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The Effect of Ecophysiological Traits on Live Fuel Moisture Content
1
La Kretz Center for California Conservation Science, University of California, 619 Charles. E. Young Drive East, Los Angeles, CA 90095, USA
2
Department of Plant Biology, Michigan State University, 612 Wilson Road, East Lansing, MI 48824, USA
3
Department of Ecology and Evolutionary Biology, University of California, 612 Charles E. Young Drive South, Los Angeles, CA 90095, USA
4
National Park Service, Santa Monica Mountains National Recreation Area, 401 West Hillcrest Drive, Thousand Oaks, CA 91360, USA
5
United States Geological Survey, Western Ecological Research Center, Sequoia-Kings Canyon Field Station, 47050 Generals Highway, Three Rivers, CA 93271, USA
*
Correspondence: [email protected]; Tel.: +1-562-881-4640
Current Address: Atmospheric Sciences and Global Change Division, Pacific Northwest National Lab, 902 Battelle Blvd, Richland, WA 99354, USA.
Received: 5 April 2019 / Accepted: 17 May 2019 / Published: 22 May 2019

Abstract

:
Live fuel moisture content (LFMC) is an important metric for fire danger ratings. However, there is limited understanding of the physiological control of LFMC or how it varies among co-occurring species. This is a problem for biodiverse yet fire-prone regions such as southern California. We monitored LFMC and water potential for 11 native woody species, and measured ecophysiological traits related to access to water, plant water status, water use regulation, and drought adaptation to answer: (1) What are the physiological mechanisms associated with changes in LFMC? and (2) How do seasonal patterns of LFMC differ among a variety of shrub species? We found that LFMC varied widely among species during the wet winter months, but converged during the dry summer months. Traits associated with LFMC patterns were those related to access to water, such as predawn and minimum seasonal water potentials (Ψ), and water use regulation, such as transpiration. The relationship between LFMC and Ψ displayed a distinct inflection point. For most species, this inflection point was also associated with the turgor loss point, an important drought-adaptation trait. Other systems will benefit from studies that incorporate physiological mechanisms into determining critical LFMC thresholds to expand the discipline of pyro-ecophysiology.
Keywords:
live fuel moisture content; water potential; fire; pyro-ecophysiology; chaparral; water relations; functional traits

1. Introduction

Live fuel moisture content (LFMC) is a landscape-level management metric that, along with weather and topography, is incorporated into rate-of-spread models and fire danger ratings [1,2,3,4]. LFMC is expressed as the ratio of water content in fresh plant tissue to the dry weight and represents the amount of moisture that needs to evaporate from a fuel source before ignition can occur. Greater fuel moisture means reduced flammability and lower likelihood of ignition [5], hence LFMC has a major effect on combustion, fire spread, and fire consumption [6,7,8]. Yet, the relationship between LFMC and fire risk is not always linear. Instead, there are thresholds. For example, wildfire risk increases as LFMC decreases until a critical threshold is reached, and then the fire risk is constant and high [9]. However, recent studies suggest that these breakpoints need to be considered with caution as field-based and laboratory-based studies often show differing results related to the magnitude of the effect of LFMC on fire rate-of-spread [2,4,10]. Hence, further LFMC studies are needed to resolve these issues.
California’s Mediterranean-type climate shrublands are especially prone to wildfires. For example, the natural fire regime in southern California historically included infrequent yet large, high-intensity fires [11]. However, modern fire frequencies are higher than normal, which threaten chaparral diversity [12,13]. For chaparral shrubs in southern California, LFMC is normally high during the winter and spring and then gradually declines with the onset of the characteristic summer drought (Figure 1) of Mediterranean-type climates (Figure 2a). This leads to a typical fire season about six months long (Figure 1). However, severe or extended drought conditions, which are becoming more common with climate change, lead to fuels drying out sooner, more quickly, to a greater extent, or for longer. In these instances, the predicted fire season starts earlier in the year and/or lasts longer. Extended droughts have been associated with high fire activity or anomalous seasonality. For example, five of nine historic “mega fires” (>50,000 ha) in southern California occurred during a period of anomalous drought [14]. Large, wind driven, spring fires can occur when LFMC is at levels that normally occur in the late summer or fall [15].
In some cases, extreme drought can lead to vegetation mortality [16] and hence increased dead fuel loads, which can also exacerbate fire risk. These drought conditions can lead to increased dead fuels, which are largely controlled by weather conditions and fuel thickness [8]. Alternatively, live fuel moisture covaries with environmental conditions, including temperature, precipitation, and soil moisture, as well as the phenological and physiological characteristics of a plant species [9,17,18]. Despite the widespread use of LFMC as a proxy for flammability and fire risk, only recently have quantitative studies emerged of how these data relate to traits of plant water relations components including water potentials, turgor loss point, relative water content, and hydraulic conductivity [19,20]. This represents a significant gap in our understanding of pyro-ecophysiology [21].
Determining the physiological mechanisms that underpin LFMC, especially in a fire-prone landscape such as California, has important implications for fire-risk management practices [22]. The objective of this research was to address: (1) What are the physiological mechanisms associated with changes in LFMC? and (2) How do seasonal patterns of LFMC differ among a variety of species? To address these questions, we measured LFMC and a variety of ecophysiological traits for 11 native woody chaparral and coastal sage scrub species. These traits included water potentials, gas exchange characteristics, and hydraulic traits. We hypothesized that the following traits would control changes in LFMC: (1a) traits associated with access to water, such as predawn water potentials and minimum seasonal water potentials, (1b) water use regulation, such as transpiration, and (1c) traits associated with drought adaptation, as determined from pressure-volume curves. We also hypothesized that: (2) these trait differences would manifest as differing seasonal LFMC patterns among species.

