Abstract
Context
No single model can capture the complex species range dynamics under changing climates—hence the need for a combination approach that addresses management concerns.
Objective
A multistage approach is illustrated to manage forested landscapes under climate change. We combine a tree species habitat model—DISTRIB II, a species colonization model—SHIFT, and knowledge-based scoring system—MODFACs, to illustrate a decision support framework.
Methods
Using shortleaf pine (Pinus echinata) and sugar maple (Acer saccharum) as examples, we project suitable habitats under two future climate change scenarios (harsh, Hadley RCP8.5 and mild CCSM RCP4.5 at ~2100) at a resolution of 10 km and assess the colonization likelihood of the projected suitable habitats at a 1 km resolution; and score biological and disturbance factors for interpreting modeled outcomes.
Results
Shortleaf pine shows increased habitat northward by 2100, especially under the harsh scenario of climate change, and with higher possibility of natural migration confined to a narrow region close to the current species range boundary. Sugar maple shows decreased habitat and has negligible possibility of migration within the US due to a large portion of its range being north of the US border. Combination of suitable habitats with colonization likelihoods also allows for identification of potential locations appropriate for assisted migration, should that be deemed feasible.
Conclusion
The combination of these multiple components using diverse approaches leads to tools and products that may help managers make management decisions in the face of a changing climate.
Similar content being viewed by others
References
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 43:1223–1232
Araújo M, Peterson A (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539
Belmaker J, Zarnetske P, Tuanmu M, Zonneveld S, Record S, Strecker A, Beaudrot L (2015) Empirical evidence for the scale dependence of biotic interactions. Glob Ecol Biogeogr (in press). doi:10.1111/geb.12311
Bowman D, Perry G, Marston JB (2015) Feedbacks and landscape-level vegetation dynamics. Trends Ecol Evol 30:255–260
Bradter U, Kunin W, Altringham J, Thorn TJ, Benton TG (2013) Identifying appropriate spatial scales of predictors in species distribution models with the random forest algorithm. Methods Ecol Evol 4:167–174
Brandt L, He H, Iverson L, Thompson FR III, Butler P, Handler S, Janowiak M, Shannon PD, Swanston C, Albrecht M, Blume-Weaver R, Deizman P, DePuy J, Dijak WD, Dinkel G, Fei S, Jones-Farrand DT, Leahy M, Matthews S, Nelson P, Oberle B, Perez J, Peters M, Prasad A, Schneiderman JE, Shuey J, Smith AB, Studyvin C, Tirpak JM, Walk JW, Wang WJ, Watts L, Weigel D, Westin S (2014) Central Hardwoods ecosystem vulnerability assessment and synthesis: a report from the Central Hardwoods Climate Change Response Framework project. U.S. Department of Agriculture, Forest Service, Northern Research Station, Gen. Tech. Rep. NRS-124, Newtown Square
Bucklin D, Basille M, Benscoter A, Brandt LA, Mazzotti FJ, Romanach SS, Speroterra C, Watling JI (2015) Comparing species distribution models constructed with different subsets of environmental predictors. Divers Distrib II 21:23–35
Butler PR, Iverson L, Thompson FR, Brandt L, Handler S, Janowiak M, Shannon PD, Swanston C, Karriker K, Bartig J, Connolly S, Dijak W, Bearer S, Blatt S, Brandon A, Byers E, Coon C, Culbreth T, Daly J, Dorsey W, Ede D, Euler C, Gillies N, Hix DM., Johnson C, Lyte L, Matthews S, McCarthy D, Minney D, Murphy D, O’Dea C, Orwan R, Peters M, Prasad A, Randall C, Reed J, Sandeno C, Schuler T, Sneddon L, Stanley B, Steele A, Stout S, Swaty R, Teets J, Tomon T, Vanderhorst J, Whatley J, Zegre N (2015) Central Appalachians forest ecosystem vulnerability assessment and synthesis: a report from the Central Appalachians Climate Change Response Framework project. U.S. Department of Agriculture, Forest Service, Northern Research Station, Gen. Tech. Rep. NRS-146, Newtown Square
