Opportunities for winter prescribed burning in mixed conifer plantations of the Sierra Nevada

Opportunities for winter prescribed burning in mixed conifer plantations of the Sierra Nevada | 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 Opportunities for winter prescribed burning in mixed conifer plantations of the Sierra Nevada Robert York, Jacob Levine, Kane Russell, Joseph Restaino This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-423745/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Young, planted forests are particularly vulnerable to wildfire. High severity effects in planted forests translate to the loss of previous reforestation investments and the loss of future ecosystem service gains. We conducted prescribed burns in three ~ 35 year-old mixed conifer plantations during February in order to demonstrate the effectiveness of winter burning, which is not common in the Sierra Nevada, California. Results On average, 59% of fine fuels were consumed and the fires reduced shrub cover by 94%. The average percent of crown volume that was damaged was 25%, with no mortality observed in overstory trees one year following the fires. A plot-level analysis of the factors of fire effects did not find strong predictors of fuel consumption. Shrub cover was reduced dramatically, regardless of the specific structure that existed in plots. We found a positive relationship between crown damage and the two variables of Pinus ponderosa relative basal area and shrub cover. But these were not particularly strong predictors. An analysis of the weather conditions that have occurred at this site over the past 20 years indicated that there has consistently been opportunities to conduct winter burns. Windows of time are short, typically one or two days, and may occur at any time during the winter season. Conclusions This study demonstrates that winter burning can be an important piece of broader strategies to reduce wildfire severity in the Sierra Nevada. Preparing forest structures so that they can be feasible to burn and also preparing burn programs so that they can be nimble enough to burn opportunistically during short windows during the winter are key strategies. Ecological Modeling Environmental Policy Forestry Pyrosilviculture plantations winter burning Figures Figure 1 Figure 2 Figure 3 Figure 4 Background In California, approximately 1 million hectares of mixed conifer forestland is less than 50 years old, with more than half of that actively managed in plantations (USDA 2021). As the extent and severity of wildfires increase, the degree to which mixed conifer plantations are exposed to wildfire will also increase (North et al. 2019 ). As such, the development and maintenance of plantation stand structures that can withstand wildfires so that basic management objectives are still met is of increasing importance. Related lines of evidence from modeling, structural attributes, and empirical data suggest that fire severity may be higher in western United States conifer plantations compared to mature forests. Wildfire behavior modeling predicts exceptionally high fireline intensities and rates of spread in plantations, resulting in the prediction of primarily crown fire behavior during high fire hazard weather conditions (Stephens and Moggadas 2005). The physical structures of many plantations, which are a result of past silvicultural treatments, are inherently prone to high severity fires. Traditionally, plantations have been characterized by homogenous canopies, moderate or high shrub components, low height to crown bases, and high densities of small trees — structural components that have long been understood to make them vulnerable to fire (e.g. Kobziar et al. 2009 ). More recently, empirical evidence from post-wildfire assessments in productive mixed conifer forests have found increased severities in plantations compared to mature forests (Thompson et al. 2011 , Zald and Dunn 2018 ). Rather than reducing even-aged management as a response to these results, most suggest that plantations be managed in specific ways to mitigate fire severity. In other words, it is not the silvicultural practice of even-aged management that inherently makes plantations vulnerable, but the lack of cultural practices designed to make them less vulnerable as they develop. These include site preparation to reduce early fuel loads (Lyons-Tinsley and Peterson 2012 ), altering planting density (North et al. 2019 ), and fuel treatments during early stand development (Zald and Dunn 2018 ). High-severity wildfires have created large areas of early seral conditions, causing multiple landowner types to consider options for young stand management regardless of whether or not they previously practiced even-aged regeneration. Following wildfires or even-aged regeneration harvests, landowners who want to quickly establish tree dominated structures have decades of timber-focused reforestation research to draw upon (Baldwin et al. In Press). By the time a mature forest structure develops, numerous treatments may have occurred. Treatment costs can add up to several thousand dollars per hectare, with future revenue possible only after several decades (Stewart et al. In Press). This investment is at risk of total loss if a wildfire occurs before trees are sufficiently large to develop fire-resistant properties, or if surface fuels are not maintained at low levels throughout stand development. The risk of loss occurs regardless of whether investments were made for timber or any other objective that prioritizes the rapid recruitment of large, vigorous trees. Thinning via mechanical tee removal is the primary treatment used to increase tree vigor in young stands, but it does not address concerns with surface fuel accumulations. In fact, it can increase rather than decrease surface fuels (Hartsough et al. 2008 ; Stephens and York 2017 ). Whole tree yarding of biomass helps reduce activity fuel increases from thinning treatments (Han et al. 2009 ), yet it does not counter surface fuel deposition inherent to developing stands where leaf area accumulates rapidly. Attention to the long-term feedbacks between treatments and expected fire behavior is central to the “ecology of fuels” concept that has been applied in southeastern US forests (Mitchell et al. 2009 ) but is less developed in in dry western US forests. Low intensity prescribed fire is the lowest-cost treatment available for reducing surface fuels in both mature (Hartsough et al. 2008 ) and young forests (Stewart et al. In Press). Prescribed fire may enhance what is thought to be resilience in plantations, but neither the feasibility nor effects are well-studied (North et al. 2019 ). The predominant strategy for managing for timber growth is to avoid fire, intentional or not, and hope to achieve a large-tree structure before the next wildfire. This assumption is arguably no longer valid, as the probability of a high severity fire occurring in a developing stand has increased across all ownership types despite increasing fire suppression efforts (Starrs et al. 2018 ). Thus, the use of fire in plantations and the protection of long-term economic investments are no longer mutually exclusive. Yet, any benefit to prescribed fire in plantations will inevitably be weighed against negative impacts to tree growth and survival. Despite the benefits of prescribed fire, its operational bluntness is in stark contrast to the high precision of other treatments to which managers are accustomed. Using prescribed fire during dry conditions in the early fall (York et al. In Press a) or when terminal buds of trees are vulnerable in the spring during active growth (Agee 1993 ) may result in levels of fire-related damage or mortality that are unacceptable, thus causing managers to discontinue the use of prescribed fire. The most obvious way to avoid unwanted outcomes is to burn during weather and fuel conditions that avoid unacceptable canopy damage. Winter burning is not a common practice and no literature describes its role in either mature or young stands. This may be because it is assumed that conditions are simply too wet or under cover of snow (Knapp et al. 2005 ). Anthropogenic snowfall reductions in the mixed conifer region, while concerning given ecosystem service and societal impacts (Huang et al. 2018 ), may provide emerging opportunities for winter season prescribed fires, which in turn may help increase fuel treatment work more broadly. Winter burning is not necessarily new, as Indigenous burning likely included winter burning at small spatial scales in between winter storms (Biswell 1989 ). A reason to be cautious with winter burns is the risk of applying silvicultural treatments that are outside of the historical disturbance regime (e.g. Seymour et al. 2002 ). To align strictly with the seasonality of the historic fire regime, prescribed burns would be applied in the summer or early fall (Keeley and Safford 2016 ). The counterargument is that studies of spring versus fall burn effects on ecological variables mainly find either small or no differences (e.g. Knapp and Keeley 2006 ), thus providing an ecological argument for conducting spring burns. Because vegetation is typically dormant, winter burn effects may not be distinctly negative. Independent of climate trends or ecological objectives, the logistical advantages to winter burning are significant. They include: elevated live fuel moisture allows managers to consider burning during wider ranges of wind and relative humidity; because of lower probabilities of escape, fewer personnel are needed to conduct burns; there is less need for patrolling following burns given the frequency of storms to provide mop-up functions; and burn permits are much easier to obtain or may not be needed at all in some locations (York et al. 2020 ). Our three study objectives are aimed at better understanding the potential for winter burning in mixed conifer forests, especially in plantations. First, we demonstrate the potential for winter burning by describing the effectiveness and conditions under which we conducted broadcast burns during the winter season. Second, we analyze pre- and post-burn forest structure data in order to assess which plot-level variables were the best predictors of what are most likely to be considered desirable prescribed fire effects. The purpose is to design pyrosilvicultural activities ( sensu York et al. In Press a) during young stand development, that facilitate successful implementation of winter burns. Finally, we evaluate the frequency and duration of weather windows that have occurred during the last 20 years at the study’s burn site in order to recommend logistical strategies for winter burns. Methods Location and forest structural context of prescribed burns Burns were completed in mixed-species plantations at Blodgett Forest Research Station (BFRS) in the mixed conifer forest on the western slopes of the central Sierra Nevada range, which has a Mediterranean climate. From 1994 to 2020, the mean total precipitation during the wet season (further defined below), was 145 cm yr − 1 . Mean daily temperature was 6.2, mean daily low was 2.6, and mean daily high was 10.9°C. A prescribed burn program at BFRS has been active for the past 20 years, during which burns occurred across a variety of seasons, age classes, and silvicultural systems. Recently, winter burning activity has increased. This is in part due to difficulties obtaining permits to burn during fall and spring windows across the Sierra Nevada region. The three stands used for this study are 8 ha in size, spanning an elevation range of 1283 to 1347 m. The stands were regenerated with clearcuts, including pile-and-burn site preparation, planting, and early vegetation control with herbicide. These are common practices for this forest type when the objective is to promote rapid tree growth after a stand replacing disturbance (Stewart et al. In Press). Stands were planted with six native species: Abies concolor, Calocedrus decurrens, Pinus lambertiana, Pinus ponderosa, Psuedotsuga menziesii, and Sequoiadendron giganteum . Although seedlings were planted in equal proportions among these species, survival was much higher for P. ponderosa and S. giganteum . Two of the stands were 36 and one was 35 years old when they were burned. All three stands were masticated five years prior and commercially thinned with mechanized felling and whole tree yarding three years prior to burning. Unthinned stands with dense canopies of this age are typically not burned at BFRS in the winter because surface fuels rarely dry out enough to carry fire. The objective of the thin was to reduce density to a target basal area of approximately 25 m 2 ha − 1 , while promoting increased tree species diversity and equidistant spacing among the largest available canopy trees. At the time of burning, the stands were typical of a plantation structure that would generally be seen as desirable for growth and yield of timber and carbon sequestration. Average tree dbh was 33 cm when considering all trees greater than 11.4 cm dbh. Data collection Permanent 0.04 ha circular plots on a 10 m grid were established prior to burns. Within plots, all trees greater than 11.4 cm dbh were tagged. Trees were identified by species and measured for dbh. Percent crown volume scorch (PCVS) for all tagged trees was visually estimated immediately following burns. PCVS is a widely used measure of crown damage (Wooley et al. 2012 ). Tree status (alive or dead) was surveyed 1 year following the burns to assess post burn mortality. Pre- and post-burn shrub cover within the 0.04 ha plots was visually estimated to the nearest 5%. Post-fire, the percent of each plot covered with ash was also estimated with the same method. To improve precision, shrub cover by species and ash cover was estimated within each quadrant of the plot, and then averaged across the quadrants to estimate plot-level cover. Only woody shrubs were considered for analysis. The three most dominant species were Ceanothus integerrimus, Ceanothus cordulatus , and Arctostaphylos patula. Surface and ground fuels were sampled before and after the burns using the line-intercept method (Brown 1974 ). Pre-fire measurements of fuel occurred on the morning immediately prior to ignitions. Post-fire measurements occurred 4 months following the burns, coincident with the beginning of the wildfire season in the Sierra Nevada. Two transects per plot were established, one along contour and the second one at + 60 degrees azimuth from the first. Post-fire transects were measured along the same azimuths. 