From Drying to Frying: Drought and fire as drivers of vegetation change in piñon-juniper- oak forests of the Chisos Mountains, Big Bend National Park, USA

From Drying to Frying: Drought and fire as drivers of vegetation change in piñon-juniper- oak forests of the Chisos Mountains, Big Bend National Park, USA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Drying to Frying: Drought and fire as drivers of vegetation change in piñon-juniper- oak forests of the Chisos Mountains, Big Bend National Park, USA Helen Mills Poulos, Andrew Marder Barton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8123967/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Piñon-juniper (PJ) forests and woodlands comprise cover vast portions of western North America, providing habitat and food to myriad species. Increasing drought and wildfire activity from climate change and fire suppression are triggering major changes in forest structure and composition in PJ systems, yet we know little about how these recent disturbances coincide or diverge from historical disturbance regimes. In an effort to fill this gap, we evaluated 20 years of forest change data from the Chisos Mountains, Texas to evaluate trends in piñon-juniper forest dynamics in response to a record 2011 drought and subsequent 2021 wildfire. Results Our results revealed that the Chisos Mountains have experienced recent, range-wide tree mortality over the last two decades, in response to drought and subsequent fire, especially within high-severity fire sites. The 100 sample plots in our study experienced approximately 56% survivorship over the pre-disturbance to post-drought sampling interval, demonstrating the major impact of the 2011 event on forest stand dynamics. Sites that experienced high-severity fire in the 2021 South Rim 4 Fire displayed even further significant losses in tree density and basal area in response to stand-replacing fire after the drought. Low- to moderate-severity fire sites remained relatively unchanged after the fire. High-severity sites experienced an average of just 14% survivorship from the post-drought to the post-fire sampling interval, which highlights the stand-replacing nature of the South Rim 4 Fire within high-severity burn patches. Conclusions We suggest that droughts and contemporary fires like the 2021 South Rim 4 Fire might have been a natural part of this regions’ fire regime prior to Euro-American settlement. Drought and wildfires in the wake of drought are becoming increasingly common throughout the region as the impacts of climate change continue to amplify, and studies like this are important for elucidating how contemporary wildfires are concordant with or diverge from historical fire regimes for sustainable PJ ecosystem management in the Anthropocene. piñon-juniper ecosystems tree mortality piñon pine acute drought mixed-severity wildfire stand-replacing fire Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Piñon-juniper (PJ) ecosystems are one of the most widespread vegetation types in western North America (Romme et al. 2009; Redmond et al. 2023; Halperin et al. 2025). These woodlands and forests are culturally and ecologically significant, providing myriad ecosystem services to people and wildlife including watershed protection, recreation, wood, food products, and habitat (Bombaci and Pejchar 2016; Shelef et al. 2017; Boone et al. 2021). Their structure and species composition have been shaped by drought and fire for millennia (Floyd et al. 2000, 2004, 2017; Romme et al. 2009). Yet, in recent decades, extreme drought and increased wildfire activity are triggering landscape-scale tree mortality and shifts in species dominance at many PJ sites in the southwestern USA (Floyd et al. 2009; Breshears et al. 2009; Flake and Weisberg 2019). This widespread tree die-off sparks concern about the resilience of PJ systems to disturbances in the Anthropocene (Floyd et al. 2009; Redmond et al. 2023). Isohydric piñon pines are particularly sensitive to prolonged drought and high-severity fire compared to co-occurring junipers and oaks, which are both anisohydric and capable of post-fire resprouting (Breshears et al. 2009, 2018a; McDowell et al. 2019; Poulos et al. 2020 a). In some recent droughts, however, even junipers and oaks are succumbing to extreme droughts with prolonged high vapor pressure deficits (Bowker et al. 2012; Poulos 2014; Kannenberg et al. 2021). When drought-killed trees become part of the fuel matrix, these same dry atmospheric conditions also create fuel beds that are vulnerable to ignition in extreme fire weather. This is especially true in the wake of drought-triggered tree mortality events where drought killed standing dead and dead and downed trees/fuels litter the landscape (Seager et al. 2015 , Higuera and Abatzoglou 2021 ). While frequent low-severity fire was historically common in many pine forest types prior to Euro-American settlement, PJ fire regimes have been well-characterized as largely infrequent, and stand-replacing in nature (Floyd et al. 2000, 2004; Romme et al. 2009). In general, piñon pines do not possess sufficiently thick bark or tall, self-pruned boles to survive even moderate-severity fire (Romme et al. 2009, Keely 2012), in contrast to more fire-resistant pine species, such as ponderosa pine. Romme et al. (2009) acknowledge that low- and moderate-severity fires can occur in open, grass-dominated PJ savannas and woodlands, and Poulos et al. ( 2009 ) and Margolis ( 2014 ) found some evidence for low-severity fire in PJ systems. However, high-severity, stand-replacing fire has and continues to be the dominant fire regime type for North American PJ systems. The combination of drought, high temperature, and wildfire is increasingly recognized as a key combined driver of contemporary landscape-scale vegetation change (Anderegg et al. 2019 ; McDowell et al. 2019). Moreover, projections call for an even a hotter, drier, and more fiery future for the American Southwest (Petrie et al. 2017 , Thorne et al. 2018 , O’Connor et al. 2020 ). Many fear that, as a result of these trajectories, forests and woodlands in this region may be approaching a tipping point that could lead to their transition from diverse, mixed species complexes to simpler drought-resistant and fire-resilient plant communities (Falk 2013 ; Gonzalez et al. 2018 ; Falk et al. 2019 ; Phillips et al. 2024 ). Many recent field studies and review papers have documented how drought or wildfire as individual disturbances influence PJ systems, but to our knowledge no one has evaluated the combined impacts of recent drought-triggered tree mortality followed by wildfire on PJ forest structure or composition. Southwestern PJ systems include a range of species, but studies characterizing the responses to drought or fire on PJ systems outside the Rocky Mountain piñon pine ( Pinus edulis )- one seed juniper ( Juniperus osteosperma ) species complex are far less common (Barton 1993a; Barton and Teeri 1993; Barton 1994; Morino et al. 2000; Poulos and Berlyn 2007; Barton and Poulos 2021 a; Poulos et al. 2021; Villanueva-Díaz et al. 2022; Azpeleta Tarancón et al. 2023; Rodriguez-Robles et al. 2023). In an effort to fill these gaps in our understanding of drought + wildfire impacts on PJ systems, we use data from a long-term plot monitoring network in the Chisos Mountains in Big Bend National Park in west Texas to quantify the impacts of a freeze-drought event in 2011 and subsequent wildfire in 2021 on Mexican piñon pine ( Pinus cembroides )-Alligator juniper ( Juniperus deppeana )-oak ( Quercus grisea , Q. gravesii, Q. emoryi ) dominated PJ forests that are common at high elevations of the US-Mexico Borderlands. We addressed the following key questions: 1) What were the independent impacts of drought and fire on forest stands?; 2) How did drought and fire interact to drive forest change? For example, did plots most affected by drought experience more severe fire?; and 3) What were the influences of topography and site moisture on drought and fire severity. Given recent increases in both drought and wildfire incidence across the southwestern USA, assessing the impacts of multiple, successive disturbances on PJ forest stand dynamics is critical for informing forest and fire management actions, and it provides an opportunity for evaluating how recent droughts and wildfires mimic or diverge from historical fires of the past in PJ systems. Methods Study system The Chisos Mountains (CM) are a small rhyolitic mountain range located entirely within Big Bend National Park. The CM rise to 2300 m asl. and are bound at lower elevations by deserts dominated by shrub and succulent desert flora, where tree establishment and growth is inhibited due to high temperatures and low precipitation. Soils are a mixture of mollisols and entisols. They are composed of moderately deep gravelly loam, which is well drained and non-calcareous (Miggins et al. 2008). PJ forests cover most of the mountain range, with woodlands occurring on drier sites and lower elevations, and forests dominating higher elevations. Major tree species include Mexican piñon pine ( Pinus cembroides Zuccarini), alligator juniper ( Juniperus deppeana vonSteudal), gray oak ( Quercus grisea Liebmann), Graves oak ( Quercus gravesii Sudworth), Emory oak ( Q. emoryi Leibmann), and weeping juniper ( J. flaccida vonSchlechtendal) (Poulos et al. 2013). Lower elevations also contain small populations of one seed juniper ( J. monosperma Englemann) and red berry juniper ( J. pinchotii Sudworth), and oak shrublands that are dominated by Q. pungens Leibmann. The climate is arid, characterized by cool winters and warm summers. Precipitation is distributed bi-modally in late summer and winter with most precipitation falling during summer storms as part of the North American Monsoon System. Mean annual precipitation for the Chisos Basin is 49.7 cm (range 10–135 cm). Mean monthly minimum temperatures are 1.8 ºC in January and 17.0 ºC in July. Maximum temperatures are 14.1 ºC in January and 29.1 ºC in July (Western Regional Climate Center 2023). Contemporary PJ forest structure and composition in the Chisos Mountains is driven by a combination of historical factors including climate, fire regimes, and land-use history (Romme et al., 2009, Shriver et al., 2025, Noel et al., 2023). Tree density generally increases with increasing soil moisture availability in PJ systems and in the CM, with open-canopy woodlands dominating drier sites and lower elevations and PJ forests occurring on wetter locations and higher elevations and canyons (Poulos and Camp 2010 ). In February 2011, the Chisos Mountains and the rest of Texas experienced a rare five-day freeze followed by the most severe one-year drought on record in the State (Neilson-Gammon 2011). These two adjacent events led to widespread tree mortality and unprecedented wildfires across Texas, although none occurred in the Chisos. That year, Texas experienced 31,000 wildfires that burned over 16,000 km 2 , including four of the largest fires in State history (InciWeb 2011 ). The freeze-drought killed an estimated 300 million trees statewide (Moore et al. 2016 ), with especially severe impacts in West Texas (National Drought Mitigation Center, 2011). Arid conditions have continued to prevail in the Chisos Mountains since the 2011 drought. For example, from September 2020 to late June 2021, Big Bend National Park experienced severe to exceptional drought (National Drought Mitigation Center 2021). During this drought, the South Rim 4 Fire ignited (April 8, 2021) in the Chisos Mountains (InciWeb 2021 ), and over the course of two weeks, burned 541 ha (1,337 acres) of the high country (Fig. 1 ). Fire severity appeared to vary substantially, with little tree damage in some sites and complete above-ground mortality elsewhere. The results reported here are the first to quantify successive impacts of the drought and the South Rim 4 Fire on PJ forest dynamics over two decades. Field Methods We have maintained a network of 226 permanent plots in the forested areas of the Chisos Mountains since they were installed by Poulos in 2003 and 2008. Poulos and Barton resampled these plots in 2019 to 1) document the effects of the 2011 freeze-drought on forest dynamics (Barton and Poulos 2021 ) and 2) estimate the effectiveness of fuel management treatments (Poulos and Barton 2021 ). The 2021 South Rim 4 Fire burned 49 of the plots in our permanent plot network (Fig. 1 ). In 2023, we resampled 51 unburned control plots and 49 burned plots in the Chisos Mountains permanent plot network in which all previously censused trees were uniquely identified with numbered brass tags. We sampled tree survival and regeneration, fire severity, and microenvironmental conditions in these burned plots as well as in the unburned (control) plots. We analyzed these data for changes in forest stand structure over time from pre-disturbance in 2003/2008 to post-drought in 2019 and from post-drought in 2019 to post-fire in 2023. In these 10-m radius fixed-area (0.03 ha) plots, we assessed each tree > 5 cm DBH as live or dead. Live trees that were < 5 cm DBH in previous censuses but surpassed that threshold by our sampling dates were measured, tagged, and added to the current plot population