Using High-Resolution Future Climate Scenarios to Forecast Bromus tectorum Invasion in Rocky Mountain National Park

National Parks are hallmarks of ecosystem preservation in the United States. The introduction of alien invasive plant species threatens protection of these areas. Bromus tectorum L. (commonly called downy brome or cheatgrass), which is found in Rocky Mountain National Park (hereafter, the Park), Colorado, USA, has been implicated in early spring competition with native grasses, decreased soil nitrogen, altered nutrient and hydrologic regimes, and increased fire intensity. We estimated the potential distribution of B. tectorum in the Park based on occurrence records (n = 211), current and future climate, and distance to roads and trails. An ensemble of six future climate scenarios indicated the habitable area of B. tectorum may increase from approximately 5.5% currently to 20.4% of the Park by the year 2050. Using ordination methods we evaluated the climatic space occupied by B. tectorum in the Park and how this space may shift given future climate change. Modeling climate change at a small extent (1,076 km2) and at a fine spatial resolution (90 m) is a novel approach in species distribution modeling, and may provide inference for microclimates not captured in coarse-scale models. Maps from our models serve as high-resolution hypotheses that can be improved over time by land managers to set priorities for surveys and removal of invasive species such as B. tectorum.

[1]  Mark E. Miller,et al.  Performance of Bromus tectorum L. in relation to soil properties, water additions, and chemical amendments in calcareous soils of southeastern Utah, USA , 2006, Plant and Soil.

[2]  Miroslav Dudík,et al.  Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation , 2008 .

[3]  C. Favier,et al.  Neotropical C3/C4 grass distributions – present, past and future , 2012 .

[4]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[5]  William L. Baker,et al.  Scale and Pattern of Cheatgrass (Bromus tectorum) Invasion in Rocky Mountain National Park , 2011 .

[6]  W. Thuiller,et al.  Predicting species distribution: offering more than simple habitat models. , 2005, Ecology letters.

[7]  P. Adler,et al.  Warming, competition, and Bromus tectorum population growth across an elevation gradient , 2014 .

[8]  M. Fortin,et al.  Measuring ecological niche overlap from occurrence and spatial environmental data , 2012 .

[9]  Bruce L. Webber,et al.  CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling , 2012 .

[10]  Sunil Kumar,et al.  Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia , 2009 .

[11]  L. Kumar,et al.  Future distributions of Fusarium oxysporum f. spp. in European, Middle Eastern and North African agricultural regions under climate change , 2014 .

[12]  J. Franklin,et al.  Modeling plant species distributions under future climates: how fine scale do climate projections need to be? , 2013, Global change biology.

[13]  J. Canadell,et al.  Global and regional drivers of accelerating CO2 emissions , 2007, Proceedings of the National Academy of Sciences.

[14]  R. Turkington,et al.  THE BIOLOGY OF CANADIAN WEEDS.: 75. Bromus tectorum L. , 1986 .

[15]  A. Peterson,et al.  Use of niche models in invasive species risk assessments , 2011, Biological Invasions.

[16]  P. Soltis,et al.  Genetic variation in Bromus tectorum (Poaceae): introduction dynamics in North America. , 1993 .

[17]  P. Marquet,et al.  A Significant Upward Shift in Plant Species Optimum Elevation During the 20th Century , 2008, Science.

[18]  L. Kumar,et al.  Climate Change Impacts on the Future Distribution of Date Palms: A Modeling Exercise Using CLIMEX , 2012, PloS one.

[19]  W. Kunin,et al.  The effect of spatial resolution on projected responses to climate warming , 2012 .

[20]  Tongli Wang,et al.  ClimateWNA—High-Resolution Spatial Climate Data for Western North America , 2012 .

[21]  P. Hernandez,et al.  The effect of sample size and species characteristics on performance of different species distribution modeling methods , 2006 .

[22]  M. Araújo,et al.  Uses and misuses of bioclimatic envelope modeling. , 2012, Ecology.

[23]  S. Meyer,et al.  Genetic variation and local adaptation at a cheatgrass (Bromus tectorum) invasion edge in western Nevada , 2009, Molecular ecology.

[24]  Matthew J. Smith,et al.  Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates , 2013, bioRxiv.

[25]  Richard E. Glor,et al.  ENMTools: a toolbox for comparative studies of environmental niche models , 2010 .

[26]  A. Townsend Peterson,et al.  Rethinking receiver operating characteristic analysis applications in ecological niche modeling , 2008 .

[27]  B. E T H A N.  Regional analysis of the impacts of climate change on cheatgrass invasion shows potential risk and opportunity , 2008 .