1-hour (0–0.64 cm) and 10-hour (0.64–2.54 cm) fuel classes were tallied between 0 and 2 m along each transect. 100-hour (2.54–7.62 cm) fuels were tallied between 0 and 3 m, and 1000-hour (> 7.62 cm) fuels were tallied between 0 and 11.3 m. Duff, litter, and total fuel depths (cm) were measured at two locations per transect. Fuel loads were calculated with species-specific coefficients developed for Sierra Nevada forests using the Rfuels package in R version 3.6.3 (van Wagtendonk et al. 1998 , Foster et al. 2018 ). Coefficients were weighted for each transect based on the relative basal area fractions of each species derived from the specific plot’s tree measurements. The two transects were averaged to obtain plot-level values. Fuel consumption was expressed proportionally, as the difference in fuel load between the post-fire and pre-fire measurements, divided by the pre-fire measurement. BFRS has a permanent weather station that has collected continuous data at 15-minute intervals since 1994. The station is centrally located within a 0.1 ha gap surrounded by a tall forest structure. We defined the winter burning season based on the combination of weather and logistical factors that realistically define burning opportunities. The winter season starts following a significant precipitation event, defined as ≥ 2.54 cm of precipitation in a 24-hour period. This amount of precipitation in this short amount of time typically ends what is considered to be the fall burning window (i.e. a “season-ending” event). We consider the end of the winter burning period to be May 1st, which is typically prior to the onset of seasonal growth by trees. Importantly, this day coincides with the typical onset of when burn permits are required, thus significantly changing the regulatory context of burning for landowners. Implicit in this timing is that conditions are generally low-risk prior to this date. Each day within this burn window was considered in prescription if relative humidity was less than 45% for at least three consecutive hours and no precipitation had fallen within the previous 10 days. This prescription is based on weather conditions that have occurred during successful winter burns in thinned mature stands conducted at BFRS over the prior decade. Wind speed was initially included in the prescription, but was never a limiting factor and so was excluded. We considered using a standard prescribed burn prescription with acceptable weather ranges and then finding days within which conditions were in prescription. However this overestimates, possibly dramatically depending on elevation, the actual number of feasible burn days. For example, low humidity and high temperatures can dry out weather station fuel sticks shortly after a snow storm, suggesting that it is a burn day when in fact there is still snow on the ground making it impossible to burn. The use of RAWS stations is also problematic for reconstructing winter burn windows, because stations are typically set up on ridge tops or open fields that are not representative of structures where understory burns would take place. Our standards for a minimum number of consecutive dry days to dry out fuels followed by at least one day with low humidity have proven to be consistent indicators of winter burn feasibility over the past decade of annual burning at BFRS. Air quality conditions limit prescribed burns although practitioners did not cite it as a major barrier in the western United States (Schulz et al. 2019). The California Air Resources Board (CARB) announces daily decisions on “burn” vs. “no burn” days based on atmospheric conditions. Archived CARB burn decisions from 1998 to 2020 were accessed from https://ww3.arb.ca.gov/smp/histor/histor.htm . BFRS is located within the North Mountain Counties air basin, where allowable burn days are further categorized as either superior, good, fair, or marginal. Burning on marginal burn days is variable from district to district, but in this region burns have typically been allowed on these days. Thus only no-burn days were considered to limit burning. Analysis Our first objective- describing the nature and effects of the burns- was met by describing the operational and environmental conditions under which the burns were conducted. Basic weather and fuel moisture data are provided. Stand-level effects are described by reporting changes in fuel load, shrub cover, ash cover, and PCVS. The second objective of describing the structural attributes that influenced fire effects was addressed using exploratory variable selection through the least absolute shrinkage and selection operator (LASSO). LASSO is a method of obtaining interpretable, predictive models by simultaneously performing variable selection and regularization (a method to avoid fitting unrealistically complex models) (Tibshirani 1996 ). We applied this method for four objective-relevant metrics of prescribed fire efficacy: fine fuel consumption (excludes 1000-hour fuel), duff consumption, shrub consumption, and PCVS. For each metric, we determined a set of plot-level stand characteristics we hypothesized a priori were most likely to influence these fire effects. These were P. ponderosa basal area, percent canopy cover, pre-fire fine fuel load, pre-fire duff load, and pre-fire shrub cover. Stand was also included. LASSO was used to determine which subset of these variables most parsimoniously predicted the observed data. We used leave-one-out cross-validation (LOOCV) to assess predictive ability at two levels: “best-fit”, and “conservative” using the package gglasso in R (3.6.3) (Yang 2020). LOOCV is conducted by fitting each model to an N-1 length subset of the full data and calculating the prediction error for the left-out data point. This process is repeated many times for each model, and the mean prediction error is then used to assess the model’s predictive ability. To make inferences from the analysis, two models and their associated explanatory variables are considered together. The best-fit model is the one which minimized out-of-sample prediction error while the conservative model is the one with the fewest predictor variables that came within one standard deviation of the minimum out-of-sample prediction error. The conservative model, as the name implies, provides an extra measure of caution against overfitting. The results of both models are reported for each metric variable (fine fuel consumption, duff consumption, shrub consumption, and PCVS). Days that satisfied the criteria for both weather and air quality conditions were summarized by winter season and month. Average burn window length frequency was calculated over all winters and binned into categories of one day, two to three days, four to seven days, and eight days or longer. A univariate autoregressive (AR) model was fit to the annual summary data to estimate a linear trend. Slope was estimated via maximum likelihood using an expectation-maximization algorithm, and an approximate 95% confidence interval was computed from the estimated Hessian matrix of the maximum likelihood parameter estimate. The Mann-Kendall trend test was also used to test for a monotonic trend. The AR model was fit using the MARSS package in R version 4.0.2 (Holmes et al. 2020 , R Core Team 2020). The Mann-Kendall trend test was performed using the Kendall package (McLeod 2011 ). Results Stand-level prescribed fire effects The stands were burned over a three-day period, from February 24th − 26th 2020. Test burns confirmed the likelihood of satisfactory litter consumption. Ignitions began at approximately 10:30 and proceeded until 14:00. 10-hour fuel moisture was 10 to 11%, and relative humidity ranged from 25 to 30%. Live fuel moisture, estimated by oven-drying two samples of live foliage and branches < 0.6 cm diameter collected the day of the burns, averaged 107%. Soil moisture was 25%. Air temperature ranged from 15.5 to 20°C. One stand was burned per day, each with a crew of 3 to 4 people. Strip-head firing with drip torches was used for completing burns at a pace of approximately 2 ha hr − 1 . Flame lengths were typically less than 1.5 m and increased slightly each day as fuels continued to dry out. These conditions were well within the winter burning prescription that has been developed at BFRS, which is different than the prescription used for fall burning (Table 1 ). Generally, it is desirable for 1-hr and 10-hr fuel to be drier during winter burns, because of the head-dampening effect that live fuel moisture and duff tend to have (Banerjee et al. 2020 ). Similarly, the minimum allowable relative humidity is generally lower for winter burns than fall burns due to the decreased risk of escape. Table 1 Environmental prescription ranges for winter burning and fall burning at BFRS. Winter Fall Parameter Low High Low High Relative humidity 45 18 65 23 6.1 m height wind speed (km hr − 1 ) 8 24 8 16 Mid-flame wind speed (km hr − 1 ) 2.4 9.7 2.4 4.8 Temperature (°C) 3 27 3 27 1-hour fuel moisture (%) 13 3 13 5 10-hour fuel moisture (%) 12 5 14 5.5 The burns spread effectively, with surface fuel consumption occurring across most areas. The percent of the ground covered with ash was 68, 80, and 90% across the three stands. Some of the unburned forest floor was caused by skid trails, where there was a lack of fuel. Each successive day of burning led to an increased amount of the forest floor being covered with fire, presumably following a climatic drying trend that was creating more receptive fuel conditions across the burning window. At the stand level, the prescribed fires consumed a substantial amount of fine fuels (59% on average) and, as expected, much lower amounts of large fuels and duff (Table 2 ). Shrub cover was reduced dramatically, by 94% on average. We observed torching of shrubs to be very common, but did not observe any torching of mid-story or canopy trees. One year after the fires, some shrubs had re-sprouted to bring average cover to 5%, which was still lower than the pre-fire cover of 21%. Table 2 Change in fuel loads and shrub cover following winter burns in mixed conifer plantations. Pre burn mean Post burn mean % reduction burn 1 % reduction burn 2 % reduction burn 3 Fine fuels 31.9 mg ha − 1 13.1 mg ha − 1 58 42 78 1000-hr 1.1 mg ha − 1 0.8 mg ha − 1 48 24 7 Duff 28.6 mg ha − 1 24.5 mg ha − 1 11 2 31 Shrub cover 21% 1% 86 96 98 Canopy tree damage and mortality one year after the fires was generally low. No canopy trees (n = 223) that were in the permanent plots died during the first year following the fires. For mid-story trees, mortality was 0–3%. This mid-story mortality may have been caused by the fires, although field crews could not attribute mortality to the fires with certainty. Crown damage (PCVS) of canopy trees averaged 25.5% and varied among the three stands, from 10, to 27, to 40%, increasing on each successive day of burning. Statistically modeled prescribed fire effects The best-fit model for explaining fine fuel consumption at the plot level