPubMed
U.S. Forest Service Tongass National Forest: Southeast Alaska GIS Library (2007) TNF Cover Type. Accessed October 5,2010Google Scholar
Walsh, JE, Chapman, WL, Romanovsky, V, Christensen, JH, Stendel, M (2008) Global climate model performance over Alaska and Greenland. J Clim 21:61566174 CrossRefGoogle Scholar
Warren, DL, Seifert, SN (2011) Environmental niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol Appl 21:335342 CrossRefGoogle ScholarPubMed
Wendler, G, Chen, L, Moore, B (2012) The first decade of the new century: a cooling trend for most of Alaska. Open Atmosph Sci J 6:111116 CrossRefGoogle Scholar
Wendler, G, Shulski, M (2009) A century of climate change for Fairbanks, Alaska. Arct Alp Res 62:295300 Google Scholar
Wiens, J (2002) Predicting species occurrences: progress, problems, and prospects. Pages 739749 in Scott, J, Heglund, P, Morrison, M, eds. Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA Island Google Scholar
Wisz, MS, Hijmans, RJ, Li, J, Peterson, AT, Graham, CH, Guisan, A, Distribut, NPS (2008) Effects of sample size on the performance of species distribution models. Divers Distrib 14:763773 CrossRefGoogle Scholar
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