Complex interactions among successional trajectories and climate govern spatial resilience after severe windstorms in central Wisconsin, USA
Resilience is a concept central to the field of ecology, but our understanding of resilience is not sufficient to predict when and where large changes in species composition might occur following disturbances, particularly under climate change.
Our objective was to estimate how wind disturbance shapes landscape-level patterns of engineering resilience, defined as the recovery of total biomass and species composition after a windstorm, under climate change in central Wisconsin.
We used a spatially-explicit, forest simulation model (LANDIS-II) to simulate how windstorms and climate change affect forest succession and used boosted regression tree analysis to isolate the important drivers of resilience.
At mid-century, biomass fully recovered to current conditions, but neither biomass nor species composition completely recovered at the end of the century. As expected, resilience was lower in the south, but by the end of the century, resilience was low throughout the landscape. Disturbance and species’ characteristics (e.g., the amount of area disturbed and the number of species) explained half of the variation in resilience, while temperature and soil moisture comprised only 17% collectively.
Our results illustrate substantial spatial patterns of resilience at landscape scales, while documenting the potential for overall declines in resilience through time. Species diversity and windstorm size were far more important than temperature and soil moisture in driving long term trends in resilience. Finally, our research highlights the utility of using machine learning (e.g., boosted regression trees) to discern the underlying mechanisms of landscape-scale processes when using complex spatially-interactive and non-deterministic simulation models.
KeywordsClimate change Windstorms Resilience Forest simulation model LANDIS-II
Funding was provided by NSF CNH Grant #1617396. We acknowledge substantial contributions by Ty Wilson who gave us access to all his nearest neighbor maps and provided technical support for our biomass calculations using USDA Forest Inventory data. We also appreciate Jared Oyler’s assistance in preparation of the climate forcing data.
- Curtis JT (1959) The vegetation of Wisconsin. An ordination of plant communities. University of Wisconsin Press, MadisonGoogle Scholar
- Greenwell B, Boehmke B, Cunningham J (2018) landscapemetrics: landscape metrics for categorical map patterns. R package. In: 2.1.5 v. (ed)Google Scholar
- Grimm V, Calabrese JM (2011) What is resilience? A short introduction. In: Viability and resilience of complex systems. Springer, Berlin, pp 3–13Google Scholar
- Gustafson EJ (2016) LANDIS-II Linear Wind Extension v1.0 Extension User Guide. The LANDIS-II FoundationGoogle Scholar
- Handler S, Duveneck MJ, Iverson L, Peters E, Scheller RM, Wythers KR, Brandt L, Butler P, Janowiak M, Shannon PD, Swanston C, Barrett K, Kolka R, McQuiston C, Palik B, Reich PB, Turner C, White M, Adams C, D’Amato A, Hagell S, Johnson P, Johnson R, Larson M, Matthews S, Montgomery R, Olson S, Peters M, Prasad A, Rajala J, Daley J, Davenport M, Emery MR, Fehringer D, Hoving CL, Johnson G, Johnson L, Neitzel D, Rissman A, Rittenhouse C, Ziel R (2014) Minnesota forest ecosystem vulnerability assessment and synthesis: a report from the Northwoods climate change response framework project. In: NRS-129 GTR (ed) USDA Forest Service, Newtown Square, PennsylvaniaGoogle Scholar
- Hesselbarth MHK, Sciaini M, With KA, Wiegand K, Nowosad J (2018) landscapemetrics: landscape metrics for categorical map patterns. R package, version 1.1Google Scholar
- Higuera PE, Metcalf AL, Miller C, Buma B, McWethy DB, Metcalf EC, Ratajczak Z, Nelson CR, Chaffin BC, Stedman RC, McCaffrey S, Schoennagel T, Harvey BJ, Hood SM, Schultz CA, Black AE, Campbell D, Haggerty JH, Keane RE, Krawchuk MA, Kulig JC, Rafferty R, Virapongse A (2019) Integrating subjective and objective dimensions of resilience in fire-prone landscapes. Bioscience 69(5):379–388PubMedPubMedCentralCrossRefGoogle Scholar
- Holling CS, Gunderson LH (2002) Resilience and adaptive cycles. In: Panarchy: understanding transformations in human and natural systems, pp 25–62Google Scholar
- Janowiak MK, Iverson LR, Mladenoff DJ, Peters E, Wythers KR, Xi W, Brandt LA, Butter P, Amman A, Bogaczyk B, Handler SD, Shannon D, Swanston CW, Parker LR, Handler C, Lesch E, Reich PB, Matthews S, Peters MP, Prasad AM, Khanal S, Liu F, Bronson D, Bal T, Burton AJ, 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 AR, 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. Gen. Tech. Rep. NRS-136. US Department of Agriculture, Forest Service, Northern Research Station, Newtown Square, PA, pp 1–247Google Scholar
- Milfred CJ, Olson GW, Hole FD (1967) Soil resources and forest ecology of Menominee County, Wisconsin: By Clarence J. Milfred, Gerald W. Olson, and Francis D. Hole. State of WisconsinGoogle Scholar
- Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2013) Package ‘vegan’. Community ecology package, version 2.9Google Scholar
- Parton WJ, Anderson DW, Cole CV, Steward JWB (1983) Simulation of soil organic matter formation and mineralization in semiarid agroecosystems. In: Lowrance RR, Todd RL, Asmussen LE, Leonard RA (eds) Nutrient cycling in agricultural ecosystems. The University of Georgia, College of Agriculture Experiment Stations, AthensGoogle Scholar
- Parton WJ, Ojima DS, Cole CV, Schimel DS (1994) A general model for soil organic matters dynamics: sensitivity to litter chemistry, texture and management. In: Bryant RB, Arnold RW (eds) Quantitative modeling of soil forming processes: proceedings of a symposium sponsored by Divisions S-5 and S-9 of the Soil Science Society of America, Minneapolis, Minnesota, USA, vol 39. Soil Science Society of America, Madison, Wisconsin, pp 147–167Google Scholar
- Reyer CP, Brouwers N, Rammig A, Brook BW, Epila J, Grant RF, Holmgren M, Langerwisch F, Leuzinger S, Lucht W, Medlyn B, Pfeifer M, Steinkamp J, Vanderwel MC, Verbeeck H, Villela DM (2015) Forest resilience and tipping points at different spatio-temporal scales: approaches and challenges. J Ecol 103:5–15CrossRefGoogle Scholar
- Scheller RM, Domingo JB (2003) LANDIS-II Base Wind v2.1 Extension User GuideGoogle Scholar
- Serra-Diaz JM, Maxwell C, Lucash MS, Scheller RM, Laflower DM, Miller AD, Tepley AJ, Epstein HE, Anderson-Teixeira K, Thompson J (2018) Disequilibrium of fire-prone forests sets the stage for a rapid decline in conifer dominance during the 21st century. Sci Rep 8:6749PubMedPubMedCentralCrossRefGoogle Scholar
- Team RDC (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- USDA FS (2004) Chequamegon-Nicolet National Forests Land and Reource Management Plan. Rhinelander, WIGoogle Scholar