included the variables ponderosa pine basal area, percent canopy cover, pre-fire fine fuel load, pre-fire duff load, and stand. It did not include pre-fire shrub cover. The greatest amount of fuel consumption occurred on the third day of burning, and also where pre-fire fuel load was higher. Most of the leverage in the relationship between pre-fire fuel load and fuel consumption came from a few plots with low fuel loads that had either zero or negative fuel consumption (Fig. 1 A). Canopy cover, which we believed could be a strong predictor of fuel consumption based on Levine et al. ( 2020 ), was at best weakly correlated despite our plots having a wide range of canopy cover values (Fig. 1 B). The conservative model did not support any of our variables as strong predictors of fuel consumption, but it did verify that meaningful fuel consumption (i.e. fuel reductions of 50% or more) occurred at the plot level. Change in duff at the plot level was not explained well with any of our variables. It was not the case that higher duff loads impeded fine fuel consumption, but duff consumption was generally low (2–31% at the plot level), as expected. Variability associated with duff load and consumption make it especially difficult to find predictors at the plot level (e.g. Westfall and Woodall 2007 ). The best-fit model for explaining change in shrub cover included pre-fire fuel load as a predictor, but the change in shrub cover with fuel load was minimal (< 1% additional increase in fuel consumed per additional Mg ha − 1 of load). The conservative model did not reveal any relationships between shrub cover changes and our measured variables. Overall, this reflects the fact that shrub cover was reduced dramatically whenever it was present on most plots regardless of the specific structure surrounding the shrubs. PCVS was the response variable where the most nuance in the predictor variables could be seen, albeit only when using the best-fit procedure. This suggested a positive relationship between PCVS and both P. Ponderosa basal area and pre-fire shrub cover; and a negative relationship with height to crown base. Particularly high levels of PCVS (> 50%) tended to occur in plots with more than 5 m 2 ha − 1 of P. ponderos a basal area (Fig. 1 C) or when there was less than 5m of average height to crown base (Fig. 1 D). Moderate levels of PCVS (> 25%) occurred when shrub cover was greater than 30% (Fig. 1 E). As with the other variables, the conservative model tended to support that PCVS was significant in magnitude (compared to zero change), but that there were no particularly strong predictors among those that we considered. Weather windows for winter burning over the past 20 years Of the 4851 days that occurred during the winter periods from 1994 to 2020, 320 of them were feasible burn days where both adequate weather and air quality conditions coincided. Annual variability was considerable (Fig. 2 ), ranging from zero days (this occurred in 1995–1996 and again in 2016–2017) to 33 days in 2013–2014, which was within an exceptional multi-year drought that occurred across the region. On average, 12 days per winter were feasible for burning using our criteria. Of the total days that were in prescription in terms of weather conditions, 33% were designated by CARB as no-burn days. There was insufficient evidence of a linear or a monotonic trend for the number of days in prescription per winter. The approximate 95% confidence interval for the slope estimate from the AR model overlapped with zero, and the Mann-Kendall trend test was not statistically significant (τ = 0.090, 2-sided P = 0.536). The month in which burning windows occurred also varied greatly from year to year (Fig. 3 ). October and January had the highest average number of days in prescription from 1994 to 2020 (2.9 and 3.1 days per winter, respectively). October had much more variation over time than January, with many years having no days in prescription during this month because the precipitation-initiating winter period sometimes did not start until at least late October. November had the lowest average number of days in prescription per winter (0.6 days). CARB no-burn decisions, which reduced feasible burn days by 33%, did not limit all months uniformly. While days in prescription in terms of weather conditions were less common in February, March, and April, fewer of these (23%) were designated as no-burn days. This percentage was higher in the first half of winter, from October to January, where 53% of days in prescription were no-burn days. The majority of windows when conditions were suitable for burning from 1994 to 2020 were one to three days long (Fig. 4 ). One-day burn windows were most frequent, occurring 1.5 times per winter on average. Occurrence of either two or three-day burn windows occurred about 1.6 times per winter on average. Windows lasting longer than three days were relatively rare, occurring on average 1.1 times per winter. Discussion Pyrosilviculture treatments to facilitate winter burns The use of prescribed fire in young stands is considered experimental because studies are sparse relative to mature forests (North et al. 2018). Yet there is enough recent work to characterize variability in mortality and damage levels (Table 3). A common experimental treatment prior to conducting burns has been mastication, an activity that was cautioned against by Kobziar (et al. 2009) when trying to lower wildfire severity in the short term. However, in mature forests mastication treatments have performed well in terms of lowering fire severity once masticated fuel decomposes (Stephens et al. 2012). When viewed as a pyrosilviculture treatment — that is, one that increases opportunities for using prescribed fire — then mastication has some possible advantages that are distinct to a winter burning approach. Mastication primarily acts to remove the mid-story component of forest structure, redistributing it in small pieces to the forest floor. Removal of the mid-story influences several fire behavior properties connected to air-flow such that fires are predicted to be hotter when occurring during more moist fuel conditions (Banerjee et al. 2020). The stands for this study were masticated five years prior to the burns and were also commercially thinned three years prior, creating relatively open mid-story conditions when they were burned. Future studies that explore mid-story removal as a pre-prescribed fire treatment, as well as studies that vary time since mastication prior to burning, could contribute to understanding effective pyrosilviculture approaches in plantations. With mastication, the removal of the mid-story translates directly to an increase in fine fuels that can increase fireline intensity during dry fuel conditions (Stephens and Moghaddas 2005). Unlike fall burns, where the weather and fuel conditions enter the prescription from the high (i.e. hot/dry) end of the range and practitioners are waiting for predicted effects to be less severe (Table 1), winter burns enter the prescription from the low (i.e. cool/moist) end of the range. This means that practitioners are waiting for conditions to improve such that the minimal level of effective fuel consumption becomes possible. This suggests that structures that increase expected fire intensity may be beneficial for winter burning because prescription windows open up earlier and provide more opportunities to burn. Hence while burning recently-masticated (<1 yr) material in the fall may be risky in terms of canopy damage (Kobziar et al. 2009, Stephens and Moghaddas 2005), it may provide amenable conditions in the winter. Mastication clearly has not always led to hot burns during the fall, as Table 3 demonstrates that it can been a prelude to both high (e.g. York et al. In Press a) and low levels of mortality (e.g. Bellows et al. 2016). While our study reports relatively low levels of damage and nearly non-existent mortality, burning in the winter can still cause high canopy damage when conditions are dry (Reiner et al 2012). Another potential pyrosilvicultural treatment in plantations is thinning that removes whole trees. As with mastication, this is likely to influence fire behavior such that intensity increases during winter burns (Banerjee et al. 2020) thus increasing surface fuels consumption when burning. The burns that followed commercial thins (this study, Busse and Girrard 2020, York et al. In Press a) all had relatively low levels of post-burn mortality. One explanation for this is that thinning removed smaller trees that were more vulnerable to fire-related mortality, leaving behind vigorous trees with thicker bark and higher crown bases. In this study, whole trees were removed and processed for sawlogs, but other options such as removing whole trees for biomass energy production or yarding and then burning at landings would have a similar effect on stand structure. While further studies may help reveal patterns, our expectation following this and other studies to date is that thinning combined with whole tree yarding and followed by winter burning may be an especially effective approach for reducing and maintaining surface fuels in plantations while avoiding high levels of damage and mortality. Importantly, revenue from commercial thinning can cover the operational costs of prescribed fires, especially low-cost winter burns, depending on market conditions and product type (Hartsough et al. 2008). Plot-level forest structural factors of prescribed fire effects Plot-level analyses of the factors of fuel consumption did not find clear correlations with plantation structural attributes. Although the plots in this study were somewhat variable in their structure (Fig. 1), this represents only a small range of conditions that can occur in mixed conifer plantations. Stand age, site productivity, species composition, and treatment history range widely in the western US. Future work could widen the range of experimental structural conditions more broadly. For example, burning plantations with different levels of mid-story removal (Banerjee et al. 2020) or testing different lengths of time between masticating and burning, could help to better understand how to prepare plantations for burning. If generally low consumption of duff is typical of winter burns, as was the case here, it may be difficult to characterize duff consumption as a factor of certain structural characteristics. Instead, the implication with respect to duff consumption during winter burns is simply that not very much of it occurs. As noted by our soil moisture measurements, winter burns occur during moist soil conditions, likely contributing to the general lack of duff combustion. Unlike generally drier fall burns in mature forests (Levine et al. 2020), these winter burns were modest at best in consuming duff. When compared to burning prior to precipitation in the fall, burning after even a small amount of precipitation can lead to considerable variation in amounts of duff consumption (Hille and Stephens 2005). Compared to litter and fine fuels, duff fuels do not appear to become receptive to consumption during these winter burning windows. While duff was recalcitrant in this regard, we also did not find them to be obstacles to consumption of fine fuels. An important context of duff consumption in plantations is that duff load is typically not very high to begin with due to earlier site preparation practices and the lack of time for it to develop. Instead of actively decreasing duff loads, therefore, winter burns may function more to limit its future development by maintaining low litter loads which are the supply of future duff loads. Crown damage was generally low across plots, although some did experience amounts of scorch (i.e. >50%) that may be considered undesirable. While plots with more ponderosa pine had more crown scorch, this species has also been observed to be capable of surviving extensive scorch well compared to other species (Harrington 1993). Given that scorch damage occurs low on the crown, where branches are less productive in terms of contributing to growth (Sprugel et al. 1991), it may be that these levels of scorch do not negatively influence long-term growth. If crown scorch is undesirable for other reasons such as aesthetic quality, then managing for fewer ponderosa pine would seem to be a logical implication. This, however, would likely be trading scorch for mortality since ponderosa pine in young stands are relatively good survivors of scorch (York et al. In Press a). Lowering shrub cover in locations with particularly high amounts of shrubs could be a reasonable way to reduce crown scorch. Our results, however, suggest that only the lowest of management tolerance for crown scorch may justify reducing shrubs prior to burning. It