2. Methods and Materials

2.1. Study Site and Species

The study was conducted from July 2015 to March 2016 at the Stunt Ranch Santa Monica Mountains Reserve, located in the Santa Monica Mountains in California (34°5′27′′ N, 118°39′27′′ W). Stunt Ranch is 125 ha field station dominated by coverage of chaparral, with elevation ranging from 392 to 472 m. With a Mediterranean-type climate, average temperature highs are 32 °C and average lows are 4 °C (Figure 2A). Mean annual precipitation is 610 mm, occurring mostly during the winter months with almost no rainfall during the summer; however, there is very high interannual rainfall variability. Stunt Ranch last burned in 1993, 22–23 years before this study. This study measured 11 common woody species in California shrub land ecosystems (Table 1).

2.2. Live Fuel Moisture Content

LFMC (%) was measured approximately every three weeks, concurrently with plant water potentials (see below). During sampling, a terminal shoot was collected from each individual, for at least three individuals per species during midday (11:00 to 13:00), approximating the procedures used by the Los Angeles County Fire Department and the Ventura County Fire Department in their monitoring of LFMC. Each sample was sealed in a separate plastic bag (Whirlpak, Nasco, and Fort Atkinson, WI, USA) and transported in a cooler to the lab.
LFMC is the ratio of water weight to dry weight of living plant tissue [23] and was determined by measuring the sample fresh weight and then placing the sample in a coin envelope and drying in an oven at 70 °C for at least 72 h, after which the dry mass was recorded. LFMC (%) was calculated as:
L F M C = F r e s h   w e i g h t D r y   w e i g h t D r y   w e i g h t 100
Minimum seasonal LFMC (LFMCMIN) was determined from the lowest LFMC value a species experienced over the course of a year, and maximum seasonal LFMC (LFMCMAX) was determined from the highest LFMC value a species experienced over the course of a year.

2.3. Plant Water Potential

At the same time, and from the same branch used for LFMC sample collection (see above), an additional terminal shoot was collected for a paired water potential (Ψ; MPa) measurement, for at least three individuals per species during midday (11:00 to 13:00). Each sample was sealed in a separate plastic bag (Whirlpak, Nasco, and Fort Atkinson, WI, USA) and transported in a cooler to the lab. Water potential was determined with a Scholander-type pressure chamber (PMS Instrument Corp., Corvallis, Oregon). In addition to midday water potentials, predawn water potentials (04:00 to 05:00) were sampled during the dry (August 2015; ΨPD;dry) and wet season (March 2016; ΨPD;wet) following the same protocol. Minimum seasonal water potential (ΨMIN) was determined from the most negative water potential a species experienced over the course of a year.

