\<methods\>
\<methodStep\>
\<description\>
\<section\>
\<title\>Study area\</title\>
\<para\>The Greater Yellowstone Ecosystem (GYE) comprises 89,000 km2 (YNP 2017) of mostly federally managed land centered on Yellowstone and Grand Teton National Parks (Figure 1). Greater Yellowstone has cold, snowy winters and mild summers, with most annual precipitation falling as snow. Average summer temperature (1981-2010) is 12.3 °C, and annual precipitation averages 644 mm at centrally located Old Faithful in Yellowstone National Park (WRCC 2021). The region is expected to get warmer and drier over the 21st century, with lengthening fire seasons and harsher conditions for germination and establishment of young tree seedlings (Westerling et al. 2011; Romme and Turner 2015). Since 1950, Greater Yellowstone has warmed +1.3 °C, and annual snowfall has decreased by 25% (Hostetler et al. 2021). Soils are primarily derived from highly infertile, volcanic rhyolite; slightly less infertile andesite; or sedimentary parent materials (Despain 1990).\</para\>
\<para\>Subalpine forests cover much of the GYE between ~1900-3000 m elevation and historically recovered rapidly after infrequent severe fire due to prevalent serotinous lodgepole pine with its fire-stimulated canopy seed bank (Turner et al. 1999). Stand-level percent serotiny of lodgepole pine is highest at lower elevations (up to ~2300-2400 m; Tinker et al. 1994, Schoennagel et al. 2003). Approximately one-third of 1984-2010 area burned in US Northern Rocky Mountains subalpine forests was stand-replacing (Harvey et al. 2016a), and 19-25% of 1984-2020 short-interval area burned in Northwest US forests was stand-replacing in both fires (Harvey et al. 2023). Mean aboveground biomass in lodgepole pine-dominated forests averages 139 Mg ha-1 (live tree) and 98 Mg ha-1 (dead woody) across a 300-year chronosequence, and stand density stabilizes to approximately 1,200 stems ha-1 after 200 years of stand development (Kashian et al. 2005; 2013). \</para\>
\<para\>Other tree species in the subalpine zone include Douglas-fir (Pseudotsuga menziesii var. glauca) and quaking aspen (Populus tremuloides) at lower elevations, Engelmann spruce (Picea engelmannii) and subalpine fir (Abies lasiocarpa) at higher elevations, and whitebark pine (Pinus albicaulis) near upper treeline (Baker 2009). Douglas-fir, Engelmann spruce, subalpine fir, and non-serotinous lodgepole pine rely on wind dispersal from nearby live seed sources, and most seeds fall within 50 m of a live tree (McCaughey and Schmidt 1987; Gill et al. 2021). Whitebark pine and quaking aspen can disperse over longer distances (Turner et al. 2003; Lorenz et al. 2011), and aspen can also resprout after fire (Baker 2009).\</para\>
\</section\>
\<section\>
\<title\>Reburn and plot selection\</title\>
\<para\>We identified recent, large fires (1994-2018; > 404 ha) that severely burned subalpine forests at both short (< 30-year; n = 16 reburns) and long (> 125-year) intervals (Figure 1a; Appendix S1; Eidenshink et al. 2007). In 2021, we sampled 22 plot pairs (1-2 pairs per reburn) that each consisted of a 0.25-ha short-interval plot burned twice as stand-replacing fire and a topographically similar, nearby 0.25-ha long-interval plot burned as stand-replacing in the same recent fire (Figure 1c). These data were augmented with paired short- and long-interval post-fire plot data collected in 2000, 12 years after the 1988 fires (Schoennagel et al. 2003; Braziunas et al. 2023). Together, these datasets included 33 plot pairs (n = 66 plots) in 27 reburns widely distributed throughout the GYE and representing 7-28-year short fire return intervals, 3-27 years since most recent fire, 1798-2769 m in elevation, 0-356 ° aspect, and 0-25 ° slope (Figure 1, Appendix S2:Table S1 and Figure S1). \</para\>
\</section\>
\<section\>
\<title\>Field data collection\</title\>