is more likely that managers with some tolerance for scorch would view our results as a way to cost-effectively reduce shrub cover with fire, as opposed to using costly mechanical or chemical treatments before applying fire. Monitoring the rate and extent of shrub recovery, as well as describing shrub dynamics during follow-up burns, will be a relevant topic to explore in the future. Winter burn windows occur but are sporadic and fleeting Periods during the winter at this location when both weather and air quality conditions allow for feasible burns do not occur during long windows. Rather, they occur during “winks” of time that can occur in any month on any given year. Developing weather prescription ranges during the winter period is complicated by the fact that by the time fuel realistically dries out enough to conduct a burn, a winter storm will soon reset fuel into saturated conditions. While consideration for local weather and fuel conditions is always a dominant factor in deciding when to burn (Biswell 1989), it may be even more important for winter burns. At higher elevations or latitudes than our site, more drying time would be needed given higher amounts of snowfall per storm. At rain-dominated lower elevations or latitudes, less time would be needed. In our experience at this site, a minimum of ten days of drying time is needed following winter storms. This varies considerably depending on the details of the fuel structure (e.g. ponderosa pine needles versus black oak leaves) and topography (e.g. south versus west facing slopes), but it has been a dependable rule of thumb. The fleeting nature of winter burn windows requires a specifically-designed approach to preparations for burning. Most importantly, it requires being nimble. If multiple days of in-prescription conditions are needed to justify burning, then the opportunity has likely already passed. For large agencies focused on increasing temporary staffing during summer wildfire seasons, this is a challenge. However, large landowners and agencies have the advantage of being able to hedge bets operationally across a wide range of conditions that occur in the winter. For example, preparing pile burn projects at higher elevations can keep personnel active until lower elevation sites become available for broadcast burning. Regardless of the exact approach, winter burning means having personnel hired and available throughout the winter period. For private landowners with smaller ownership sizes, winter burning may come easier than for large programs, or it may be the only feasible option to burn at all. In California, permitting during the fall period can restrict burning, even when conditions are ideal (York et al. 2020). The removal or lessening of the permitting constraint during the winter period is thus a significant advantage. Importantly, our burns demonstrate a lower complexity of winter burns, as each 8 ha stand was burned with a four person crew. Striplin et al. (2020) also considered burn window opportunities in the Sierra Nevada, and documented that winter burn windows existed in conifer forests that were drier but also colder and with precipitation much more dominated by snow compared to our site. Whereas Striplin et al. (2020) found approximately half of winter days to be in prescription in terms of fuel and weather conditions, we found it to be much lower (7%). There are several reasons for this difference. First, our study used a weather station that was located near the burn locations and thus avoided low humidity readings that can come from exposure to dry winds on ridge tops. Second, unlike in Striplin et al. (2020) which did not apply any minimum drying out time, this study applied a minimum period of 10 days following the last precipitation. Finally, this study applied a prescription that was specific to winter burns and was not a general prescription applied year-round. Whereas Striplin et al. (2020) labeled a burn day as one where any hour during the day had humidity less than 50% and 10-hour moisture less than 20%, we required burn days to have at least three hours at less than 45% relative humidity in order to be realistic in terms of burn operation efficiency. We did not include dropping below a maximum 10-hour fuel moisture, which has been observed to be underestimated when using RAWS stations (Estes et al. 2012). The experience-derived winter burning prescription developed at BFRS requires on-site 10-hour fuels to be less than 12%, considerably lower than the 20% maximum used at the higher elevation site. We point out these differences to underscore the importance of deriving specific winter burn prescriptions at a local level. Burns at BFRS have been allowed on all fair and most marginal burn days. Additionally, exceptions have been given to conduct small winter burns on no-burn days when a justification is provided. This experience explains our different methodology in labelling days as burn versus no-burn days compared to Striplin et al. (2020), who did not include any marginal or even fair burn days as acceptable air quality burn days. Despite our inclusive approach to labelling days as acceptable in terms of air quality, the number of days on which air quality restricted burning was surprisingly high. Although it is often listed as a constraint (e.g. Quinn-Davidson and Varner 2012), air quality has recently been described by practitioners across the western United States as not being a major impediment except in some locales (Schultz et al. 2019). Our analysis suggests that air quality is a significant constraint in this region during the winter period. That air quality was more of a constraint toward the end of the winter period as it transitions to the spring season does not conform to the expectation that the fall / early winter season has atmospheric conditions that are less conducive to burning (Cahill et al. 1996). The net effect was a smoothing-out on the frequency of burning opportunities across the winter period (Fig. 3). Conclusions And Management Implications The potential logistic, economic, and risk-avoidance advantages of winter burning should be appealing for managers who want to both insulate economic investments and also protect future ecosystem services gains that can come from developing mixed-species plantations. Until results from winter burning trials are evaluated, however, it remains a concept with appeal but lacking in proof. Here, we contribute evidence that winter burning can be feasible and effective for common prescribed burn objectives, albeit with limitations. As with numerous other forest types throughout the western United States, there is an urgency to increase the use of prescribed fire in mixed conifer forests. Yet actual implementation of broadcast burning lags far behind desired goals. In the 2016–2017 fiscal year, the most recent period for which information is available, state agency burning occurred on less than 5,700 hectares across the state (Brown et al. 2018 ). This could be a vast overestimate of broadcast burning on forestlands, since it includes both pile burning and grassland burning. Winter burning, especially in young stands managed with pyrosilvicultural treatments prior to burning, may be an overlooked opportunity to lower wildfire severity in structures that are otherwise particularly vulnerable. In the context of young stands managed for timber with even-aged management, prescribed fires could be used multiple times in the lifespan of a managed stand. At our study site, long-term tracking of mean annual volume increments suggest that approximately 100 years is an optimal rotation age in terms of maximizing yield (BFRS, unpublished data). In fully-stocked plantations with increasing leaf areas, fuel loads will recover quickly following prescribed burns, suggesting a frequent prescribed burning frequency in order to maintain low fuel loads (York et al. In Press b). Each subsequent burn ostensibly will have less canopy damage, as trees get larger and lower portions of crowns that are scorched are no longer present. One approach to burn timing may be to conduct burns following commercial thins, thus taking advantage of lower canopy densities to dry fuel and also to consume activity fuel associated with harvesting. This would likely translate to three or four burns prior to rotation age, depending on the type of thinning regime that is applied. On forestlands not managed with a timber objective, avoiding or staying below certain levels of mortality may not be as relevant. Instead fire damage and tree mortality may be desirable if it is relied upon as the primary density management tool. However, even with a more broad range of acceptable outcomes following prescribed fires, federal mixed conifer lands also lag far behind compared to desired use of fire (North et al. 2012 ). For landowners who place the reintroduction of fire as a high priority, the winter period may represent an overlooked opportunity to advance overall burn program objectives. Abbreviations AR: Autoregresssive BFRS: Blodgett Forest Research Station CARB: California Air Resources Board LASSO: Least Absolute Shrinkage and Selection Operator LOOVC: Leave One Out Cross Validation PCVS: Percent Crown Volume Scorch RAWS: Remote Automated Weather Station US: United States USDA: United States Department of Agriculture Declarations Ethics approval and consent to participate This study did not involve the use of any animal or human data or tissue. Consent for publication This study did not contain data from any individual person. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors acknowledge funding support from the SPARx-Cal (Smart Practices and Architectures for Rx fires) project under the University of California Laboratory Fees Research Program funded by the UC Office of the President (UCOP), grant ID LFR-20-653572, entitled ’Transforming Prescribed Fire Practices for California.” Authors' contributions RY was the burn boss, supervised data collection, conceptualized the study, and wrote the manuscript JL conducted analysis and edited the manuscript KR collected data, conducted analysis and edited the manuscript JR helped conceptualize the study and edited the manuscript Acknowledgements We acknowledge A. Roughton and H. Noble for assistance with burning. B. Collins provided a helpful review of the manuscript. References Agee, JK. 1993. Fire Ecology of Pacific Northwest Forests. Island Press, Covelo, CA, USA, p. 493. Baldwin, H, W Stewart, S Sommarstrom. In Press. Reforesting California. In: Stewart, W. (ed). Reforestation Practices for Conifers in California. Davis, CA. University of California Agriculture and Natural Resources. https://www.fvmc.org/ Banerjee, T, W Heilman, S Goodrick, JK Heirs, and R Linn. 2020. Effects of canopy midstory management and fuel moisture on wildfire behavior. Nature Scientific Reports 10: 17312 Bellows, RS, AC Thomson, KJ Helmstedt, RA York, and MD Potts. 2016. Damage and mortality patterns in young mixed conifer plantations following prescribed fires in the Sierra Nevada, California. Forest Ecology and Management 376: 193-204 Biswell, HH 1989. Prescribed Burning In California Wildlands Vegetation Management. University of California Press, Berkeley, CA, USA pp. 44 Brown, JK 1974. Handbook for inventorying downed woody material. USDA Forest Service, General Technical Report INT-16. Brown, EG, J Laird, and K Pimlott. 2018. California’s Forest and Rangelands: 2017 Assessment. http://frap.fire.ca.gov/assessment2017 Busse, M, and R Gerrard. 2020. Thinning and burning effects on long-term litter accumulation and function in young ponderosa pine forests. Forest Science 66(6): 761-769 Cahill, TA, JJ Carroll, D Campbell, and TE Gill. 1996. Air quality. In: Sierra Nevada Ecosystem Project, Final Report to Congress, vol. II, Assessments and Scientific Basis for Management Options. University of California Davis, Centers for Water and Wildland Resources, pp. 1227–1260. Estes, BL, EE Knapp, CN Skinner, and FCC Uzoh. 2012. Seasonal variation in surface fuel moisture between unthinned and thinned mixed conifer forest, northern California, USA. International Journal of Wildland Fire 21: 428-435 Han, S-K., H-S Han, DS Page-Dumroese, and LR Johnson. 2009. Soil compaction associated with cut-to-length and whole-tree harvesting of a coniferous forest. Canadian Journal of Forest Research 39: 976-989. Harrington, MG 1993. Predicting Pinus ponderosa mortality from dormant season and growing season fire injury. International Journal of Wildland Fire 3(2): 65-72 Hartsough, BR, S Abrams, RJ Barbour, ES Drews, JD McIver, JJ Moghaddas, and SL Stephens. 2008. The economics of alternative fuel reduction treatments in western United States dry forests: Financial and policy implications from the National Fire and Fire Surrogate Study. Forest Policy and Economics , 10 (6), 344–354. https://doi.org/10.1016/J.FORPOL.2008.02.001 Hille, MG and SL Stephens. 