2.4. Gas Exchange

Photosynthetic gas exchange was measured using a portable photosynthesis system (LI-6400, LI-COR Biosciences, Lincoln, NE, USA) with a red-blue light source. Measurements were frequently taken from September 2014 to April 2016, during the morning and midday hours (9:00 to 14:00) on the most recently mature leaf. We determined the light-saturated rate of photosynthetic CO2 assimilation per area (AMAX; µmol·m−2·s−1), stomatal conductance to water vapor (gS; mol·m−2·s−1), transpiration (E; mol·m−2·s−1), and intrinsic water use efficiency (A/gS; µmol·mol−1).

2.5. Pressure-Volume Curves

Pressure-volume curves were determined on six terminal shoots per species using the bench-dehydration method [24,25] during August and September, which are dry summer months. Samples were collected from the field, sealed in plastic bags, and placed inside a cooler for transport back to the lab. In the lab, shoots were recut under water and placed in beakers with the distal cut stem end in water, and the entire sample covered in plastic to rehydrate overnight. The next day, samples were weighed and measured for water potential using a Scholander-type pressure chamber, and then dried on a lab bench. Weighing, measuring water potential, and drying samples were repeated until achieving a water potential of about −4 to −6 MPa, depending on the species. Subsequently, samples were put in a paper bag and dried in an oven at 70 °C for at least 48 h to determine dry mass. We calculated saturated water content (SWC; %), water potential at turgor loss point (ΨTLP; MPa), relative water content at turgor loss point (RWCTLP; %), osmotic potential (πo; MPa), modulus elasticity (ε; MPa), capacitance at full turgor (CFT; MPa−1), capacitance at turgor loss point (CTLP; MPa−1), and leaf dry matter content (LDMC; g·g−1) using the Excel spreadsheet available on PrometheusWiki [26].

2.6. Statistical Analyses

All statistical tests were performed in R Studio (ver 0.99.485) [27]. Piecewise regression using the ‘segmented’ package was used to find the inflection point, standard error, and 95% confidence intervals for the relationship between water potential and LFMC (LFMCIP) for each individual species and for all species combined. We tested for differences in ΨTLP among species using ANOVA with the ‘aov’ function from the ‘stats’ package and a Tukey post-hoc test. Pearson correlation was performed with the ‘rcorr’ function from the ‘Hmisc’ package to produce a correlation matrix and evaluate the relationships among all measured traits [28,29]. Before running correlations, traits were tested for normal distribution using the Shapiro-Wilk test of normality [30] and transformed if necessary. Transformed traits included AMAX, gS, E, SWC, and LFMCMAX. The correlogram was constructed using the ‘corrplot’ function.

