Towards predicting flammability of Sierra Nevada mixed conifer forests: drought stress and fuel moisture are strongly linked in angiosperms but decoupled in gymnosperms

Towards predicting flammability of Sierra Nevada mixed conifer forests: drought stress and fuel moisture are strongly linked in angiosperms but decoupled in gymnosperms | 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 Towards predicting flammability of Sierra Nevada mixed conifer forests: drought stress and fuel moisture are strongly linked in angiosperms but decoupled in gymnosperms Indra Boving, Joe V. Celebrezze, Leander DL Anderegg, Max Moritz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5939800/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Fire Ecology → Version 1 posted 5 You are reading this latest preprint version Abstract Background Drought and wildfire are linked by their relationships with plant hydration, yet drought- and fire-focused research use discipline-specific hydration metrics to capture plant stress and fuel flammability. We investigated potential drought-wildfire dynamics in angiosperms and gymnosperms common to Sierra Nevada mixed conifer forests by relating plant drought status (water potential) to water content (live fuel moisture, LFM), and to flammability. We conducted laboratory flammability tests coupled with a benchtop drydown and measurements of physiological drought-response (the turgor loss point, TLP). We measured water potential, LFM, and phenology during a seasonal drydown in the field. Results Gymnosperms showed inconsistent relationships between water content and water potential across drydown type (field vs. lab), complicating how we measure their potential drought-wildfire relationships. In contrast, angiosperms had consistent hydration relationships, showing promise for relating drought stress to flammability. Physiological adjustments occurring near the TLP and phenological patterns impacting dry matter development drove differences across functional groups. Decreased plant hydration increased flammability, and LFM predicted flammability better than water potential – likely because LFM captured both hydration and shifts in leaf and branch dry matter associated with phenology. Conclusions Our results suggest that phenological development and drought response mediate the relationship between drought stress, tissue water content, and tissue flammability in seed-producing plants (i.e., angio- vs gymnosperms). This work links key ecological processes, drought and wildfire, and advances our ability to predict wildfire risk using species composition and drought stress. flammability live fuel moisture water potential pyro-ecophysiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background As droughts and wildfires increase in both frequency and intensity, understanding the links between these interrelated processes is key to resilience-focused forest management (Dale et al. 2001; Allen et al. 2010). Drought and wildfire share a relationship with plant hydration: the moisture content of vegetation is an important determinant of wildfire risk, and reductions in plant hydration associated with drought can have long-term effects on plants (Allen et al. 2010; Ma et al. 2021). As soil water availability decreases, both plant water content (the amount of water in leaves) and water potential (the potential energy in plants responsible for water movement) decrease in turn. Reductions in plant water content and water potential have repercussions for growth and whole-plant function, as cellular processes are inhibited and leaf chemistry shifts, eventually leading to mortality if water availability is not restored (Álvarez-Cansino et al. 2022; Bartlett et al. 2016; 2016; Blackman 2018; Lawlor and Cornic 2002; Trifilò et al. 2023). Lowered water content also influences the ignition risk of plant tissues directly via the effect of moisture on ignitability, and indirectly via shifts in foliar chemistry (e.g., volatile organic compound concentration) or structural changes following drought (e.g. leaf or branch shedding) that impact live fuel connectivity (Alessio et al. 2008; Bianchi et al. 2019; Dickman et al. 2023; Guerrero et al. 2024; W. M. Jolly et al. 2012; Pausas et al. 2016). A measurement of plant water content, live fuel moisture (LFM), is commonly used in assessments of fire risk from the front lines of wildland firefighting to satellite-based risk monitoring (Peterson, Roberts, and Dennison 2008; Yebra et al. 2013). LFM is defined as the proportion of water relative to dry matter in a plant tissue sample and is usually on a percent bases. At the tissue scale (e.g. individual leaves or plant organs at the centimeter-scale) reductions in LFM has been linked to increases in ignition likelihood and flame durations (Anderson 1970; Dimitrakopoulos and Papaioannou 2001; Alessio et al. 2008; de Magalhães and Schwilk 2012; Alam et al. 2019; Bianchi et al. 2019; Tumino et al. 2019; Boving et al. 2023; Celebrezze, Boving, and Moritz 2023). At the scale of whole plants or plant stands, the effects of LFM are less well-documented due to challenges associated with conducting intermediate-scale flammability tests (e.g. difficulties with holding all factors but LFM constant, or reaching sufficiently large ranges of LFM in field settings), yet LFM likely does have an effect on fire behavior at these intermediate scales by influencing ignitions and spread rates (Alexander and Cruz 2013; Pimont et al. 2019; Rossa and Fernandes 2018). On the landscape-level (in large vegetated areas, kilometer-scale), LFM has been shown to affect fire spread, size, and severity (Dennison and Moritz 2009; Jurdao, Chuvieco, and Arevalillo 2012; Yebra et al. 2013; Park, Fauss, and Moritz 2022). Despite the importance of LFM in understanding wildfire dynamics, its relationship with physiological drought status (i.e., water potential and drought-response traits) is less well understood, as is the relationship between physiological drought status and flammability itself. Since both wildfire and stand-level drought impacts are linked to tissue-level processes, relating physiological drought status to flammability is key for understanding how drought might influence landscape-level wildfire dynamics (Schwilk and Caprio 2011; Krix and Murray 2018; W. Jolly and Johnson 2018; Nolan et al. 2018). To assess flammability, laboratory studies typically measure a suite of flammability parameters that describe different aspects of ignition and combustion. These include time to ignition, flame duration, glow duration, flame height, and changes in temperature or heat release (Martin et al. 1993; Alam et al. 2019; Tumino et al. 2019; Celebrezze, Boving, and Moritz 2023). These types of studies help disentangle the importance of different plant traits for predicting flammability and provide the foundation for many landscape-level models of wildfire dynamics and risk (Andrews, Cruz, and Rothermel 2013; Grootemaat et al. 2015; Alam et al. 2019; Mitchell and Martin 2023). Time to ignition and flame characteristics related to fire spread, such as flame duration and height, are especially relevant for wildfire dynamics at the landscape level where ignitions and flame spread are key determinants of overall fire risk and severity (Anderson 1970; Molina et al. 2017). Drought responses can be expected to vary based on functional group (i.e. broadleaf angiosperms vs. gymnosperms) or water use strategy (e.g. the spectrum between isohydric and anisohydric stomatal behaviors) (Carnicer et al. 2013; Lusk, Wright, and Reich 2003; Pirasteh-Anosheh et al. 2016). Indeed, plant functional groups are often used in global vegetation models and models of fire behavior due to shared morphological and drought response traits within plant groups (Wullschleger et al. 2014). A common adjustment to limit water loss during drought is stomatal closure, which typically occurs near a plants turgor loss point (TLP) (Chen et al. 2022). The TLP is a plant trait that can help describe a plant’s water use strategy: very negative TLPs can indicate drought tolerance , while less negative TLPs are slightly more complicated to interpret, either indicating a drought avoidance strategy or vulnerability to drought (Bartlett, Scoffoni, and Sack 2012; Bartlett et al. 2016; Farrell, Szota, and Arndt 2017; Pivovaroff, Cook, and Santiago 2018). In combination with an understanding of plant-level water access and a species` stomatal sensitivity, it has also been used to predict drought response (Álvarez-Cansino et al. 2022; Blackman 2018; Petruzzellis et al. 2021). Pressure volume (PV) curves, which relate water potential to relative water content for a single leaf, are used to determine the TLP by identifying where the relationship between -1/water potential and water content shifts from curvilinear to linear (Abrams and Menges 1992; Tyree and Hammel 1972). Recent studies in Mediterranean systems have found similarities between PV curves and landscape-level relationships between water potential and water content, with larger decreases in water content relative to water potential at hydration levels higher than the TLP, and a linear relationship following the TLP (Nolan et al. 2018; 2022; Pivovaroff et al. 2019). Here, we extend previous work in Mediterranean systems into temperate mixed-conifer forests in the Sierra Nevada, California, USA where increasing prevalence of large fires and severe droughts indicates a dire need for better understanding of regional drought-fire dynamics (Gutierrez et al. 2021; Stevens et al. 2017). We investigate water use strategies and flammability dynamics in gymnosperm and angiosperm species with contrasting structural characteristics, leaf habits, and physiological responses to drought. These taxonomic groups have historically been compared in the context of fire-driven trait evolution (particularly in leaf litter production and regeneration strategies), but have rarely been compared in terms of plant flammability or modern drought-fire relationships (Belcher and Hudspith 2017; Bond and Midgley 2012; Burger and Bond 2015; Cornwell et al. 2015). To better measure and model how drought might impact wildfire risk and severity, we must understand what drives variation in the relationship between water content and water potential, two key metrics of plant hydration that both influence flammability. This is especially important for systems with increased risk of both drought and fire, such as in California’s Sierra Nevadas. Here, we focus six common tree and shrub species from this region: three gymnosperms (white fir ( Abies concolor ), Jeffrey pine ( Pinus jeffreyi ), and incense cedar ( Calocedrus decurrens )) and three angiosperms (California black oak ( Quercus kelloggii ), mountain whitethorn ( Ceanothus cordulatus ), and greenleaf manzanita ( Arctosaphylos patula )). We asked: 1) How well do reductions in plant hydration (water potential and LFM) measured in the lab reflect field-based patterns measured over a season? 2) How do water potential and LFM differ in their relationship with flammability, and how does this vary across taxonomic group? 3) How does flammability change over the course of a season (i.e. as shifts in plant hydration and phenology occur) and how does this vary across species? To answer these, we collected synchronous measurements of LFM and water potential in the field between snow-melt and the end of the summer dry season, in the lab as branches dried on a benchtop, and using traditional physiology methods (PV curves). These, in combination with flammability tests conducted across a range of hydration levels, enabled us to test the link between hydration and tissue-level flammability in species with varying physiological characteristics. Materials and Methods 1.1: Study area and species We investigated dominant tree and shrub species in the Providence Creek Watershed in the Sierra National Forest, California, USA. Vegetation in the watershed consists of mixed-conifer forest with a variety of distinct plant functional types. The site is situated in the rain-snow transition zone with a mean annual precipitation of 1015 mm/year and mean annual temperature of 8°C (O’Geen et al. 2018) (Site 1: 37º 4’ 4” N, 119º 11’ 43” W, 2100 m elevation; Site 2: 37º 3’ 5” N, 119º 11’ 0” W, 1930 m elevation). Precipitation largely occurs between October and April, allowing for a seasonal period of relative water deficit during the summer months (Halofsky 2021). Soils at these sites are primarily the Gerle series of Humic Dystroxerepts with occasional rock outcroppings (Soil Survey Staff 2022). We investigated gymnosperm species white fir [ Abies concolor (Gord. & Glend.) Lindl. ex Hildebr.], Jeffrey pine ( Pinus jeffreyi Balf.), and incense cedar ( Calocedrus decurrens), and angiosperms California black oak ( Quercus kelloggii) , whitehorn ceanothus ( Ceanothus cordulatus) and Greenleaf manzanita (Arctostaphylos patula) . Trees were interspersed with shrubs in patchy mosaics. As Qu. kelloggii and Pi. jeffreyi were not present at both sites, we only sampled these species at site 1. We collected samples from both sites every two to three weeks throughout the summer drydown period (June 2020 - October 2020 and April 2021 - November 2021), except when the 2020 Creek Fire closed access to the Providence Creek sites in September and October 2020, during which we sampled in a nearby location similar in species composition, elevation, and environment (36° 42' 55" N, -118° 58' 5.415" W). Flammability tests were conducted on samples collected in September and October 2020. 1.2: LFM and midday water potential measurements We conducted simultaneous measurements of LFM and water potential at midday, near solar noon (11:00 h -14:00 h), and collected predawn water potential measurements on the same day (03:00 h - 05:00 h). Midday water potential measurements characterize maximum water stress based on stomatal openness and evaporative demand, while predawn measurements characterize water availability. Sites 1 and 2 were visited on consecutive days approximately every three weeks. 