Fire legacies impact conifer regeneration across environmental gradients in the U.S. northern Rockies
- 996 Downloads
An increase in the incidence of large wildfires worldwide has prompted concerns about the resilience of forest ecosystems, particularly in the western U.S., where recent changes are linked with climate warming and 20th-century land management practices.
To study forest resilience to recent wildfires, we examined relationships among fire legacies, landscape features, ecological conditions, and patterns of post-fire conifer regeneration.
We quantified regeneration across 182 sites in 21 recent large fires in dry mixed-conifer forests of the U.S. northern Rockies. We used logistic and negative binomial regression to predict the probability of establishment and abundance of conifers 5–13 years post-fire.
Seedling densities varied widely across all sites (0–127,500 seedlings ha−1) and were best explained by variability in distance to live seed sources (β = −0.014, p = 0.002) and pre-fire tree basal area (β = 0.072, p = 0.008). Beyond 95 m from the nearest live seed source, the probability of seedling establishment was low. Across all the fires we studied, 75 % of the burned area with high tree mortality was within this 95-m threshold, suggesting the presence of live seed trees to facilitate natural regeneration.
Combined with the mix of species present within the burn mosaic, dry mixed-conifer forests will be resilient to large fires across our study region, provided that seedlings survive, fire do not become more frequent, high-severity patches do not get significantly larger, and post-fire climate conditions remain suitable for seedling establishment and survival.
KeywordsTree regeneration Mixed-severity Wildfire Patch size Distance to seed source Resilience
We thank K. Baker, M. Chaney, and A. Wells for assistance with data collection, S. Busby, R. Ramsey, and O. Guthrie for assistance with data collection and entry, Tim Johnson for helpful insights and assistance with statistical analysis, Zack Holden for providing the R script for calculating the distances to patch edges, and John Abatzoglou for providing downscaled climate data. This work was supported by grants from the National Aeronautics and Space Administration under award NNX11AO24G (PM), the National Science Foundation under awards DGE-0903479 (PM, KBK) and IIA-0966472 (PEH), the Joint Fire Science Program Graduate Research Innovation program under award 12-3-1-13 (KBK, PEH), and the University of Idaho Stillinger Trust Forest Science fellowship (KBK).
- Arno SF, Parsons DJ, Keane RE (2000) Mixed-severity fire regimes in the northern Rocky Mountains: consequences of fire exclusion and options for the future. In: Cole D, McCool S, Borrie W, O’Laughlin J (eds) Wilderness science in a time of change, Missoula, MT. 1999. vol 5: Wilderness ecosystems, threats, and management. USDA Forest Service, Rocky Mountain Research Station, pp 225-232Google Scholar
- Baker WL (2009) Fire Ecology in Rocky Mountain Landscapes. Island Press, United StatesGoogle Scholar
- Bivand R, Keitt T, Rowlingson B (2014) rgdal: Bindings for the Geospatial Data Abstraction Library. R package version 0.8-16. Available from http://CRAN.R-project.org/package=rgdal
- Calvo L, Torres O, Valbuena L, Luis-Calabuig E (2013) Short Communication. Recruitment and early growth of Pinus pinaster seedlings over five years after a wildfire in NW Spain. Forest Systems 22(3):582–586Google Scholar
- Dillon GK, Holden ZA, Morgan P, Crimmins MA, Heyerdahl EK, Luce CH (2011) Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006. Ecosphere 2(12):art130Google Scholar
- Gibson CE, Morgan P, Wilson AM (2014) Atlas of digital polygon fire extents for Idaho and western Montana. 2nd edn. Forest Service Research Data Archive, Fort Collins, CO. DOI: 10.2737/RDS-2009-0006-2
- Halofsky JE, Donato DC, Hibbs DE et al (2011) Mixed-severity fire regimes: lessons and hypotheses from the Klamath-Siskiyou Ecoregion. Ecosphere 2(4):art40Google Scholar
- Jackman S (2012) pscl: Classes and Methods for R Developed in the Political Science Computational Labratory. Department of Political Science, Stanford University, Stanford, California. Available from http://cran.r-project.org/web/packages/pscl/pscl.pdf
- Johnstone JF, McIntire EJB, Pedersen EJ, King G, Pisaric MJF (2010b) A sensitive slope: estimating landscape patterns of forest resilience in a changing climate. Ecosphere 1(6):art14Google Scholar
- Keeley JE, Ne’eman G, Fotheringham C (1999) Immaturity risk in a fire-dependent pine. J of Mediterranean Ecol 1:41–48Google Scholar
- Keyser TL, Lentile LB, Smith FW, Shepperd WD (2008) Changes in forest structure after a large, mixed-severity wildfire in ponderosa pine forests of the Black Hills, South Dakota. USA. For Sci 54(3):328–338Google Scholar
- LANDFIRE (2010) Existing vegetation type layer. U.S. Geological Survey, Department of Interior. Available from http://landfire.cr.usgs.gov/viewer/ (accessed 30 March 2012)
- Littell JS (2011) Impacts in the next few decades and the next century: fire and climate. In: Council NR (ed) Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millennia. The National Academies Press, Washington, D.C., pp 178–180Google Scholar
- McCaughey WW, Schmidt WC, Shearer RC (1986) Seed dispersal characteristics of conifers in the inland mountain West. In: Shearer RC (ed) Conifer tree seed in the inland mountain West, Missoula, MT. USFS Gen Tech Rep INT-023. Intermountain Research Station, pp 50–62Google Scholar
- McKenzie D, Tinker D (2012) Fire-induced shifts in overstory tree species composition and associated understory plant composition in Glacier National Park, Montana. Plant Ecol:1-18Google Scholar
- Morgan P, Heyerdahl EK, Miller C, Wilson AM, Gibson CE (2014) Northern Rockies pyrogeography: An example of fire atlas utility. Fire Ecol 10(1):14–30Google Scholar
- MTBS (2011) Monitoring Trends in Burn Severity Project Data Access. U.S. Geologic Survey, Department of the Interior. Available from www.mtbs.gov/dataaccess.html (accessed 30 March 2012)
- PRISM (2014) PRISM Climate Group. Oregon State University, Corvallis, OR, USA. Available from http://prism.oregonstate.edu (accessed June 3 2014)
- R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from http://www.R-project.org/
- Rogers BM, Neilson RP, Drapek R et al (2011) Impacts of climate change on fire regimes and carbon stocks of the U.S. Pacific Northwest. J Geophys Res: Biogeosci 116(G3):G03037Google Scholar
- Shatford J, Hibbs D, Puettmann K (2007) Conifer regeneration after forest fire in the Klamath-Siskiyous: how much, how soon? J For 105(3):139–146Google Scholar
- USDA (2014) Web Soil Survey. US Department of Agriculture. Available from http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx (accessed Dec. 3 2014)
- USFS (2012) Ch. 500: Planning for Cone and Seed Production. Forest Service Handbook Northern Region (R1) Seed Handbook. USDA Forest Service, Missoula, MT, USA. pp 31Google Scholar
- van Etten J (2014) gdistance: Distances and routes on geographical grids. R package version 1.1-5. Available from http://CRAN.R-project.org/package=gdistance
- Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer Science & Business Media, LLC. New York, NY, U.S.AGoogle Scholar
- Zuur AF, Savaliev AA, Ieno EN (2012) Zero inflated models and generalized linear mixed models with R. Highland Statistics Ltd., Newburg, U.K.Google Scholar