Clark JS, Silman M, Kern R, Macklin E, Hillerisambers J (1999) Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80:1475–1494
Cole K (2010) Vegetation response to early Holocene warming as an analog for current and future changes. Conserv Biol 24:29–37
Crase B, Liedloff A, Vesk P, Fukuda Y, Wintle BA (2014) Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts. Glob Chang Biol 20:2566–2579
Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris PP (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol 28:2031–2064
Davis MB, Shaw RG (2001) Range shifts and adaptive responses to Quaternary climate change. Science 292:673–679
Diniz-Filho J, Bini L (2008) Macroecology, global change and the shadow of forgotten ancestors. Glob Ecol Biogeogr 17:11–17
Dobrowski S (2011) A climatic basis for microrefugia: the influence of terrain on climate. Glob Change Biol 17:1022–1035
Dormann C, Schymanski S, Cabral J, Chuine I, Graham C, Hartig F, Kearney M, Morin X, Romermann C, Schroder B, Singer A (2012) Correlation and process in species distribution models: bridging a dichotomy. J Biogeogr 39:2119–2131
Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342
ESRI (2015) ArcGIS [GIS software], version 10.3.1. Environmental Systems Research Institute, Inc., Redlands
Franklin J (2013) Species distribution models in conservation biogeography: developments and challenges. Divers Distrib II 19:1217–1223
Gent PR, Danabasoglu G, Donner LJ, Holland MM, Hunke EC, Jayne SR, Lawrence DM, Neale RB, Rasch PJ, Vertenstein M, Worley PH, Yang ZL, Zhang M (2011) The Community Climate System Model, Version 4. J Clim 24:4973–4991
Godman RM, Yawney HW, Tubbs CH (1990) Acer saccharum Marsh, sugar maple. In: Burns RM, Honkala BH (eds) Silvics of North America: 1. Conifers. USDA Forest Service Agricultural Handbook 654, Washington DC, pp 194–215
Guisan A, Tingley R, Baumgartner JB, Naujokaitis-Lewis I, Sutcliffe PR, Tulloch AIT, Regan TJ, Brotons L, McDonald-Madden E, Mantyka-Pringle C, Martin TG, Rhodes JR, Maggini R, Setterfield SA, Elith J, Schwartz MW, Wintle BA, Broennimann O, Austin M, Ferrier S, Kearney MR, Possingham HP, Buckley YM (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435
Guth PL (2006) Geomorphometry from SRTM: comparison to NED. Photogramm Eng Remote Sens 72:269–277
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York
Higgins SI, Clark JS, Nathan R, Hovestadt T, Schurr F, Fragoso JM, Aguiar R, Ribbens E, Lavorel S (2003) Forecasting plant migration rates: managing uncertainty for risk assessment. J Ecol 91:341–347
Iverson LR, Matthews SN, Prasad AM, Peters MP, Yohe G (2012) Development of risk matrices for evaluating climatic change responses of forested habitats. Clim Change 114:231–243
Iverson LR, Prasad A, Matthews S, Peters M (2008) Estimating potential habitat for 134 eastern US tree species under six climate scenarios. For Ecol Manag 254:390–406
Iverson LR, Prasad AM, Matthews S, Peters M (2011) Lessons learned while integrating habitat, dispersal, disturbance, and life-history traits into species habitat models under climate change. Ecosystems 14:1005–1102
Iverson LR, Schwartz MW, Prasad AM (2004) How fast and far might tree species migrate in the eastern United States due to climate change? Glob Ecol Biogeogr 13:209–219
Jackson H, Fahrig L (2015) Are ecologists conducting research at the optimal scale? Glob Ecol Biogeogr 24:52–63
Janowiak MK, Iverson LR, Mladenoff DJ, Peters E, Wythers KR, Xi W, Brandt LA, Butler PR, Handler SD, Shannon P, Swanston C, Parker LR, Amman AJ, Bogaczyk B, Handler C, Lesch E, Reich PB, Matthews S, Peters M, Prasad A, Khanal S, Liu F, Bal T, Bronson D, Burton A, Ferris J, Fosgitt J, Hagan S, Johnston E, Kane E, Matula C, O'Connor R, Higgins D, St Pierre M, Daley J, Davenport M, Emery MR, Fehringer D, Hoving CL, Johnson G, Neitzel D, Notaro M, Rissman A, Rittenhouse C, Ziel R (2014) Forest ecosystem vulnerability assessment and synthesis for northern Wisconsin and western Upper Michigan: a report from the Northwoods Climate Change Response Framework project. U.S. Department of Agriculture, Forest Service, Northern Research Station, Gen. Tech. Rep. NRS-136, Newtown Square
