Regional climate model downscaling may improve the prediction of alien plant species distributions

Abstract

Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models). We also compared species distributions based on either GCM-based or RCM-based models for the present (1990–1999) to the future (2046–2055).

RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa (Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

Keywords

climate change / species distribution model / Maxent / downscaling

References

[1] Allouche O, Tsoar A, Kadmon R (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol, 43(6): 1223–1232

[2] Araújo M B, Pearson R G, Thuiller W, Erhard M (2005). Validation of species-climate impact models under climate change. Glob Change Biol, 11(9): 1504–1513

[3] Beaumont L J, Gallagher R V, Thuiller W, Downey P O, Leishman M R, Hughes L (2009). Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Divers Distrib, 15(3): 409–420

[4] Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012). Impacts of climate change on the future of biodiversity. Ecol Lett, 15(4): 365–377

[5] Bromberg J E, Kumar S, Brown C S, Stohlgren T J (2011). Distributional changes and range predictions of downy brome (Bromus tectorum) in Rocky Mountain National Park. Invasive Plant Science and Management, 4(2): 173–182

[6] Collins W D, Bitz C M, Blackmon M L, Bonan G B, Bretherton C S, Carton J A, Chang P, Doney S C, Hack J J, Henderson T B, Kiehl J T, Large W G, McKenna D S, Santer B D, Smith R D (2006). The Community Climate System Model version 3 (CCSM3). J Clim, 19(11): 2122–2143

[7] Cook D C, Thomas M B, Cunningham S A, Anderson D L, DeBarro P J (2007). Predicting the economic impact of an invasive species on an ecosystem service. Ecol Appl, 17(6): 1832–1840

[8] Davis A J, Jenkinson L S, Lawton J H, Shorrocks B, Wood S (1998). Making mistakes when predicting shifts in species range in response to global warming. Nature, 391(6669): 783–786

[9] Elith J, Graham C H, Anderson R P, Dudík M, Ferrier S, Guisan A, Hijmans R J, Huettmann F, Leathwick J R, Lehmann A, Li J, Lohmann L G, Loiselle B A, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J M, Peterson A T, Phillips S J, Richardson K, Scachetti-Pereira R, Schapire R E, Soberón J, Williams S, Wisz M S, Zimmermann N E (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29: 129–151

[10] Elith J, Leathwick J R (2009). Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst, 40(1): 677–697

[11] Elith J, Phillips S J, Hastie T, Dudík M, Chee Y E, Yates C J (2011). A statistical explanation of Maxent for ecologists. Divers Distrib, 17(1): 43–57

[12] Fielding A H, Bell J F (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv, 24(1): 38–49

[13] Franklin J, Davis F W, Ikegami M, Syphard A D, Flint L E, Flint A L, Hannah L (2013). Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Glob Change Biol, 19(2): 473–483

[14] Hernandez P C, Graham C, Master L, Albert D (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography, 29(5): 773–785

[15] 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. Int J Climatol, 25(15): 1965–1978

[16] Holcombe T R, Stohlgren T J, Jarnevich C S (2010). From points to forecasts: predicting invasive species habitat suitability in the near term. Diversity, 2(5): 738–767

[17] IPCC (Intergovernmental Panel on Climate Change) (2007). Climate Change 2007: The physical Science Basis. In: Solomon S, Qin D, Manning M, Marquis M, Averyt K, Tignor M M B, Miller H L Jr., Chen Z, eds. Contribution of Working Group I to the Fourth Assessment Report of the IPCC. New York: Cambridge University Press

[18] Jarnevich C S, Evangelista P, Stohlgren T J, Morisette J (2011). Improving national-scale invasion maps: tamarisk in the western United States. West N Am Nat, 71(2): 164–175

[19] Jarnevich C S, Stohlgren T J (2009). Near term climate projections for invasive species distributions. Biol Invasions, 11(6): 1373–1379

[20] Kumar S, Spaulding S A, Stohlgren T J, Hermann K A, Schmidt T S, Bahls L L (2009). Potential habitat distribution for the freshwater diatom Didymosphenia geminate in the continental US. Front Ecol Environ, 7(8): 415–420

[21] Liang X Z, Li L, Kunkel K E, Ting M, Wang J X L (2004). Regional climate model simulation of U.S. precipitation during 1982–2002. Part I: annual cycle. J Clim, 17(18): 3510–3529

[22] Liang X-Z, Pan J, Zhu J, Kunkel K E, Wang J X L, Dai A (2006). Regional climate model downscaling of the U.S. summer climate and future change. Journal of Geophysical Research-Atmosphere, 111, D10108

[23] Liang X Z, Xu M, Yuan X, Ling T, Choi H I, Zhang F, Chen L, Liu S, Su S, Qiao F, He Y, Wang J X L, Kunkel K E, Gao W, Joseph E, Morris V, Yu T W, Dudhia J, Michalakes J (2012). Regional climate-weather research and forecasting model. Bull Am Meteorol Soc, 93(9): 1363–1387

[24] Liu L, Berry P M, Dawson T P, Pearson R G (2005). Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28(3): 385–393

[25] Mack R N, Simberloff D, Lonsdale W M, Evans H, Clout M, Bazzaz F A (2000). Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl, 10(3): 689–710

[26] Manel S, Williams H C, Ormerod S J (2001). Evaluating presences-absence models in ecology: the need to account for prevalence. J Appl Ecol, 38(5): 921–931

[27] McPherson J M, Jetz W, Rogers D J (2004). The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact? J Appl Ecol, 41(5): 811–823

[28] Morisette J T, Jarnevich C S, Ullah A, Cai W, Pedelty J A, Gentle J, Stohlgren T J, Schnase J L (2006). A tamarisk habitat suitability map for the continental United States. Front Ecol Environ, 4(1): 11–17