2-3 foliage samples were harvested per individual. We sampled 36 individuals at site 1 (six individuals x six species), and 20 individuals at Site 2 (five individuals x four species). We stored water potential samples in sealed thick plastic bags in a cold cooler until we measured them in the field, typically 30-60 minutes after collection, using a Scholander Pressure chamber (PMS instruments, Corvallis, OR). LFM was measured according to United States Forest Service protocols after separating new (current year) and old (older than current year) growth, when possible (Countryman and Dean 1979; Norum and Miller 1984; Weise, Hartford, and Mahaffey 1998). Approximately 25g of healthy, sun-exposed new and old tissues were collected from each individual and placed in preweighed airtight containers; we took care to avoid collection of woody stems greater than 3.5mm diameter, dead plant matter, or reproductive organs (Norum and Miller 1984; Zahn, S. and Henson, C. 2011); for the Pinus species in our study, only needles were collected following established protocols (W. M. Jolly and Hadlow 2012). Following collection, we weighed samples, dried them at 100ºC for 24 hours, and then weighed them again for their oven-dry weight. LFM was determined using Eq. 1. Eq. 1 1.3: Pressure-Volume Curves In October 2020 and 2021, healthy 20-40 cm-long branches were collected from the 3-6 individuals per species from Site 1 shortly after dawn, sealed in moistened plastic bags, and transported to the laboratory in a cooler. Samples were either immediately processed within 1 hour of sample collection or stored in a refrigerator at 4°C between processing (maximally 12 hours after collection). For processing, we cut branches incrementally underwater into smaller branchlets and rehydrated them in cool darkness for 2-4 hours, after which we excised healthy non-necrotic leaves or small leafy shoots from the branch using a razor blade. To avoid the effects of oversaturation, leaves and stems that had been underwater during rehydration were avoided when conducting PV curves (Meinzer et al. 2014). For Pi. jeffreyi fascicles were rehydrated by placing the entire excised fascicle in water (Rehschuh and Ruehr 2021). To construct PV curves, water potential (-MPa) and leaf weight (to 0.0001 g) were determined on rehydrated leaves or branchlets (one per individual) following established methods (Tyree and Hammel 1972). While measuring water potential, we slowly adjusted pressure (0.1 -MPa/sec) to avoid rapid cooling of the sample. Consecutive water potential and weight measurements were taken on each leaf and plotted as -1/MPa versus sample weight. This plot informed when we had reached the TLP, and we ceased measurements after three to five points of the linear section of the curve were reached. We determined saturated water content (SWC) by extrapolating the plot of water potential and water weight to water potential = 0, which was used to determine relative water content (RWC) and LFM using Eq. 1. The LFM, RWC, and water potential at TLP corresponded with the point at which the exponential section of the curve met the linear section, determined by maximizing the linear fit (r 2 ) of the tail of the curve. The osmotic potential at full turgor was calculated as the y-intercept of the linear section of the curve. PV parameters were determined using the excel spreadsheet available on Prometheus wiki (“Leaf Pressure-Volume Curve Parameters” 2010). All pressure-volume parameters are available in Supporting Information Table S1. 1.4: Flammability Drydown Tests We conducted flammability drydown tests during the end of the summer dry season 2020, during which we measured LFM, water potential, and flammability simultaneously on leaves and shoots of each species during a benchtop drydown using established methods (Boving et al. 2023). We collected branches from 9-11 individuals per species shortly after dawn, cut these underwater into 9-12 smaller branchlets containing at least three smaller shoots or leaves, and rehydrated them following the same procedure as for PV curves. Branchlets dried over the course of 3-7 days, during which we took periodic measurements of water potential, LFM, and flammability (one from each of the three leaves or shoots per branchlet) at approximate intervals of 1 MPa. As we conducted flammability tests at the end of the growing season when all tissues were done growing, new and old growth were not separated for LFM or flammability measurements as they had been in the field. Prior to measurements, branchlets were stored covered with a dark plastic bag for at least 15 minutes to allow equilibration of water potential across all tissues. We conducted flammability drydown tests until branchlets had reached -10 MPa or were well below their naturally observed minimum seasonal water potentials. To measure flammability, we used a hotplate-based device, where the entire experiment was conducted under a laboratory fume hood to control for external temperature, humidity, and wind. A 15 cm square hotplate (Corning, Model COR-6795-600D) was heated to a surface temperature of 550 °C, and 10 cm plant samples were placed on a 1.27cm gridded brass mesh 1 cm above the heated surface, such that samples were situated at a steady temperature of around 270°C at the height of the sample. Type K thermocouples were used to record temperatures 1 cm and 5 cm above the mesh. A propane gas Bunsen burner pilot flame was centered 6 cm above the plant sample allowing for sufficient space between the wire mesh and flame to avoid the plant sample touching the flame while being close enough to ignite emitted volatiles. The device was designed to allow burning of large 10-15cm shoots (compared to other studies that typically only measure plant samples within the 1-3 cm range) and to capture radiative and convective heat exposure by elevating samples on a wire mesh (rather than only capturing conductive heat, which is what similar studies using epiradiator-type devices typically capture when samples are placed directly on the heated surface) (Celebrezze, Boving, and Moritz 2023). For more detailed descriptions, see the Supplementary Information. While the hotplate-based device exhibits improvements relative to other existing methods, it likely fails to capture some key aspects of actual wildfire conditions. Primarily, a flame front would likely involve markedly higher temperatures and heating rates that would lead to ‘fast pyrolysis’, while our flammability device better represents conditions of ‘slow pyrolysis’ where preheated vegetation smolders prior to ignition (as in a slow moving fire, or among still-smoldering fuels after a fast-moving surface fire) (Santoso et al. 2019; Lin, Goos, and Riedel 2013; Safdari et al. 2019) . In addition to potentially unrealistic heating conditions, all tissue-level (rather than plant-level) flammability tests may induce unrealistic chemical changes when branches are severed from an intact plant (Ganteaume et al. 2021; Guerrero et al. 2024; Jolly et al. 2012) . Despite potential misalignments between laboratory flammability testing and a moving flame front, laboratory flammability testing allows for controlled experiments to tease apart trait-flammability relationships and to better understand interspecific differences in flammability. A plant was considered a ‘non-ignition’ if it went through all glowing phases without a flaming phase. Burn tests were recorded using iPads (iPad Air 2, MGTY2LL/A). Videos were visually analyzed for the metrics used to describe ignitability (time from sample placement to ignition, temperature at ignition), combustibility (maximum temperature between ignition and 5 seconds post-flame extinction, change in temperature from ignition to maximum, and maximum flame height), consumability (glow phase durations), and sustainability (flame duration). 1.5 Statistical analyses Comparing lab and field drydown methods and the relationship between water potential and LFM We measured the relationship between LFM and water potential using three different drydown methods: 1) classic pressure-volume curves, where water content (i.e. LFM) and water potential are measured on the same leaves/branchlets through time as each leaf/branchlet dries, 2) benchtop flammability drying, where LFM and water potential were measured on different shoots or leaves from the same branch as branches became progressively drier, and 3) in the field, where water potential and LFM were measured on different leaves/stems from the same individual between spring and fall. To determine the relationships between LFM and water potential for each drying method (Question 1), we built mixed effects models using the lmer() from the lme4 package (Bates et al. 2014). These models related LFM to water potential, with species and timing (year of collection for field data, and year + month of sampling for benchtop curves) as fixed effects and with individual plant as a random effect. For field collected data we additionally included tissue age (new or old growth) as a fixed effect in models using field-collected data. To determine the contribution of different predictors to estimates of LFM, we ran a variance decomposition using vardecomp() from the R package variancePartition (Hoffman 2017; Hoffman and Schadt 2016). Mirroring model selection, we included water potential, timing (year or year + month), species, individual plant, and (for field data) tissue age. Effect of plant hydration on flammability To determine how water potential and LFM differ in their relationships with flammability (Question 2), we first condensed the eight flammability metrics we measured into those that best describe key axes of flammability (e.g. ignitability, combustibility, consumability, and sustainability). We used a principal component analysis (PCA) to determine the relationship among flammability metrics, using prcomp() from the stats package (R Core Team, 2023) on all samples that ignited. To compare species and functional groups, normal data ellipses were utilized in PCA visualizations. To determine proportion ignited (a metric of likelihood of ignition), we divided the number of successful ignitions by the number of total attempted. We selected the flammability parameters time to ignition (i.e. ignitability), flame height, flame duration, temperature change, and glow duration as the parameters to focus on due to either their high contribution to PCA loadings (Supporting Information Table S2) or ecological relevance (e.g. flame height was not a highly loading variable, but is important for crown fire spread). We built a family of linear mixed effects models relating each plant hydration variable to each flammability parameter identified as most representative of distinct axes of flammability in the PCA using lmer() from the lme4 and lmerTest packages (Bates et al. 2014; Kuznetsova, Brockhoff, and Christensen 2017) , accounting for repeated measurements of individual plants (9-12 per species) by including a random effect for plant ID. Plant hydration variables (water potential and LFM) were highly colinear (VIF > 8) (Daoud 2017), so were used as predictors in separate models. Each flammability metric was modeled with either a hydration variable or a species x hydration interaction, with site and sample wet mass as covariates (Supporting Information Tables S3, S4). Numerical predictors were scaled and centered. To identify the most parsimonious set of predictors for each flammability metric, we used Akaike’s information criterion (AIC) (Akaike 1987) to select the most parsimonious model from among the family of models including LFM, water potential, species x LFM/water potential interaction, site and sample wet mass and all their nested subsets, selecting the least complicated model within a difference in AIC of < 2 from the lowest AIC model. Models were fit for model selection using Maximum Likelihood estimation and then the best model was refit for parameter interpretation using Restricted Maximum Likelihood (Zuur et al. 2008). We then extracted the standardized regression coefficients, marginal R 2 , and conditional R 2 from the best model for each flammability metric using the performance package (Lüdecke et al. 2021; Nakagawa and Schielzeth 2013; Schielzeth and Nakagawa 2013). P-values were calculated using conditional F-tests with Kenward-Roger approximation for the degrees of freedom in the package sJPlot (Lüdecke 2018) . Predicting seasonal changes in flammability To determine how interspecific differences in flammability might manifest based on LFM and water potential values observed in the field (Question 3), we used the top performing mixed-effect models relating each flammability metric to hydration metrics (built on flammability data from tissue collected in fall 2020, Table 1) and LFM data collected in the field over summer 2021 to predict different flammability metrics over time using the package modelr ( Wickham 2023 ) . We combined old and new growth LFM samples for predictions to capture what is most likely to be present at the whole-plant level. Location was not included in the model (as all flammability observations were from the same location), and a constant sample wet mass (1gram) was utilized for all species as we were interested only in the effect of hydration, not in effects of tissue size. Results The effect of drydown types on LFM ~ water potential relationship: The strongest seasonal LFM changes occurred in current year growth, with significantly higher LFM in new growth relative to old growth early in the season (May – July; Fig. 1, 2). New and old growth converged at similar LFMs at the onset of fruiting/end of flowering for angiosperms and following full leaf-out for Ab. concolor and Pi. jeffreyi (Fig. 1). Tissue age was a significant predictor in models relating field LFM to water potential (P < 0.001) and explained a relatively large portion of the variation in water potential (variance decomposition results: 18.4% for angiosperms, and 22.5% for gymnosperms, Fig. 3). Analyses were qualitatively similar using a 1/LFM transformation for the field data, which stabilized the variance of the LFM~water potential relationship across a range of water potentials (Fig. S1, Fig. S2). In angiosperms, variance decompositions showed that water potential explained most of the variation in LFM for individual branch drydowns (54% for PV curves where LFM and water potential were directly measured on the same sample, or 72% for flammability curves where LFM and water potential were measured on different branchlets from the same branch), and 23% of variance in field-collected values (where LFM and water potential were measured on different samples from the same tree at the same time over a season, Fig. 3). In contrast, for gymnosperms, water potential explained 36% of variation in LFM during flammability curve drydowns, but only 6% in PV curves and < 1% for field collected data. Field-collected LFM vs. water potential relationships were considerably different from field-collected values, particularly for gymnosperm species where the slopes of field-collected data were in the opposite direction of benchtop drydowns (Fig 3b). Effect of hydration on flammability across species: The first two principal components in our analysis across flammability metrics explained 49% of the variation, with PC1 (25%) capturing ignitability and PC2 (24%) capturing glow-related metrics (Table S2). PC1 was strongly associated with time to ignition (1/TTI, or, ignitability, 0.85 loading) and most negatively associated with time to first glow (-0.96). PC2 was negatively associated with glow-related metrics (glow duration, -0.96; post-flame glow, -0.96). PC3 and PC4 (SI) captured glow to ignition (0.96, PC3), flame duration (-0.59, PC4), flame height (-0.50, PC4) and temperature change (-0.82, PC4). Many of the species were similarly flammable (Fig. 4); however, Qu. kelloggii ignited much faster (ANCOVA, p < 0.001) than all other species. There were stronger interspecific differences for ignitability ( proportion ignited metric) than other flammability components (e.g., consumability, as indicated by glow duration). Differences in ignitability were dictated by functional group, as broadleaf angiosperms ignited less frequently than conifers, which ignited nearly 100% of the time (8% of attempts for Ce. cordulatus , 81% for both Ar. patula and Qu. kelloggii vs. 97-99% for conifers). In flammability drydown tests, flammability typically related to tissue hydration as expected (e.g. decreased time to ignition and increased temperature change during ignition in drier samples) though the relationships were relatively weak (Table 1, Fig. 5, Fig S3). LFM was selected over water potential in the top-performing linear mixed-effects models for all five flammability metrics (∆AIC > 2). However, LFM models only explained an additional 1-5% of variation compared to water potential models, and models with an interaction between water potential and species performed similarly to LFM models with