census. Seedlings (0–5 cm dbh) were tallied by species in 5-m radius circular plots (0.008 ha), nested inside of the larger census plots. We also noted the presence/absence of maturing cones on piñon pines in each sample plot. Finally, we visually assessed fire severity in the field in burned plots as low severity (tree mortality < 33%), moderate severity (tree mortality 33–66% mortality), and high severity (< 66% tree mortality). In total, we sampled 15 low-, 14 moderate-, and 20 high-severity burned plots. In addition to our field surveys, we also derived differenced normalized burn ratio (dNBR) fire severity data (Miller and Thode 2007 ) for our 49 burned plots. We compared the utility of 30-m resolution Landsat 8 and 10-m resolution Sentinel 2 dNBR products for evaluating fire severity impacts on Chisos Mountains forests, comparing them to our field estimates of fire severity. We extracted Landsat and Sentinel2 dNBR fire severity data and Landsat dNBR classed fire severity data (1–5 from unburned to high severity fire) for each sample plot location using the point sampling tool plugin in QGIS (QGIS.org 2024). Statistical analyses indicated a better fit between Landsat dNBR and field estimates; we therefore chose this remotely sensed dataset over Sentinel 2 for all subsequent analyses. A key goal of this study was to investigate how topography shaped the response of forests in the Chisos Mountains to the 2011 freeze-drought and the 2021 South Rim 4 Fire. Accordingly, in each plot, we recorded the slope direction (in degrees), slope inclination (in degrees), position on slope (valley bottom, lower slope, middle slope, upper slope, and ridgetop), and surface configuration (concave, concave-straight, straight, convex-straight, convex). We used these variables to calculate the topographic relative moisture index (TRMI), which provides a ranking of sites along a xeric-mesic gradient (Parker 1982 ), with low scores signifying warm, dry sites and high scores the opposite. Statistical Analyses We assessed the impacts of drought and fire on forest stands over the study period using zero-inflated linear mixed effects (LME) models and Kolmogorov-Smirnov (KS) tests. Zero-inflated models can handle data with a large proportion of zeros, as was the case for many post-drought and post-fire plots where all trees died. LME models also account for the covariance structures of the repeated measures sampling design. We fit LME models to test for significant differences in tree and seedling density and basal area by fire severity (unburned control, low-, moderate-, or high-severity) and timestep (pre-disturbance in 2003 or 2008, post-drought in 2019, and post-fire in 2023) and the interactions between these two independent factors. Sample plot was designated as a random effect for all models. Changes in these stand characteristics were assessed for all species combined and for junipers, oaks, and piñons, separately. LME models were fit using the glmmTMB package in R (Brooks et al. 2017 ). Significant post-hoc pairwise differences among fire severities and timesteps were evaluated using estimated marginal means in the emmeans package (Lenth et al. 2019 ). Differences in tree size distributions from pre-disturbance to post-drought to post-fire and among fire severity classes were evaluated using two sample KS tests. Size distribution differences were assessed for all species combined and for junipers, oaks, and piñons, separately. All statistical analyses were conducted in R (R Core Team 2024). We utilized linear regression and the TRMI data for all 100 plots to investigate the role of site moisture in tree survival from pre-disturbance to post-drought to test the hypothesis that plots in drier sites experienced higher levels of mortality. Then, we performed a second set of regresions regression using TRMI and Landsat dNBR fire severity data from the 49 burn plots to test 1) whether fire severity differed by moisture variation across the landscape, and 2) if plots subject to high levels of 2011 drought-induced tree death subsequently burned at higher fire severities. Lastly, we performed a regression to estimate the relationship between Landsat dNBR and basal area change over the post-drought to post-fire sampling interval to test the hypothesis that dNBR was a good estimator of field fire-severity or tree death. Results 3.1 Fire behavior The Landsat dNBR fire severity maps performed far better than the Sentinel2 maps for all analyses, and, accordingly, we report results for Landsat dNBR data only for the remainder of the report. The Landsat classed dNBR maps indicated that, within the South Rim 4 Fire perimeter, 23% remained unburned, 73% of the area burned at low severity, 4% experienced moderate-severity fire, and 0% of the landscape burned at high-severity (Figs. 1 – 2 , Table 1 ). dNBR values peaked at 700, but only two plots had values higher than 400. Landsat dNBR was a significant predictor of basal area change over the post-drought to post-fire sampling interval within burned plots (Fig. 3 ), and mean values of dNBR within each field-determined fire severity class also displayed expected trends of increased dNBR with higher field-determined fire severity. However, the dNBR fire severity proxy contradicted our field observations and fire effects assessments. In contrast to the dNBR maps, we detected in the field 2 unburned, 15 low-, 14 moderate-, and 20 high-severity plots in our systematic sampling grid of plots within the burn perimeter; 41% of the plots burned at high-severity, killing more than 60% of the trees from the post-drought to post-fire sampling interval. Overall agreement between our field estimates of fire severity and the classed/thresholded Landsat dNBR data was just 53%. Agreement was good for unburned and low-severity plots, with ~ 80% agreement between field and Landsat-derived fire severity. However, plots that we assessed in the field as burning at moderate- or high-severity showed zero agreement with the Landsat-derived fire-severity classes. The fire severity results suggest that dNBR may not capture fire-induced vegetation mortality within short stature and often open-grown piñon-juniper-oak forests, at least in West Texas. We therefore focus the rest of our analyses on characterizing forest change over time based on our field-determined fire severity classes. High-severity sample plots burned at higher elevations within the CM, which is no surprise given that the fire ignition occurred near the edge of the South Rim (Table 1 ). Our regression results indicated that TRMI was not a significant predictor of tree survival over the pre-disturbance to post-drought sampling interval ( P > 0.05), or fire severity over the post0drought to post-fire sampling interval, likely because this set of 100 sample plots did not display large variation in landscape soil moisture. The multiple regression of environmental variables and tree density change from the post-drought to post-fire interval indicated that only slope and dNBR were significant predictors of tree density change (Fig. 3 , Table 2 ). On steeper slopes, the fire was more severe, and the tree mortality impacts greater from the fire (i.e., decline in tree density; rsq = 0.27). Alternatively, we found no significant impact of pre-fire tree mortality (i.e., pre-disturbance to post-drought) on dNBR ( P > 0.05, data not shown). We note that the poor fit between our field estimates of fire severity and dNBR limit this analysis of the effects of tree mortality on fire severity. Table 1 Mean environmental characteristics of sample plots by fire-severity in the Chisos Mountains. Treatment Elevation Slope TRMI Sentinel2 dNBR Landsat dNBR control 1952.6 14.9 28.3 -18.1 17.1 low-severity 2198.0 10.5 25.9 36.4 119.2 moderate-severity 2217.7 11.7 26.7 102.3 171.1 high-severity 2208.8 13.8 27.4 116.7 283.9 Table 2 Environmental predictors of tree density change over the post-drought to post-fire sampling intervals in the Chisos Mountains. Multiple regression results for predictors of tree density change Variable Coefficient Standard Error t-value P-value 95% CI Lower 95% CI Upper Intercept 0 0.137 0 1 -0.276 0.276 Elevation 0.252 0.147 1.713 0.094 -0.045 0.549 Slope 0.323 0.154 2.101 0.042 0.012 0.634 TRMI 0.214 0.158 1.355 0.183 -0.105 0.532 Landsat dNBR 0.290 0.143 2.023 0.050 0.000 0.580 Forest changes in Chisos Mountains forests The zero-inflated LME models revealed a strong freeze-drought response of forests within unburned control plots, as evidenced by significant declines in tree density and basal area from the pre-disturbance to post-drought sampling interval (Figs. 4 – 7 , Appendix A-B). We saw no further significant changes in forest structure within control plots since 2019, indicating that these plots can be used as a baseline control for estimating the magnitude of fire effects within burned plots over the post-drought to post-fire sampling interval. As in the unburned control plots, burned plots also experienced significant declines in tree density and basal area in response to the freeze-drought (i.e., over the pre-disturbance to post-drought sampling interval, P < 0.001). Then, sites that subsequently experienced moderate- or high-severity fire displayed further significant declines in total tree density and basal area over the post-drought to post-fire sampling intervals for all tree species combined ( P < 0.001, Figs. 4 – 7 , Appendix A-B). In contrast, low-severity fire sites experienced no further changes in forest structure in response to the fire according to the zero-inflated LME models (i.e., from the post-drought to post-fire sampling interval, P > 0.05). For individual taxonomic groups, our results corroborated our prior reports on the impacts of the 2011 freeze-drought on piñon pine. It was the only tree type in both burned and control plots to experience significant declines in abundance and basal area from the pre-disturbance to post-drought interval. Over the post-drought to post-fire sampling interval, the impacts of moderate-severity fire on forest structure disappeared in our individual taxonomic analyses of oaks, junipers, and piñons. Individually, oaks, junipers, and piñons experienced significant declines in abundance and basal area within high-severity fire sites only. Seedling abundances were stable over the entire study in control plots and within areas that experienced low- to moderate-severity fire ( P > 0.05, Appendix B). As in our other metrics of forest change over time, however, high-severity fire plots displayed significant declines in seedling abundances from the post-drought to post-fire period for all species combined. The taxonomic group analysis revealed that seedling declines were most pronounced in junipers, while oak and piñon seedling abundances did not change significantly over time. Together, these forest change data suggest that, contrary to the Landsat dNBR results, high-severity fire occurred in many plots, and at those locations, forests experienced stand-replacing wildfire. 3.3 Forest size structure changes All sample plots experienced an apparent shift towards larger diameter trees. However, the Kolmogorov-Smirnov tests did not detect significant differences in forest diameter distributions over time in either the control or the burn plots, when comparing the size distributions across all fire severities combined (Fig. 8 , P > 0.05). Since we observed significant changes in tree and seedling densities and basal area within high-severity plots, we also evaluated whether high-severity fire triggered a change in tree diameter distributions within those plots. Although high-severity fire resulted in losses of smaller diameter trees, diameter distributions shifted to larger trees only for oaks and not for piñon, junipers, or all species combined. While resprouting can be common in Sky Island oaks (Barton and Poulos 2018; Poulos et al. 2020 b), including in the Chisos Mountains, we saw little evidence of post-fire oak sprouting in high-severity fire plots after the South Rim 4 Fire. These results suggest that, with the exception of oaks, tree abundances dropped across all size classes in response to the freeze-drought and fire, rather than shifting towards large surviving stems with subsequent disturbances. Post-fire pinecone production We noted a high number of piñons bearing cones within burned plots, especially within low- and moderate-severity fire sites, where many trees survived. We saw piñons bearing mature cones in 42% of our burned sample plots across all fire severities. Mature piñon pinecone prevalence was significantly higher in burned plots than in control plots, where we observed cone-bearing trees in just 21% of our sample plots (Chi-square Test, P < 0.001). Discussion Together, the 2011 freeze-drought and 2021 South Rim 4 Fire in the Chisos Mountains resulted in the landscape-scale death of thousands of trees across our sample plot network. Our analysis revealed two clear mortality patterns in PJ stands. First, the 2011 winter freeze-drought triggered significant declines in live tree abundances and basal area throughout the mountain range. Trees within the 100 plots experienced approximately 56% survivorship over the pre-disturbance to post-drought sampling interval, demonstrating