[29] Nix H A (1986). A biogeographic analysis of Australian elapid snakes. In: Longmore R, ed. Australian Flora and Fauna Series 8. Canberra: Australian Government Publishing Service

[30] Parmesan C, Yohe G (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421(6918): 37–42

[31] Pearson R G, Dawson T P (2003). Predicting the impacts of climate change on the distribution of species: are bioclimatic envelope models useful? Glob Ecol Biogeogr, 12(5): 361–371

[32] Pearson R G, Thuiller W, Araújo M B, Martinez-Meyer E, Brotons L, McClean C, Miles L, Segurado P, Dawson T P, Lees D C (2006). Model-based uncertainty in species range prediction. J Biogeogr, 33(10): 1704–1711

[33] Phillips S J (2005). A brief tutorial on Maxent (from http://www.cs.princeton.edu/~schapire/maxent/tutorial/tutorial.doc).

[34] Phillips S J, Anderson R P, Schapire R E (2006). Maximum entropy modeling of species geographic distributions. Ecol Modell, 190(3–4): 231–259

[35] Pielke R S Sr, Wilby R L (2012). Regional climate downscaling: what’s the point? Eos Transactions American Geophysical Union, 93(5): 52–53

[36] Pimentel D, Zuniga R, Morrison D (2005). Update on the environmental and economic costs of associated with alien-invasive species in the United States. Ecol Econ, 52(3): 273–288

[37] Rejmánek M, Pitcairn M J (2002). When is eradication of exotic pest plants a realistic goal? In: Veitch C R, Clout M N, eds. Turning the Tide: the Eradication of Invasive Species. Gland and Cambridge: IUCN SSC Invasive Species Specialist Group, 249–253

[38] Root T L, Price J T, Hall K R, Schneider S H, Rosenzweig C, Pounds J A (2003). Fingerprints of global warming on wild animals and plants. Nature, 421(6918): 57–60

[39] Segurado P, Araújo M B (2004). An evaluation of methods for modelling species distributions. J Biogeogr, 31(10): 1555–1568

[40] Stockwell D R B, Peterson A T (2002). Effects of sample size on accuracy of species distribution models. Ecol Modell, 148(1): 1–13

[41] Stohlgren T J, Barnett D T, Jarnevich C S, Flather C, Kartesz J (2008). The myth of plant species saturation. Ecol Lett, 11(4): 313–322

[42] Stohlgren T J, Pyšek P, Kartesz J, Nishino M, Pauchard A, Winter M, Pino J, Richardson D M, Wilson J R U, Murray B R, Phillips M L, Celesti-Grapow L, Graham J (2013). Globalization effects on common plant species. In: Levin S, ed. Encyclopedia of Biodiversity (Second Edition). Waltham, MA: Academic Press, 3: 700–706

[43] Stohlgren T J, Schnase J L (2006). Risk analysis for biological hazards: what we need to know about invasive species. Risk Anal, 26(1): 163–173

[44] Swets J A (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857): 1285–1293

[45] Tebaldi C, Smith R, Nychka D, Mearns L O (2005). Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multi-model ensembles. J Clim, 18(10): 1524–1540

[46] Thomas C D, Bodsworth E J, Wilson R J, Simmons A D, Davies Z G, Musche M, Conradt L (2001). Ecological and evolutionary processes at expanding range margins. Nature, 411(6837): 577–581

[47] Thomas C D, Cameron A, Green R E, Bakkenes M, Beaumont L J, Collingham Y, Erasmus B F N, de Siqueira M F, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld A S, Midgley G F, Miles L J, Ortega-Huerta M A, Peterson A T, Philips O, Williams S E (2004). Extinction risk from climate change. Nature, 427(6970): 145–148

[48] Thornton P E, Running S W, White M A (1997). Generating surfaces of daily meteorological variables over large regions of complex terrain. J Hydrol (Amst), 190(3–4): 214–251

[49] Thuiller W (2003). BIOMOD: optimizing predictions of species distributions and projecting potential future shifts under global change. Glob Change Biol, 9(10): 1353–1362

[50] Thuiller W (2004). Patterns and uncertainties of species’ ranges shifts under climate change. Glob Change Biol, 10(12): 2020–2027

[51] Thuiller W, Richardson D M, Pyšek P, Midgley G F, Hughes G O, Rouget M (2005). Niche-based modeling as a tool for predicting the risk of alien plant invasions at a global scale. Glob Change Biol, 11(12): 2234–2250

[52] Vose R S, Applequist S, Menne M J, Williams C N Jr, Thorne P (2012). An intercomparison of temperature trends in the U.S. historical climatology network and recent atmospheric reanalyses. Geophys Res Lett, 39(10): L10703

[53] Walther G R, Post E, Convey P, Menzel A, Parmesan C, Beebee T J, Fromentin J M, Hoegh-Guldberg O, Bairlein F (2002). Ecological responses to recent climate change. Nature, 416(6879): 389–395

[54] Wiley E O, McNyset K M, Peterson A T, Robins C R, Stewart A M (2003). Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography (Wash DC), 16(3): 120–127

[55] Yates C J, McNeill A, Elith J, Midgley G F (2010). Assessing the impacts of climate change and land transformation on Banksia in the South West Australian Floristic Region. Divers Distrib, 16(1): 187–201

Acknowledgements

This research was supported by USDA CSREES/NRI 2008-35615-04666. The TJS contribution was supported by the U.S. Geological Survey, Fort Collins Science Center. The views expressed are those of the authors and do not necessarily reflect those of the sponsoring agencies and the Earth System Science Interdisciplinary Center, University of Maryland. Any use of trade, produce, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. government.

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