\<para\>Forest recovery and fuels were sampled in 0.25-ha plots following standard methods (Turner et al. 2019; Nelson et al. 2016). Sapling/seedling (< 1.4 m height), tree (> 1.4 m height), and standing dead (> 1.4 m height) stem density were tallied in three parallel 2-m x 50-m belt transects. At 5-m intervals, we measured height, crown base height, and diameter at breast height (DBH) of the closest live tree by species; height and DBH of the closest standing dead snag; height of the closest sapling/seedling by species; and cover and average height of shrubs by species in 0.25-m2 quadrats (n = 25 quadrats for 6.25-m2 per plot). Downed woody and forest floor fuels were quantified with five 20-m Brown’s planar intersect transects (Brown 1974) oriented randomly from plot center (total length = 100 m per plot). We recorded 1-h (< 0.64 cm diameter) and 10-h (0.64-2.54 cm) fuels along the first 3 m, 100-h (2.54-7.60 cm) fuels along the first 10 m, and sound and rotten coarse woody debris (> 7.6 cm diameter, 1000-h fuel) along the full 20 m. Litter and duff depth were recorded at 2-m intervals at three locations per transect (n = 15 measurements per plot). At plot center we measured aspect, slope, and distance to unburned live forest edge. If live edge was not visible or too far to measure in the field, this distance was estimated in ArcGIS Desktop 10.6 from aerial imagery and burn severity perimeters. Field data from 2000 included live stem densities by species counted in four parallel 2-m x 50-m belt transects spaced 25 m apart (Schoennagel et al. 2003). \</para\>
\</section\>
\<section\>
\<title\>Biomass and fuels calculations\</title\>
\<para\>We derived live tree, dead snag, lodgepole pine sapling, and shrub aboveground biomass using allometric equations (Appendix S1). Snag biomass was summarized by size classes corresponding to downed wood (i.e., 1-, 10-, 100-, and 1000-h based on DBH). Canopy fuel load and bulk density were estimated from conifer tree crown biomass. Dead woody fuel biomass was computed for 1-, 10-, 100-, and 1000-h pools following Brown (1974) and correcting for slope. Litter and duff biomass were quantified based on average depth and bulk densities for lodgepole pine forest types (Brown et al. 1982; Nelson et al. 2016).\</para\>
\</section\>
\<section\>
\<title\>Question 1: Effects of interacting drivers on forest regeneration\</title\>
\<para\>We tested whether live stem densities (including all seedlings, saplings, and trees) were lower in short- versus long-interval fire with a one-sided, paired Wilcoxon signed rank test (n = 33 pairs, lower densities expected in reburns). Differences were also evaluated by species. For lodgepole pine, which was present in all plots, a two-sided, paired Wilcoxon signed rank test was used. For other species, which were absent from many plots and exhibited high variance relative to mean values, differences in presence and density between pairs were tested with zero-inflated negative binomial regression models adjusted for matched data (McElduff et al. 2010; Abadie and Spiess 2022). Simulated model residuals were evaluated to determine that these distributions appropriately represented underlying data (Appendix S2:Figures S2-S3). Subsequent analyses only used live conifer stem densities (i.e., excluding aspen).\</para\>
\<para\>Post-fire climate was characterized with water-year (October-September) climate water deficit and summer (June-August) vapor pressure deficit (VPD; Harvey et al. 2016b; Stevens-Rumann et al. 2018; Davis et al. 2019). We used 4-km resolution climate data (TerraClimate; Abatzoglou et al. 2018) and summarized 30-year normal (1989-2018) and 3-year post-fire anomaly (z-score relative to normal; Appendix S2:Figures S4-S7). We assessed whether differences in conifer stem density were associated with warmer-drier climate using Spearman’s rank correlations because pairwise bivariate distributions were not normal. \</para\>
\<para\>The relative importance of drivers of post-fire stem density was tested with multiple linear regression models (n = 66 observations). Predictors included climate (climate water deficit normal and post-fire summer VPD anomaly), short- versus long-interval fire, lower (< 2350) versus higher elevation as a proxy for stand-level serotiny, topography (heat load index and topographic position index; Appendix S1), and distance to live edge. Continuous predictors were not strongly correlated (Pearson’s |r| < 0.5) and were rescaled to have a mean of 0 and standard deviation of 1. Conifer stem density was log10-transformed to meet assumptions of linearity, normality, and equal variance, which were assessed with residual and quantile-quantile plots (Appendix S2:Figures S10-S11). We fit a full model including interactions between each predictor and short- versus long-interval fire. We used exhaustive model selection to identify the most important factors based on model Bayesian Information Criterion (BIC), retaining all models with difference in BIC < 2 (see Appendix S2:Table S2 for additional models).\</para\>