Jones CD, Hughes JK, Bellouin N, Hardiman SC, Jones GS, Knight J, Liddicoat S, O'Connor FM, Andres RJ, Bell C, Boo K.-O, Bozzo A, Butchart N, Cadule P, Corbin KD, Doutriaux-Boucher M, Friedlingstein P, Gornall J, Gray L, Halloran PR, Hurtt G, Ingram WJ, Lamarque J.-F, Law RM, Meinshausen M, Osprey S, Palin EJ, Parsons Chini L, Raddatz T, Sanderson MG, Sellar AA, Schurer A, Valdes P, Wood N, Woodward S, Yoshioka M, Zerroukat M (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570
Kühn I, Böhning-Gaese K, Cramer W, Klotz S (2008) Macroecology meets global change research. Glob Ecol Biogeogr 17:3–4
Lawler JJ, White D, Neilson RP et al (2006) Predicting climate-induced range shifts: model differences and model reliability. Glob Change Biol 12:1568–1584
Lawson ER (1990) Pinus echinata Mill, shortleaf pine. In: Burns RM, Honkala BH (eds) Silvics of North America: 1. Conifers. USDA Forest Service Agricultural Handbook 654, Washington DC, pp 316–32
Levins R (1966) The strategy of model building in population biology. Am Sci 54:421–431
Levins R (1993) A response to Orzack and Sober: formal analysis and the fluidity of science. Q Rev Biol 68:547–555
Little EL Jr (1971) Atlas of United States trees, vol 1. Conifers and important hardwoods. USDA Forest Service Miscellaneous Publication 1146, Washington
Matthews S, Iverson L, Prasad A, Peters MP, Rodewald PG (2011) Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history-factors. For Ecol Manag 262:1460–1472
McGill BJ (2010) Matters of scale. Science 328:575–576
McLachlan J, Clark J (2004) Reconstructing historical ranges with fossil data at continental scales. For Ecol Manag 197:139–147
Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, Matsumoto K, Montzka SA, Raper SCB (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109:213–241
Merow C, Smith M, Edwards TC, Guisan A, McMahon SM, Normand S, Thuiller W, Wüest RO, Zimmermann NE, Elith J (2014) What do we gain from simplicity versus complexity in species distribution models? Ecography 37:1267–1281
Miller J, Franklin J, Aspinall R (2007) Incorporating spatial dependence in predictive vegetation models. Ecol Model 202:225–242
Monahan WB, Cook T, Melton F, Connor J, Bobowski B (2013) Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park. PLoS ONE 8(12):e83163
Morin X, Thuiller W (2009) Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology 90:1301–1313
Nathan R, Schurr FM, Spiegel O, Steinitz O, Trakhtenbrot A, Tsoar A (2008) Mechanisms of long-distance seed dispersal. Trends Ecol Evol 23:638–647
Nowacki GJ, Abrams MD (2008) The demise of fire and “mesophication” of forests in the eastern United States. Bioscience 58:123–138
NRCS (Natural Resources Conservation Service) (2009) Soil Survey Geographic (SSURGO). http://soildatamart.nrcs.usda.gov/State.aspx. Accessed between Aug 2009 and Nov 2010
Orzack SH, Sober E (1993) A critical assessment of Levins’s the strategy of model building in population biology (1966). Q Rev Biol 68:533–546
Pederson N, D’Amato A, Dyer J, Foster DR, Goldblum D, Hart JL, Hessl AE, Iverson LR, Jackson ST, Martin-Benito D, McCarthy BC, McEwan RW, Mladenoff DJ, Parker AJ, Shuman B, Williams JW (2015) Climate remains an important driver of post-European vegetation change in the eastern United States. Glob Chang Biol 21:2105–2110
Pedlar JH, McKenney DW, Aubin I, Beardmore T, Beaulieu J, Iverson L, Neill GAO, Winder RS, Ste-marie C (2012) Placing forestry in the assisted migration debate. Bioscience 62:835–884
Peters MP, Iverson LR, Prasad AM, Matthews SN (2013a) Integrating fine-scale soil data into species distribution models: preparing Soil Survey Geographic (SSURGO) data from multiple counties, p 70. US Department of Agriculture, Forest Service, Northern Research Station, Newtown Square
Peters MP, Matthews SN, Iverson LR, Prasad AM (2013b) Delineating generalized species boundaries from species distribution data and a species distribution model. Int J Geogr Inf Sci 28:1547–1560