the major impact of the event on tree densities. Second, sites that experienced high-severity fire displayed even further significant losses in tree density and basal area, while low- to moderate-severity fire sites remained relatively unchanged. High-severity fire sites experienced an average of just 14% survivorship from the post-drought to the post-fire sampling interval, demonstrating the stand-replacing nature of the South Rim 4 Fire within these burn patches. Our results clearly reveal that this fire had a much larger impact than recent West Texas wildfires burning within similar piñon-juniper-oak woodlands, and much more than indicated by remote-sensing. Drought Impacts The 2011 freeze-drought and the 2021 South Rim 4 Fire caused distinctly different impacts. The freeze-drought caused high mortality among all tree species, all tree sizes, and in all plots, killing nearly 50% of the trees. Plots occurring in drier sites (e.g., south-facing, steeper, ridgetops) exhibited the highest levels of mortality, suggesting some level of refuge for trees growing in more protected sites. These results are consistent with our past reports on this plot network by Poulos (2014) and Barton and Poulos (2022), as well as Waring and Schwilk ( 2014 ) for the Chisos Mountains and the state of Texas, as a whole (Lawal et al. 2025). In piñon-juniper woodlands throughout much of the western U.S., severe drought (and associated diseases, insects, and parasites) is the most important driver of forest dynamics, more so even than fire (Floyd et al. 2009; Meddens et al. 2018). The impacts of the freeze-drought revealed in the present study will no doubt reverberate in the dynamics of these forests for many decades, if not centuries. How did these trees die? Plant hydraulic system failure the a primary cause of death from water stress in response to both drought and plant tissue freezing (Brodribb et al. 2020; Marchin et al. 2022). Water moves through the xylem tubes of vascular plants because of the negative tension created by transpiration of H 2 O out of leaf stomates into the air; this tension pulls water into the roots and through the plant. As moisture stress, or evaporative demand, increases so does internal water tension, which can cause the formation of bubbles in the water stream, or cavitation, blocking water flow (Sperry and Tyree 1990; Pittermann et al. 2010; Lens et al. 2013). When plants freeze and thaw, they can experience the same effect of hydraulic breakdown from freeze-triggered cavitation of xylem and death (Pittermann et al. 2010; Lens et al. 2013). It is likely, therefore, that some combination of drought- and freeze-induced hydraulic failure led to the high levels of tree mortality from the pre-disturbance to post-freeze-drought sampling interval in the CM. Mortality stemming from the freeze-drought events of 2011 was most pronounced in drier, hotter sites. Topographically, these were lower locations that were more exposed, steeper, and on south-facing slopes. Topography is a key determinant of forest structure, species composition, and processes in the Sky Islands of southwestern North America (Barton 1993; Coblentz and Riitters 2004; Poulos et al. 2007), and our results reveal how its role plays out during key tree mortality events. Our mortality patterns are similar to the findings of others that drought-induced tree mortality is usually more pronounced on drier portions of environmental gradients (Allen and Breshears 1998; Gitlin et al. 2006; Breshears et al. 2009), but cold air drainage in mesic valley bottoms during the 2011 February freeze may have also triggered some of the tree death in CM. Most of the fire ecology literature on piñon-juniper (PJ) woodlands shows that piñon pines are particularly vulnerable to prolonged acute drought relative to oaks and junipers, which can better tolerate prolonged water deficits (Breshears et al. 2005 , Wion 2022). In the current study, we saw a reduction in tree densities over the pre-disturbance to post-drought sampling interval across all tree species, and not just in piñon pine alone. This, coupled with the size distribution analysis, suggest that trees of all species and across a wide range of tree sizes were negatively impacted by the drought, not just the less drought tolerant piñons. Wildfire impacts The subsequent 2021 South Rim 4 Fire triggered further significant declines in tree abundances. These effects were much more variable across plots than for the freeze-drought, however. Fire-induced tree mortality was minimal where the fire burned at low and moderate severity, but nearly 100% at high-severities. Mortality was not concentrated in smaller size classes, as in some fires (Barton & Poulos, 2018), but instead killed stems regardless of size. This impact is likely a result of the high intensity of the fire in some plots combined with the pronounced fire sensitivity of piñons, junipers, and oaks. Compared to truly fire-resistant tree species like ponderosa pine, these species lack traits, such as thick bark, self-pruning, and high crowns, which confer a capacity to survive fire (Romme et al. 2009). In contrast to the lack of size-dependent mortality in all species combined, mortality in oaks was relatively higher in smaller trees, suggesting especially high sensitivity to fire/topkill in this group of species. Compared to piñons and junipers, xerophytic oaks in fire-prone environments depend more on resprouting after top-kill from disturbances such as droughts and fire (e.g., Barton and Poulos 2018, Poulos et al. 2020 ). Yet in the CM, we found surprisingly low levels of oak resprouting after top-kill in the South Rim 4 Fire, possibly a result of very high fire severity, although we have observed oaks elsewhere in the Sky Islands exhibit almost universally vigorous post-fire resprouting regardless of fire severity (Barton and Poulos 2018, Poulos et al. 2021). In these other mountain ranges, including the Davis Mountains just to the north, this response of oaks has led to projections of the development of dense oak shrublands after high-severity fire (Barton and Poulos 2018; Guiterman et al. 2022 ). The low levels of oak resprouting we documented here suggest that oak fire effects can be variable, and the death of oaks in the CM within high-severity patches is likely to have important consequences, such as a lack of future dominance by oaks, for vegetation composition in recovering woodlands after this fire. Interactions of freeze-drought and wildfire Disturbances such as drought, insect outbreaks, and fire often interact in their effects on PJ woodlands and forests (Romme et al. 2009). Barton and Poulos ( 2021 ) identified increased dead fuel loads resulting from the 2011 freeze-drought, leading us to hypothesize that plots exhibiting higher levels of mortality from that disturbance event would burn at relatively higher fire severities. Our analysis of pre-fire tree mortality impacts on Landsat fire severity did not support this hypothesis. Several explanations could account for this pattern. First, the poor performance of Landsat dNBR could have obscured a relationship between past mortality and fire severity. Second, we intentionally avoided resampling plots that had experienced total tree mortality from the drought in our most recent post-fire sampling effort. Therefore, sites that experienced heavy/total tree death from the drought, and subsequent high standing dead and surface fuels after the drought may not be well represented in our sample. Lastly, it is possible that, ten years after the freeze-drought, any remaining increase in dead fuel from the drought was not sufficient to amplify fire severity. Piñon pinecone production The abundant cone production of piñon pines in burned compared to unburned control plots was a surprise. Prior studies on post-fire cone production have only been documented in serotinous species like Aleppo Pine (Daskalakou and Thanos 1996; Alfaro-Sánchez et al. 2015). Piñon pine cones require two growing seasons to mature (Wauer and Riskind 1977), meaning that the prevalence of maturing pine cones in 2023 within the burn perimeter in CM could represent a seed production event in the wake of the fire, especially given the scant cone production in unburned control plots. While the year of the fire in 2021 was hot and very dry, 2022 was wetter and cooler ( www.climateengine.org ) with almost normal precipitation. It is possible that reduced competition by surviving trees after the fire triggered a widespread seed production event fueled by more normal precipitation and cooler temperatures in the year after the wildfire. This could be good news for post-fire piñon regeneration and seed source availability to regenerate piñons after the drought and wildfire. However, summer precipitation for seedling germination has remained low in the years since the South Rim4 Fire, even with intermittent severe thunderstorm activity in spring and fall of 2023 and summer 2025. Pine seedling germination requires both bare mineral soil and precipitation for germination of the cone crop we observed in the field, which dispersed its seeds in fall 2023. Future studies on cone production and post-fire piñon pine regeneration could elucidate the long-term impacts of unusual events like this in non-serotinous obligate seeder pine species. Forest management applications Our results have important implications for future forest management of the Chisos Mountains, specifically, and PJ systems of the southwestern USA, in general. Climate projections predict a hotter and drier climate with increased fire activity in southwestern PJ forests and woodlands (Wasserman and Mueller 2023; Harris et al. 2024). This is predicted to lead to shrinking PJ distributions (Noel et al. 2025). These projections suggest an amplification of the impacts we identified here, with associated higher tree mortality and reduced stand density and basal area from increasing multiple disturbances. Drought stress may also accentuate the impacts of disease and insects, as well, exacerbating disturbance effects (Breshears et al. 2021). If climate changes proceeds as predicted, these impacts may lead to transitions to other vegetation types, such as shrubland or grassland systems that are devoid of trees (Falk et al. 2019 ; Guiterman et al. 2022 ). While these resultant plant communities are more resilient to drought and fire, they may trigger a long-term loss of forest cover across the Southwest if trees cannot recruit in the post-fire and post-drought environment (Coop et al. 2020). Such forest losses would in turn result in loss or at least changes in the unique environmental services provided by trees (e.g., wood, shaded recreation). Forest managers cannot exert control over the incidence of drought events, which are meteorological events imposed regardless of forest conditions. On the other hand, reducing the forest density through thinning and prescribed burning can increase water availability and drought resilience in ponderosa pine and similar forests across the West (Sankey and Tatum 2022 ; Sankey et al. 2025). There is no evidence, however, that these management practices would translate into similar returns in PJ, although many managers have cleared fuels within this forest type to reduce both drought and wildfire risk (Redmond et al. 2023). A century or more of fire suppression in ponderosa pine forests has dramatically increased tree density in stands that prior to Euro-American impacts were maintained in open, low-density condition by frequent surface fires. Although some PJ stands might have approached this structure, abundant evidence suggests that these forests were typically denser and subject to infrequent, stand-replacing fire (Floyd et al. 2000, 2004, 2017; Romme et al. 2009). Fire suppression and many other impacts imposed over the past century have led to increased tree densities in some of these stands, but elsewhere such evidence is lacking. The key point here is that ponderosa pine forests differ strongly from PJ systems, and no evidence yet recommends stand management for drought resilience within this forest type. Forest managers potentially have much more control over fire, which is as much a biological as a meteorological phenomenon. Managers can influence these biological components by manipulating the crown and surface fuel matrix. Throughout the world, practices such as suppression, thinning, and prescribed burning have drastically altered the incidence and severity of wildfire (e.g., Pollet and Omi 2002 ; Brodie et al. 2024 ). In the CM, fire suppression has clearly reduced the incidence of wildfire in the Chisos Mountains (e.g., Poulos et al. 2009 ). Previously, we showed that low-severity thinning and prescribed burning did not have detectable impacts on dead fuel loads, stand density, basal area, or species composition within the Chisos, and that all stands responded similarly to the 2011 drought regardless of prior restoration treatment history (Barton and Poulos 2021 b). Higher intensities of thinning and prescribed burning, however, might have stronger impacts on dead and live fuel loads and thus reduce the incidence and especially the severity of future wildfires, impacts found in other forests (e.g. Pollet and Omi 2002 ; Brodie