Invasive species are expected to shift their ranges to track preferred environments as climate changes. This map indicates the agreement of future climate models predicting county-level presence for the selected species by 2041 - 2060 based on currently available evidence. Higher numbers indicate higher agreement among models that the county will be suitable for the chosen species by 2041 - 2060.
Occurrence data were compiled from 33 data sources. Distribution models were run using MaxEnt software (Phillips et al. 2006) using average January minimum temperature (a proxy for cold tolerance), average maximum July temperature (a proxy for heat tolerance), and average annual precipitation (a proxy for water requirements) as predictor variables for current (1950-2000 average) and future (2041-2060 average) climate assembled from WorldClim (Hijmans et al. 2005) . Future climate assumed Relative Concentration Pathway 4.5, which represents a target forcing of 4.5 W m− 2 above the pre-industrial baseline by 2100, with stabilization after that date (Collins et al. 2013). Models were run at a 5 km spatial resolution, thresholded to include 95% of occurrence records within the predicted range, and summarized to counties.
Full modeling details can be found in:
Allen, J.M. and B.A. Bradley. 2016. Out of the weeds? Reduced plant invasion risk with climate change in the continental United States. Biological Conservation 203: 306-312.PDF Summary
Research was conducted in collaboration with the Northeast Regional Invasive Species and Climate Change Management Network (https://www.risccnetwork.org/northeast) which seeks to build stronger scientist-manager communities to address the compounding effects of invasive species and climate change.
This map was funded by the Northeastern IPM Center through Grant #2014-70006-22484 and supported by Southern IPM Center through Grant #2018-70006-28884 from the USDA National Institute of Food and Agriculture, Crop Protection and Pest Management, Regional Coordination Program
Citations:
Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver, M. Wehner. 2013. Long-term climate change: projections, commitments and irreversibility. In: T. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, P.M. Midgley (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, New York, NY, pp. 1029-1136.
Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones, and A. Jarvis. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.
Phillips, S.J., R.P. Anderson, and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259.