Peterson AT, Soberón J (2012) Species distribution modeling and ecological niche modeling: getting the concepts right. Nat Conserv 10:1–6
Portnoy S, Willson MF (1993) Seed dispersal curves: behavior of the tail of the distribution. Evol Ecol 7:25–44
Prasad A, Gardiner J, Iverson L, Matthews SN, Peters M (2013) Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change. Glob Change Biol 19:2196–2208
Prasad A, Iverson L, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199
Prasad AM (2015) Macroscale intraspecific variation and environmental heterogeneity: analysis of cold and warm zone abundance, mortality, and regeneration distributions of four eastern US tree species. Ecol Evol 5:5033–5048
PRISM Climate Group. Oregon State University. http://prism.oregonstate.edu
Randin C, Dirnböck T, Dullinger S, Zimmermann NE, Zappa M, Guisan A (2006) Are niche-based species distribution models transferable in space? J Biogeogr 33:1689–1703
Rehfeldt GE, Worrall JJ, Marchetti SB, Crookston NL (2015) Adapting forest management to climate change using bioclimate models with topographic drivers. Forestry 88:528–539
Schaetzl RJ, Krist FJ Jr, Miller BA (2012) A taxonomically based ordinal estimate of soil productivity for landscape-scale analyses. Soil Sci 177:288–299
Schwartz M (1992) Modeling effects of habitat fragmentation on the ability of trees to respond to climatic warming. Biodivers Conserv 2:51–61
Schwartz MW, Hellmann JJ, Lachlan JMM, Sax DF, Borevitz JO, Brennan J, Camacho AE, Ceballos G, Clark JR, Doremus H, Early R, Etterson JR, Fielder D, Gill JL, Gonzalez P, Green N, Hannah L, Jamieson DW, Javeline D, Minteer BA, Odenbaugh J, Polasky S, Richardson DM, Root TL, Safford HD, Sala O, Schneider SH, Thompson AR, Williams JW, Vellend M, Vitt P, Zellmer S (2012) Managed relocation: integrating the scientific, regulatory, and ethical challenges. Bioscience 62:732–774
Svenning J, Gravel D, Holt R, Schurr FM, Thuiller W, Münkemüller T, Schiffers KH, Dullinger S, Edwards TC, Hickler T, Higgins SI, Nabel JEMS, Pagel J, Normand S (2014) The influence of interspecific interactions on species range expansion rates. Ecography 37:1198–1209
Svenning J, Skov F (2007) Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecol Lett 10:453–460
Thornthwaite C, Mather J (1957) Instructions and tables for computing potential evapotranspiration and the water balance. Publ Climatol 10:185–311
Thrasher B, Xiong J, Wang W, Melton F, Michaelis A, Nemani R (2013) Downscaled climate projections suitable for resource management. Trans Am Geophys Union 94:321–323
Thuiller W, Albert C, Araújo M, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–152
Warren D (2012) In defense of “niche modeling”. Trends Ecol Evol 27:497–500
Wiens JA, Stralberg D, Jongsomjit D (2009) Niches, models, and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci USA 106:19729–19736
Woudenberg SW, Conkling BL, O’Connell BM, LaPoint EB, Turner JA, Waddell KL (2010) The Forest Inventory and Analysis Database: database description and user’s manual version 4.0 for Phase 2. Gen. Tech. Rep. RMRSGTR-245, p. 336. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins
Yansa C (2006) The timing and nature of Late Quaternary vegetation changes in the northern Great Plains, USA and Canada: a re-assessment of the spruce phase. Quat Sci Rev 25:263–281
Acknowledgments
Thanks to the Northern Research Station, USDA Forest Service, for funding, and external reviewers Maria Janowiak and Laura Leites for their help in improving the manuscript. We declare no conflict of interest to the best of our knowledge.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Prasad, A.M., Iverson, L.R., Matthews, S.N. et al. A multistage decision support framework to guide tree species management under climate change via habitat suitability and colonization models, and a knowledge-based scoring system. Landscape Ecol 31, 2187–2204 (2016). https://doi.org/10.1007/s10980-016-0369-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10980-016-0369-7