et al. 2024 ). Contemporary fire in PJ forests The recent transition from frequent low-severity to frequent high-severity fire across many western forests begs the question whether the fire behavior of South Rim 4 Fire deviated from natural piñon-juniper-oak fire regimes (Landres et al. 1999 ). Was the 2011 freeze-drought followed by the 2021 wildfire anomalous or were such events common in pre-Euro-American disturbance regimes? The answer to this question has important implications regarding whether management should aim at reducing the risk of drought and wildfires fires on PJ mortality (by vegetation management or suppression, for example) or should managers take a hands-off approach? Prior research in the CM suggested that the natural fire regime of piñon-juniper-oak woodlands was primarily frequent, low severity surface fires (Poulos et al. 2009 ). Historical droughts have also been repeatedly identified in PJ stands in the dendrochronological record (Floyd et al. 2009 and many others). Conclusions regarding CM PJ fire regimes were based on size and age data from across the mountain range and fire scar samples from large, widely scattered Mexican piñon pines. In their fire history paper from 24 fire-scarred trees in CM, Poulos et al. ( 2009 ) interpreted the fire-scar record of relatively frequent landscape fire (MFI = 36.5 years) and the reverse-J tree size and age-class distributions with abundant, small/young piñon pines as indicators of historically low- to mixed-severity fire, with a fire-suppression-induced pulse in tree regeneration after the last major fire in 1926 (Fig. 9 ). However, our analysis of the contemporary fire behavior of the South Rim 4 Fire suggests an alternative fire regime for the Chisos Mountains characterized by mixed-severity fire with large patches of high-severity, stand-replacing fire. This scenario is consistent with the age data-based literature on fire behavior and stand dynamics in other piñon-juniper-oak woodlands, including with some of the species in the present study. For example, the comprehensive review by Romme et al (2009) clearly demonstrated that Rocky Mountain piñon ( P. edulis )-juniper woodlands across the western US were characterized by an infrequent, stand-replacing fire regime, although low-severity fires probably also occurred infrequently. Baisan and Morino (2000) came to a similar conclusion for border piñon ( P. discolor )-juniper-oak forests in the Chiricahua Mountains in southeastern Arizona. Further, comprehensive studies, such as Huffman et al. (2006, 2009), demonstrated that, although small-scale, stand-replacing fires were major drivers of forest dynamics historically in piñon-juniper woodlands, fire suppression has nevertheless increased stand density above pre-Euro-American levels, as suggested for the CM and other more open PJ woodlands (Poulos et al. 2009 ; Margolis 2014 ). Resolving the historic range of variation (HRV) for fire in piñon-oak-juniper woodlands of the Chisos Mountains (and other Sky Island PJ communities) will require a fine-grained, spatially-explicit analysis of both fire-scar evidence and pre-1920s piñon age distributions and cohorts (see, for example, Huffman et al. 2008). In Poulos et al. ( 2009 ), of the 24 fire-scarred specimens, six samples recorded multiple fire events, with just two cross-sections recording > 3 fire events. It is possible that these trees were located in areas that burned repeatedly at lower-severity within a high-severity fire matrix. Until we have such additional evidence, our best scientific approach to fire HRV for these woodlands is to be open to the possibility that wildfires such as the 2021 South Rim 4 Fire might have been a natural part of the fire regime prior to Euro-American settlement. Declarations Funding Funding for this project was provided by the National Park Service via agreement P22AC01677. The Schumann Institute of the Bailey College of the Environment at Wesleyan University funded two summer field internships for two undergraduates to conduct our surveys. University of Maine at Farmington also supported two summer undergraduate stipends to support this research. The original field inventory was funded by Joint Fire Sciences Program of the U.S. Department of the Interior (03-3-3-13). Post-drought sampling in 2019 was funded by NPS agreements P18AC00722 and P18AC01162. Author Contribution HP and AB conceptualized the study, wrote the proposal, conducted the field work, analyzed the data, and wrote the paper together. We worked equally on all parts. Acknowledgement AcknowledgementsWe thank Richard Gatewood, John Morlock, and DW Ivans of the Big Bend National Park Fire Management Office for logistical support for this project. This work would not have been possible without the assistance of NPS mule packers, Gavin Monson and Lanadawn Nusz. 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Additional Declarations No competing interests reported. Supplementary Files Appendixs.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 18 Dec, 2025 Reviewers invited by journal 15 Dec, 2025 Editor assigned by journal 17 Nov, 2025 Submission checks completed at journal 17 Nov, 2025 First submitted to journal 15 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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11:23:10","extension":"xml","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134638,"visible":true,"origin":"","legend":"","description":"","filename":"71a33972a6ca49779f041ac8f793d9b51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/7aa7ba0b8b61f8a511fd0c91.xml"},{"id":98509368,"identity":"505a11a7-5f4d-4333-9928-a43521c36c89","added_by":"auto","created_at":"2025-12-18 11:23:11","extension":"html","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148642,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/ca2900657b0f3ea819ec7cd3.html"},{"id":98509320,"identity":"2ab4525e-c1d7-4616-b303-805ff3ca80d1","added_by":"auto","created_at":"2025-12-18 11:23:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":30493,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map of the South Rim 4 Fire in the Chisos Mountains in West Texas, USA. Sample plots are displayed in black on the classed dNBR fire severity product from the Monitoring Trends in Burn Severity database: \u003ca href=\"https://www.mtbs.gov/\"\u003ehttps://www.mtbs.gov\u003c/a\u003e.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/0f6b75af7e2955317b418670.jpg"},{"id":98509343,"identity":"ae249e7f-5972-4655-a26a-8835cb39c504","added_by":"auto","created_at":"2025-12-18 11:23:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":839395,"visible":true,"origin":"","legend":"\u003cp\u003eVisual impacts from the 2011 freeze-drought event (light gray) and South Rim 4 Fire (dark gray) in the Chisos Mountains. Mortality from the drought was nearly 50%. High-severity fire sites like these resulted in near total tree death in the wake of the 2021 wildfire. Photo credit: Helen Poulos\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/f0a823388690347310eccbea.png"},{"id":98509334,"identity":"43247602-c8b6-4c61-988b-75f08415056b","added_by":"auto","created_at":"2025-12-18 11:23:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":256030,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression results for evaluating the performance of Landsat dNBR in predicting field-derived tree density change over the post-drought to post-fire interval. The linear regression resulted in a weak, but significant positive relationship between Landsat-derived fire severity and basal area change over the post-drought to post-fire sampling interval.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/669efff43d69c66a1106ce8a.png"},{"id":98624092,"identity":"03cec7f9-9880-4d42-9152-2c73bdbdb020","added_by":"auto","created_at":"2025-12-19 17:08:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90451,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of juniper, oak, piñon pine and total tree density (# ha\u003csup\u003e-1\u003c/sup\u003e) in control (dark gray) and burn (light gray) plots over the three sampling intervals in the Chisos Mountains of Big Bend National Park. Tree density declined significantly over the time in all sample plots over the pre-disturbance to post-drought sampling interval. Burned plots experienced significant declines in tree densities in response to the fire, while control plots experienced little change according to zero-inflated linear mixed effects models (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/eeee44a2290c0c0c6745c9b0.png"},{"id":98624360,"identity":"13bb24cd-fccb-4f7e-9b84-55442a9fbe1c","added_by":"auto","created_at":"2025-12-19 17:08:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85245,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of juniper, oak, piñon pine and total tree basal area (m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e-1\u003c/sup\u003e) in control (dark gray) and burn (light gray) plots over the three sampling intervals in the Chisos Mountains of Big Bend National Park. Tree basal area declined significantly over the time in all sample plots over the pre-disturbance to post-drought sampling interval. Burned plots experienced significant declines in tree densities in response to the fire, while control plots experienced little change according to zero-inflated linear mixed effects models (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/f486ee3fe8c9a3418f747530.png"},{"id":98509324,"identity":"63316aa3-7bb0-48e5-a5e8-6649c11604f5","added_by":"auto","created_at":"2025-12-18 11:23:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":504199,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of juniper, oak, piñon pine and total seedling density (ha\u003csup\u003e-1\u003c/sup\u003e) in control (dark gray) and burn (light gray) plots over the three sampling intervals in the Chisos Mountains of Big Bend National Park. Seedling density was stable in all sample plots over the pre-disturbance to post-drought sampling interval. Burned plots experienced significant declines in seedling densities in response to the fire, while control plots remained stable. Junipers were largely responsible for this trend according to the zero-inflated linear mixed effects models (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/38d28715abca0835581bd8a5.png"},{"id":98509326,"identity":"904d35e0-67ce-4b19-b771-b408259592ac","added_by":"auto","created_at":"2025-12-18 11:23:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":295935,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of tree survival in control plots over the pre-disturbance to post-drought (control-drought) and post-drought to post-fire (control fire) sampling intervals, and over the post-drought to post-fire interval for low-, moderate-, and high-severity fire. Groups are significantly different according to a zero-inflated linear mixed effects model with estimated marginal means post-hoc tests (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/2d0f44bcd715ddd8da4d6614.png"},{"id":98509351,"identity":"72180655-f458-494f-8e41-f02cfcb1d3ac","added_by":"auto","created_at":"2025-12-18 11:23:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":109164,"visible":true,"origin":"","legend":"\u003cp\u003eDiameter distributions of trees (DBH) in control plots, and high severity plots for all trees, junipers, piñons, and oaks. While tree sizes decreased over time in all burned plots, Kolmogorov-Smirnov tests revealed that the size distributions differed significantly over all sampling intervals only in oaks (KS \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/704f49bad5c8245a2b623347.png"},{"id":98509338,"identity":"f1517b46-f1f7-42c0-908c-0c8b6b2a7e02","added_by":"auto","created_at":"2025-12-18 11:23:09","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":140095,"visible":true,"origin":"","legend":"\u003cp\u003eMexican piñon pine (\u003cem\u003ePinus cembroides\u003c/em\u003e) A)\u003cem\u003e \u003c/em\u003esize distribution and B) age data from the Chisos Mountains in 2003 prior to any recent disturbance event, and C) a mature fire-scarred \u003cem\u003ePinus cembroides\u003c/em\u003e within an unburnt forest patch in 2023. Note the large size, thick bark, and self-pruning of the tree which may aid its fire survival.