no species interaction. Top model estimates are presented in Tables S3 and S4. Table 1. Mixed effects model selection table, showing top hydration model results for selected flammability metrics (time to ignition, flame height, temperature change, flame duration, and glow duration) using live fuel moisture (LFM), water potential (MPa), species, sample weight and location (not shown) as predictors. The hydration coefficient column represents the effect size of the hydration metric (if one is included in the model) and asterisks represent significance (*0.05>p>0.01, **0.01>p>0.001, ***p<0.001). Species-only model shown for comparison. The ∆AIC column represents the difference in AIC between the indicated model and the top-performing model in the model selection, with a ∆AIC<2 exhibiting no statistically significant differences between models. Also shown are Nakagawa’s marginal and conditional R 2 values (Nakagawa and Schielzeth 2013). Full model comparisons shown in Table S4. Flammability Metric Predictors Hydration Coefficient ∆AIC Marginal R 2 / Conditional R 2 Time to Ignition LFM*species + weight 8.097 *** 0 0.586 / 0.651 MPa*species + weight 11.567 *** 50.113 0.558 / 0.653 Species NA 174.13 0.512 / 0.611 Flame Height LFM*species + weight -0.374 0 0.252 / 0.361 MPa*species + weight 1.990 11.199 0.249 / 0.374 Species NA 49.822 0.226 / 0.345 Temp. Change LFM * species + weight -5.605 0 0.233 / 0.264 MPa*species + weight -8.595 38.617 0.237 / 0.271 Species NA 131.438 0.211 / 0.239 Flame Duration LFM*species + weight -2.013 0 0.439 / 0.522 MPa*species + weight -3.626 * 20.007 0.429 / 0.520 Species NA 108.252 0.368 / 0.515 Glow Duration LFM*species + weight 3.174 0 0.179 / 0.219 MPa*species + weight -10.238 23.6 0.174 / 0.216 Species NA 128.806 0.158 / 0.215 Seasonal shifts in flammability: We combined measurements of flammability from the flammability drydown tests with field measurements of tissue hydration to investigate seasonal patterns of predicted flammability (Fig. 5). Interspecific variation and seasonal changes were largely attributable to the presence of new high LFM tissues during the growing season, which resulted in overall decreased flammability when these newer leaves were present. Pi. jeffreyi and Ca. decurrens were the most combustible (greatest predicted temperature change during combustion), while Qu. kelloggii showed the highest ignitability. Combustibility differences among species remained consistent throughout the year despite seasonal variation in some species. However, seasonal decreases in LFM were sufficient in Pi. jeffreyi and Ar. patula to change the species rank order of ignitability in July and August. Discussion We investigated the relationships between water content, water potential, and flammability using a combination of field and laboratory-based methods. We compared benchtop drying methods (both on multiple branches from flammability curves and on single leaves from PV Curves) to natural changes in LFM and water potential measured as summer drought progressed in intact plants. We found that LFM and water potential were more strongly related in angiosperms than gymnosperms across all drydown types. To compare different metrics of plant water status to flammability, we measured LFM, water potential, and flammability simultaneously as plants dried, and used field measurements to predict seasonal shifts in flammability based on the relationships between LFM and flammability observed in flammability tests. We found that LFM was a stronger predictor of flammability than water potential, and that cross-species flammability rankings shifted as phenology-driven changes in LFM occurred during the growing season. Relationship between water potential and water content differ based on functional group We found that gymnosperm and angiosperm species differed in their relationships between water content and water potential. Consequently, interactions between drought stress and flammability differed between groups. For both plant types in field-measured samples, we found that LFM tended to decrease quickly during the start of the dry season until it reached the water potential at a given specie’s turgor loss point (Fig. 1 ), a seasonal dynamic previously observed in Mediterranean systems (Ma et al. 2021 ; Nolan et al. 2022 ; Pivovaroff et al. 2019 ). In our study system, a patchy shrub and mixed-conifer forest, these rapid early-season LFM decreases were associated with the expansion and development of new growth (bud break and young leaf growth). Beyond this point (in mid-July in this study) the relationship between water potential and water content became shallower and more linear for angiosperms (Fig. 2 ). Gymnosperms, particularly the Pinaceae ( Ab. Concolor and Pi. jeffreyi ), showed similar drastic decreases in LFM in new growth early in the season, but this variation was never strongly related to plant water potential (Fig. 3 , variance decompositions). Indeed, field-measured water potentials did not decline far below the water potential at TLP for these gymnosperm species, likely due to strong stomatal control of transpiration as the dry season progressed (Klein 2014 ; Anderegg and HilleRisLambers 2016 ; Voelker et al. 2018 ), or decoupling between soil moisture and LFM, which has been observed in other systems (Brown et al. 2022 ). Ca. decurrens exhibited more fluctuation in its water potential compared to Ab. Concolor and Pi. Jeffreyi , reaching water potentials well below the TLP, indicating that Ca. decurrens is perhaps less likely to close stomata in response to reductions in water availability (T. J. Brodribb et al. 2014 ). For all gymnosperm species, however, LFM did drop considerably below the LFM at TLP despite stomatal control and reduced water loss, likely due to ongoing increases in leaf dry matter even after stomatal closure. For gymnosperms measured in laboratory drydowns, we found that single timepoint drydowns were largely dissimilar from field-based observations, likely due to benchtop curves failing to capture seasonal stomatal adjustments and changes in dry matter content that occur in intact plants (e.g. shifts in the proportion of leaves to branches or increasing lignin content in leaves) (Fig. 3 ). This high degree of cross-method variation poses a challenge for translating from lab-measured relationships to those present naturally on the landscape, and we argue that caution should be used when attempting to generalize relationships between water content and water potential (and thereby water potential and flammability, particularly when flammability ~ hydration relationships have been established using LFM) in gymnosperm species. Gymnosperm hydration relationships were almost entirely dominated by factors outside of water potential (cross-plant, cross-species, and cross-timepoint variation in plant structure/dry mass), indicating that these factors should not be ignored when characterizing LFM ~ water potential relationships. The angiosperms in our study system, Ce. cordulatus, Ar. patula , and Qu. kelloggii , showed reductions in water potential even below their TLP in field-based measurements, indicating a weaker relationship between TLP, as measured in the lab, and stomatal closure in situ . In general, we observed stronger relationships between water potential and LFM in the field for angiosperms, and less interspecific and cross-method variation overall in angiosperm water content ~ water potential relationships (e.g. in variance decompositions of LFM, Fig. 3 ). This low intraspecific variation is likely due to more similar leaf morphology, phenological patterns, and drought response (e.g. later stomatal closure) across angiosperm species. As such, relationships between LFM and water potential are likely more generalizable in angiosperms than in gymnosperms across species or across drydown methods (benchtop vs. field). Additionally, LFM and drought stress appear to be more strongly coupled in angiosperms, making LFM (and water content more broadly) a potentially good indicator of drought stress for angiosperm species. Our results show that repeated measurements of LFM and water potential will be necessary to accurately characterize the relationship between water potential and water content during early season growth for both plant groups. Additionally, measurements of leaf structural characteristics related to dry matter content (e.g. increases in LDMC and LMA as leaves transition from new to old growth) will help characterize water content ~ water potential relationships later in the season for evergreen gymnosperms. Both water potential and LFM at the TLP helped describe seasonal LFM dynamics in the field, particularly for gymnosperms. Thus, we stress the importance of field-collected water content ~ water potential relationships to contextualize classic benchtop drydown curves. Effects of hydration and species on flammability On the landscape scale, LFM is commonly used in fire risk assessments because of its relationship with ignitions and fire spread (Chuvieco et al. 2009 ; Dennison and Moritz 2009 ; Jurdao, Chuvieco, and Arevalillo 2012 ; Park, Fauss, and Moritz 2022 ). At the tissue scale, we found that LFM was consistently a significant, if sometimes subtle, predictor of nearly all measures of flammability (ignition, flaming, and heating dynamics, Table 1 ) mirroring these landscape-scale findings and those of other laboratory-based studies (Grootemaat et al. 2015 ; Kauf et al. 2014 ; Tumino et al. 2019 ). While water potential did show some relationship with flammability (Fig. 5 ), it generally performed slightly worse than LFM in model selection, explaining similar variation in flammability (similar R 2 as LFM models) but in a less parsimonious manner (higher AIC). Due to these differences, we posit that physiological drought stress alone (which strongly controls tissue hydration if all else is held constant, e.g. in a rapid laboratory drydowns such as those used to create PV curves) can influence flammability, but its effects are mediated by tissue attributes such as dry matter content and those related to species morphology. LFM, which is influenced by both instantaneous hydration status and tissue morphology (e.g. leaf mass per area and leaf or stem dry matter content), is thus more directly linked to tissue-level flammability, even for different portions of the same branch. As such, LFM is an effective all-around predictor of flammability because it gives insight into both drought status and morphology. In addition, the consistent inclusion of sample weight and a strong species-effect in top-performing models of flammability points to the importance of morphological characteristics in estimates of flammability (Fig. 4 ). In fact, species differences (i.e. differences in mean flammability) alone explained substantial variation in our flammability trials, without information on hydration. The inclusion of hydration-specific variables only improved model fit by a few percent (maximum increase of 7.4%) while species alone explained up to 51% of variation in flammability metrics (Table 1 , though some of the species-only variance explained is likely shared variance with hydration). Qu. kelloggii showed faster ignitions than other species across all hydration levels and in predicted flammability, and all angiosperms showed lowered ignition probabilities than gymnosperms (Fig. 4 ). Ce. cordulatus had such low ignition probabilities that gathering flammability data on other metrics was nearly impossible. Interspecific differences in flammability are likely driven by variation in structural and chemical characteristics across species. Qu. kelloggii’s faster ignitions and relatively low glow durations likely were a result of its relatively large area and low LMA leaves, which had higher surface areas exposed to heating and less leaf mass to burn through during the combustion and glowing phases. Ar. patula was the second-most ignitable in terms of time to ignition and had relatively large leaves compared to the other species, while its longer glow durations were likely due to the higher density of its leaves and stems. The gymnosperms were more like each other in terms of flammability metrics, with marginally shorter ignition times for the higher surface-area Ca. decurrens . Further research into the specific traits that mediate flammability – via comprehensive studies that measure a large suite of traits – might elucidate more specific patterns of what makes plants more or less flammable and should emphasize differences in leaf area and tissue density or volume both within and across species. These studies could help determine the relative contributions of specific traits to flammability, as well as how different traits might interact to influence specific components of flammability. In additional to morphological difference, plant chemistry likely also plays a role in species-difference in flammability. Conifers store VOCs in resin ducts in leaves and tend to have higher VOC concentrations overall than broadleaf angiosperm species (Vázquez-González et al. 2020 ). Given extended exposure to a heat and ignition source such as a pilot flame or surface fire, a conifer’s high VOC content and waxy leaves might ensure that it ignites even if times to ignition are prolonged, while the high heating value of VOCs could increase both temperatures and flame heights (Ormeño et al. 2009 ; Ganteaume et al. 2021 ; T. Brodribb and Hill 1997 ). Within species, these flammability-related chemical and physiological traits may be driven by access to water and drought status, which comparative studies across water access gradients might help elucidate. While species differences were the most important driver of flammability, our predictions based on lab flammability tests and field-observed LFM highlight that drought physiology has the potential to meaningfully alter flammability, to the point where species rankings change (Fig. 5 ). For angiosperms, we observed a strong correlation between phenology and flammability and an overall more consistent relationship between LFM and water potential both in field and laboratory drydowns, suggesting a correlation between drought stress and morphology that can be used to inform flammability predictions for these species. Gymnosperms, conversely, showed little to no correlation between drought stress and LFM or phenology, making seasonal flammability ~ water potential relationships difficult to interpret without additional information regarding morphology (LFM or old/new growth ratios). Capturing all the drivers of flammability will require flammability testing across plant developmental stages throughout a fire season in living plants, with a focus on phenological phases linked to changes in water availability. These phenological signals and physiological changes likely drive a portion of the changes in flammability observed at the landscape level (i.e., Dennison and Moritz 2009 ; Park, Fauss, and Moritz 2022 ), as seasonal shifts in carbon allocation and leaf chemistry change plant structure and volatility, and changes in tissue water holding capacity shift the relative contribution of water content to overall plant mass. As we only tested flammability at the end of the dry season, our flammability testing likely did not capture some of these dynamics. To disentangle the effects of phenology vs. water access on LFM and flammability, we suggest joint phenology, LFM, and flammability monitoring in plants with contrasting levels of water access (i.e. water potentials) at the same time of