3. Results

LFMC and water potentials (Ψ) varied among species and seasons (Figure 2B,C). LFMC was highest and most variable among species during the wet months of January through April, while species tended to converge towards low LFMC during the dry season. LFMCMIN occurred in October and November, while LFMCMAX occurred in February and March. Ψ were lowest during the dry season, particularly in October, November, and December, right before winter rains began. Variability in Ψ among species was greatest during the dry season. When winter rains started in January, species converged towards less negative Ψ.
Salvia mellifera had the highest LFMCMAX, while Quercus berberidifolia had the lowest LFMCMAX. Eriogonum fasciculatum had the lowest LFMCMIN, while Malosma laurina had the highest LFMCMIN. Salvia mellifera had the lowest ΨMIN, and Malosma laurina had the highest ΨMIN. In fact, deeply rooted Malosma laurina did not show a substantial seasonal change in Ψ or LFMC.
In comparing LFMC to Ψ (Figure 3, Supplemental Figure S1), LFMC initially decreased rapidly with only a small decline in Ψ, before reaching an inflection point (LFMCIP) and leveling off. After LFMCIP, there were large declines in Ψ with only a small decline in LFMC. In addition, the confidence intervals for LFMCIP overlapped with the confidence intervals for ΨTLP for the compiled data set (Figure 3), which included all study species, as well as for individual species, except for A. fasciculatum, Heteromeles arbutifolia, M. laurina, and S. leucophylla (Supplemental Figure S1). For H. arbutifolia and M. laurina, the relationship between LFMC and Ψ was not tightly constrained, which made it difficult for the model to converge on an inflection point. For A. fasciculatum, ΨTLP was more negative than the inflection point, while for S. leucophylla, ΨTLP was less negative than the inflection point. For the remaining species, E. fasciculatum had the lowest LFMCIP at 67%, while C. spinosus had the highest LFMCIP at 121%.
Species with lower LFMCMIN also had lower (more negative) ΨMIN, lower ΨPD;wet, and lower ΨPD;dry (Figure 4). For example, A. fasciculatum had the second lowest ΨMIN and the lowest ΨPD;wet and ΨPD;dry. LFMCMAX was correlated with AMAX, E, SWC, CFT, CTLP, and LDMC. LFMCIP was correlated with SWC.
ΨTLP among species ranged from −1.47 MPa to −3.40 Mpa (Table 1). S. leucophylla had the highest (least negative) ΨTLP and A. fasciculatum had the lowest (most negative) ΨTLP. While S. leucophylla had the lowest ΨTLP, it was not significantly different from Q. berberidifolia, C. betuloides, M. laurina, or S. mellifera. While E. fasciculatum and A. fasciculatum had the two lowest (most negative) ΨTLP and were also significantly different from the five previously mentioned species, they were not significantly different from A. glauca, Q. agrifolia, H. arbutifolia, and C. spinosus.

4. Discussion

4.1. Overview

Our study aimed to answer, (1) What are the physiological mechanisms associated with changes in LFMC? We found that minimum seasonal live fuel moisture content (LFMCMIN) was correlated with water access traits, including wet season predawn water potential (ΨPD;wet), dry season predawn water potential (ΨPD;dry), and minimum seasonal water potential (ΨMIN), confirming hypothesis 1a as well as previous work by [19]. Maximum seasonal live fuel moisture content (LFMCMAX) was correlated with photosynthesis (AMAX) and transpiration (E), confirming hypothesis 1b. LFMCMAX was correlated with drought-adaptation traits, including SWC, relative CFT, and relative capacitance at zero turgor (CTLP), LDMC, supporting hypothesis 1c.
We also asked, (2) How do seasonal patterns of LFMC differ among a variety of species? We found that species trait differences were associated with differing seasonal LFMC patterns among species, confirming hypothesis 2. LFMC varied among species to the greatest extent during the wet winter months, but all species converged on low LFMC values during the dry season. LFMCMAX occurred during the wet season, along with maximum rate of AMAX and E, while LFMCMIN and ΨMIN occurred during the dry season. The coastal sage scrub species S. mellifera and S. leucophylla, which are drought-deciduous and shallow-rooted, had much higher LFMCMAX values compared to any other species during the wet season. However, Salvia species displayed the lowest ΨMIN. Malosma laurina, which is an evergreen chaparral species characterized by deep roots, maintained the highest LFMC during the dry season, never dropping below 87%.