2005. Mixed conifer forest duff consumption during prescribed fires: Tree crown impacts. Forest Science 51(5): 417-424 Holmes, E, E Ward, M Scheuerell, and K Willis. 2020. MARSS: Multivariate autoregressive state-space modeling. R package version 3.11.3 https://cran.r-project.org/web/packages/MARSS/MARSS.pdf Huang, X., AD Hall, and N Berg. 2018. Anthropogenic warming impacts on today’s Sierra Nevada snowpack and flood risk. Geophysical Research Letters 45(12): 6215-6222 Keeley, JE and HD Safford. 2016. Fire as an Ecosystem Process. In: Mooney, H. and E. Zavaleta (eds). Ecosystems of California. Oakland, CA, USA. University of California Press pp. 27-45. Kobziar, LN, JR McBride, and SL Stephens. 2009. The efficacy of fire and fuels reduction treatments in a Sierra Nevada pine plantation. International Journal of Wildland Fire 20(8): 932-945 Knapp, EE, JE Keeley, EA Ballenger, and TJ Brennan. 2005. Fuel reduction and coarse woody debris dynamics with early season and late season prescribed fire in a Sierra Nevada mixed conifer forest. Forest Ecology and Management 208: 383-397 Knapp, EE and JE Keeley. 2006. Heterogeneity in fire severity within early season and late season prescribed burns in a mixed-conifer forest. International Journal of Wildland Fire 15: 37-45 Knapp, EE, CN Skinner, MP North, and BE Estes. 2013. Long-term overstory and understory change following logging and fire exclusion in a Sierra Nevada mixed-conifer forest. Forest Ecology and Management 310: 903-914. Kobziar, LN, JR McBride, and S Stephens. 2009. The efficacy of fire and fuels reduction treatments in a Sierra Nevada pine plantation. International Journal of Wildland Fire 18: 791-801 Levine, JI, BC Collins, RA York, DE Foster, DL Fry, and SL Stephens. 2020. Forest stand and site characteristics influence fuel consumption in repeat prescribed burns. International Journal of Wildland Fire 29(2): 148-159 Lyons-Tinsley, C, and DL Peterson. 2012. Surface fuel treatments in young, regenerating stands affect wildfire severity in a mixed conifer forest, eastside Cascade Range, Washington, USA. Forest Ecology and Management , 270 , 117–125. https://doi.org/10.1016/j.foreco.2011.04.016 McLeod, AI. 2011. Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.2. https://CRAN.R-project.org/package=Kendall . Mitchell, RJ, JK Heirs, J O’Brien, and G Starr. 2009. Ecological forestry in the southeast: Understanding ecology of fuels. Journal of Forestry December: 391-397 North, M, BM Collins, and S Stephens. 2012. Using fire to increase the scale, benefits, and future maintenance of fuels treatments. Journal of Forestry 110(7): 392-401 North, M, Werner, CM, Safford, HD, Walsh, D, Tompkins, RE, Coppoletta, M, Latimer, AM, … Estes, BL 2019. Tamm Review: Reforestation for resilience in dry western U.S. forests. Forest Ecology and Management , 432 : 209–224. https://doi.org/10.1016/j.foreco.2018.09.007 Reiner, AL, NM Valliant, and SN Dailey. 2012. Mastication and prescribed fire influences on tree mortality and predicted fire behavior in ponderosa pine. Western Journal of Applied Forestry 27(1): 36-41 Schultz, CA, SM McCaffrey, and HR Huber-Stearns. 2019. Policy barriers and opportunities for prescribed fire application in the western United States. International Journal of Wildland Fire 28: 874-884 Seymour RS, AS White, and PG deMaynadier. 2002. Natural disturbance regimes in northeastern North America – evaluating silvicultural systems using natural scales and frequencies. Forest Ecology and Management 155 : 357-367. Sprugel, DG, TM Hinckley, and W Schaap 1991. The theory and practice of branch autonomy. Annu. Rev. Ecol. Syst. 22 (1): 309–334. doi:10.1146/annurev.es.22.110191.001521. Starrs, CF, V Butsic, C Stephens, and W Stewart. 2018. The impact of ownership, firefighting, and reserve status on fire probability in California. Environmental Research Letters 13: 1-11 Stephens, CW and RA York. 2017. An evaluation of stand age as a factor of mastication efficiency and effectiveness in the Central Sierra Nevada, California. Northwest Science 91(4): 389-398. https://doi.org/10.3955/046.091.0408 Stephens, SL and JM Moghaddas. 2005. Experimental fuel treatment impacts on forest structure, potential fire behavior, and predicted tree mortality in a California mixed conifer forest. Forest Ecology and Management 215: 21-36 Stephens, SL, RC Heald, and JJ Moghaddas. 2005. Silvicultural and reserve impacts on potential fire behavior and forest conservation: Twenty-five years of experience from Sierra Nevada mixed conifer forests. Biological Conservation 125: 369-379 Stephens, SL, BM Collins, and G Roller. 2012. Fuel treatment longevity in a Sierra Nevada mixed conifer forest. Forest Ecology and Management 285: 204-212 Stewart, W, R Standiford, S Kocher, J Webster. In Press. In: Stewart, WC (ed) Reforestation Practices for Conifers in California. Davis, CA, University of California Agriculture and Natural Resources. https://www.fvmc.org/ Striplin, R, SA McAfee, HD Safford, and MJ Papa. 2020. Retrospective analysis of burn windows for fire and fuels management: an example from the Lake Tahoe Basin, California, USA. Fire Ecology 16:13 Thompson, J. R., Spies, T. A., & Olsen, K. A. (2011). Canopy damage to conifer plantations within a large mixed-severity wildfire varies with stand age. Forest Ecology and Management , 262 , 355–360. https://doi.org/10.1016/j.foreco.2011.04.001 Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (methodological). Wiley. 58 (1): 267–88. USDA Forest Service, Forest Inventory and Analysis Program, Mon Mar 01 17:58:28 GMT 2021. Forest Inventory EVALIDator web-application Version 1.8.0.01. St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern Research Station. [Available only on internet: http://apps.fs.usda.gov/Evalidator/evalidator.jsp ] van Wagtendonk JW, Benedict JM, Sydoriak WM (1998) Fuel bed characteristics of Sierra Nevada conifers. Western Journal of Applied Forestry 13 , 73–84. Westfall, JA and CW Woodall. 2007. Measurement repeatability of a large-scale inventory of forest fuels. Forest Ecology and Management 253: 171-176 Wooley, T, KC Shaw, LM Ganio, and S Fitzgerald. 2012. A review of logistic regression models used to predict post-fire tree mortality of western North American conifers. International Journal of Wildland Fire 21(1): 1-35 York, RA, H Noble, L Quinn-Davidson, and JJ Battles. In Press. Pyrosilviculture: Combining prescribed fire with gap-based silviculture in mixed-conifer forests of the Sierra Nevada. Canadian Journal of Forest Research. York, RA, J Levine, D Foster, S Stephens, and BM Collins. In Press. Silviculture can facilitate repeat prescribed burn programs. California Agriculture York, RA, A Roughton, R Tompkins, and S Kocher. 2020. Burn permits need to facilitate – not prevent- “good fire” in California. California Agriculture 74(2): 62-66 Zald, H, and C Dunn. 2018. Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape. Ecological Applications 28 (4): 1068-1080. Zhang, J, KA Finley, and EE Knapp. 2020. Resilience of a ponderosa pine plantation to a backfiring operation during a mid-summer wildfire. International Journal of Wildland Fire 28(12): 981-992 Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2021 Reviewers invited by journal 29 Apr, 2021 Editor assigned by journal 17 Apr, 2021 First submitted to journal 13 Apr, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-423745","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":24475233,"identity":"949e747b-1876-435a-8dee-24ba3fc33abb","order_by":0,"name":"Robert York","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3NoQrCQBzH8f8VLacznjDmK0wWVnyYjQOTWQyCWi7tAW4gPoMryzcOZpnmhYWtLLtmUBBNJm9rgvdtP/h/+APodD8a2q4EAAYk3lO0ItmbQAeyY12Iy2nahIfCcgMpRMPAGubed2LmcxpGce2YZ+YlIQNnrCKELBxUxdLnBrblgIF/bEf2csMN4yofDDbtSLSVHhkEIBEDz1YSXFPE03rKcWonwYVMw6xUkD6VKFgXE4JpVd6Ws8nwpPgCo88D1COK81eG+Fz3FkKn0+n+rico4E0dOFNpgwAAAABJRU5ErkJggg==","orcid":"","institution":"UC Berkeley: University of California Berkeley","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"York","suffix":""},{"id":24475234,"identity":"aeccb343-2a4c-4763-bdd4-c5fb5ea9b75b","order_by":1,"name":"Jacob Levine","email":"","orcid":"","institution":"Princeton University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Levine","suffix":""},{"id":24475235,"identity":"abf430df-1032-44bb-a7c5-80374e65c0bf","order_by":2,"name":"Kane Russell","email":"","orcid":"","institution":"UC Berkeley","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kane","middleName":"","lastName":"Russell","suffix":""},{"id":24475236,"identity":"bad8c886-8736-45b8-bce6-122d396aa470","order_by":3,"name":"Joseph Restaino","email":"","orcid":"","institution":"California Department of Forestry and Fire Protection","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Restaino","suffix":""}],"badges":[],"createdAt":"2021-04-14 19:40:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-423745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-423745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":8756081,"identity":"e3f1dafd-4b41-4022-9d4f-cc125c50d8bc","added_by":"auto","created_at":"2021-05-04 14:28:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10714,"visible":true,"origin":"","legend":"Points in panels A-E show the observed relationships between A) pre-fire fuel load and fuel consumption, B) canopy cover and fuel consumption, C) P. ponderosa basal area and PCVS, D) height to crown base and PCVS, and E) shrub cover and PCVS. Solid lines indicate the predicted values from the best-fit models for each response variable.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-423745/v1/e2df286f4a8dd8572d454095.png"},{"id":8755768,"identity":"9e69b64f-f45c-4c47-909a-72d418f652be","added_by":"auto","created_at":"2021-05-04 14:25:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11367,"visible":true,"origin":"","legend":"Number of days in prescription (gray) and, of those days, CARB allowable-burn days (dark gray) per winter season at BFRS. Results of Mann-Kendall trend test are shown at the top left. *CARB data available from 1998-2020 only.","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-423745/v1/711664aa917b532e3ea4e264.png"},{"id":8756082,"identity":"cd1d4a37-40ef-470b-91a4-70fe89cbb70b","added_by":"auto","created_at":"2021-05-04 14:28:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":15801,"visible":true,"origin":"","legend":"Number of days in prescription (gray) and CARB burn day (dark gray) by month and winter season at BFRS. *CARB data available from 1998-2020 only.","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-423745/v1/99ce4c6a5bed296d2a41aaaa.png"},{"id":8756310,"identity":"75a5415d-fa54-45a2-81d8-ed75efa5cbec","added_by":"auto","created_at":"2021-05-04 14:31:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7897,"visible":true,"origin":"","legend":"Average occurrence of burn window lengths per winter season at BFRS, 1994-2020.","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-423745/v1/76ac68d26f77d232778e7a5b.png"},{"id":13690472,"identity":"afe0e5db-0c08-430e-b9b1-89b9fd51416b","added_by":"auto","created_at":"2021-09-17 12:33:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":480227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-423745/v1/c0a14dcf-6e69-493f-8b71-4924b0a3b592.pdf"}],"financialInterests":"","formattedTitle":"Opportunities for winter prescribed burning in mixed conifer plantations of the Sierra Nevada","fulltext":[{"header":"Background","content":" \u003cp\u003eIn California, approximately 1\u0026nbsp;million hectares of mixed conifer forestland is less than 50 years old, with more than half of that actively managed in plantations (USDA 2021). As the extent and severity of wildfires increase, the degree to which mixed conifer plantations are exposed to wildfire will also increase (North et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As such, the development and maintenance of plantation stand structures that can withstand wildfires so that basic management objectives are still met is of increasing importance.