year, or in living plants with similar water access but throughout a seasonal drydown. Conclusion Drought and wildfire are dynamic disturbances in vegetated regions, and their severity and impact are likely shaped by similar physiological traits at the plant level. To determine how flammability-related and drought-related traits interact – and when they intersect – we took seasonal measurements of drought stress, LFM, and flammability. We found that plant hydration drives many aspects of flammability and that LFM better describes flammability than water potential. Seasonal shifts in morphology and physiology (the development of new tissues, and stomatal closure that limits water loss), drastically influence the relationship between LFM and water potential. Importantly, our finding of a strong and generalizable relationship between drought stress and LFM (and thereby drought stress and flammability) in angiosperms might help simplify efforts to understand drought and fire interactions across scales. However, the more complex relationship in gymnosperms warrants further mechanistic investigation. Declarations Author contributions: MM and IB designed the research. IB and JVC conducted lab and fieldwork. IB and JVC performed analysis, with guidance from LLA and MM. IB wrote the first draft of the manuscript. IB, JVC, LLA, and MM revised the manuscript. Data availability statement: All data and code for this project will be made available on github https://github.com/bovingi/sierra-flammability/tree/main. Competing interests: None declared. Funding statement: This research was funded by the University of California’s National Laboratories (UCNL) 542 Laboratory Fees grant program under grant number LFR-18-542511 (as a part of the California 543 Ecosystems Futures project). Acknowledgements: We would like to thank field assistants and volunteers Anne-Marie Parkinson, Ariana Escobedo, Jasper Romero, Kristina Fauss, and Boots. We acknowledge the Traditional Custodians and Owners of California and recognize their continuing connection to the land upon which this research was conducted, particularly the Chumash on whose traditional territory UC Santa Barbara sits. References Abrams, M. D., & Menges, E. S. (1992). Leaf Ageing and Plateau Effects on Seasonal Pressure-Volume Relationships in Three Sclerophyllous Quercus Species in South-Eastern USA. Functional Ecology , 6 (3), 353–360. JSTOR. https://doi.org/10.2307/2389527 Akaike, H. (1987). Factor analysis and AIC. 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H., Foster, B., Griebel, A., Choat, B., Medlyn, B. E., Yebra, M., Younes, N., & Boer, M. M. (2022). Drought-related leaf functional traits control spatial and temporal dynamics of live fuel moisture content. Agricultural and Forest Meteorology , 319 , 108941. https://doi.org/10.1016/j.agrformet.2022.108941 Nolan, R. H., Hedo, J., Arteaga, C., Sugai, T., & Resco de Dios, V. (2018). Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest. Agricultural and Forest Meteorology , 263 , 417–427. https://doi.org/10.1016/j.agrformet.2018.09.011 Norum, R. A., & Miller, Melanie. (1984). Measuring fuel moisture content in Alaska: Standard methods and procedures. (PNW-GTR-171; p. PNW-GTR-171). U.S. Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station. https://doi.org/10.2737/PNW-GTR-171 O’Geen, A. (Toby), Safeeq, M., Wagenbrenner, J., Stacy, E., Hartsough, P., Devine, S., Tian, Z., Ferrell, R., Goulden, M., Hopmans, J. W., & Bales, R. (2018). Southern Sierra Critical Zone Observatory and Kings River Experimental Watersheds: A Synthesis of Measurements, New Insights, and Future Directions. Vadose Zone Journal , 17 (1), 180081. https://doi.org/10.2136/vzj2018.04.0081 Ormeño, E., Céspedes, B., Sánchez, I. A., Velasco-García, A., Moreno, J. M., Fernandez, C., & Baldy, V. (2009). The relationship between terpenes and flammability of leaf litter. Forest Ecology and Management , 257 (2), 471–482. https://doi.org/10.1016/j.foreco.2008.09.019 Park, I., Fauss, K., & Moritz, M. A. (2022). Forecasting Live Fuel Moisture of Adenostema fasciculatum and Its Relationship to Regional Wildfire Dynamics across Southern California Shrublands. Fire , 5 (4), 110. https://doi.org/10.3390/fire5040110 Pausas, J. G., Alessio, G. A., Moreira, B., & Segarra-Moragues, J. G. (2016). Secondary compounds enhance flammability in a Mediterranean plant. Oecologia , 180 (1), 103–110. https://doi.org/10.1007/s00442-015-3454-8 Peterson, S. H., Roberts, D. A., & Dennison, P. E. (2008). Mapping live fuel moisture with MODIS data: A multiple regression approach. Remote Sensing of Environment , 112 (12), 4272–4284. https://doi.org/10.1016/j.rse.2008.07.012 Petruzzellis, F., Tordoni, E., Di Bonaventura, A., Tomasella, M., Natale, S., Panepinto, F., Bacaro, G., & Nardini, A. (2021). Turgor loss point and vulnerability to xylem embolism predict species‐specific risk of drought‐induced decline of urban trees. Plant Biology , 24 (7), 1198–1207. https://doi.org/10.1111/plb.13355 Pimont, F., Ruffault, J., Martin-StPaul, N. K., & Dupuy, J.-L. (2019). Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands? 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Unrevealing water and carbon relations during and after heat and hot drought stress in Pinus sylvestris (p. 2021.06.29.450316). bioRxiv. https://doi.org/10.1101/2021.06.29.450316 Rossa, C. G., & Fernandes, P. M. (2018). Live Fuel Moisture Content: The ‘Pea Under the Mattress’ of Fire Spread Rate Modeling? Fire , 1 (3), Article 3. https://doi.org/10.3390/fire1030043 Safdari, M.-S., Amini, E., Weise, D. R., & Fletcher, T. H. (2019). Heating rate and temperature effects on pyrolysis products from live wildland fuels. Fuel , 242 , 295–304. https://doi.org/10.1016/j.fuel.2019.01.040 Santoso, M. A., Christensen, E. G., Yang, J., & Rein, G. (2019). Review of the Transition From Smouldering to Flaming Combustion in Wildfires. Frontiers in Mechanical Engineering , 5 . https://doi.org/10.3389/fmech.2019.00049 Schielzeth, H., & Nakagawa, S. (2013). Nested by design: Model fitting and interpretation in a mixed model era. 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Critical water contents at leaf, stem and root level leading to irreversible drought-induced damage in two woody and one herbaceous species. Plant, Cell & Environment , 46 (1), 119–132. https://doi.org/10.1111/pce.14469 Tumino, B. J., Duff, T. J., Goodger, J. Q. D., & Cawson, J. G. (2019). Plant traits linked to field-scale flammability metrics in prescribed burns in Eucalyptus forest. PLOS ONE , 14 (8), e0221403. https://doi.org/10.1371/journal.pone.0221403 Tyree, M. T., & Hammel, H. T. (1972). The Measurement of the Turgor Pressure and the Water Relations of Plants by the Pressure-bomb Technique. Journal of Experimental Botany , 23 (1). https://fdocuments.us/document/the-measurement-of-the-turgor-pressure-and-the-water-relations-of-plants-by.html Vázquez-González, C., Zas, R., Erbilgin, N., Ferrenberg, S., Rozas, V., & Sampedro, L. (2020). Resin ducts as resistance traits in conifers: Linking dendrochronology and resin-based defences. Tree Physiology , 40 (10), 1313–1326. https://doi.org/10.1093/treephys/tpaa064 Voelker, S. L., DeRose, R. J., Bekker, M. F., Sriladda, C., Leksungnoen, N., & Kjelgren, R. K. (2018). Anisohydric water use behavior links growing season evaporative demand to ring-width increment in conifers from summer-dry environments. Trees. 32: 735-749. , 32 , 735–749. https://doi.org/10.1007/s00468-018-1668-1 Weise, D., Hartford, R., & Mahaffey, L. (1998). ASSESSING LIVE FUEL MOISTURE FOR FIRE MANAGEMENT APPLICATIONS . Wickham, H. (2023). modelr: Modelling Functions that Work with the Pipe. (Version R package version 0.1.11) [Computer software]. Wullschleger, S. D., Epstein, H. E., Box, E. O., Euskirchen, E. S., Goswami, S., Iversen, C. M., Kattge, J., Norby, R. J., van Bodegom, P. M., & Xu, X. (2014). Plant functional types in Earth system models: Past experiences and future directions for application of dynamic vegetation models in high-latitude ecosystems. Annals of Botany , 114 (1), 1–16. https://doi.org/10.1093/aob/mcu077 Yebra, M., Dennison, P. E., Chuvieco, E., Riaño, D., Zylstra, P., Hunt, E. R., Danson, F. M., Qi, Y., & Jurdao, S. (2013). A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. Remote Sensing of Environment , 136 , 455–468. https://doi.org/10.1016/j.rse.2013.05.029 Zahn, S. & Henson, C. (2011, May). A Syntheis of Fuel Moistyre Collection Methods and Equipment—A Desk Guide . U.S. Department of Agriculure, Forest Service, National Technology and Development Program. Supplementary Files SI20250606.docx Supporting information Table S1: Parameters from pressure volume curves for each year and species. Table S2: PCA table for all flammability metrics. Table S3: Summary table of top models relating flammability metrics to LFM. Table S4: Summary table of top models relating flammability metrics to water potential. Fig. S1: Water potential vs. transformed LFM as used in models of plant hydration dynamics. Fig. S2: Variance decomposition using transformed LFM (1/LFM). Fig. S3: Relationships between LFM and flammability and water potential and flammability. Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2025 Read the published version in Fire Ecology → Version 1 posted Editorial decision: Accept 24 Jul, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor assigned by journal 06 Jun, 2025 First submitted to journal 06 Jun, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5939800","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475892202,"identity":"380a126f-db79-4370-8e3a-c432f753269f","order_by":0,"name":"Indra Boving","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACAyA+wMAgIQek2YCYmXgtxqRpAYHEBqK1mLP3GB4u+GWRvuH48WcPGCqsQXrxA8ueMwaHZ/ZJ5G44k2NuwHAmnbAWgxtpCYd5e4BabvCwSTC2HSZeS7rBDfZnEoz/iNKSfOAwzw+JBIMbDGYSjA1EaLHsOXzgMG+DhOFMkF8SjqUbE9Rizt7Y/JnnT508HyjEPtRYyxLUAgaMbVBGAlHKweAP8UpHwSgYBaNgBAIAwFVBBukvUAoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2176-2819","institution":"UC Santa Barbara: University of California Santa Barbara","correspondingAuthor":true,"prefix":"","firstName":"Indra","middleName":"","lastName":"Boving","suffix":""},{"id":475892203,"identity":"1dd040c9-8ca2-401b-a23c-9ef46580231b","order_by":1,"name":"Joe V. Celebrezze","email":"","orcid":"","institution":"University of California Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Joe","middleName":"V.","lastName":"Celebrezze","suffix":""},{"id":475892204,"identity":"d9fd18f3-a5bf-489e-9df6-6c6d1d9d1a7f","order_by":2,"name":"Leander DL Anderegg","email":"","orcid":"","institution":"University of California Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Leander","middleName":"DL","lastName":"Anderegg","suffix":""},{"id":475892205,"identity":"5ab72863-5016-4b00-bf31-dca48501a7c0","order_by":3,"name":"Max Moritz","email":"","orcid":"","institution":"UCSB Bren School: University of California Santa Barbara David Bren School of Environmental Science and Management","correspondingAuthor":false,"prefix":"","firstName":"Max","middleName":"","lastName":"Moritz","suffix":""}],"badges":[],"createdAt":"2025-02-01 04:49:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5939800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5939800/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42408-025-00396-x","type":"published","date":"2025-09-02T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85559311,"identity":"f0b0f631-ea9a-43a8-9b36-4b3877a27b7a","added_by":"auto","created_at":"2025-06-27 12:17:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260181,"visible":true,"origin":"","legend":"\u003cp\u003eObserved LFM, midday water potential, and phenophases during summer 2021. During collection LFM (dark blue) was split into current year growth (i.e. new, developing tissues; open circles) and previous year growth (fully developed tissues; closed circles) when easily distinguishable, mean and standard deviation shown for each time point. Corresponding water potential shown in light blue. Phenology of new growth (leaf development) and reproductive phases were tracked throughout the season, indicated by colored bars at base of each plot. The horizontal dotted line on each plot indicates species-specific LFM and corresponding water potential at turgor loss point.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/de09a630bed9241ac5360744.png"},{"id":85559892,"identity":"e4528309-d567-4a25-bfb7-3935d672e228","added_by":"auto","created_at":"2025-06-27 12:25:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142113,"visible":true,"origin":"","legend":"\u003cp\u003eThe \u0026nbsp;relationship between live fuel moisture (LFM) and midday water potential for \u0026nbsp;all species. Data collected between April - October in 2020 and 2021. LFM \u0026nbsp;samples were split into already-developed tissue (previous year, dark grey) \u0026nbsp;and newly developing tissue (current year, light grey).Vertical dotted lines indicate mean TLP for each species from PV Curves measured during fall 2020 and 2021, horizontal lines indicate the mean LFM at TLP (Table S1).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/31a79f0409612bb3296f021c.png"},{"id":85559318,"identity":"d83d1c81-d44f-4426-bea9-b93c710fea1f","added_by":"auto","created_at":"2025-06-27 12:17:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":259083,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between live fuel moisture and water potential from three different dehydration methods for angiosperms (left panels) and gymnosperms (right panels), and variance decompositions showing the contribution of each predictor. A-B) Field collected data for midday water potentials and LFM collected approximately fortnightly April- October in 2020 and 2021, with old and new growth combined for visualization of linear models but separated in variance decompositions. C-D) PV Curve data measured on individual leaves. E-F) Water potential and LFM relationships established during a benchtop drydown while building flammability curves (‘Flam. Data’), in which separate branchlets were measured for each datapoint. Vertical lines show the mean TLP for each functional group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/d72ac8c369fe08351cc468c2.png"},{"id":85559316,"identity":"e6535980-783a-4438-ae3a-5f0944a3c94e","added_by":"auto","created_at":"2025-06-27 12:17:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":281739,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies-level differences in flammability metrics. Density plots of time to ignition (a) and glow duration (b) are for all species except \u003cem\u003eCe. cordulatus, \u003c/em\u003ewhich did not ignite at high enough frequency to be included. Principal component analysis indicates covariation among measure measured flammability metrics (c). The proportion ignited plot (inset) includes all species and indicates the percent of all samples that ignited without touching an ignition source.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/be69ad8bb43c09d9ce3ed0c0.png"},{"id":85559314,"identity":"b4dee35a-b868-4bf8-9fb9-7711c5e64673","added_by":"auto","created_at":"2025-06-27 12:17:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":292070,"visible":true,"origin":"","legend":"\u003cp\u003eObserved time to ignition and temperature change of Sierran tree and shrub species as a function of LFM (a, d) and water potential (b, e). Predicted flammability from LFM observed over summer 2021, using averaged new and old-growth LFM for evergreen species, and just current year growth for Qu. kelloggii. Increases in time to ignition and decreases in temperature in July/August due to the contribution of more hydrated new-growth LFM to the average used in models. Models built on flammability data collected in fall 2020 using the interaction between LFM and species as the primary predictor (c, f).