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

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.

Keywords

Canada thistle, Cirsium arvense (L.) Scop.reed canarygrass, Phalaris arundinacea L.white sweetclover, Melilotus albus Medik.Alaskabioclimatic modelingCentaurea stoebeCirsium arvenseclimate changeFallopia japonica (Polygonum cuspidatum) complexinvasive speciesMelilotus albusPhalaris arundinacea
Type
Research Article
Information
Invasive Plant Science and Management , Volume 7 , Issue 3 , September 2014 , pp. 491 - 502
Copyright
Copyright © Weed Science Society of America 

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References

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. http://aknhp.uaa.alaska.edu/maps/akepic/. Accessed 10 May, 2010Google Scholar
Amor, RL, Harris, RV (1974) Distribution and seed production of Cirsium arvense (L.) Scop, in Victoria, Australia. Weed Res 14:317–323 Google Scholar
Araujo, MB, Guisan, A (2006) Five (or so) challenges for species distribution modelling. J Biogeogr 33:1677–1688 Google Scholar
Araújo, MB, New, M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47 Google Scholar
Araújo, MB, Peterson, AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539 Google Scholar
Ashcroft, MB, French, KO, Chisholm, LA (2011) An evaluation of environmental factors affecting species distributions. Ecol Model 222:524–531 Google 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 205–222 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 11:1135–1146 Google Scholar
Bella, EM (2009) Invasive Plant Species Response to Climate Change in Alaska: Bioclimatic models of current and predicted future ranges: HDR Alaska, Inc. http://alaska.fws.gov/fisheries/invasive/reports.htm. Accessed November 1, 2011Google Scholar
Bella, EM (2011) Invasion prediction on Alaska trails: distribution, habitat, and trail use. Invasive Plant Sci Manage 4:296–305 Google 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 227:275–283 Google Scholar
Carlson, ML, Cortes-Burns, H (2013) Rare vascular plant distributions in Alaska: evaluating patterns of habitat suitability. Pages 1–18 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 111–127 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 35:198–202 Google Scholar
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 58:531–540 Google Scholar
Conn, J, Gronquist, R, Mueller, M (2003) Invasive plants in Alaska: assessment of research priorities. Agroborealis 35:13–18 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 23:60–72 Google Scholar
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 29:129–151 Google Scholar
Elith, J, Kearney, M, Phillips, S (2010) The art of modelling range-shifting species. Meth Ecol Evol 1:330–342 Google 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 14:808–817 Google Scholar
Fielding, AH, Bell, JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49 Google Scholar
Franklin, J (1995) Predictive vegetation mapping: Geographic modelling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:474–499 Google 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 21:1126–1136 Google 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. 5:637–648 Google Scholar
Grinnell, J (1917) The niche relationships of the California thrasher. Auk 34:427–433 Google Scholar
Guisan, A, Thuiller, W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009 Google Scholar
Hijmans, RJ (2006) MkBCvars.AML version 2.3. http://worldclim.org/bioclim.htm. 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 66:686–690 Google Scholar
Jeschke, JM, Strayer, DL (2008) Usefulness of bioclimatic models for studying climate change and invasive species. Ann N Y Acad Sci 1134:1–24 Google Scholar
Jimenez-Valverde, A, Lobo, JM, Hortal, J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14:885–890 Google Scholar
Jones, CC (2012) Challenges in predicting the future distributions of invasive plant species. For Ecol Manag 284:69–77 Google Scholar
Lassuy, D, Lewis, P (2013) Invasive Species: Human-Induced Arctic Biodiversity Assessment. Pages 560–563 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 23:415–429 Google Scholar
Luoto, M, Virkkala, R, Heikkinen, RK (2007) The role of land cover in bioclimatic models depends on spatial resolution. Glob Ecol Biogeogr 16:34–42 Google Scholar
Moore, RJ (1975) The biology of Canadian weeds. 13. Cirsium arvense (L.) Scop. Can J Plant Sci 55:1033–1048 Google 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 36:129–135 Google Scholar
Moritz, RE, Bitz, CM, Steig, EJ (2002) Dynamics of recent climate change in the Arctic. Science 297:1497–1502 Google Scholar
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 27:285–298 Google 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 34:102–117 Google 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 pGoogle Scholar
Phillips, SJ, Anderson, RP, Schapire, RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259 Google Scholar
Phillips, SJ, Dudik, M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175 Google 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 19:181–197 Google Scholar
Reshetnikov, A, Ficetola, G (2011) Potential range of the invasive fish rotan (Perccottus glenii) in the Holarctic. Biol Invasions 13:2967–2980 Google 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 11:1017–1031 Google 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/cps033Google Scholar
Scheffer, M, Carpenter, S, Foley, JA, Folke, C, Walker, B (2001) Catastrophic shifts in ecosystems. Nature 413:591–596 Google Scholar
Serreze, MC (2010) Understanding recent climate change. Conserv Biol 24:10–17 Google Scholar
Serreze, MC. (2000) Observational evidence of recent change in the northern high-latitude environment. Clim Change 46:159–207 Google Scholar
[SNAP] Scenarios Network for Alaska Planning (2010) Alaska Climate Datasets. http://www.snap.uaf.edu/gis-maps. 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 56:274–296 Google Scholar
Stafford, JM, Wendler, G, Curtis, J (2000) Temperature and precipitation of Alaska: 50 year trend analysis. Theor Appl Clim 67:33–44 Google Scholar
Swets, JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293 Google Scholar
U.S. Forest Service Tongass National Forest: Southeast Alaska GIS Library (2007) TNF Cover Type. http://seakgis03.alaska.edu/geoportal/catalog/main/home.page. 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 21:6156–6174 Google 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 21:335–342 Google Scholar
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 6:111–116 Google Scholar
Wendler, G, Shulski, M (2009) A century of climate change for Fairbanks, Alaska. Arct Alp Res 62:295–300 Google Scholar
Wiens, J (2002) Predicting species occurrences: progress, problems, and prospects. Pages 739–749 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 14:763–773 Google Scholar
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