[28]  J. Paruelo,et al.  Relative Abundance of Plant Functional Types in Grasslands and Shrublands of North America , 1996 .

[29]  R. Shaw,et al.  Range shifts and adaptive responses to Quaternary climate change. , 2001, Science.

[30]  Damaris Zurell,et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance , 2013 .

[31]  Sunil Kumar,et al.  Distributional Changes and Range Predictions of Downy Brome (Bromus tectorum) in Rocky Mountain National Park , 2011, Invasive Plant Science and Management.

[32]  N. Reid,et al.  Climate Change and the Potential Distribution of an Invasive Shrub, Lantana camara L , 2012, PloS one.

[33]  P. Vitousek,et al.  Biological invasions by exotic grasses, the grass/fire cycle, and global change , 1992 .

[34]  Sunil Kumar,et al.  Environmental conditions associated with bat white‐nose syndrome mortality in the north‐eastern United States , 2012 .

[35]  Brendan A. Wintle,et al.  Correlative and mechanistic models of species distribution provide congruent forecasts under climate change , 2010 .

[36]  L. Chisholm,et al.  Climate change at the landscape scale: predicting fine‐grained spatial heterogeneity in warming and potential refugia for vegetation , 2009 .

[37]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[38]  S. Reed,et al.  Eco-evolutionary responses of Bromus tectorum to climate change: implications for biological invasions , 2013, Ecology and evolution.

[39]  David R. Anderson,et al.  Avoiding pitfalls when using information-theoretic methods , 2002 .

[40]  G. McCabe,et al.  Regional patterns and proximal causes of the recent snowpack decline in the Rocky Mountains, U.S. , 2013 .

[41]  C. Delong,et al.  Corroboration of biogeoclimatic ecosystem classification climate zonation by spatially modelled climate data , 2009, Journal of Ecosystems and Management.

[42]  Catherine S. Jarnevich,et al.  Modelling invasion for a habitat generalist and a specialist plant species , 2008 .

[43]  B. Huntley,et al.  The relative importance of climate and habitat in determining the distributions of species at different spatial scales: a case study with ground beetles in Great Britain , 2012 .

[44]  Catherine S. Jarnevich,et al.  Near term climate projections for invasive species distributions , 2009, Biological Invasions.

[45]  Sunil Kumar,et al.  Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US , 2009 .

[46]  A. Gitelman,et al.  Predicting timing of downy brome (Bromus tectorum) seed production using growing degree days , 2004, Weed Science.

[47]  M. Austin,et al.  Improving species distribution models for climate change studies: variable selection and scale , 2011 .

[48]  R. Pearson.  Species’ Distribution Modeling for Conservation Educators and Practitioners , 2010 .

[49]  K. J. Willis,et al.  The ability of climate envelope models to predict the effect of climate change on species distributions , 2007 .

[50]  Antonio DiTommaso,et al.  Climate change and weed adaptation: can evolution of invasive plants lead to greater range expansion than forecasted? , 2011 .

[51]  M. Zappa,et al.  Climate change and plant distribution: local models predict high‐elevation persistence , 2009 .

[52]  Tongli Wang,et al.  Potential effects of climate change on ecosystem and tree species distribution in British Columbia. , 2006, Ecology.

[53]  John B. Bradford,et al.  Controls over invasion of Bromus tectorum: The importance of climate, soil, disturbance and seed availability , 2006 .

[54]  Christine S. Sheppard.  How does selection of climate variables affect predictions of species distributions? A case study of three new weeds in New Zealand , 2013 .

[55]  A. Peterson,et al.  Effects of sample size on the performance of species distribution models , 2008 .

[56]  T. Stohlgren,et al.  A Modified-Whittaker nested vegetation sampling method , 1995, Vegetatio.

[57]  Jessica C. Stanton,et al.  Combining static and dynamic variables in species distribution models under climate change , 2012 .

[58]  K. Schierenbeck,et al.  Hybridization as a stimulus for the evolution of invasiveness in plants? , 2006, Euphytica.

[59]  M. Kearney,et al.  Correlation and process in species distribution models: bridging a dichotomy , 2012 .

[60]  Richard N. Mack,et al.  Invasion of Bromus tectorum L. into Western North America: An ecological chronicle , 1981 .

[61]  Wilfried Thuiller,et al.  Invasive species distribution models – how violating the equilibrium assumption can create new insights , 2012 .

[62]  Mark New,et al.  Ensemble forecasting of species distributions. , 2007, Trends in ecology & evolution.

[63]  Antoine Guisan,et al.  Niche dynamics in space and time. , 2008, Trends in ecology & evolution.