4.2. Physiological Controls of LFMC

Traits associated with water access and water use regulation were critical determinants of LFMC among species. One of the most important traits that controlled LFMC in this semi-arid plant community was water potential. Predawn water potentials and minimum seasonal water potentials, which are indicative of species access to water [34] and regulation of water loss when stomata are closed (i.e., cuticular conductance) [35], were correlated with LFMCMIN (Figure 4). Deeply rooted plants have greater access to soil moisture for longer into the dry season than shallow-rooted plants, supporting either the delay of LFMC decline or keeping minimum LFMC high [36]. In addition to access to water, traits associated with water use regulation played a significant role in controlling LFMC. For example, species with the highest LFMCMAX also had high AMAX and high E, providing evidence that species with high gas exchange rate rates also have high moisture content in their tissue. Overall, this points to a suite of traits that coordinate water access, water use regulation, and plant water status [37].
By plotting concurrently measured LFMC and Ψ together on the same plot, we examined the relationship between a landscape-level management metric used for assaying fire danger (LFMC) and a well-established physiological measurement of plant water status (Ψ). As plants started to dry out, LFMC rapidly declined with only a small change in Ψ, before reaching an inflection point (LFMCIP), after which there were large declines in Ψ with only a small change in LFMC. The confidence intervals for LFMCIP overlapped with turgor loss point (ΨTLP) confidence intervals for the compiled data set. ΨTLP is an important drought-adaptation trait; species with a lower (more negative) ΨTLP can withstand more negative Ψ before losing leaf turgor, or before wilting. At the point that leaves have lost so much water that they lose turgor, they would also be highly flammable as the amount of moisture in the leaves would be low, hence ignition could easily occur. However, this would correspond to a higher critical LFMC threshold than the current 60% used by fire departments in southern California. This finding confirms work by Dennison & Moritz [38], which used a different approach to determine a critical LFMC threshold involving precipitation records, and builds on the recent LFMC versus fire behavior breakpoint work of Pimont [10,39], with our physiological data.
While the LFMCIP corresponded to the ΨTLP for the compiled data set, this was not the case for each individual species (Supplemental Figure S1). There are a few possibilities for this result. One cause could be phenology [40] and, more specifically, growth. Dry matter “accumulates” when plants grow, which would exert a strong seasonal effect on the relationship between LFMCIP and ΨTLP for species with differing phenological patterns [41,42]. Another potential factor is related to water storage and water release dynamics of different plant species, including the release of capillary water versus water released from cavitation events [43,44]. Using a physiologically meaningful LFMC threshold, as supported by flammability and precipitation data, along with an appropriate indicator species is important when monitoring fire-risk conditions.

4.3. Indicator Species Choice Can Impact Fire Danger Rating

As we have shown, LFMC and therefore fire-risk monitoring will vary depending on the indicator species. By comparing 11 dominant species in southern California, a region with high plant biodiversity, we can quantitatively compare LFMC of Adenostoma fasciculatum, the current indicator species, against other co-occurring species. Adenostoma fasciculatum is used as an LFMC indicator species in southern California because of its widespread distribution and abundance. In fact, it is present in 70% of California chaparral communities [45]. In addition, it is viewed as being highly flammable due to its oily leaves (giving rise to its other common name “greasewood”) and multi-stemmed canopy structure that consists of dense clusters of small stems, branches, and flower stalks [46]; about 60% of stems are <1.27 cm in diameter [47]. This makes it a fine fuel with small stems and leaves, especially in comparison to co-occurring large branching or broad-leafed species. Our data show that of the 11 study species examined, A. fasciculatum was one of the first species to reach the 79% critical LFMC threshold proposed by [38] and it stayed below that threshold during the seasonal dry period. In fact, it often also fell below the 60% LFMC threshold used by LACFD when many other species did not. The only other species to consistently do so was C. betuloides. Hence, A. fasciculatum is a good indicator species for measuring LFMC in southern California chaparral mountains. The high seasonal LFMC values of M. laurina, H. arbutifolia, and Q. agrifolia make them less optimal as LFMC indicator species.

5. Conclusions

In their 2018 Perspective paper, Jolly and Johnson [21] argue,
The discipline of ecophysiology is rich and mostly unleveraged in live fuel research, yet it has the potential to link plant flammability traits at both the leaf and plant level to fundamental laws that govern how plants functions.
Our study takes the initial steps to address this and shows that traits associated with water access and water use regulation were critical determinants of seasonal LFMC patterns among species. Other systems would benefit from physiological studies of their chosen indicator species and critical LFMC thresholds. This understanding is crucial as the number and size of large fires in the western United States have increased, especially when coincident with drought [48]. Increasing temperatures and longer, more intense, and/or more frequent droughts will become common with climate change, so accurately forecasting wildfire risk is an immediate issue.