\</section\>
\<section\>
\<title\>Question 2: Forest biomass and fuels after short- versus long-interval fire\</title\>
\<para\>We assessed whether total live and dead tree biomass were lower in short- versus long-interval fire with one-sided, paired t-tests (n = 22 pairs for live and n = 21 for dead fuels, lower biomass expected following reburns). Individual fuel pool differences were tested using either two-sided, paired t-tests or two-sided, paired Wilcoxon signed rank tests. Fuels were transformed as needed to meet normality based on quantile-quantile plots (Appendix S2:Figure S12), and a Wilcoxon test was used if transformations did not result in normal distributions. Trees (> 1.4 m height), canopy fuels, and 1-h and 10-h snags were absent from > 40% of plots and were not tested for differences. Finally, biomass pools were averaged over 0-10, 10-20, and 20-30 years since fire to explore trajectories of biomass change and recovery following short- versus long-interval fire.\</para\>
\<para\>All analyses and visualizations were performed in ArcGIS Desktop 10.6 and R 4.1.3 (R Core Team 2022). See Appendix S1 for supplemental detail on methods and R packages.\</para\>
\</section\>
\<section\>
\<para\>See published manuscript for additional information and appendices. This repository includes field data and code for reproducing all analyses in data_code_deposit.zip.\</para\>
\</section\>
\<section\>
\<title\>References\</title\>
\<para\>Abadie, A., and J. Spiess. 2022. “Robust Post-Matching Inference.” Journal of the American Statistical Association 117 (538): 983–95.\</para\>
\<para\>Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, and K.C. Hegewisch. 2018. “Terraclimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958-2015.” Scientific Data 5: 170191. (Accessed 2022-10-30). https://www.climatologylab.org/terraclimate.html.\</para\>
\<para\>Baker, W.L. 2009. Fire Ecology in Rocky Mountain Landscapes. Vol. 36. Washington, DC: Island Press.\</para\>
\<para\>Braziunas, K.H., N.G. Kiel, and M.G. Turner. 2023. “Less Fuel for the next Fire? Interacting Drivers Amplify Effects of Short-Interval Fire on Forest Recovery, Greater Yellowstone Ecosystem, Montana and Wyoming. Version 1.” Environmental Data Initiative. (Accessed DATE.) http://placeholder.\</para\>
\<para\>Brown, J.K. 1974. “Handbook for Inventorying Downed Woody Material.” Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-16.\</para\>
\<para\>Brown, J.K., R.D. Oberheu, and C.M. Johnston. 1982. “Handbook for Inventorying Surface Fuels and Biomass in the Interior West.” Ogden, UT: USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-129.\</para\>
\<para\>Davis, K.T., S.Z. Dobrowski, P.E. Higuera, Z.A. Holden, T.T. Veblen, M.T. Rother, S.A. Parks, A. Sala, and M.P. Maneta. 2019. “Wildfires and Climate Change Push Low-Elevation Forests across a Critical Climate Threshold for Tree Regeneration.” Proceedings of the National Academy of Sciences 116 (13): 6193–98.\</para\>
\<para\>Despain, D.G. 1990. Yellowstone Vegetation: Consequences of Environment and History in a Natural Setting. Boulder, CO: Roberts Rinehart.\</para\>
\<para\>Eidenshink, J., B. Schwind, K. Brewer, Z.-L. Zhu, B. Quayle, and S. Howard. 2007. “A Project for Monitoring Trends in Burn Severity.” Fire Ecology 3 (1): 3–21.\</para\>
\<para\>Gill, N.S., T.J. Hoecker, and M.G. Turner. 2021. “The Propagule Doesn’t Fall Far from the Tree, Especially after Short-Interval, High-Severity Fire.” Ecology 102 (1): e03194.\</para\>
\<para\>Harvey, B.J., M.S. Buonanduci, and M.G. Turner. 2023. “Spatial Interactions among Short-Interval Fires Reshape Forest Landscapes.” Global Ecology and Biogeography (In press).\</para\>