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/5b7dc0703f1ca619a24435ca.jpg"},{"id":98774890,"identity":"c7e8a61c-230b-4797-a927-70ce301d3762","added_by":"auto","created_at":"2025-12-22 12:16:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3339403,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/0f02af41-2a0d-47a9-b989-5716487158b6.pdf"},{"id":98624082,"identity":"06db8601-c9e4-4eaf-8509-f56867e1a418","added_by":"auto","created_at":"2025-12-19 17:07:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":372785,"visible":true,"origin":"","legend":"","description":"","filename":"Appendixs.docx","url":"https://assets-eu.researchsquare.com/files/rs-8123967/v1/be3f4c90e34efdaeb4c726c4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Drying to Frying: Drought and fire as drivers of vegetation change in piñon-juniper- oak forests of the Chisos Mountains, Big Bend National Park, USA","fulltext":[{"header":"Background","content":"\u003cp\u003ePi\u0026ntilde;on-juniper (PJ) ecosystems are one of the most widespread vegetation types in western North America (Romme et al. 2009; Redmond et al. 2023; Halperin et al. 2025). These woodlands and forests are culturally and ecologically significant, providing myriad ecosystem services to people and wildlife including watershed protection, recreation, wood, food products, and habitat (Bombaci and Pejchar 2016; Shelef et al. 2017; Boone et al. 2021). Their structure and species composition have been shaped by drought and fire for millennia (Floyd et al. 2000, 2004, 2017; Romme et al. 2009). Yet, in recent decades, extreme drought and increased wildfire activity are triggering landscape-scale tree mortality and shifts in species dominance at many PJ sites in the southwestern USA (Floyd et al. 2009; Breshears et al. 2009; Flake and Weisberg 2019). This widespread tree die-off sparks concern about the resilience of PJ systems to disturbances in the Anthropocene (Floyd et al. 2009; Redmond et al. 2023).\u003c/p\u003e \u003cp\u003eIsohydric pi\u0026ntilde;on pines are particularly sensitive to prolonged drought and high-severity fire compared to co-occurring junipers and oaks, which are both anisohydric and capable of post-fire resprouting (Breshears et al. 2009, 2018a; McDowell et al. 2019; Poulos et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea). In some recent droughts, however, even junipers and oaks are succumbing to extreme droughts with prolonged high vapor pressure deficits (Bowker et al. 2012; Poulos 2014; Kannenberg et al. 2021). When drought-killed trees become part of the fuel matrix, these same dry atmospheric conditions also create fuel beds that are vulnerable to ignition in extreme fire weather. This is especially true in the wake of drought-triggered tree mortality events where drought killed standing dead and dead and downed trees/fuels litter the landscape (Seager et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Higuera and Abatzoglou \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile frequent low-severity fire was historically common in many pine forest types prior to Euro-American settlement, PJ fire regimes have been well-characterized as largely infrequent, and stand-replacing in nature (Floyd et al. 2000, 2004; Romme et al. 2009). In general, pi\u0026ntilde;on pines do not possess sufficiently thick bark or tall, self-pruned boles to survive even moderate-severity fire (Romme et al. 2009, Keely 2012), in contrast to more fire-resistant pine species, such as ponderosa pine. Romme et al. (2009) acknowledge that low- and moderate-severity fires can occur in open, grass-dominated PJ savannas and woodlands, and Poulos et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Margolis (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found some evidence for low-severity fire in PJ systems. However, high-severity, stand-replacing fire has and continues to be the dominant fire regime type for North American PJ systems.\u003c/p\u003e \u003cp\u003eThe combination of drought, high temperature, and wildfire is increasingly recognized as a key combined driver of contemporary landscape-scale vegetation change (Anderegg et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; McDowell et al. 2019). Moreover, projections call for an even a hotter, drier, and more fiery future for the American Southwest (Petrie et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Thorne et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, O\u0026rsquo;Connor et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many fear that, as a result of these trajectories, forests and woodlands in this region may be approaching a tipping point that could lead to their transition from diverse, mixed species complexes to simpler drought-resistant and fire-resilient plant communities (Falk \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gonzalez et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Falk et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Phillips et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany recent field studies and review papers have documented how drought or wildfire as individual disturbances influence PJ systems, but to our knowledge no one has evaluated the combined impacts of recent drought-triggered tree mortality followed by wildfire on PJ forest structure or composition. Southwestern PJ systems include a range of species, but studies characterizing the responses to drought or fire on PJ systems outside the Rocky Mountain pi\u0026ntilde;on pine (\u003cem\u003ePinus edulis\u003c/em\u003e)- one seed juniper (\u003cem\u003eJuniperus osteosperma\u003c/em\u003e) species complex are far less common (Barton 1993a; Barton and Teeri 1993; Barton 1994; Morino et al. 2000; Poulos and Berlyn 2007; Barton and Poulos \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003ea; Poulos et al. 2021; Villanueva-D\u0026iacute;az et al. 2022; Azpeleta Taranc\u0026oacute;n et al. 2023; Rodriguez-Robles et al. 2023). In an effort to fill these gaps in our understanding of drought\u0026thinsp;+\u0026thinsp;wildfire impacts on PJ systems, we use data from a long-term plot monitoring network in the Chisos Mountains in Big Bend National Park in west Texas to quantify the impacts of a freeze-drought event in 2011 and subsequent wildfire in 2021 on Mexican pi\u0026ntilde;on pine (\u003cem\u003ePinus cembroides\u003c/em\u003e)-Alligator juniper (\u003cem\u003eJuniperus deppeana\u003c/em\u003e)-oak (\u003cem\u003eQuercus grisea\u003c/em\u003e, \u003cem\u003eQ. gravesii, Q. emoryi\u003c/em\u003e) dominated PJ forests that are common at high elevations of the US-Mexico Borderlands.\u003c/p\u003e \u003cp\u003eWe addressed the following key questions: 1) What were the independent impacts of drought and fire on forest stands?; 2) How did drought and fire interact to drive forest change? For example, did plots most affected by drought experience more severe fire?; and 3) What were the influences of topography and site moisture on drought and fire severity. Given recent increases in both drought and wildfire incidence across the southwestern USA, assessing the impacts of multiple, successive disturbances on PJ forest stand dynamics is critical for informing forest and fire management actions, and it provides an opportunity for evaluating how recent droughts and wildfires mimic or diverge from historical fires of the past in PJ systems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy system\u003c/h2\u003e \u003cp\u003eThe Chisos Mountains (CM) are a small rhyolitic mountain range located entirely within Big Bend National Park. The CM rise to 2300 m asl. and are bound at lower elevations by deserts dominated by shrub and succulent desert flora, where tree establishment and growth is inhibited due to high temperatures and low precipitation. Soils are a mixture of mollisols and entisols. They are composed of moderately deep gravelly loam, which is well drained and non-calcareous (Miggins et al. 2008).\u003c/p\u003e \u003cp\u003ePJ forests cover most of the mountain range, with woodlands occurring on drier sites and lower elevations, and forests dominating higher elevations. Major tree species include Mexican pi\u0026ntilde;on pine (\u003cem\u003ePinus cembroides\u003c/em\u003e Zuccarini), alligator juniper (\u003cem\u003eJuniperus deppeana\u003c/em\u003e vonSteudal), gray oak (\u003cem\u003eQuercus grisea\u003c/em\u003e Liebmann), Graves oak (\u003cem\u003eQuercus gravesii\u003c/em\u003e Sudworth), Emory oak (\u003cem\u003eQ. emoryi\u003c/em\u003e Leibmann), and weeping juniper (\u003cem\u003eJ. flaccida\u003c/em\u003e vonSchlechtendal) (Poulos et al. 2013). Lower elevations also contain small populations of one seed juniper (\u003cem\u003eJ. monosperma\u003c/em\u003e Englemann) and red berry juniper (\u003cem\u003eJ. pinchotii\u003c/em\u003e Sudworth), and oak shrublands that are dominated by \u003cem\u003eQ. pungens\u003c/em\u003e Leibmann.\u003c/p\u003e \u003cp\u003eThe climate is arid, characterized by cool winters and warm summers. Precipitation is distributed bi-modally in late summer and winter with most precipitation falling during summer storms as part of the North American Monsoon System. Mean annual precipitation for the Chisos Basin is 49.7 cm (range 10\u0026ndash;135 cm). Mean monthly minimum temperatures are 1.8 \u0026ordm;C in January and 17.0 \u0026ordm;C in July. Maximum temperatures are 14.1 \u0026ordm;C in January and 29.1 \u0026ordm;C in July (Western Regional Climate Center 2023).\u003c/p\u003e \u003cp\u003eContemporary PJ forest structure and composition in the Chisos Mountains is driven by a combination of historical factors including climate, fire regimes, and land-use history (Romme et al., 2009, Shriver et al., 2025, Noel et al., 2023). Tree density generally increases with increasing soil moisture availability in PJ systems and in the CM, with open-canopy woodlands dominating drier sites and lower elevations and PJ forests occurring on wetter locations and higher elevations and canyons (Poulos and Camp \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn February 2011, the Chisos Mountains and the rest of Texas experienced a rare five-day freeze followed by the most severe one-year drought on record in the State (Neilson-Gammon 2011). These two adjacent events led to widespread tree mortality and unprecedented wildfires across Texas, although none occurred in the Chisos. That year, Texas experienced 31,000 wildfires that burned over 16,000 km\u003csup\u003e2\u003c/sup\u003e, including four of the largest fires in State history (InciWeb \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The freeze-drought killed an estimated 300\u0026nbsp;million trees statewide (Moore et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with especially severe impacts in West Texas (National Drought Mitigation Center, 2011). Arid conditions have continued to prevail in the Chisos Mountains since the 2011 drought. For example, from September 2020 to late June 2021, Big Bend National Park experienced severe to exceptional drought (National Drought Mitigation Center 2021). During this drought, the South Rim 4 Fire ignited (April 8, 2021) in the Chisos Mountains (InciWeb \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and over the course of two weeks, burned 541 ha (1,337 acres) of the high country (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Fire severity appeared to vary substantially, with little tree damage in some sites and complete above-ground mortality elsewhere. The results reported here are the first to quantify successive impacts of the drought and the South Rim 4 Fire on PJ forest dynamics over two decades.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField Methods\u003c/h3\u003e\n\u003cp\u003eWe have maintained a network of 226 permanent plots in the forested areas of the Chisos Mountains since they were installed by Poulos in 2003 and 2008. Poulos and Barton resampled these plots in 2019 to 1) document the effects of the 2011 freeze-drought on forest dynamics (Barton and Poulos \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and 2) estimate the effectiveness of fuel management treatments (Poulos and Barton \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The 2021 South Rim 4 Fire burned 49 of the plots in our permanent plot network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2023, we resampled 51 unburned control plots and 49 burned plots in the Chisos Mountains permanent plot network in which all previously censused trees were uniquely identified with numbered brass tags. We sampled tree survival and regeneration, fire severity, and microenvironmental conditions in these burned plots as well as in the unburned (control) plots. We analyzed these data for changes in forest stand structure over time from pre-disturbance in 2003/2008 to post-drought in 2019 and from post-drought in 2019 to post-fire in 2023.\u003c/p\u003e \u003cp\u003eIn these 10-m radius fixed-area (0.03 ha) plots, we assessed each tree\u0026thinsp;\u0026gt;\u0026thinsp;5 cm DBH as live or dead. Live trees that were \u0026lt;\u0026thinsp;5 cm DBH in previous censuses but surpassed that threshold by our sampling dates were measured, tagged, and added to the current plot population census. Seedlings (0\u0026ndash;5 cm dbh) were tallied by species in 5-m radius circular plots (0.008 ha), nested inside of the larger census plots. We also noted the presence/absence of maturing cones on pi\u0026ntilde;on pines in each sample plot. Finally, we visually assessed fire severity in the field in burned plots as low severity (tree mortality\u0026thinsp;\u0026lt;\u0026thinsp;33%), moderate severity (tree mortality 33\u0026ndash;66% mortality), and high severity (\u0026lt;\u0026thinsp;66% tree mortality). In total, we sampled 15 low-, 14 moderate-, and 20 high-severity burned plots.