Supplementary Materials

The following are available online at https://www.mdpi.com/2571-6255/2/2/28/s1: Figure S1: Water potential versus live fuel moisture content for 11 species, measured at the Stunt Ranch Santa Monica Mountains Reserve, with the piecewise linear regression used to determine the inflection point.

Author Contributions

Conceptualization, A.L.P. with input from M.R.S., M.W., J.E.K., and P.W.R. Methodology, A.L.P. Formal analysis, A.L.P. with input from N.E., M.R.S., M.W., J.E.K., and P.W.R. Writing–Original draft preparation, A.L.P. Writing–Reviewing and editing, A.L.P., N.E., M.R.S., M.W., J.E.K., and P.W.R.

Funding

This work was supported by funding provided by a La Kretz Center for California Conservation Science Postdoctoral Fellowship to A.L.P. and the Bloom-Hays Ecological Research Grant from Sea and Sage Audubon to A.L.P.

Acknowledgments

We thank Kim Riley, Linda Moua, Mindy Phan, and Tai Michaels for help in the field and lab; Aaron Ramirez, the Sork lab, and the Sack lab for useful feedback on the data; Mario Colon and Gary Bucciarelli for logistical support at the Stunt Ranch Santa Monica Mountains Reserve. We acknowledge that this study was conducted on the traditional territory of the Chumash and Tongva people.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

TraitAbbreviationUnits
Live fuel moisture contentLFMC%
Minimum seasonal live fuel moisture contentLFMCMIN%
Maximum seasonal live fuel moisture contentLFMCMAX%
Live fuel moisture inflection pointLFMCIP%
Water potentialΨMPa
Wet season predawn water potentialΨPD;wetMPa
Dry season predawn water potentialΨPD;dryMPa
Minimum seasonal water potentialΨMINMPa
Maximum seasonal water potentialΨMAXMPa
Maximum photosynthetic carbon gainAMAXµmol·m−2·s−1
Stomatal conductancegSmol·m−2·s−2
TranspirationEmol·m−2·s−2
Water use efficiencyA/gSµmol·mol−1
Saturated water contentSWC%
Water potential at turgor loss pointΨTLPMPa
Relative water content at turgor loss pointRWCTLP%
Osmotic potentialπoMPa
Modulus of elasticityεMPa
Capacitance at full turgorCFTMPa−1
Capacitance at turgor loss pointCTLPMPa−1
Leaf dry matter contentLDMCg·g−1