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/b5940c948fc2096995023c83.png"},{"id":90828097,"identity":"b6a81e71-9e15-4a7e-a477-d2ff2e583e69","added_by":"auto","created_at":"2025-09-08 16:05:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1811266,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/d4f3fe41-72a2-4669-8564-35b9ef6c99ed.pdf"},{"id":85559896,"identity":"c88bc2bb-9f14-45bc-ab4e-0a2404032a96","added_by":"auto","created_at":"2025-06-27 12:25:27","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2553714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable S1: Parameters from pressure volume curves for each year and species.\u003c/p\u003e\n\u003cp\u003eTable S2: PCA table for all flammability metrics.\u003c/p\u003e\n\u003cp\u003eTable S3: Summary table of top models relating flammability metrics to LFM.\u003c/p\u003e\n\u003cp\u003eTable S4: Summary table of top models relating flammability metrics to water potential.\u003c/p\u003e\n\u003cp\u003eFig. S1: Water potential vs. transformed LFM as used in models of plant hydration dynamics.\u003c/p\u003e\n\u003cp\u003eFig. S2: Variance decomposition using transformed LFM (1/LFM).\u003c/p\u003e\n\u003cp\u003eFig. S3: Relationships between LFM and flammability and water potential and flammability.\u003c/p\u003e","description":"","filename":"SI20250606.docx","url":"https://assets-eu.researchsquare.com/files/rs-5939800/v1/5f14b162f7f8d5427ca66c21.docx"}],"financialInterests":"","formattedTitle":"Towards predicting flammability of Sierra Nevada mixed conifer forests: drought stress and fuel moisture are strongly linked in angiosperms but decoupled in gymnosperms","fulltext":[{"header":"Background","content":"\u003cp\u003eAs droughts and wildfires increase in both frequency and intensity, understanding the links between these interrelated processes is key to resilience-focused forest management (Dale et al. 2001; Allen et al. 2010). Drought and wildfire share a relationship with plant hydration: the moisture content of vegetation is an important determinant of wildfire risk, and reductions in plant hydration associated with drought can have long-term effects on plants (Allen et al. 2010; Ma et al. 2021). As soil water availability decreases, both plant water content (the amount of water in leaves) and water potential (the potential energy in plants responsible for water movement) decrease in turn. Reductions in plant water content and water potential have repercussions for growth and whole-plant function, as cellular processes are inhibited and leaf chemistry shifts, eventually leading to mortality if water availability is not restored (Álvarez-Cansino et al. 2022; Bartlett et al. 2016; 2016; Blackman 2018; Lawlor and Cornic 2002; Trifilò\u0026nbsp;et al. 2023).\u0026nbsp;Lowered water content also influences the ignition risk of plant tissues directly via the effect of moisture on ignitability, and indirectly via shifts in foliar chemistry (e.g., volatile organic compound concentration) or structural changes following drought (e.g. leaf or branch shedding) that impact live fuel connectivity\u0026nbsp;(Alessio et al. 2008; Bianchi et al. 2019; Dickman et al. 2023; Guerrero et al. 2024; W. M. Jolly et al. 2012; Pausas et al. 2016).\u003c/p\u003e\n\u003cp\u003eA measurement of plant water content, live fuel moisture (LFM), is commonly used in assessments of fire risk from the front lines of wildland firefighting to satellite-based risk monitoring (Peterson, Roberts, and Dennison 2008; Yebra et al. 2013). LFM is defined as the proportion of water relative to dry matter in a plant tissue sample and is usually on a percent bases. At the tissue scale (e.g. individual leaves or plant organs at the centimeter-scale) reductions in LFM has been linked to increases in ignition likelihood and flame durations (Anderson 1970; Dimitrakopoulos and Papaioannou 2001; Alessio et al. 2008; de Magalhães and Schwilk 2012; Alam et al. 2019; Bianchi et al. 2019; Tumino et al. 2019; Boving et al. 2023; Celebrezze, Boving, and Moritz 2023). At the scale of whole plants or plant stands, the effects of LFM are less well-documented due to challenges associated with conducting intermediate-scale flammability tests (e.g. difficulties with holding all factors but LFM constant, or reaching sufficiently large ranges of LFM in field settings), yet LFM likely does have an effect on fire behavior at these intermediate scales by influencing ignitions and spread rates\u0026nbsp;(Alexander and Cruz 2013; Pimont et al. 2019; Rossa and Fernandes 2018). On the landscape-level (in large vegetated areas, kilometer-scale), LFM has been shown to affect fire spread, size, and severity\u0026nbsp;(Dennison and Moritz 2009; Jurdao, Chuvieco, and Arevalillo 2012; Yebra et al. 2013; Park, Fauss, and Moritz 2022). Despite the importance of LFM in understanding wildfire dynamics, its relationship with physiological drought status (i.e., water potential and drought-response traits) is less well understood, as is the relationship between physiological drought status and flammability itself. Since both wildfire and stand-level drought impacts are linked to tissue-level processes, relating physiological drought status to flammability is key for understanding how drought might influence landscape-level wildfire dynamics\u0026nbsp;(Schwilk and Caprio 2011; Krix and Murray 2018; W. Jolly and Johnson 2018; Nolan et al. 2018).\u003c/p\u003e\n\u003cp\u003eTo assess flammability, laboratory studies typically measure a suite of flammability parameters that describe different aspects of ignition and combustion. These include time to ignition, flame duration, glow duration, flame height, and changes in temperature or heat release (Martin et al. 1993; Alam et al. 2019; Tumino et al. 2019; Celebrezze, Boving, and Moritz 2023). These types of studies help disentangle the importance of different plant traits for predicting flammability and provide the foundation for many landscape-level models of wildfire dynamics and risk (Andrews, Cruz, and Rothermel 2013; Grootemaat et al. 2015; Alam et al. 2019; Mitchell and Martin 2023). Time to ignition and flame characteristics related to fire spread, such as flame duration and height, are especially relevant for wildfire dynamics at the landscape level where ignitions and flame spread are key determinants of overall fire risk and severity (Anderson 1970; Molina et al. 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrought responses can be expected to vary based on functional group (i.e. broadleaf angiosperms vs. gymnosperms) or water use strategy (e.g. the spectrum between isohydric and anisohydric stomatal behaviors) (Carnicer et al. 2013; Lusk, Wright, and Reich 2003; Pirasteh-Anosheh et al. 2016). Indeed, plant functional groups are often used in global vegetation models and models of fire behavior due to shared morphological and drought response traits within plant groups (Wullschleger et al. 2014). A common adjustment to limit water loss during drought is stomatal closure, which typically occurs near a plants turgor loss point (TLP) (Chen et al. 2022). The TLP is a plant trait that can help describe a plant’s water use strategy: very negative TLPs can indicate \u003cem\u003edrought tolerance\u003c/em\u003e, while less negative TLPs are slightly more complicated to interpret, either indicating a drought avoidance strategy or vulnerability to drought (Bartlett, Scoffoni, and Sack 2012; Bartlett et al. 2016; Farrell, Szota, and Arndt 2017; Pivovaroff, Cook, and Santiago 2018). \u0026nbsp;In combination with an understanding of plant-level water access and a species` stomatal sensitivity, it has also been used to predict drought response\u0026nbsp;(Álvarez-Cansino et al. 2022; Blackman 2018; Petruzzellis et al. 2021).\u0026nbsp;Pressure volume (PV) curves, which relate water potential to relative water content for a single leaf, are used to determine the TLP by identifying where the relationship between -1/water potential and water content shifts from curvilinear to linear\u0026nbsp;(Abrams and Menges 1992; Tyree and Hammel 1972).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent studies in Mediterranean systems have found similarities between PV curves and landscape-level relationships between water potential and water content, with larger decreases in water content relative to water potential at hydration levels higher than the TLP, and a linear relationship following the TLP (Nolan et al. 2018; 2022; Pivovaroff et al. 2019). Here, we extend previous work in Mediterranean systems into temperate mixed-conifer forests in the Sierra Nevada, California, USA where increasing prevalence of large fires and severe droughts indicates a dire need for better understanding of regional drought-fire dynamics (Gutierrez et al. 2021; Stevens et al. 2017). We investigate water use strategies and flammability dynamics in gymnosperm and angiosperm species with contrasting structural characteristics, leaf habits, and physiological responses to drought. These taxonomic groups have historically been compared in the context of fire-driven trait evolution (particularly in leaf litter production and regeneration strategies), but have rarely been compared in terms of plant flammability or modern drought-fire relationships (Belcher and Hudspith 2017; Bond and Midgley 2012; Burger and Bond 2015; Cornwell et al. 2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo better measure and model how drought might impact wildfire risk and severity, we must understand what drives variation in the relationship between water content and water potential, two key metrics of plant hydration that both influence flammability. This is especially important for systems with increased risk of both drought and fire, such as in California’s Sierra Nevadas. Here, we focus six common tree and shrub species from this region: three gymnosperms (white fir (\u003cem\u003eAbies concolor\u003c/em\u003e), Jeffrey pine (\u003cem\u003ePinus jeffreyi\u003c/em\u003e), and incense cedar (\u003cem\u003eCalocedrus decurrens\u003c/em\u003e)) and three angiosperms (California black oak (\u003cem\u003eQuercus kelloggii\u003c/em\u003e), mountain whitethorn (\u003cem\u003eCeanothus cordulatus\u003c/em\u003e), and greenleaf manzanita (\u003cem\u003eArctosaphylos patula\u003c/em\u003e)). We asked:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1)\u0026nbsp; \u0026nbsp;How well do reductions in plant hydration (water potential and LFM) measured in the lab reflect field-based patterns measured over a season?\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2)\u0026nbsp; \u0026nbsp;How do water potential and LFM differ in their relationship with flammability, and how does this vary across taxonomic group?\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3)\u0026nbsp; \u0026nbsp;How does flammability change over the course of a season (i.e. as shifts in plant hydration and phenology occur) and how does this vary across species?\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo answer these, we collected synchronous measurements of LFM and water potential in the field between snow-melt and the end of the summer dry season, in the lab as branches dried on a benchtop, and using traditional physiology methods (PV curves). These, in combination with flammability tests conducted across a range of hydration levels, enabled us to test the link between hydration and tissue-level flammability in species with varying physiological characteristics.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cem\u003e1.1: Study area and species\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated dominant tree and shrub species in the Providence Creek Watershed in the Sierra National Forest, California, USA. Vegetation in the watershed consists of mixed-conifer forest with a variety of distinct plant functional types. The site is situated in the rain-snow transition zone with a mean annual precipitation of 1015 mm/year and mean annual temperature of\u0026nbsp;8\u0026deg;C\u0026nbsp;(O\u0026rsquo;Geen et al. 2018)\u0026nbsp;(Site 1: 37\u0026ordm; 4\u0026rsquo; 4\u0026rdquo; N, 119\u0026ordm; 11\u0026rsquo; 43\u0026rdquo; W, 2100 m elevation; Site 2: 37\u0026ordm; 3\u0026rsquo; 5\u0026rdquo; N, 119\u0026ordm; 11\u0026rsquo; 0\u0026rdquo; W, 1930 m elevation). Precipitation largely occurs between October and April, allowing for a seasonal period of relative water deficit during the summer months\u0026nbsp;(Halofsky 2021).\u0026nbsp;Soils at these sites are primarily the Gerle series of Humic Dystroxerepts with occasional rock outcroppings\u0026nbsp;(Soil Survey Staff 2022).\u0026nbsp;We investigated gymnosperm species\u0026nbsp;white fir [\u003cem\u003eAbies concolor\u003c/em\u003e (Gord. \u0026amp; Glend.) Lindl. ex Hildebr.], Jeffrey pine (\u003cem\u003ePinus jeffreyi\u003c/em\u003e Balf.),\u0026nbsp;and incense cedar (\u003cem\u003eCalocedrus decurrens),\u0026nbsp;\u003c/em\u003eand angiosperms California black oak (\u003cem\u003eQuercus kelloggii)\u003c/em\u003e, whitehorn ceanothus (\u003cem\u003eCeanothus cordulatus)\u0026nbsp;\u003c/em\u003eand Greenleaf manzanita\u003cem\u003e\u0026nbsp;(Arctostaphylos patula)\u003c/em\u003e. Trees were interspersed with shrubs in patchy mosaics. As \u003cem\u003eQu. kelloggii\u003c/em\u003e and \u003cem\u003ePi. jeffreyi\u0026nbsp;\u003c/em\u003ewere not present at both sites, we only sampled these species at site 1. We collected samples from both sites every two to three weeks throughout the summer drydown period (June 2020 - October 2020 and April 2021 - November 2021), except when the 2020 Creek Fire closed access to the Providence Creek sites in September and October 2020, during which we sampled in a nearby location similar in species composition, elevation, and\u0026nbsp;environment\u0026nbsp;(36\u0026deg; 42\u0026apos; 55\u0026quot; N, -118\u0026deg; 58\u0026apos; 5.415\u0026quot; W).\u0026nbsp;Flammability tests were conducted on samples collected in September and October 2020.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.2: LFM and midday water potential measurements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted simultaneous measurements of LFM and water potential at midday, near solar noon (11:00 h -14:00 h), and collected predawn water potential measurements on the same day (03:00 h - 05:00 h). Midday water potential measurements characterize maximum water stress based on stomatal openness and evaporative demand, while predawn measurements characterize water availability. Sites 1 and 2 were visited on consecutive days approximately every three weeks. 2-3 foliage samples were harvested per individual. We sampled 36 individuals at site 1 (six individuals x six species), and 20 individuals at Site 2 (five individuals x four species). We stored water potential samples in sealed thick plastic bags in a cold cooler until we measured them in the field, typically 30-60 minutes after collection, using a Scholander Pressure chamber (PMS instruments, Corvallis, OR). LFM was measured according to United States Forest Service protocols after separating new (current year) and old (older than current year) growth, when possible (Countryman and Dean 1979; Norum and Miller 1984; Weise, Hartford, and Mahaffey 1998). Approximately 25g of healthy, sun-exposed new and old tissues were collected from each individual and placed in preweighed airtight containers; we took care to avoid collection of woody stems greater than 3.5mm diameter, dead plant matter, or reproductive organs (Norum and Miller 1984; Zahn, S. and Henson, C. 2011); for the \u003cem\u003ePinus\u003c/em\u003e species in our study, only needles were collected following established protocols (W. M. Jolly and Hadlow 2012). Following collection, we weighed samples, dried them at 100\u0026ordm;C for 24 hours, and then weighed them again for their oven-dry weight. LFM was determined using Eq. 