\u003c/p\u003e \u003cp\u003eRelated lines of evidence from modeling, structural attributes, and empirical data suggest that fire severity may be higher in western United States conifer plantations compared to mature forests. Wildfire behavior modeling predicts exceptionally high fireline intensities and rates of spread in plantations, resulting in the prediction of primarily crown fire behavior during high fire hazard weather conditions (Stephens and Moggadas 2005). The physical structures of many plantations, which are a result of past silvicultural treatments, are inherently prone to high severity fires. Traditionally, plantations have been characterized by homogenous canopies, moderate or high shrub components, low height to crown bases, and high densities of small trees \u0026mdash; structural components that have long been understood to make them vulnerable to fire (e.g. Kobziar et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). More recently, empirical evidence from post-wildfire assessments in productive mixed conifer forests have found increased severities in plantations compared to mature forests (Thompson et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Zald and Dunn \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Rather than reducing even-aged management as a response to these results, most suggest that plantations be managed in specific ways to mitigate fire severity. In other words, it is not the silvicultural practice of even-aged management that inherently makes plantations vulnerable, but the lack of cultural practices designed to make them less vulnerable as they develop. These include site preparation to reduce early fuel loads (Lyons-Tinsley and Peterson \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), altering planting density (North et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and fuel treatments during early stand development (Zald and Dunn \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). High-severity wildfires have created large areas of early seral conditions, causing multiple landowner types to consider options for young stand management regardless of whether or not they previously practiced even-aged regeneration.\u003c/p\u003e \u003cp\u003eFollowing wildfires or even-aged regeneration harvests, landowners who want to quickly establish tree dominated structures have decades of timber-focused reforestation research to draw upon (Baldwin et al. In Press). By the time a mature forest structure develops, numerous treatments may have occurred. Treatment costs can add up to several thousand dollars per hectare, with future revenue possible only after several decades (Stewart et al. In Press). This investment is at risk of total loss if a wildfire occurs before trees are sufficiently large to develop fire-resistant properties, or if surface fuels are not maintained at low levels throughout stand development. The risk of loss occurs regardless of whether investments were made for timber or any other objective that prioritizes the rapid recruitment of large, vigorous trees. Thinning via mechanical tee removal is the primary treatment used to increase tree vigor in young stands, but it does not address concerns with surface fuel accumulations. In fact, it can increase rather than decrease surface fuels (Hartsough et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Stephens and York \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Whole tree yarding of biomass helps reduce activity fuel increases from thinning treatments (Han et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), yet it does not counter surface fuel deposition inherent to developing stands where leaf area accumulates rapidly. Attention to the long-term feedbacks between treatments and expected fire behavior is central to the \u0026ldquo;ecology of fuels\u0026rdquo; concept that has been applied in southeastern US forests (Mitchell et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) but is less developed in in dry western US forests.\u003c/p\u003e \u003cp\u003eLow intensity prescribed fire is the lowest-cost treatment available for reducing surface fuels in both mature (Hartsough et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and young forests (Stewart et al. In Press). Prescribed fire may enhance what is thought to be resilience in plantations, but neither the feasibility nor effects are well-studied (North et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The predominant strategy for managing for timber growth is to avoid fire, intentional or not, and hope to achieve a large-tree structure before the next wildfire. This assumption is arguably no longer valid, as the probability of a high severity fire occurring in a developing stand has increased across all ownership types despite increasing fire suppression efforts (Starrs et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, the use of fire in plantations and the protection of long-term economic investments are no longer mutually exclusive. Yet, any benefit to prescribed fire in plantations will inevitably be weighed against negative impacts to tree growth and survival. Despite the benefits of prescribed fire, its operational bluntness is in stark contrast to the high precision of other treatments to which managers are accustomed. Using prescribed fire during dry conditions in the early fall (York et al. In Press a) or when terminal buds of trees are vulnerable in the spring during active growth (Agee \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) may result in levels of fire-related damage or mortality that are unacceptable, thus causing managers to discontinue the use of prescribed fire.\u003c/p\u003e \u003cp\u003eThe most obvious way to avoid unwanted outcomes is to burn during weather and fuel conditions that avoid unacceptable canopy damage. Winter burning is not a common practice and no literature describes its role in either mature or young stands. This may be because it is assumed that conditions are simply too wet or under cover of snow (Knapp et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Anthropogenic snowfall reductions in the mixed conifer region, while concerning given ecosystem service and societal impacts (Huang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), may provide emerging opportunities for winter season prescribed fires, which in turn may help increase fuel treatment work more broadly. Winter burning is not necessarily new, as Indigenous burning likely included winter burning at small spatial scales in between winter storms (Biswell \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). A reason to be cautious with winter burns is the risk of applying silvicultural treatments that are outside of the historical disturbance regime (e.g. Seymour et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). To align strictly with the seasonality of the historic fire regime, prescribed burns would be applied in the summer or early fall (Keeley and Safford \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The counterargument is that studies of spring versus fall burn effects on ecological variables mainly find either small or no differences (e.g. Knapp and Keeley \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), thus providing an ecological argument for conducting spring burns. Because vegetation is typically dormant, winter burn effects may not be distinctly negative. Independent of climate trends or ecological objectives, the logistical advantages to winter burning are significant. They include: elevated live fuel moisture allows managers to consider burning during wider ranges of wind and relative humidity; because of lower probabilities of escape, fewer personnel are needed to conduct burns; there is less need for patrolling following burns given the frequency of storms to provide mop-up functions; and burn permits are much easier to obtain or may not be needed at all in some locations (York et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur three study objectives are aimed at better understanding the potential for winter burning in mixed conifer forests, especially in plantations. First, we demonstrate the potential for winter burning by describing the effectiveness and conditions under which we conducted broadcast burns during the winter season. Second, we analyze pre- and post-burn forest structure data in order to assess which plot-level variables were the best predictors of what are most likely to be considered desirable prescribed fire effects. The purpose is to design pyrosilvicultural activities (\u003cem\u003esensu\u003c/em\u003e York et al. In Press a) during young stand development, that facilitate successful implementation of winter burns. Finally, we evaluate the frequency and duration of weather windows that have occurred during the last 20 years at the study\u0026rsquo;s burn site in order to recommend logistical strategies for winter burns.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLocation and forest structural context of prescribed burns\u003c/h2\u003e \u003cp\u003eBurns were completed in mixed-species plantations at Blodgett Forest Research Station (BFRS) in the mixed conifer forest on the western slopes of the central Sierra Nevada range, which has a Mediterranean climate. From 1994 to 2020, the mean total precipitation during the wet season (further defined below), was 145 cm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Mean daily temperature was 6.2, mean daily low was 2.6, and mean daily high was 10.9\u0026deg;C. A prescribed burn program at BFRS has been active for the past 20 years, during which burns occurred across a variety of seasons, age classes, and silvicultural systems. Recently, winter burning activity has increased. This is in part due to difficulties obtaining permits to burn during fall and spring windows across the Sierra Nevada region.\u003c/p\u003e \u003cp\u003eThe three stands used for this study are 8 ha in size, spanning an elevation range of 1283 to 1347 m. The stands were regenerated with clearcuts, including pile-and-burn site preparation, planting, and early vegetation control with herbicide. These are common practices for this forest type when the objective is to promote rapid tree growth after a stand replacing disturbance (Stewart et al. In Press). Stands were planted with six native species: \u003cem\u003eAbies concolor, Calocedrus decurrens, Pinus lambertiana, Pinus ponderosa, Psuedotsuga menziesii, and Sequoiadendron giganteum\u003c/em\u003e. Although seedlings were planted in equal proportions among these species, survival was much higher for \u003cem\u003eP. ponderosa and S. giganteum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTwo of the stands were 36 and one was 35 years old when they were burned. All three stands were masticated five years prior and commercially thinned with mechanized felling and whole tree yarding three years prior to burning. Unthinned stands with dense canopies of this age are typically not burned at BFRS in the winter because surface fuels rarely dry out enough to carry fire. The objective of the thin was to reduce density to a target basal area of approximately 25 m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, while promoting increased tree species diversity and equidistant spacing among the largest available canopy trees. At the time of burning, the stands were typical of a plantation structure that would generally be seen as desirable for growth and yield of timber and carbon sequestration. Average tree dbh was 33 cm when considering all trees greater than 11.4 cm dbh.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003ePermanent 0.04 ha circular plots on a 10 m grid were established prior to burns. Within plots, all trees greater than 11.4 cm dbh were tagged. Trees were identified by species and measured for dbh. Percent crown volume scorch (PCVS) for all tagged trees was visually estimated immediately following burns. PCVS is a widely used measure of crown damage (Wooley et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Tree status (alive or dead) was surveyed 1 year following the burns to assess post burn mortality. Pre- and post-burn shrub cover within the 0.04 ha plots was visually estimated to the nearest 5%. Post-fire, the percent of each plot covered with ash was also estimated with the same method. To improve precision, shrub cover by species and ash cover was estimated within each quadrant of the plot, and then averaged across the quadrants to estimate plot-level cover. Only woody shrubs were considered for analysis. The three most dominant species were \u003cem\u003eCeanothus integerrimus, Ceanothus cordulatus\u003c/em\u003e, and \u003cem\u003eArctostaphylos patula.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSurface and ground fuels were sampled before and after the burns using the line-intercept method (Brown \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Pre-fire measurements of fuel occurred on the morning immediately prior to ignitions. Post-fire measurements occurred 4 months following the burns, coincident with the beginning of the wildfire season in the Sierra Nevada. Two transects per plot were established, one along contour and the second one at +\u0026thinsp;60 degrees azimuth from the first. Post-fire transects were measured along the same azimuths. 1-hour (0\u0026ndash;0.64 cm) and 10-hour (0.64\u0026ndash;2.54 cm) fuel classes were tallied between 0 and 2 m along each transect. 