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Figure 1. Mean live fuel moisture content (LFMC) ± standard error, measured for Adenostoma fasciculatum at Stunt Ranch Santa Monica Mountains Research Reserve from 2006–2016, represented by the solid black symbols and line. The red dashed line indicates the 60% critical LFMC threshold used by local fire departments, while the orange dash-dotted line indicates the 79% threshold from Dennison and Moritz (2009).
Figure 1. Mean live fuel moisture content (LFMC) ± standard error, measured for Adenostoma fasciculatum at Stunt Ranch Santa Monica Mountains Research Reserve from 2006–2016, represented by the solid black symbols and line. The red dashed line indicates the 60% critical LFMC threshold used by local fire departments, while the orange dash-dotted line indicates the 79% threshold from Dennison and Moritz (2009).
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Figure 2. (A) Weather data for July 2015 to May 2016, including mean daily air temperature (red line), total daily precipitation (blue line), and mean daily soil moisture (green line), measured from the Stunt Ranch Santa Monica Mountains Research Reserve weather station. (B) Live fuel moisture content including the 60% critical threshold used by Los Angeles County Fire Department and (C) midday water potential measured for 11 species at Stunt Ranch.
Figure 2. (A) Weather data for July 2015 to May 2016, including mean daily air temperature (red line), total daily precipitation (blue line), and mean daily soil moisture (green line), measured from the Stunt Ranch Santa Monica Mountains Research Reserve weather station. (B) Live fuel moisture content including the 60% critical threshold used by Los Angeles County Fire Department and (C) midday water potential measured for 11 species at Stunt Ranch.
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Figure 3. Live fuel moisture content (LMFC) versus water potential (Ψ) for 11 species measured at the Stunt Ranch Santa Monica Mountains Research Reserve from July 2015–May 2016 (grey symbols). Each grey symbol represents a data point. The solid black line represents the 60% critical LFMC threshold used by fire departments in southern California. Dotted lines represent the 95% confidence intervals for Ψ and LFMC; the intersection of the confidence intervals represents the LFMC versus Ψ inflection point (LFMCIP). At the top of the figure, there is a conceptual explanation of abiotic conditions and physiological responses associated with LFMC and Ψ changes.
Figure 3. Live fuel moisture content (LMFC) versus water potential (Ψ) for 11 species measured at the Stunt Ranch Santa Monica Mountains Research Reserve from July 2015–May 2016 (grey symbols). Each grey symbol represents a data point. The solid black line represents the 60% critical LFMC threshold used by fire departments in southern California. Dotted lines represent the 95% confidence intervals for Ψ and LFMC; the intersection of the confidence intervals represents the LFMC versus Ψ inflection point (LFMCIP). At the top of the figure, there is a conceptual explanation of abiotic conditions and physiological responses associated with LFMC and Ψ changes.
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Figure 4. Correlogram of all traits measured for 11 species at the Stunt Ranch Santa Monica Mountain Research Reserve from July 2015–May 2016. Blue indicates a positive correlation while red indicates a negative correlation. Blank squares indicate the correlation was not significant at α = 0.05. Abbreviations and units are found under the “Abbreviations” table.
Figure 4. Correlogram of all traits measured for 11 species at the Stunt Ranch Santa Monica Mountain Research Reserve from July 2015–May 2016. Blue indicates a positive correlation while red indicates a negative correlation. Blank squares indicate the correlation was not significant at α = 0.05. Abbreviations and units are found under the “Abbreviations” table.
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Table 1. Study species, including family, leaf phenology, post-fire regeneration type, the live fuel moisture content inflection point (LFMCIP) ± standard error, and the turgor loss point (ΨTLP; MPa) ± standard error at the Stunt Ranch Santa Monica Mountains Reserve. Sources of post-fire regeneration type: [31]1, [32]2, [33]3. For ΨTLP, different letter superscripts indicate significant differences (p < 0.05).
Table 1. Study species, including family, leaf phenology, post-fire regeneration type, the live fuel moisture content inflection point (LFMCIP) ± standard error, and the turgor loss point (ΨTLP; MPa) ± standard error at the Stunt Ranch Santa Monica Mountains Reserve. Sources of post-fire regeneration type: [31]1, [32]2, [33]3. For ΨTLP, different letter superscripts indicate significant differences (p < 0.05).
SpeciesFamilyLeaf PhenologyPost-Fire Regeneration TypeLFMCIPΨTLP
Adenostoma fasciculatumRosaceaeEvergreenFacultative sprouter 277% ± 5−3.40 ± 0.17 B
Arctostaphylos glandulosaEricaceaeEvergreenFacultative sprouter 286% ± 4−2.51 ± 0.27 B,C
Ceanothus spinosusRhamnaceaeEvergreenFacultative sprouter 1121% ± 19−2.80 ± 0.29 B,C
Cercocarpus betuloidesRosaceaeEvergreenObligate sprouter 271% ± 5−2.02 ± 0.09 A,C
Eriogonum fasciculatumPolygonaceaeSemi-deciduousReseeder 367% ± 5−3.23 ± 0.20 B
Heteromeles arbutifoliaRosaceaeEvergreenObligate sprouter 298% ± 14−2.70 ± 0.21 B,C
Malosma laurinaAnacardiaceaeEvergreenFacultative sprouter 292% ± 10−2.07 ± 0.14 A,C
Quercus agrifoliaFagaceaeEvergreenObligate sprouter 293% ± 12−2.61 ± 0.27 B,C
Quercus berberidifoliaFagaceaeEvergreenObligate sprouter 281% ± 4−1.97 ± 0.22 A,C
Salvia leucophyllaLamiaceaeDrought-deciduousReseeder 385% ± 4−1.47 ± 0.11 A
Salvia melliferaLamiaceaeDrought-deciduousReseeder 3116% ± 8−2.08 ± 0.14 A,C

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