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEq. 1\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.3: Pressure-Volume Curves\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003eIn October 2020 and 2021, healthy 20-40 cm-long branches were collected from the 3-6 individuals per species from Site 1 shortly after dawn, sealed in moistened plastic bags, and transported to the laboratory in a cooler. Samples were either immediately processed within 1 hour of sample collection or stored in a refrigerator at 4\u0026deg;C between processing (maximally 12 hours after collection). For processing, we cut branches incrementally underwater into smaller branchlets and rehydrated them in cool darkness for 2-4 hours, after which we excised healthy non-necrotic leaves or small leafy shoots from the branch using a razor blade. To avoid the effects of oversaturation, leaves and stems that had been underwater during rehydration were avoided when conducting PV curves (Meinzer et al. 2014). For \u003cem\u003ePi. jeffreyi\u003c/em\u003e fascicles were rehydrated by placing the entire excised fascicle in water (Rehschuh and Ruehr 2021).\u003c/p\u003e\n\u003cp\u003eTo construct PV curves, water potential (-MPa) and leaf weight (to 0.0001 g) were determined on rehydrated leaves or branchlets (one per individual) following established methods (Tyree and Hammel 1972). While measuring water potential, we slowly adjusted pressure (0.1 -MPa/sec) to avoid rapid cooling of the sample. Consecutive water potential and weight measurements were taken on each leaf and plotted as -1/MPa versus sample weight. This plot informed when we had reached the TLP, and we ceased measurements after three to five points of the linear section of the curve were reached.\u0026nbsp;We determined saturated water content (SWC) by extrapolating the plot of water potential and water weight to water potential = 0, which was used to determine relative water content (RWC) and LFM using Eq. 1. The LFM, RWC, and water potential at TLP corresponded with the point at which the exponential section of the curve met the linear section, determined by maximizing the linear fit (r\u003csup\u003e2\u003c/sup\u003e) of the tail of the curve. The osmotic potential at full turgor was calculated as the y-intercept of the linear section of the curve. PV parameters were determined using the excel spreadsheet available on Prometheus wiki (\u0026ldquo;Leaf Pressure-Volume Curve Parameters\u0026rdquo; 2010). All pressure-volume parameters are available in Supporting Information Table S1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.4: Flammability Drydown Tests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;We conducted flammability drydown tests during the end of the summer dry season 2020, during which we measured LFM, water potential, and flammability simultaneously on leaves and shoots of each species during a benchtop drydown using established methods (Boving et al. 2023). We collected branches from 9-11 individuals per species shortly after dawn, cut these underwater into 9-12 smaller branchlets containing at least three smaller shoots or leaves, and rehydrated them following the same procedure as for PV curves. Branchlets dried over the course of 3-7 days, during which we took periodic measurements of water potential, LFM, and flammability (one from each of the three leaves or shoots per branchlet) at approximate intervals of 1 MPa. As we conducted flammability tests at the end of the growing season when all tissues were done growing, new and old growth were not separated for LFM or flammability measurements as they had been in the field. Prior to measurements, branchlets were stored covered with a dark plastic bag for at least 15 minutes to allow equilibration of water potential across all tissues. We conducted flammability drydown tests until branchlets had reached -10 MPa or were well below their naturally observed minimum seasonal water potentials.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo measure flammability, we used a hotplate-based device, where the entire experiment was conducted under a laboratory fume hood to control for external temperature, humidity, and wind. A 15 cm square hotplate (Corning, Model COR-6795-600D) was heated to a surface temperature of 550\u0026nbsp;\u0026deg;C, and 10 cm plant samples were placed on a 1.27cm gridded brass mesh 1 cm above the heated surface, such that samples were situated at a\u0026nbsp;steady temperature of around 270\u0026deg;C at the height of the sample.\u0026nbsp;Type K thermocouples were used to record temperatures 1 cm and 5 cm above the mesh. A propane gas Bunsen burner pilot flame was centered 6 cm above the plant sample allowing for sufficient space between the wire mesh and flame to avoid the plant sample touching the flame while being close enough to ignite emitted volatiles. The device was designed to allow burning of large 10-15cm shoots (compared to other studies that typically only measure plant samples within the 1-3 cm range) and to capture radiative and convective heat exposure by elevating samples on a wire mesh (rather than only capturing conductive heat, which is what similar studies using epiradiator-type devices typically capture when samples are placed directly on the heated surface)\u0026nbsp;(Celebrezze, Boving, and Moritz 2023). For more detailed descriptions, see the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the hotplate-based device exhibits improvements relative to other existing methods, it likely fails to capture some key aspects of actual wildfire conditions. Primarily, a flame front would likely involve markedly higher temperatures and heating rates that would lead to \u0026lsquo;fast pyrolysis\u0026rsquo;, while our flammability device better represents conditions of \u0026lsquo;slow pyrolysis\u0026rsquo; where preheated vegetation smolders prior to ignition (as in a slow moving fire, or among still-smoldering fuels after a fast-moving surface fire) \u003cu\u003e(Santoso et al. 2019; Lin, Goos, and Riedel 2013; Safdari et al. 2019)\u003c/u\u003e . In addition to potentially unrealistic heating conditions, all tissue-level (rather than plant-level) flammability tests may induce unrealistic chemical changes when branches are severed from an intact plant \u003cu\u003e(Ganteaume et al. 2021; Guerrero et al. 2024; Jolly et al. 2012)\u003c/u\u003e. Despite potential misalignments between laboratory flammability testing and a moving flame front, laboratory flammability testing allows for controlled experiments to tease apart trait-flammability relationships and to better understand interspecific differences in flammability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA plant was considered a \u0026lsquo;non-ignition\u0026rsquo; if it went through all glowing phases without a flaming phase. Burn tests were recorded using iPads (iPad Air 2, MGTY2LL/A). Videos were visually analyzed for the metrics used to describe ignitability (time from sample placement to ignition, temperature at ignition), combustibility (maximum temperature between ignition and 5 seconds post-flame extinction, change in temperature from ignition to maximum, and maximum flame height), consumability (glow phase durations), and sustainability (flame duration).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.5 Statistical analyses\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComparing lab and field drydown methods and the relationship between water potential and LFM\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe measured the relationship between LFM and water potential using three different drydown methods: 1) classic pressure-volume curves, where water content (i.e. LFM) and water potential are measured on the same leaves/branchlets through time as each leaf/branchlet dries, 2) benchtop flammability drying, where LFM and water potential were measured on different shoots or leaves from the same branch as branches became progressively drier, and 3) in the field, where water potential and LFM were measured on different leaves/stems from the same individual between spring and fall.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo determine the relationships between LFM and water potential for each drying method (Question 1), we built mixed effects models using the \u003cem\u003elmer()\u003c/em\u003e from the \u003cem\u003elme4\u0026nbsp;\u003c/em\u003epackage (Bates et al. 2014). These models related LFM to water potential, with species and timing (year of collection for field data, and year + month of sampling for benchtop curves) as fixed effects and with individual plant as a random effect. For field collected data we additionally included tissue age (new or old growth) as a fixed effect in models using field-collected data. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; To determine the contribution of different predictors to estimates of LFM, we ran a variance decomposition using \u003cem\u003evardecomp()\u0026nbsp;\u003c/em\u003efrom the R package\u0026nbsp;\u003cem\u003evariancePartition\u003c/em\u003e (Hoffman 2017; Hoffman and Schadt 2016). Mirroring model selection, we included water potential, timing (year or year + month), species, individual plant, and (for field data) tissue age.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEffect of plant hydration on flammability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine how water potential and LFM differ in their relationships with flammability (Question 2), we first condensed the eight flammability metrics we measured into those that best describe key axes of flammability (e.g. ignitability, combustibility, consumability, and sustainability). We used a principal component analysis (PCA) to determine the relationship among flammability metrics, using \u003cem\u003eprcomp()\u003c/em\u003e from the \u003cem\u003estats\u0026nbsp;\u003c/em\u003epackage (R Core Team, 2023) on all samples that ignited. To compare species and functional groups, normal data ellipses were utilized in PCA visualizations. To determine proportion ignited (a metric of likelihood of ignition), we divided the number of successful ignitions by the number of total attempted. We selected the flammability parameters time to ignition (i.e. ignitability), flame height, flame duration, temperature change, and glow duration as the parameters to focus on due to either their high contribution to PCA loadings (Supporting Information Table S2) or ecological relevance (e.g. flame height was not a highly loading variable, but is important for crown fire spread).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe built a family of linear mixed effects models relating each plant hydration variable to each flammability parameter identified as most representative of distinct axes of flammability in the PCA using \u003cem\u003elmer()\u003c/em\u003e from the \u003cem\u003elme4\u0026nbsp;\u003c/em\u003eand \u003cem\u003elmerTest\u0026nbsp;\u003c/em\u003epackages \u003cu\u003e(Bates et al. 2014; Kuznetsova, Brockhoff, and Christensen 2017)\u003c/u\u003e, accounting for repeated measurements of individual plants (9-12 per species) by including a random effect for plant ID. Plant hydration variables (water potential and LFM) were highly colinear (VIF \u0026gt; 8) (Daoud 2017), so were used as predictors in separate models. Each flammability metric was modeled with either a hydration variable or a species x hydration interaction, with site and sample wet mass as covariates (Supporting Information Tables S3, S4). Numerical predictors were scaled and centered. To identify the most parsimonious set of predictors for each flammability metric, we used Akaike\u0026rsquo;s information criterion (AIC) (Akaike 1987) to select the most parsimonious model from among the family of models including LFM, water potential, species x LFM/water potential interaction, site and sample wet mass and all their nested subsets, selecting the least complicated model within a difference in AIC of \u0026lt; 2 from the lowest AIC model. Models were fit for model selection using Maximum Likelihood estimation and then the best model was refit for parameter interpretation using Restricted Maximum Likelihood (Zuur et al. 2008). We then extracted the standardized regression coefficients, marginal R\u003csup\u003e2\u003c/sup\u003e, and conditional R\u003csup\u003e2\u003c/sup\u003e from the best model for each flammability metric using the \u003cem\u003eperformance\u003c/em\u003e package (L\u0026uuml;decke et al. 2021; Nakagawa and Schielzeth 2013; Schielzeth and Nakagawa 2013). P-values were calculated using conditional F-tests with Kenward-Roger approximation for the degrees of freedom in the package \u003cem\u003esJPlot\u0026nbsp;\u003c/em\u003e(L\u0026uuml;decke 2018)\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePredicting seasonal changes in flammability\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To determine how interspecific differences in flammability might manifest based on LFM and water potential values observed in the field (Question 3), we used the top performing mixed-effect models relating each flammability metric to hydration metrics (built on flammability data from tissue collected in fall 2020, Table 1) and LFM data collected in the field over summer 2021 to predict different flammability metrics over time using the package \u003cem\u003emodelr\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003eWickham 2023\u003cem\u003e)\u003c/em\u003e. We combined old and new growth LFM samples for predictions to capture what is most likely to be present at the whole-plant level. Location was not included in the model (as all flammability observations were from the same location), and a constant sample wet mass (1gram) was utilized for all species as we were interested only in the effect of hydration, not in effects of tissue size.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eThe effect of drydown types on LFM ~ water potential relationship:\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe strongest seasonal LFM changes occurred in current year growth, with significantly higher LFM in new growth relative to old growth early in the season (May \u0026ndash; July; Fig. 1, 2). New and old growth converged at similar LFMs at the onset of fruiting/end of flowering for angiosperms and following full leaf-out for \u003cem\u003eAb. concolor\u003c/em\u003e and \u003cem\u003ePi. jeffreyi\u0026nbsp;\u003c/em\u003e(Fig. 1). Tissue age was a significant predictor in models relating field LFM to water potential (P \u0026lt; 0.001) and explained a relatively large portion of the variation in water potential (variance decomposition results: 18.4% for angiosperms, and 22.5% for gymnosperms, Fig. 3). \u0026nbsp;Analyses were qualitatively similar using a 1/LFM transformation for the field data, which stabilized the variance of the LFM~water potential relationship across a range of water potentials (Fig. S1, Fig. S2).