100-hour (2.54\u0026ndash;7.62 cm) fuels were tallied between 0 and 3 m, and 1000-hour (\u0026gt;\u0026thinsp;7.62 cm) fuels were tallied between 0 and 11.3 m. Duff, litter, and total fuel depths (cm) were measured at two locations per transect. Fuel loads were calculated with species-specific coefficients developed for Sierra Nevada forests using the Rfuels package in R version 3.6.3 (van Wagtendonk et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Foster et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Coefficients were weighted for each transect based on the relative basal area fractions of each species derived from the specific plot\u0026rsquo;s tree measurements. The two transects were averaged to obtain plot-level values. Fuel consumption was expressed proportionally, as the difference in fuel load between the post-fire and pre-fire measurements, divided by the pre-fire measurement.\u003c/p\u003e \u003cp\u003eBFRS has a permanent weather station that has collected continuous data at 15-minute intervals since 1994. The station is centrally located within a 0.1 ha gap surrounded by a tall forest structure. We defined the winter burning season based on the combination of weather and logistical factors that realistically define burning opportunities. The winter season starts following a significant precipitation event, defined as \u0026ge;\u0026thinsp;2.54 cm of precipitation in a 24-hour period. This amount of precipitation in this short amount of time typically ends what is considered to be the fall burning window (i.e. a \u0026ldquo;season-ending\u0026rdquo; event). We consider the end of the winter burning period to be May 1st, which is typically prior to the onset of seasonal growth by trees. Importantly, this day coincides with the typical onset of when burn permits are required, thus significantly changing the regulatory context of burning for landowners. Implicit in this timing is that conditions are generally low-risk prior to this date. Each day within this burn window was considered in prescription if relative humidity was less than 45% for at least three consecutive hours and no precipitation had fallen within the previous 10 days. This prescription is based on weather conditions that have occurred during successful winter burns in thinned mature stands conducted at BFRS over the prior decade. Wind speed was initially included in the prescription, but was never a limiting factor and so was excluded. We considered using a standard prescribed burn prescription with acceptable weather ranges and then finding days within which conditions were in prescription. However this overestimates, possibly dramatically depending on elevation, the actual number of feasible burn days. For example, low humidity and high temperatures can dry out weather station fuel sticks shortly after a snow storm, suggesting that it is a burn day when in fact there is still snow on the ground making it impossible to burn. The use of RAWS stations is also problematic for reconstructing winter burn windows, because stations are typically set up on ridge tops or open fields that are not representative of structures where understory burns would take place. Our standards for a minimum number of consecutive dry days to dry out fuels followed by at least one day with low humidity have proven to be consistent indicators of winter burn feasibility over the past decade of annual burning at BFRS.\u003c/p\u003e \u003cp\u003eAir quality conditions limit prescribed burns although practitioners did not cite it as a major barrier in the western United States (Schulz et al. 2019). The California Air Resources Board (CARB) announces daily decisions on \u0026ldquo;burn\u0026rdquo; vs. \u0026ldquo;no burn\u0026rdquo; days based on atmospheric conditions. Archived CARB burn decisions from 1998 to 2020 were accessed from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ww3.arb.ca.gov/smp/histor/histor.htm\u003c/span\u003e\u003c/span\u003e. BFRS is located within the North Mountain Counties air basin, where allowable burn days are further categorized as either superior, good, fair, or marginal. Burning on marginal burn days is variable from district to district, but in this region burns have typically been allowed on these days. Thus only no-burn days were considered to limit burning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eOur first objective- describing the nature and effects of the burns- was met by describing the operational and environmental conditions under which the burns were conducted. Basic weather and fuel moisture data are provided. Stand-level effects are described by reporting changes in fuel load, shrub cover, ash cover, and PCVS.\u003c/p\u003e \u003cp\u003eThe second objective of describing the structural attributes that influenced fire effects was addressed using exploratory variable selection through the least absolute shrinkage and selection operator (LASSO). LASSO is a method of obtaining interpretable, predictive models by simultaneously performing variable selection and regularization (a method to avoid fitting unrealistically complex models) (Tibshirani \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). We applied this method for four objective-relevant metrics of prescribed fire efficacy: fine fuel consumption (excludes 1000-hour fuel), duff consumption, shrub consumption, and PCVS. For each metric, we determined a set of plot-level stand characteristics we hypothesized a priori were most likely to influence these fire effects. These were \u003cem\u003eP. ponderosa\u003c/em\u003e basal area, percent canopy cover, pre-fire fine fuel load, pre-fire duff load, and pre-fire shrub cover. Stand was also included. LASSO was used to determine which subset of these variables most parsimoniously predicted the observed data. We used leave-one-out cross-validation (LOOCV) to assess predictive ability at two levels: \u0026ldquo;best-fit\u0026rdquo;, and \u0026ldquo;conservative\u0026rdquo; using the package gglasso in R (3.6.3) (Yang 2020). LOOCV is conducted by fitting each model to an N-1 length subset of the full data and calculating the prediction error for the left-out data point. This process is repeated many times for each model, and the mean prediction error is then used to assess the model\u0026rsquo;s predictive ability. To make inferences from the analysis, two models and their associated explanatory variables are considered together. The best-fit model is the one which minimized out-of-sample prediction error while the conservative model is the one with the fewest predictor variables that came within one standard deviation of the minimum out-of-sample prediction error. The conservative model, as the name implies, provides an extra measure of caution against overfitting. The results of both models are reported for each metric variable (fine fuel consumption, duff consumption, shrub consumption, and PCVS).\u003c/p\u003e \u003cp\u003eDays that satisfied the criteria for both weather and air quality conditions were summarized by winter season and month. Average burn window length frequency was calculated over all winters and binned into categories of one day, two to three days, four to seven days, and eight days or longer. A univariate autoregressive (AR) model was fit to the annual summary data to estimate a linear trend. Slope was estimated via maximum likelihood using an expectation-maximization algorithm, and an approximate 95% confidence interval was computed from the estimated Hessian matrix of the maximum likelihood parameter estimate. The Mann-Kendall trend test was also used to test for a monotonic trend. The AR model was fit using the MARSS package in R version 4.0.2 (Holmes et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, R Core Team 2020). The Mann-Kendall trend test was performed using the Kendall package (McLeod \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eStand-level prescribed fire effects\u003c/h2\u003e\n\u003cp\u003eThe stands were burned over a three-day period, from February 24th \u0026minus;\u0026thinsp;26th 2020. Test burns confirmed the likelihood of satisfactory litter consumption. Ignitions began at approximately 10:30 and proceeded until 14:00. 10-hour fuel moisture was 10 to 11%, and relative humidity ranged from 25 to 30%. Live fuel moisture, estimated by oven-drying two samples of live foliage and branches\u0026thinsp;\u0026lt;\u0026thinsp;0.6 cm diameter collected the day of the burns, averaged 107%. Soil moisture was 25%. Air temperature ranged from 15.5 to 20\u0026deg;C. One stand was burned per day, each with a crew of 3 to 4 people. Strip-head firing with drip torches was used for completing burns at a pace of approximately 2 ha hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Flame lengths were typically less than 1.5 m and increased slightly each day as fuels continued to dry out. These conditions were well within the winter burning prescription that has been developed at BFRS, which is different than the prescription used for fall burning (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Generally, it is desirable for 1-hr and 10-hr fuel to be drier during winter burns, because of the head-dampening effect that live fuel moisture and duff tend to have (Banerjee et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, the minimum allowable relative humidity is generally lower for winter burns than fall burns due to the decreased risk of escape.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eEnvironmental prescription ranges for winter burning and fall burning at BFRS.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eWinter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFall\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRelative humidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.1 m height wind speed (km hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMid-flame wind speed (km hr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1-hour fuel moisture (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10-hour fuel moisture (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe burns spread effectively, with surface fuel consumption occurring across most areas. The percent of the ground covered with ash was 68, 80, and 90% across the three stands. Some of the unburned forest floor was caused by skid trails, where there was a lack of fuel. Each successive day of burning led to an increased amount of the forest floor being covered with fire, presumably following a climatic drying trend that was creating more receptive fuel conditions across the burning window. At the stand level, the prescribed fires consumed a substantial amount of fine fuels (59% on average) and, as expected, much lower amounts of large fuels and duff (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Shrub cover was reduced dramatically, by 94% on average. We observed torching of shrubs to be very common, but did not observe any torching of mid-story or canopy trees. One year after the fires, some shrubs had re-sprouted to bring average cover to 5%, which was still lower than the pre-fire cover of 21%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eChange in fuel loads and shrub cover following winter burns in mixed conifer plantations.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePre burn mean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePost burn mean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% reduction burn 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% reduction burn 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% reduction burn 3\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFine fuels\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.9 mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.1 mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1000-hr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1 mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8 mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuff\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.6 mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.5 mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShrub cover\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCanopy tree damage and mortality one year after the fires was generally low. No canopy trees (n\u0026thinsp;=\u0026thinsp;223) that were in the permanent plots died during the first year following the fires. For mid-story trees, mortality was 0\u0026ndash;3%. This mid-story mortality may have been caused by the fires, although field crews could not attribute mortality to the fires with certainty. Crown damage (PCVS) of canopy trees averaged 25.5% and varied among the three stands, from 10, to 27, to 40%, increasing on each successive day of burning.