\<para\>Harvey, B.J., D.C. Donato, and M.G. Turner. 2016a. “Burn Me Twice, Shame on Who? Interactions between Successive Forest Fires across a Temperate Mountain Region.” Ecology 97 (9): 2272–82.\</para\>
\<para\>———. 2016b. “High and Dry: Post-Fire Tree Seedling Establishment in Subalpine Forests Decreases with Post-Fire Drought and Large Stand-Replacing Burn Patches.” Global Ecology and Biogeography 25 (6): 655–69.\</para\>
\<para\>Hostetler, S., C. Whitlock, B. Shuman, D. Liefert, C.W. Drimal, and S. Bischke. 2021. “Greater Yellowstone Climate Assessment: Past, Present, and Future Climate Change in Greater Yellowstone Watersheds.” Bozeman, MT: Montana State University, Institute on Ecosystems.\</para\>
\<para\>Kashian, D.M., W.H. Romme, D.B. Tinker, and M.G. Turner. 2013. “Postfire Changes in Forest Carbon Storage over a 300-Year Chronosequence of Pinus Contorta-Dominated Forests.” Ecological Monographs 83 (1): 49–66.\</para\>
\<para\>Kashian, D.M., M.G. Turner, and W.H. Romme. 2005. “Variability in Leaf Area and Stemwood Increment along a 300-Year Lodgepole Pine Chronosequence.” Ecosystems 8 (1): 48–61.\</para\>
\<para\>Lorenz, T.J., K.A. Sullivan, A. v. Bakian, and C.A. Aubry. 2011. “Cache-Site Selection in Clark’s Nutcracker (Nucifraga Columbiana).” Auk 128 (2): 237–47.\</para\>
\<para\>McCaughey, W.W., and W.C. Schmidt. 1987. “Seed Dispersal of Engelmann Spruce in the Intermountain West.” Northwest Science 61 (1): 1–6.\</para\>
\<para\>McElduff, F., M. Cortina-Borja, S.K. Chan, and A. Wade. 2010. “When T-Tests or Wilcoxon-Mann-Whitney Tests Won’t Do.” American Journal of Physiology - Advances in Physiology Education 34 (3): 128–33.\</para\>
\<para\>Nelson, K.N., M.G. Turner, W.H. Romme, and D.B. Tinker. 2016. “Landscape Variation in Tree Regeneration and Snag Fall Drive Fuel Loads in 24-Year Old Post-Fire Lodgepole Pine Forests.” Ecological Applications 26 (8): 2422–36.\</para\>
\<para\>R Core Team. 2022. “R: A Language and Environment for Statistical Computing.” Vienna, Austria.\</para\>
\<para\>Romme, W.H., and M.G. Turner. 2015. “Ecological Implications of Climate Change in Yellowstone: Moving into Uncharted Territory?” Yellowstone Science 23 (1): 6–13.\</para\>
\<para\>Schoennagel, T., M.G. Turner, and W.H. Romme. 2003. “The Influence of Fire Interval and Serotiny on Postfire Lodgepole Pine Density in Yellowstone National Park.” Ecology 84 (11): 2967–78.\</para\>
\<para\>Stevens-Rumann, C.S., K.B. Kemp, P.E. Higuera, B.J. Harvey, M.T. Rother, D.C. Donato, P. Morgan, and T.T. Veblen. 2018. “Evidence for Declining Forest Resilience to Wildfires under Climate Change.” Ecology Letters 21 (2): 243–52.\</para\>
\<para\>Tinker, D.B., W.H. Romme, W.W. Hargrove, R.H. Gardner, and M.G. Turner. 1994. “Landscape-Scale Heterogeneity in Lodgepole Pine Serotiny.” Canadian Journal of Forest Research 24 (5): 897–903.\</para\>
\<para\>Turner, M.G., K.H. Braziunas, W.D. Hansen, and B.J. Harvey. 2019. “Short-Interval Severe Fire Erodes the Resilience of Subalpine Lodgepole Pine Forests.” Proceedings of the National Academy of Sciences 116 (23): 11319–28.\</para\>
\<para\>Turner, M.G., W.H. Romme, and R.H. Gardner. 1999. “Prefire Heterogeneity, Fire Severity, and Early Postfire Plant Reestablishment in Subalpine Forests of Yellowstone National Park, Wyoming.” International Journal Of Wildland Fire 9 (1): 21–36.\</para\>
\<para\>Turner, M.G., W.H. Romme, R.A. Reed, and G.A. Tuskan. 2003. “Post-Fire Aspen Seedling Recruitment across the Yellowstone (USA) Landscape.” Landscape Ecology 18 (2): 127–40.\</para\>
\<para\>Westerling, A.L., M.G. Turner, E.A.H. Smithwick, W.H. Romme, and M.G. Ryan. 2011. “Continued Warming Could Transform Greater Yellowstone Fire Regimes by Mid-21st Century.” Proceedings of the National Academy of Sciences 108 (32): 13165–70.\</para\>
\<para\>Western Regional Climate Center (WRCC). 2021. “Old Faithful, Wyoming (486845) NCDC 1981-2010 Monthly Normals.” (Accessed 2021-03-11). https://wrcc.dri.edu/cgi-bin/cliMAIN.pl?wy6845.\</para\>
\<para\>Yellowstone National Park (YNP). 2017. Yellowstone Resources and Issues Handbook: 2017. Yellowstone National Park, WY.\</para\>
\</section\>
\</description\>
\</methodStep\>
\</methods\>