\u003c/p\u003e \u003cp\u003eIn addition to our field surveys, we also derived differenced normalized burn ratio (dNBR) fire severity data (Miller and Thode \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) for our 49 burned plots. We compared the utility of 30-m resolution Landsat 8 and 10-m resolution Sentinel 2 dNBR products for evaluating fire severity impacts on Chisos Mountains forests, comparing them to our field estimates of fire severity. We extracted Landsat and Sentinel2 dNBR fire severity data and Landsat dNBR classed fire severity data (1\u0026ndash;5 from unburned to high severity fire) for each sample plot location using the point sampling tool plugin in QGIS (QGIS.org 2024). Statistical analyses indicated a better fit between Landsat dNBR and field estimates; we therefore chose this remotely sensed dataset over Sentinel 2 for all subsequent analyses.\u003c/p\u003e \u003cp\u003eA key goal of this study was to investigate how topography shaped the response of forests in the Chisos Mountains to the 2011 freeze-drought and the 2021 South Rim 4 Fire. Accordingly, in each plot, we recorded the slope direction (in degrees), slope inclination (in degrees), position on slope (valley bottom, lower slope, middle slope, upper slope, and ridgetop), and surface configuration (concave, concave-straight, straight, convex-straight, convex). We used these variables to calculate the topographic relative moisture index (TRMI), which provides a ranking of sites along a xeric-mesic gradient (Parker \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), with low scores signifying warm, dry sites and high scores the opposite.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eWe assessed the impacts of drought and fire on forest stands over the study period using zero-inflated linear mixed effects (LME) models and Kolmogorov-Smirnov (KS) tests. Zero-inflated models can handle data with a large proportion of zeros, as was the case for many post-drought and post-fire plots where all trees died. LME models also account for the covariance structures of the repeated measures sampling design. We fit LME models to test for significant differences in tree and seedling density and basal area by fire severity (unburned control, low-, moderate-, or high-severity) and timestep (pre-disturbance in 2003 or 2008, post-drought in 2019, and post-fire in 2023) and the interactions between these two independent factors. Sample plot was designated as a random effect for all models. Changes in these stand characteristics were assessed for all species combined and for junipers, oaks, and pi\u0026ntilde;ons, separately. LME models were fit using the glmmTMB package in R (Brooks et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Significant post-hoc pairwise differences among fire severities and timesteps were evaluated using estimated marginal means in the emmeans package (Lenth et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Differences in tree size distributions from pre-disturbance to post-drought to post-fire and among fire severity classes were evaluated using two sample KS tests. Size distribution differences were assessed for all species combined and for junipers, oaks, and pi\u0026ntilde;ons, separately. All statistical analyses were conducted in R (R Core Team 2024).\u003c/p\u003e \u003cp\u003eWe utilized linear regression and the TRMI data for all 100 plots to investigate the role of site moisture in tree survival from pre-disturbance to post-drought to test the hypothesis that plots in drier sites experienced higher levels of mortality. Then, we performed a second set of regresions regression using TRMI and Landsat dNBR fire severity data from the 49 burn plots to test 1) whether fire severity differed by moisture variation across the landscape, and 2) if plots subject to high levels of 2011 drought-induced tree death subsequently burned at higher fire severities. Lastly, we performed a regression to estimate the relationship between Landsat dNBR and basal area change over the post-drought to post-fire sampling interval to test the hypothesis that dNBR was a good estimator of field fire-severity or tree death.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003e3.1 Fire behavior\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Landsat dNBR fire severity maps performed far better than the Sentinel2 maps for all analyses, and, accordingly, we report results for Landsat dNBR data only for the remainder of the report. The Landsat classed dNBR maps indicated that, within the South Rim 4 Fire perimeter, 23% remained unburned, 73% of the area burned at low severity, 4% experienced moderate-severity fire, and 0% of the landscape burned at high-severity (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). dNBR values peaked at 700, but only two plots had values higher than 400.\u003c/p\u003e \u003cp\u003eLandsat dNBR was a significant predictor of basal area change over the post-drought to post-fire sampling interval within burned plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and mean values of dNBR within each field-determined fire severity class also displayed expected trends of increased dNBR with higher field-determined fire severity. However, the dNBR fire severity proxy contradicted our field observations and fire effects assessments. In contrast to the dNBR maps, we detected in the field 2 unburned, 15 low-, 14 moderate-, and 20 high-severity plots in our systematic sampling grid of plots within the burn perimeter; 41% of the plots burned at high-severity, killing more than 60% of the trees from the post-drought to post-fire sampling interval. Overall agreement between our field estimates of fire severity and the classed/thresholded Landsat dNBR data was just 53%. Agreement was good for unburned and low-severity plots, with ~\u0026thinsp;80% agreement between field and Landsat-derived fire severity. However, plots that we assessed in the field as burning at moderate- or high-severity showed zero agreement with the Landsat-derived fire-severity classes. The fire severity results suggest that dNBR may not capture fire-induced vegetation mortality within short stature and often open-grown pi\u0026ntilde;on-juniper-oak forests, at least in West Texas. We therefore focus the rest of our analyses on characterizing forest change over time based on our field-determined fire severity classes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHigh-severity sample plots burned at higher elevations within the CM, which is no surprise given that the fire ignition occurred near the edge of the South Rim (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our regression results indicated that TRMI was not a significant predictor of tree survival over the pre-disturbance to post-drought sampling interval (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), or fire severity over the post0drought to post-fire sampling interval, likely because this set of 100 sample plots did not display large variation in landscape soil moisture. The multiple regression of environmental variables and tree density change from the post-drought to post-fire interval indicated that only slope and dNBR were significant predictors of tree density change (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On steeper slopes, the fire was more severe, and the tree mortality impacts greater from the fire (i.e., decline in tree density; rsq\u0026thinsp;=\u0026thinsp;0.27).\u003c/p\u003e \u003cp\u003eAlternatively, we found no significant impact of pre-fire tree mortality (i.e., pre-disturbance to post-drought) on dNBR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, data not shown). We note that the poor fit between our field estimates of fire severity and dNBR limit this analysis of the effects of tree mortality on fire severity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean environmental characteristics of sample plots by fire-severity in the Chisos Mountains.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTRMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSentinel2 dNBR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLandsat dNBR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1952.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow-severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2198.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e119.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate-severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2217.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e171.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh-severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2208.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e283.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental predictors of tree density change over the post-drought to post-fire sampling intervals in the Chisos Mountains.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eMultiple regression results for predictors of tree density change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlope\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.154\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.101\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.634\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLandsat dNBR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.290\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.143\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.050\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.580\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eForest changes in Chisos Mountains forests\u003c/h3\u003e\n\u003cp\u003eThe zero-inflated LME models revealed a strong freeze-drought response of forests within unburned control plots, as evidenced by significant declines in tree density and basal area from the pre-disturbance to post-drought sampling interval (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Appendix A-B). We saw no further significant changes in forest structure within control plots since 2019, indicating that these plots can be used as a baseline control for estimating the magnitude of fire effects within burned plots over the post-drought to post-fire sampling interval.\u003c/p\u003e \u003cp\u003eAs in the unburned control plots, burned plots also experienced significant declines in tree density and basal area in response to the freeze-drought (i.e., over the pre-disturbance to post-drought sampling interval, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Then, sites that subsequently experienced moderate- or high-severity fire displayed further significant declines in total tree density and basal area over the post-drought to post-fire sampling intervals for all tree species combined (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Appendix A-B). In contrast, low-severity fire sites experienced no further changes in forest structure in response to the fire according to the zero-inflated LME models (i.e., from the post-drought to post-fire sampling interval, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor individual taxonomic groups, our results corroborated our prior reports on the impacts of the 2011 freeze-drought on pi\u0026ntilde;on pine. It was the only tree type in both burned and control plots to experience significant declines in abundance and basal area from the pre-disturbance to post-drought interval. Over the post-drought to post-fire sampling interval, the impacts of moderate-severity fire on forest structure disappeared in our individual taxonomic analyses of oaks, junipers, and pi\u0026ntilde;ons. Individually, oaks, junipers, and pi\u0026ntilde;ons experienced significant declines in abundance and basal area within high-severity fire sites only.\u003c/p\u003e \u003cp\u003eSeedling abundances were stable over the entire study in control plots and within areas that experienced low- to moderate-severity fire (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Appendix B). As in our other metrics of forest change over time, however, high-severity fire plots displayed significant declines in seedling abundances from the post-drought to post-fire period for all species combined. The taxonomic group analysis revealed that seedling declines were most pronounced in junipers, while oak and pi\u0026ntilde;on seedling abundances did not change significantly over time. Together, these forest change data suggest that, contrary to the Landsat dNBR results, high-severity fire occurred in many plots, and at those locations, forests experienced stand-replacing wildfire.\u003c/p\u003e \u003cp\u003e \u003cem\u003e3.3 Forest size structure changes\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAll sample plots experienced an apparent shift towards larger diameter trees. However, the Kolmogorov-Smirnov tests did not detect significant differences in forest diameter distributions over time in either the control or the burn plots, when comparing the size distributions across all fire severities combined (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Since we observed significant changes in tree and seedling densities and basal area within high-severity plots, we also evaluated whether high-severity fire triggered a change in tree diameter distributions within those plots. Although high-severity fire resulted in losses of smaller diameter trees, diameter distributions shifted to larger trees only for oaks and not for pi\u0026ntilde;on, junipers, or all species combined. While resprouting can be common in Sky Island oaks (Barton and Poulos 2018; Poulos et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb), including in the Chisos Mountains, we saw little evidence of post-fire oak sprouting in high-severity fire plots after the South Rim 4 Fire. These results suggest that, with the exception of oaks, tree abundances dropped across all size classes in response to the freeze-drought and fire, rather than shifting towards large surviving stems with subsequent disturbances.