\u003c/p\u003e\n\u003cp\u003eIn angiosperms, variance decompositions showed that water potential explained most of the variation in LFM for individual branch drydowns (54% for PV curves where LFM and water potential were directly measured on the same sample, or 72% for flammability curves where LFM and water potential were measured on different branchlets from the same branch), and 23% of variance in field-collected values (where LFM and water potential were measured on different samples from the same tree at the same time over a season, Fig. 3). In contrast, for gymnosperms, water potential explained 36% of variation in LFM during flammability curve drydowns, but only 6% in PV curves and \u0026lt; 1% for field collected data. Field-collected LFM vs. water potential relationships were considerably different from field-collected values, particularly for gymnosperm species where the slopes of field-collected data were in the opposite direction of benchtop drydowns (Fig 3b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eEffect of hydration on flammability across species:\u003c/em\u003e The first two principal components in our analysis across flammability metrics explained 49% of the variation, with PC1 (25%) capturing ignitability and PC2 (24%) capturing glow-related metrics (Table S2). PC1 was strongly associated with time to ignition (1/TTI, or, ignitability, 0.85 loading) and most negatively associated with time to first glow (-0.96). PC2 was negatively associated with glow-related metrics (glow duration, -0.96; post-flame glow, -0.96). PC3 and PC4 (SI) captured glow to ignition (0.96, PC3), flame duration (-0.59, PC4), flame height (-0.50, PC4) and temperature change (-0.82, PC4). Many of the species were similarly flammable (Fig. 4); however, \u003cem\u003eQu. kelloggii\u003c/em\u003e ignited much faster (ANCOVA, p \u0026lt; 0.001) than all other species. There were stronger interspecific differences for ignitability (\u003cem\u003eproportion ignited\u003c/em\u003e metric) than other flammability components (e.g., consumability, as indicated by glow duration). Differences in ignitability were dictated by functional group, as broadleaf angiosperms ignited less frequently than conifers, which ignited nearly 100% of the time (8% of attempts for \u003cem\u003eCe. cordulatus\u003c/em\u003e, 81% for both \u003cem\u003eAr. patula\u0026nbsp;\u003c/em\u003eand \u003cem\u003eQu. kelloggii\u003c/em\u003e vs. 97-99% for conifers).\u003c/p\u003e\n\u003cp\u003eIn flammability drydown tests, flammability typically related to tissue hydration as expected (e.g. decreased time to ignition and increased temperature change during ignition in drier samples) though the relationships were relatively weak (Table 1, Fig. 5, Fig S3). LFM was selected over water potential in the top-performing linear mixed-effects models for all five flammability metrics (∆AIC \u0026gt; 2). However, LFM models only explained an additional 1-5% of variation compared to water potential models, and models with an interaction between water potential and species performed similarly to LFM models with no species interaction. Top model estimates are presented in Tables S3 and S4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eMixed effects model selection table, showing top hydration model results for selected flammability metrics (time to ignition, flame height, temperature change, flame duration, and glow duration) using live fuel moisture (LFM), water potential (MPa), species, sample weight and location (not shown) as predictors. The hydration coefficient column represents the effect size of the hydration metric (if one is included in the model) and asterisks represent significance (*0.05\u0026gt;p\u0026gt;0.01, **0.01\u0026gt;p\u0026gt;0.001, ***p\u0026lt;0.001). Species-only model shown for comparison. The ∆AIC column represents the difference in AIC between the indicated model and the top-performing model in the model selection, with a ∆AIC\u0026lt;2 exhibiting no statistically significant differences between models. Also shown are Nakagawa\u0026rsquo;s marginal and conditional R\u003csup\u003e2\u003c/sup\u003e values (Nakagawa and Schielzeth 2013). Full model comparisons shown in Table S4.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlammability Metric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydration Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆AIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarginal R\u003csup\u003e2\u003c/sup\u003e / Conditional R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eTime to Ignition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLFM*species + weight\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8.097 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.586 / 0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMPa*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003e11.567 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e50.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.558 / 0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSpecies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e174.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.512 / 0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFlame Height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLFM*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.252 / 0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMPa*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e11.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.249 / 0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSpecies\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e49.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.226 / 0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eTemp. Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLFM * species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-5.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.233 / 0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMPa*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-8.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e38.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.237 / 0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e131.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.211 / 0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eFlame Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLFM*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-2.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.439 / 0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMPa*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-3.626 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e20.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.429 / 0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e108.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.368 / 0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eGlow Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eLFM*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e3.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.179 / 0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eMPa*species + weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-10.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.174 / 0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 171px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e128.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e0.158 / 0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSeasonal shifts in flammability:\u0026nbsp;\u003c/em\u003eWe combined measurements of flammability from the flammability drydown tests with field measurements of tissue hydration to investigate seasonal patterns of predicted flammability (Fig. 5). Interspecific variation and seasonal changes were largely attributable to the presence of new high LFM tissues during the growing season, which resulted in overall decreased flammability when these newer leaves were present. \u003cem\u003ePi. jeffreyi\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCa. decurrens\u0026nbsp;\u003c/em\u003ewere the most combustible (greatest predicted temperature change during combustion), while \u003cem\u003eQu. kelloggii\u0026nbsp;\u003c/em\u003eshowed the highest ignitability. Combustibility \u0026nbsp;differences among species remained consistent throughout the year despite seasonal variation in some species. However, seasonal decreases in LFM were sufficient in \u003cem\u003ePi. jeffreyi\u003c/em\u003e and \u003cem\u003eAr. patula\u003c/em\u003e to change the species rank order of ignitability in July and August.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe investigated the relationships between water content, water potential, and flammability using a combination of field and laboratory-based methods. We compared benchtop drying methods (both on multiple branches from flammability curves and on single leaves from PV Curves) to natural changes in LFM and water potential measured as summer drought progressed in intact plants. We found that LFM and water potential were more strongly related in angiosperms than gymnosperms across all drydown types. To compare different metrics of plant water status to flammability, we measured LFM, water potential, and flammability simultaneously as plants dried, and used field measurements to predict seasonal shifts in flammability based on the relationships between LFM and flammability observed in flammability tests. We found that LFM was a stronger predictor of flammability than water potential, and that cross-species flammability rankings shifted as phenology-driven changes in LFM occurred during the growing season.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between water potential and water content differ based on functional group\u003c/h2\u003e \u003cp\u003eWe found that gymnosperm and angiosperm species differed in their relationships between water content and water potential. Consequently, interactions between drought stress and flammability differed between groups. For both plant types in field-measured samples, we found that LFM tended to decrease quickly during the start of the dry season until it reached the water potential at a given specie\u0026rsquo;s turgor loss point (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e), a seasonal dynamic previously observed in Mediterranean systems (Ma et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nolan et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pivovaroff et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In our study system, a patchy shrub and mixed-conifer forest, these rapid early-season LFM decreases were associated with the expansion and development of new growth (bud break and young leaf growth). Beyond this point (in mid-July in this study) the relationship between water potential and water content became shallower and more linear for angiosperms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Gymnosperms, particularly the Pinaceae (\u003cem\u003eAb. Concolor\u003c/em\u003e and \u003cem\u003ePi. jeffreyi\u003c/em\u003e), showed similar drastic decreases in LFM in new growth early in the season, but this variation was never strongly related to plant water potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e, variance decompositions). Indeed, field-measured water potentials did not decline far below the water potential at TLP for these gymnosperm species, likely due to strong stomatal control of transpiration as the dry season progressed (Klein \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Anderegg and HilleRisLambers \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Voelker et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), or decoupling between soil moisture and LFM, which has been observed in other systems (Brown et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eCa. decurrens\u003c/em\u003e exhibited more fluctuation in its water potential compared to \u003cem\u003eAb. Concolor\u003c/em\u003e and \u003cem\u003ePi. Jeffreyi\u003c/em\u003e, reaching water potentials well below the TLP, indicating that \u003cem\u003eCa. decurrens\u003c/em\u003e is perhaps less likely to close stomata in response to reductions in water availability (T. J. Brodribb et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For all gymnosperm species, however, LFM did drop considerably below the LFM at TLP despite stomatal control and reduced water loss, likely due to ongoing increases in leaf dry matter even after stomatal closure.\u003c/p\u003e \u003cp\u003eFor gymnosperms measured in laboratory drydowns, we found that single timepoint drydowns were largely dissimilar from field-based observations, likely due to benchtop curves failing to capture seasonal stomatal adjustments and changes in dry matter content that occur in intact plants (e.g. shifts in the proportion of leaves to branches or increasing lignin content in leaves) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This high degree of cross-method variation poses a challenge for translating from lab-measured relationships to those present naturally on the landscape, and we argue that caution should be used when attempting to generalize relationships between water content and water potential (and thereby water potential and flammability, particularly when flammability\u0026thinsp;~\u0026thinsp;hydration relationships have been established using LFM) in gymnosperm species. Gymnosperm hydration relationships were almost entirely dominated by factors outside of water potential (cross-plant, cross-species, and cross-timepoint variation in plant structure/dry mass), indicating that these factors should not be ignored when characterizing LFM\u0026thinsp;~\u0026thinsp;water potential relationships.\u003c/p\u003e \u003cp\u003eThe angiosperms in our study system, \u003cem\u003eCe. cordulatus, Ar. patula\u003c/em\u003e, and \u003cem\u003eQu. kelloggii\u003c/em\u003e, showed reductions in water potential even below their TLP in field-based measurements, indicating a weaker relationship between TLP, as measured in the lab, and stomatal closure \u003cem\u003ein situ\u003c/em\u003e. In general, we observed stronger relationships between water potential and LFM in the field for angiosperms, and less interspecific and cross-method variation overall in angiosperm water content\u0026thinsp;~\u0026thinsp;water potential relationships (e.g. in variance decompositions of LFM, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This low intraspecific variation is likely due to more similar leaf morphology, phenological patterns, and drought response (e.g. later stomatal closure) across angiosperm species. As such, relationships between LFM and water potential are likely more generalizable in angiosperms than in gymnosperms across species or across drydown methods (benchtop vs. field). Additionally, LFM and drought stress appear to be more strongly coupled in angiosperms, making LFM (and water content more broadly) a potentially good indicator of drought stress for angiosperm species.