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePost-fire pinecone production\u003c/h2\u003e \u003cp\u003eWe noted a high number of pi\u0026ntilde;ons bearing cones within burned plots, especially within low- and moderate-severity fire sites, where many trees survived. We saw pi\u0026ntilde;ons bearing mature cones in 42% of our burned sample plots across all fire severities. Mature pi\u0026ntilde;on pinecone prevalence was significantly higher in burned plots than in control plots, where we observed cone-bearing trees in just 21% of our sample plots (Chi-square Test, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTogether, the 2011 freeze-drought and 2021 South Rim 4 Fire in the Chisos Mountains resulted in the landscape-scale death of thousands of trees across our sample plot network. Our analysis revealed two clear mortality patterns in PJ stands. First, the 2011 winter freeze-drought triggered significant declines in live tree abundances and basal area throughout the mountain range. Trees within the 100 plots experienced approximately 56% survivorship over the pre-disturbance to post-drought sampling interval, demonstrating the major impact of the event on tree densities. Second, sites that experienced high-severity fire displayed even further significant losses in tree density and basal area, while low- to moderate-severity fire sites remained relatively unchanged. High-severity fire sites experienced an average of just 14% survivorship from the post-drought to the post-fire sampling interval, demonstrating the stand-replacing nature of the South Rim 4 Fire within these burn patches. Our results clearly reveal that this fire had a much larger impact than recent West Texas wildfires burning within similar pi\u0026ntilde;on-juniper-oak woodlands, and much more than indicated by remote-sensing.\u003c/p\u003e\n\u003ch3\u003eDrought Impacts\u003c/h3\u003e\n\u003cp\u003eThe 2011 freeze-drought and the 2021 South Rim 4 Fire caused distinctly different impacts. The freeze-drought caused high mortality among all tree species, all tree sizes, and in all plots, killing nearly 50% of the trees. Plots occurring in drier sites (e.g., south-facing, steeper, ridgetops) exhibited the highest levels of mortality, suggesting some level of refuge for trees growing in more protected sites. These results are consistent with our past reports on this plot network by Poulos (2014) and Barton and Poulos (2022), as well as Waring and Schwilk (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) for the Chisos Mountains and the state of Texas, as a whole (Lawal et al. 2025). In pi\u0026ntilde;on-juniper woodlands throughout much of the western U.S., severe drought (and associated diseases, insects, and parasites) is the most important driver of forest dynamics, more so even than fire (Floyd et al. 2009; Meddens et al. 2018). The impacts of the freeze-drought revealed in the present study will no doubt reverberate in the dynamics of these forests for many decades, if not centuries.\u003c/p\u003e\n\u003cp\u003eHow did these trees die? Plant hydraulic system failure the a primary cause of death from water stress in response to both drought and plant tissue freezing (Brodribb et al. 2020; Marchin et al. 2022). Water moves through the xylem tubes of vascular plants because of the negative tension created by transpiration of H\u003csub\u003e2\u003c/sub\u003eO out of leaf stomates into the air; this tension pulls water into the roots and through the plant. As moisture stress, or evaporative demand, increases so does internal water tension, which can cause the formation of bubbles in the water stream, or cavitation, blocking water flow (Sperry and Tyree 1990; Pittermann et al. 2010; Lens et al. 2013). When plants freeze and thaw, they can experience the same effect of hydraulic breakdown from freeze-triggered cavitation of xylem and death (Pittermann et al. 2010; Lens et al. 2013). It is likely, therefore, that some combination of drought- and freeze-induced hydraulic failure led to the high levels of tree mortality from the pre-disturbance to post-freeze-drought sampling interval in the CM.\u003c/p\u003e\n\u003cp\u003eMortality stemming from the freeze-drought events of 2011 was most pronounced in drier, hotter sites. Topographically, these were lower locations that were more exposed, steeper, and on south-facing slopes. Topography is a key determinant of forest structure, species composition, and processes in the Sky Islands of southwestern North America (Barton 1993; Coblentz and Riitters 2004; Poulos et al. 2007), and our results reveal how its role plays out during key tree mortality events. Our mortality patterns are similar to the findings of others that drought-induced tree mortality is usually more pronounced on drier portions of environmental gradients (Allen and Breshears 1998; Gitlin et al. 2006; Breshears et al. 2009), but cold air drainage in mesic valley bottoms during the 2011 February freeze may have also triggered some of the tree death in CM.\u003c/p\u003e\n\u003cp\u003eMost of the fire ecology literature on pi\u0026ntilde;on-juniper (PJ) woodlands shows that pi\u0026ntilde;on pines are particularly vulnerable to prolonged acute drought relative to oaks and junipers, which can better tolerate prolonged water deficits (Breshears et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e, Wion 2022). In the current study, we saw a reduction in tree densities over the pre-disturbance to post-drought sampling interval across all tree species, and not just in pi\u0026ntilde;on pine alone. This, coupled with the size distribution analysis, suggest that trees of all species and across a wide range of tree sizes were negatively impacted by the drought, not just the less drought tolerant pi\u0026ntilde;ons.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eWildfire impacts\u003c/h2\u003e\n\u003cp\u003eThe subsequent 2021 South Rim 4 Fire triggered further significant declines in tree abundances. These effects were much more variable across plots than for the freeze-drought, however. Fire-induced tree mortality was minimal where the fire burned at low and moderate severity, but nearly 100% at high-severities. Mortality was not concentrated in smaller size classes, as in some fires (Barton \u0026amp; Poulos, 2018), but instead killed stems regardless of size. This impact is likely a result of the high intensity of the fire in some plots combined with the pronounced fire sensitivity of pi\u0026ntilde;ons, junipers, and oaks. Compared to truly fire-resistant tree species like ponderosa pine, these species lack traits, such as thick bark, self-pruning, and high crowns, which confer a capacity to survive fire (Romme et al. 2009). In contrast to the lack of size-dependent mortality in all species combined, mortality in oaks was relatively higher in smaller trees, suggesting especially high sensitivity to fire/topkill in this group of species. Compared to pi\u0026ntilde;ons and junipers, xerophytic oaks in fire-prone environments depend more on resprouting after top-kill from disturbances such as droughts and fire (e.g., Barton and Poulos 2018, Poulos et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Yet in the CM, we found surprisingly low levels of oak resprouting after top-kill in the South Rim 4 Fire, possibly a result of very high fire severity, although we have observed oaks elsewhere in the Sky Islands exhibit almost universally vigorous post-fire resprouting regardless of fire severity (Barton and Poulos 2018, Poulos et al. 2021). In these other mountain ranges, including the Davis Mountains just to the north, this response of oaks has led to projections of the development of dense oak shrublands after high-severity fire (Barton and Poulos 2018; Guiterman et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The low levels of oak resprouting we documented here suggest that oak fire effects can be variable, and the death of oaks in the CM within high-severity patches is likely to have important consequences, such as a lack of future dominance by oaks, for vegetation composition in recovering woodlands after this fire.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eInteractions of freeze-drought and wildfire\u003c/h2\u003e\n\u003cp\u003eDisturbances such as drought, insect outbreaks, and fire often interact in their effects on PJ woodlands and forests (Romme et al. 2009). Barton and Poulos (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified increased dead fuel loads resulting from the 2011 freeze-drought, leading us to hypothesize that plots exhibiting higher levels of mortality from that disturbance event would burn at relatively higher fire severities. Our analysis of pre-fire tree mortality impacts on Landsat fire severity did not support this hypothesis. Several explanations could account for this pattern. First, the poor performance of Landsat dNBR could have obscured a relationship between past mortality and fire severity. Second, we intentionally avoided resampling plots that had experienced total tree mortality from the drought in our most recent post-fire sampling effort. Therefore, sites that experienced heavy/total tree death from the drought, and subsequent high standing dead and surface fuels after the drought may not be well represented in our sample. Lastly, it is possible that, ten years after the freeze-drought, any remaining increase in dead fuel from the drought was not sufficient to amplify fire severity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003ePi\u0026ntilde;on pinecone production\u003c/h2\u003e\n\u003cp\u003eThe abundant cone production of pi\u0026ntilde;on pines in burned compared to unburned control plots was a surprise. Prior studies on post-fire cone production have only been documented in serotinous species like Aleppo Pine (Daskalakou and Thanos 1996; Alfaro-S\u0026aacute;nchez et al. 2015). Pi\u0026ntilde;on pine cones require two growing seasons to mature (Wauer and Riskind 1977), meaning that the prevalence of maturing pine cones in 2023 within the burn perimeter in CM could represent a seed production event in the wake of the fire, especially given the scant cone production in unburned control plots. While the year of the fire in 2021 was hot and very dry, 2022 was wetter and cooler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.climateengine.org\u003c/span\u003e\u003c/span\u003e) with almost normal precipitation. It is possible that reduced competition by surviving trees after the fire triggered a widespread seed production event fueled by more normal precipitation and cooler temperatures in the year after the wildfire. This could be good news for post-fire pi\u0026ntilde;on regeneration and seed source availability to regenerate pi\u0026ntilde;ons after the drought and wildfire. However, summer precipitation for seedling germination has remained low in the years since the South Rim4 Fire, even with intermittent severe thunderstorm activity in spring and fall of 2023 and summer 2025. Pine seedling germination requires both bare mineral soil and precipitation for germination of the cone crop we observed in the field, which dispersed its seeds in fall 2023. Future studies on cone production and post-fire pi\u0026ntilde;on pine regeneration could elucidate the long-term impacts of unusual events like this in non-serotinous obligate seeder pine species.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eForest management applications\u003c/h2\u003e\n\u003cp\u003eOur results have important implications for future forest management of the Chisos Mountains, specifically, and PJ systems of the southwestern USA, in general. Climate projections predict a hotter and drier climate with increased fire activity in southwestern PJ forests and woodlands (Wasserman and Mueller 2023; Harris et al. 2024). This is predicted to lead to shrinking PJ distributions (Noel et al. 2025). These projections suggest an amplification of the impacts we identified here, with associated higher tree mortality and reduced stand density and basal area from increasing multiple disturbances. Drought stress may also accentuate the impacts of disease and insects, as well, exacerbating disturbance effects (Breshears et al. 2021). If climate changes proceeds as predicted, these impacts may lead to transitions to other vegetation types, such as shrubland or grassland systems that are devoid of trees (Falk et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Guiterman et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). While these resultant plant communities are more resilient to drought and fire, they may trigger a long-term loss of forest cover across the Southwest if trees cannot recruit in the post-fire and post-drought environment (Coop et al. 2020). Such forest losses would in turn result in loss or at least changes in the unique environmental services provided by trees (e.g., wood, shaded recreation).\u003c/p\u003e\n\u003cp\u003eForest managers cannot exert control over the incidence of drought events, which are meteorological events imposed regardless of forest conditions. On the other hand, reducing the forest density through thinning and prescribed burning can increase water availability and drought resilience in ponderosa pine and similar forests across the West (Sankey and Tatum \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sankey et al. 2025). There is no evidence, however, that these management practices would translate into similar returns in PJ, although many managers have cleared fuels within this forest type to reduce both drought and wildfire risk (Redmond et al. 2023). A century or more of fire suppression in ponderosa pine forests has dramatically increased tree density in stands that prior to Euro-American impacts were maintained in open, low-density condition by frequent surface fires. Although some PJ stands might have approached this structure, abundant evidence suggests that these forests were typically denser and subject to infrequent, stand-replacing fire (Floyd et al. 2000, 2004, 2017; Romme et al. 2009). Fire suppression and many other impacts imposed over the past century have led to increased tree densities in some of these stands, but elsewhere such evidence is lacking. The key point here is that ponderosa pine forests differ strongly from PJ systems, and no evidence yet recommends stand management for drought resilience within this forest type.\u003c/p\u003e\n\u003cp\u003eForest managers potentially have much more control over fire, which is as much a biological as a meteorological phenomenon. Managers can influence these biological components by manipulating the crown and surface fuel matrix. Throughout the world, practices such as suppression, thinning, and prescribed burning have drastically altered the incidence and severity of wildfire (e.g., Pollet and Omi \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Brodie et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the CM, fire suppression has clearly reduced the incidence of wildfire in the Chisos Mountains (e.g., Poulos et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Previously, we showed that low-severity thinning and prescribed burning did not have detectable impacts on dead fuel loads, stand density, basal area, or species composition within the Chisos, and that all stands responded similarly to the 2011 drought regardless of prior restoration treatment history (Barton and Poulos \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003eb). Higher intensities of thinning and prescribed burning, however, might have stronger impacts on dead and live fuel loads and thus reduce the incidence and especially the severity of future wildfires, impacts found in other forests (e.g. Pollet and Omi \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Brodie et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eContemporary fire in PJ forests\u003c/h2\u003e\n\u003cp\u003eThe recent transition from frequent low-severity to frequent high-severity fire across many western forests begs the question whether the fire behavior of South Rim 4 Fire deviated from natural pi\u0026ntilde;on-juniper-oak fire regimes (Landres et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Was the 2011 freeze-drought followed by the 2021 wildfire anomalous or were such events common in pre-Euro-American disturbance regimes? The answer to this question has important implications regarding whether management should aim at reducing the risk of drought and wildfires fires on PJ mortality (by vegetation management or suppression, for example) or should managers take a hands-off approach?\u003c/p\u003e\n\u003cp\u003ePrior research in the CM suggested that the natural fire regime of pi\u0026ntilde;on-juniper-oak woodlands was primarily frequent, low severity surface fires (Poulos et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Historical droughts have also been repeatedly identified in PJ stands in the dendrochronological record (Floyd et al. 2009 and many others). Conclusions regarding CM PJ fire regimes were based on size and age data from across the mountain range and fire scar samples from large, widely scattered Mexican pi\u0026ntilde;on pines. In their fire history paper from 24 fire-scarred trees in CM, Poulos et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) interpreted the fire-scar record of relatively frequent landscape fire (MFI\u0026thinsp;=\u0026thinsp;36.5 years) and the reverse-J tree size and age-class distributions with abundant, small/young pi\u0026ntilde;on pines as indicators of historically low- to mixed-severity fire, with a fire-suppression-induced pulse in tree regeneration after the last major fire in 1926 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). However, our analysis of the contemporary fire behavior of the South Rim 4 Fire suggests an alternative fire regime for the Chisos Mountains characterized by mixed-severity fire with large patches of high-severity, stand-replacing fire.\u003c/p\u003e\n\u003cp\u003eThis scenario is consistent with the age data-based literature on fire behavior and stand dynamics in other pi\u0026ntilde;on-juniper-oak woodlands, including with some of the species in the present study. For example, the comprehensive review by Romme et al (2009) clearly demonstrated that Rocky Mountain pi\u0026ntilde;on (\u003cem\u003eP. edulis\u003c/em\u003e)-juniper woodlands across the western US were characterized by an infrequent, stand-replacing fire regime, although low-severity fires probably also occurred infrequently. Baisan and Morino (2000) came to a similar conclusion for border pi\u0026ntilde;on (\u003cem\u003eP. discolor\u003c/em\u003e)-juniper-oak forests in the Chiricahua Mountains in southeastern Arizona. Further, comprehensive studies, such as Huffman et al. (2006, 2009), demonstrated that, although small-scale, stand-replacing fires were major drivers of forest dynamics historically in pi\u0026ntilde;on-juniper woodlands, fire suppression has nevertheless increased stand density above pre-Euro-American levels, as suggested for the CM and other more open PJ woodlands (Poulos et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Margolis \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Resolving the historic range of variation (HRV) for fire in pi\u0026ntilde;on-oak-juniper woodlands of the Chisos Mountains (and other Sky Island PJ communities) will require a fine-grained, spatially-explicit analysis of both fire-scar evidence and pre-1920s pi\u0026ntilde;on age distributions and cohorts (see, for example, Huffman et al. 2008). In Poulos et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), of the 24 fire-scarred specimens, six samples recorded multiple fire events, with just two cross-sections recording\u0026thinsp;\u0026gt;\u0026thinsp;3 fire events. It is possible that these trees were located in areas that burned repeatedly at lower-severity within a high-severity fire matrix. Until we have such additional evidence, our best scientific approach to fire HRV for these woodlands is to be open to the possibility that wildfires such as the 2021 South Rim 4 Fire might have been a natural part of the fire regime prior to Euro-American settlement.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFunding for this project was provided by the National Park Service via agreement P22AC01677. The Schumann Institute of the Bailey College of the Environment at Wesleyan University funded two summer field internships for two undergraduates to conduct our surveys. University of Maine at Farmington also supported two summer undergraduate stipends to support this research. The original field inventory was funded by Joint Fire Sciences Program of the U.S. Department of the Interior (03-3-3-13). Post-drought sampling in 2019 was funded by NPS agreements P18AC00722 and P18AC01162.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHP and AB conceptualized the study, wrote the proposal, conducted the field work, analyzed the data, and wrote the paper together. We worked equally on all parts.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgementsWe thank Richard Gatewood, John Morlock, and DW Ivans of the Big Bend National Park Fire Management Office for logistical support for this project. This work would not have been possible without the assistance of NPS mule packers, Gavin Monson and Lanadawn Nusz. We thank them for the many pack trips into the Chisos Mountains high country to support this research. We are also grateful for field assistance by Darren Wallis, Maya Lopansri, Christopher Houdeshell, Alexander Debo, Leo Miranda, Isaiah Reed, Jordan Fried, Kelsey Wogan, Patricia Manning, Kelon Crawford, Harold Slater, and Mark Kurowski in support of this project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eI will supply data upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, H. D., G. A. Barron-Gafford, R. L. Minor, A. A. Gardea, L. P. Bentley, D. J. Law, D. D. Breshears, and N. G. McDowell. and T. E. Huxman. 2017a. 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Dead again: predictions of repeat tree die-off under hotter droughts confirm mortality thresholds for a dryland conifer species. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e 17:7: 074031.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"piñon-juniper ecosystems, tree mortality, piñon pine, acute drought, mixed-severity wildfire, stand-replacing fire","lastPublishedDoi":"10.21203/rs.3.rs-8123967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8123967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePi\u0026ntilde;on-juniper (PJ) forests and woodlands comprise cover vast portions of western North America, providing habitat and food to myriad species. Increasing drought and wildfire activity from climate change and fire suppression are triggering major changes in forest structure and composition in PJ systems, yet we know little about how these recent disturbances coincide or diverge from historical disturbance regimes. In an effort to fill this gap, we evaluated 20 years of forest change data from the Chisos Mountains, Texas to evaluate trends in pi\u0026ntilde;on-juniper forest dynamics in response to a record 2011 drought and subsequent 2021 wildfire.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results revealed that the Chisos Mountains have experienced recent, range-wide tree mortality over the last two decades, in response to drought and subsequent fire, especially within high-severity fire sites. The 100 sample plots in our study experienced approximately 56% survivorship over the pre-disturbance to post-drought sampling interval, demonstrating the major impact of the 2011 event on forest stand dynamics. Sites that experienced high-severity fire in the 2021 South Rim 4 Fire displayed even further significant losses in tree density and basal area in response to stand-replacing fire after the drought. Low- to moderate-severity fire sites remained relatively unchanged after the fire. High-severity sites experienced an average of just 14% survivorship from the post-drought to the post-fire sampling interval, which highlights the stand-replacing nature of the South Rim 4 Fire within high-severity burn patches.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe suggest that droughts and contemporary fires like the 2021 South Rim 4 Fire might have been a natural part of this regions\u0026rsquo; fire regime prior to Euro-American settlement. Drought and wildfires in the wake of drought are becoming increasingly common throughout the region as the impacts of climate change continue to amplify, and studies like this are important for elucidating how contemporary wildfires are concordant with or diverge from historical fire regimes for sustainable PJ ecosystem management in the Anthropocene.\u003c/p\u003e","manuscriptTitle":"From Drying to Frying: Drought and fire as drivers of vegetation change in piñon-juniper- oak forests of the Chisos Mountains, Big Bend National Park, USA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 11:23:02","doi":"10.21203/rs.3.rs-8123967/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-03T19:49:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T01:23:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270518937485332737996979036408729461734","date":"2026-01-09T21:57:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T00:04:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133899301148528150197065193730715922174","date":"2025-12-18T16:29:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-15T14:41:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T12:42:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T12:37:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2025-11-15T19:20:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8739e194-0679-4a95-a169-f4d9cdbc6aea","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-02T18:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 11:23:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8123967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8123967","identity":"rs-8123967","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}