\u003c/p\u003e \u003cp\u003eOur results show that repeated measurements of LFM and water potential will be necessary to accurately characterize the relationship between water potential and water content during early season growth for both plant groups. Additionally, measurements of leaf structural characteristics related to dry matter content (e.g. increases in LDMC and LMA as leaves transition from new to old growth) will help characterize water content\u0026thinsp;~\u0026thinsp;water potential relationships later in the season for evergreen gymnosperms. Both water potential and LFM at the TLP helped describe seasonal LFM dynamics in the field, particularly for gymnosperms. Thus, we stress the importance of field-collected water content\u0026thinsp;~\u0026thinsp;water potential relationships to contextualize classic benchtop drydown curves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEffects of hydration and species on flammability\u003c/h2\u003e \u003cp\u003eOn the landscape scale, LFM is commonly used in fire risk assessments because of its relationship with ignitions and fire spread (Chuvieco et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dennison and Moritz \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jurdao, Chuvieco, and Arevalillo \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Park, Fauss, and Moritz \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the tissue scale, we found that LFM was consistently a significant, if sometimes subtle, predictor of nearly all measures of flammability (ignition, flaming, and heating dynamics, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) mirroring these landscape-scale findings and those of other laboratory-based studies (Grootemaat et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kauf et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tumino et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While water potential did show some relationship with flammability (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e), it generally performed slightly worse than LFM in model selection, explaining similar variation in flammability (similar R\u003csup\u003e2\u003c/sup\u003e as LFM models) but in a less parsimonious manner (higher AIC).\u003c/p\u003e \u003cp\u003eDue to these differences, we posit that physiological drought stress alone (which strongly controls tissue hydration if all else is held constant, e.g. in a rapid laboratory drydowns such as those used to create PV curves) can influence flammability, but its effects are mediated by tissue attributes such as dry matter content and those related to species morphology. LFM, which is influenced by both instantaneous hydration status and tissue morphology (e.g. leaf mass per area and leaf or stem dry matter content), is thus more directly linked to tissue-level flammability, even for different portions of the same branch. As such, LFM is an effective all-around predictor of flammability because it gives insight into both drought status \u003cem\u003eand\u003c/em\u003e morphology. In addition, the consistent inclusion of sample weight and a strong species-effect in top-performing models of flammability points to the importance of morphological characteristics in estimates of flammability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In fact, species differences (i.e. differences in mean flammability) alone explained substantial variation in our flammability trials, without information on hydration. The inclusion of hydration-specific variables only improved model fit by a few percent (maximum increase of 7.4%) while species alone explained up to 51% of variation in flammability metrics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, though some of the species-only variance explained is likely shared variance with hydration). \u003cem\u003eQu. kelloggii\u003c/em\u003e showed faster ignitions than other species across all hydration levels and in predicted flammability, and all angiosperms showed lowered ignition probabilities than gymnosperms (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eCe. cordulatus\u003c/em\u003e had such low ignition probabilities that gathering flammability data on other metrics was nearly impossible.\u003c/p\u003e \u003cp\u003eInterspecific differences in flammability are likely driven by variation in structural and chemical characteristics across species. \u003cem\u003eQu. kelloggii\u0026rsquo;s\u003c/em\u003e faster ignitions and relatively low glow durations likely were a result of its relatively large area and low LMA leaves, which had higher surface areas exposed to heating and less leaf mass to burn through during the combustion and glowing phases. \u003cem\u003eAr. patula\u003c/em\u003e was the second-most ignitable in terms of time to ignition and had relatively large leaves compared to the other species, while its longer glow durations were likely due to the higher density of its leaves and stems. The gymnosperms were more like each other in terms of flammability metrics, with marginally shorter ignition times for the higher surface-area \u003cem\u003eCa. decurrens\u003c/em\u003e. Further research into the specific traits that mediate flammability \u0026ndash; via comprehensive studies that measure a large suite of traits \u0026ndash; might elucidate more specific patterns of what makes plants more or less flammable and should emphasize differences in leaf area and tissue density or volume both within and across species. These studies could help determine the relative contributions of specific traits to flammability, as well as how different traits might interact to influence specific components of flammability.\u003c/p\u003e \u003cp\u003eIn additional to morphological difference, plant chemistry likely also plays a role in species-difference in flammability. Conifers store VOCs in resin ducts in leaves and tend to have higher VOC concentrations overall than broadleaf angiosperm species (V\u0026aacute;zquez-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given extended exposure to a heat and ignition source such as a pilot flame or surface fire, a conifer\u0026rsquo;s high VOC content and waxy leaves might ensure that it ignites even if times to ignition are prolonged, while the high heating value of VOCs could increase both temperatures and flame heights (Orme\u0026ntilde;o et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ganteaume et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; T. Brodribb and Hill \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Within species, these flammability-related chemical and physiological traits may be driven by access to water and drought status, which comparative studies across water access gradients might help elucidate.\u003c/p\u003e \u003cp\u003eWhile species differences were the most important driver of flammability, our predictions based on lab flammability tests and field-observed LFM highlight that drought physiology has the potential to meaningfully alter flammability, to the point where species rankings change (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For angiosperms, we observed a strong correlation between phenology and flammability and an overall more consistent relationship between LFM and water potential both in field and laboratory drydowns, suggesting a correlation between drought stress and morphology that can be used to inform flammability predictions for these species. Gymnosperms, conversely, showed little to no correlation between drought stress and LFM or phenology, making seasonal flammability\u0026thinsp;~\u0026thinsp;water potential relationships difficult to interpret without additional information regarding morphology (LFM or old/new growth ratios).\u003c/p\u003e \u003cp\u003eCapturing \u003cem\u003eall\u003c/em\u003e the drivers of flammability will require flammability testing across plant developmental stages throughout a fire season in living plants, with a focus on phenological phases linked to changes in water availability. These phenological signals and physiological changes likely drive a portion of the changes in flammability observed at the landscape level (i.e., Dennison and Moritz \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Park, Fauss, and Moritz \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as seasonal shifts in carbon allocation and leaf chemistry change plant structure and volatility, and changes in tissue water holding capacity shift the relative contribution of water content to overall plant mass. As we only tested flammability at the end of the dry season, our flammability testing likely did not capture some of these dynamics. To disentangle the effects of phenology vs. water access on LFM and flammability, we suggest joint phenology, LFM, and flammability monitoring in plants with contrasting levels of water access (i.e. water potentials) at the same time of year, or in living plants with similar water access but throughout a seasonal drydown.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDrought and wildfire are dynamic disturbances in vegetated regions, and their severity and impact are likely shaped by similar physiological traits at the plant level. To determine how flammability-related and drought-related traits interact \u0026ndash; and when they intersect \u0026ndash; we took seasonal measurements of drought stress, LFM, and flammability. We found that plant hydration drives many aspects of flammability and that LFM better describes flammability than water potential. Seasonal shifts in morphology and physiology (the development of new tissues, and stomatal closure that limits water loss), drastically influence the relationship between LFM and water potential. Importantly, our finding of a strong and generalizable relationship between drought stress and LFM (and thereby drought stress and flammability) in angiosperms might help simplify efforts to understand drought and fire interactions across scales. However, the more complex relationship in gymnosperms warrants further mechanistic investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eMM and IB designed the research. IB and JVC conducted lab and fieldwork. IB and JVC performed analysis, with guidance from LLA and MM. IB wrote the first draft of the manuscript. IB, JVC, LLA, and MM revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e All data and code for this project will be made available on github https://github.com/bovingi/sierra-flammability/tree/main.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone declared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e This research was funded by the University of California\u0026rsquo;s National Laboratories (UCNL) 542 Laboratory Fees grant program under grant number LFR-18-542511 (as a part of the California 543 Ecosystems Futures project).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to thank field assistants and volunteers Anne-Marie Parkinson, Ariana Escobedo, Jasper Romero, Kristina Fauss, and Boots. We acknowledge the Traditional Custodians and Owners of California and recognize their continuing connection to the land upon which this research was conducted, particularly the Chumash on whose traditional territory UC Santa Barbara sits.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbrams, M. D., \u0026amp; Menges, E. S. (1992). Leaf Ageing and Plateau Effects on Seasonal Pressure-Volume Relationships in Three Sclerophyllous Quercus Species in South-Eastern USA. \u003cem\u003eFunctional Ecology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 353\u0026ndash;360. JSTOR. https://doi.org/10.2307/2389527\u003c/li\u003e\n \u003cli\u003eAkaike, H. (1987). Factor analysis and AIC. \u003cem\u003ePsychometrika\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 317\u0026ndash;332. https://doi.org/10.1007/BF02294359\u003c/li\u003e\n \u003cli\u003eAlam, M. A., Wyse, S. V., Buckley, H. L., Perry, G. L. W., Sullivan, J. J., Mason, N. W. 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A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, \u003cem\u003e136\u003c/em\u003e, 455\u0026ndash;468. https://doi.org/10.1016/j.rse.2013.05.029\u003c/li\u003e\n \u003cli\u003eZahn, S. \u0026amp; Henson, C. (2011, May). \u003cem\u003eA Syntheis of Fuel Moistyre Collection Methods and Equipment\u0026mdash;A Desk Guide\u003c/em\u003e. U.S. Department of Agriculure, Forest Service, National Technology and Development Program.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"flammability, live fuel moisture, water potential, pyro-ecophysiology","lastPublishedDoi":"10.21203/rs.3.rs-5939800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5939800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDrought and wildfire are linked by their relationships with plant hydration, yet drought- and fire-focused research use discipline-specific hydration metrics to capture plant stress and fuel flammability. We investigated potential drought-wildfire dynamics in angiosperms and gymnosperms common to Sierra Nevada mixed conifer forests by relating plant drought status (water potential) to water content (live fuel moisture, LFM), and to flammability. We conducted laboratory flammability tests coupled with a benchtop drydown and measurements of physiological drought-response (the turgor loss point, TLP). We measured water potential, LFM, and phenology during a seasonal drydown in the field.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGymnosperms showed inconsistent relationships between water content and water potential across drydown type (field vs. lab), complicating how we measure their potential drought-wildfire relationships. In contrast, angiosperms had consistent hydration relationships, showing promise for relating drought stress to flammability. Physiological adjustments occurring near the TLP and phenological patterns impacting dry matter development drove differences across functional groups. Decreased plant hydration increased flammability, and LFM predicted flammability better than water potential \u0026ndash; likely because LFM captured both hydration and shifts in leaf and branch dry matter associated with phenology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur results suggest that phenological development and drought response mediate the relationship between drought stress, tissue water content, and tissue flammability in seed-producing plants (i.e., angio- vs gymnosperms). This work links key ecological processes, drought and wildfire, and advances our ability to predict wildfire risk using species composition and drought stress.\u003c/p\u003e","manuscriptTitle":"Towards predicting flammability of Sierra Nevada mixed conifer forests: drought stress and fuel moisture are strongly linked in angiosperms but decoupled in gymnosperms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 12:17:22","doi":"10.21203/rs.3.rs-5939800/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2025-07-24T08:13:50+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-24T18:25:04+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T17:05:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-07T01:37:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2025-06-06T19:57:09+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":"b30a0750-0ff3-4756-bd49-1c84c0d35ef6","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T16:04:13+00:00","versionOfRecord":{"articleIdentity":"rs-5939800","link":"https://doi.org/10.1186/s42408-025-00396-x","journal":{"identity":"fire-ecology","isVorOnly":false,"title":"Fire Ecology"},"publishedOn":"2025-09-02 15:57:44","publishedOnDateReadable":"September 2nd, 2025"},"versionCreatedAt":"2025-06-27 12:17:22","video":"","vorDoi":"10.1186/s42408-025-00396-x","vorDoiUrl":"https://doi.org/10.1186/s42408-025-00396-x","workflowStages":[]},"version":"v1","identity":"rs-5939800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5939800","identity":"rs-5939800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}