\u003c/p\u003e\n\u003ch2\u003eStatistically modeled prescribed fire effects\u003c/h2\u003e\n\u003cp\u003eThe best-fit model for explaining fine fuel consumption at the plot level included the variables ponderosa pine basal area, percent canopy cover, pre-fire fine fuel load, pre-fire duff load, and stand. It did not include pre-fire shrub cover. The greatest amount of fuel consumption occurred on the third day of burning, and also where pre-fire fuel load was higher. Most of the leverage in the relationship between pre-fire fuel load and fuel consumption came from a few plots with low fuel loads that had either zero or negative fuel consumption (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Canopy cover, which we believed could be a strong predictor of fuel consumption based on Levine et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), was at best weakly correlated despite our plots having a wide range of canopy cover values (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The conservative model did not support any of our variables as strong predictors of fuel consumption, but it did verify that meaningful fuel consumption (i.e. fuel reductions of 50% or more) occurred at the plot level.\u003c/p\u003e\n\u003cp\u003eChange in duff at the plot level was not explained well with any of our variables. It was not the case that higher duff loads impeded fine fuel consumption, but duff consumption was generally low (2\u0026ndash;31% at the plot level), as expected. Variability associated with duff load and consumption make it especially difficult to find predictors at the plot level (e.g. Westfall and Woodall \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The best-fit model for explaining change in shrub cover included pre-fire fuel load as a predictor, but the change in shrub cover with fuel load was minimal (\u0026lt;\u0026thinsp;1% additional increase in fuel consumed per additional Mg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of load). The conservative model did not reveal any relationships between shrub cover changes and our measured variables. Overall, this reflects the fact that shrub cover was reduced dramatically whenever it was present on most plots regardless of the specific structure surrounding the shrubs.\u003c/p\u003e\n\u003cp\u003ePCVS was the response variable where the most nuance in the predictor variables could be seen, albeit only when using the best-fit procedure. This suggested a positive relationship between PCVS and both \u003cem\u003eP. Ponderosa\u003c/em\u003e basal area and pre-fire shrub cover; and a negative relationship with height to crown base. Particularly high levels of PCVS (\u0026gt;\u0026thinsp;50%) tended to occur in plots with more than 5 m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of \u003cem\u003eP. ponderos\u003c/em\u003ea basal area (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC) or when there was less than 5m of average height to crown base (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). Moderate levels of PCVS (\u0026gt;\u0026thinsp;25%) occurred when shrub cover was greater than 30% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). As with the other variables, the conservative model tended to support that PCVS was significant in magnitude (compared to zero change), but that there were no particularly strong predictors among those that we considered.\u003c/p\u003e\n\u003ch2\u003eWeather windows for winter burning over the past 20 years\u003c/h2\u003e\n\u003cp\u003eOf the 4851 days that occurred during the winter periods from 1994 to 2020, 320 of them were feasible burn days where both adequate weather and air quality conditions coincided. Annual variability was considerable (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), ranging from zero days (this occurred in 1995\u0026ndash;1996 and again in 2016\u0026ndash;2017) to 33 days in 2013\u0026ndash;2014, which was within an exceptional multi-year drought that occurred across the region. On average, 12 days per winter were feasible for burning using our criteria. Of the total days that were in prescription in terms of weather conditions, 33% were designated by CARB as no-burn days. There was insufficient evidence of a linear or a monotonic trend for the number of days in prescription per winter. The approximate 95% confidence interval for the slope estimate from the AR model overlapped with zero, and the Mann-Kendall trend test was not statistically significant (\u0026tau;\u0026thinsp;=\u0026thinsp;0.090, 2-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.536).\u003c/p\u003e\n\u003cp\u003eThe month in which burning windows occurred also varied greatly from year to year (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). October and January had the highest average number of days in prescription from 1994 to 2020 (2.9 and 3.1 days per winter, respectively). October had much more variation over time than January, with many years having no days in prescription during this month because the precipitation-initiating winter period sometimes did not start until at least late October. November had the lowest average number of days in prescription per winter (0.6 days).\u003c/p\u003e\n\u003cp\u003eCARB no-burn decisions, which reduced feasible burn days by 33%, did not limit all months uniformly. While days in prescription in terms of weather conditions were less common in February, March, and April, fewer of these (23%) were designated as no-burn days. This percentage was higher in the first half of winter, from October to January, where 53% of days in prescription were no-burn days.\u003c/p\u003e\n\u003cp\u003eThe majority of windows when conditions were suitable for burning from 1994 to 2020 were one to three days long (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). One-day burn windows were most frequent, occurring 1.5 times per winter on average. Occurrence of either two or three-day burn windows occurred about 1.6 times per winter on average. Windows lasting longer than three days were relatively rare, occurring on average 1.1 times per winter.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003ch2\u003ePyrosilviculture treatments to facilitate winter burns\u003c/h2\u003e\n\u003cp\u003eThe use of prescribed fire in young stands is considered experimental because studies are sparse relative to mature forests (North et al. 2018). Yet there is enough recent work to characterize variability in mortality and damage levels (Table 3). A common experimental treatment prior to conducting burns has been mastication, an activity that was cautioned against by Kobziar (et al. 2009) when trying to lower wildfire severity in the short term. However, in mature forests mastication treatments have performed well in terms of lowering fire severity once masticated fuel decomposes (Stephens et al. 2012). When viewed as a pyrosilviculture treatment \u0026mdash; that is, one that increases opportunities for using prescribed fire \u0026mdash; then mastication has some possible advantages that are distinct to a winter burning approach. Mastication primarily acts to remove the mid-story component of forest structure, redistributing it in small pieces to the forest floor. Removal of the mid-story influences several fire behavior properties connected to air-flow such that fires are predicted to be hotter when occurring during more moist fuel conditions (Banerjee et al. 2020). The stands for this study were masticated five years prior to the burns and were also commercially thinned three years prior, creating relatively open mid-story conditions when they were burned. Future studies that explore mid-story removal as a pre-prescribed fire treatment, as well as studies that vary time since mastication prior to burning, could contribute to understanding effective pyrosilviculture approaches in plantations.\u003c/p\u003e\n\u003cp\u003eWith mastication, the removal of the mid-story translates directly to an increase in fine fuels that can increase fireline intensity during dry fuel conditions (Stephens and Moghaddas 2005). Unlike fall burns, where the weather and fuel conditions enter the prescription from the high (i.e. hot/dry) end of the range and practitioners are waiting for predicted effects to be less severe (Table 1), winter burns enter the prescription from the low (i.e. cool/moist) end of the range. This means that practitioners are waiting for conditions to improve such that the minimal level of effective fuel consumption becomes possible. This suggests that structures that \u003cem\u003eincrease\u003c/em\u003e expected fire intensity may be beneficial for winter burning because prescription windows open up earlier and provide more opportunities to burn. Hence while burning recently-masticated (\u0026lt;1 yr) material in the fall may be risky in terms of canopy damage (Kobziar et al. 2009, Stephens and Moghaddas 2005), it may provide amenable conditions in the winter. Mastication clearly has not always led to hot burns during the fall, as Table 3 demonstrates that it can been a prelude to both high (e.g. York et al. In Press a) and low levels of mortality (e.g. Bellows et al. 2016). While our study reports relatively low levels of damage and nearly non-existent mortality, burning in the winter can still cause high canopy damage when conditions are dry (Reiner et al 2012).\u003c/p\u003e\n\u003cp\u003eAnother potential pyrosilvicultural treatment in plantations is thinning that removes whole trees. As with mastication, this is likely to influence fire behavior such that intensity increases during winter burns (Banerjee et al. 2020) thus increasing surface fuels consumption when burning. The burns that followed commercial thins (this study, Busse and Girrard 2020, York et al. In Press a) all had relatively low levels of post-burn mortality. One explanation for this is that thinning removed smaller trees that were more vulnerable to fire-related mortality, leaving behind vigorous trees with thicker bark and higher crown bases. In this study, whole trees were removed and processed for sawlogs, but other options such as removing whole trees for biomass energy production or yarding and then burning at landings would have a similar effect on stand structure. While further studies may help reveal patterns, our expectation following this and other studies to date is that thinning combined with whole tree yarding and followed by winter burning may be an especially effective approach for reducing and maintaining surface fuels in plantations while avoiding high levels of damage and mortality. Importantly, revenue from commercial thinning can cover the operational costs of prescribed fires, especially low-cost winter burns, depending on market conditions and product type (Hartsough et al. 2008).\u003c/p\u003e\n\u003ch2\u003ePlot-level forest structural factors of prescribed fire effects\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003ePlot-level analyses of the factors of fuel consumption did not find clear correlations with plantation structural attributes. Although the plots in this study were somewhat variable in their structure (Fig. 1), this represents only a small range of conditions that can occur in mixed conifer plantations. Stand age, site productivity, species composition, and treatment history range widely in the western US. Future work could widen the range of experimental structural conditions more broadly. For example, burning plantations with different levels of mid-story removal (Banerjee et al. 2020) or testing different lengths of time between masticating and burning, could help to better understand how to prepare plantations for burning.\u003c/p\u003e\n\u003cp\u003eIf generally low consumption of duff is typical of winter burns, as was the case here, it may be difficult to characterize duff consumption as a factor of certain structural characteristics. Instead, the implication with respect to duff consumption during winter burns is simply that not very much of it occurs. As noted by our soil moisture measurements, winter burns occur during moist soil conditions, likely contributing to the general lack of duff combustion. Unlike generally drier fall burns in mature forests (Levine et al. 2020), these winter burns were modest at best in consuming duff. When compared to burning prior to precipitation in the fall, burning after even a small amount of precipitation can lead to considerable variation in amounts of duff consumption (Hille and Stephens 2005). Compared to litter and fine fuels, duff fuels do not appear to become receptive to consumption during these winter burning windows. While duff was recalcitrant in this regard, we also did not find them to be obstacles to consumption of fine fuels. An important context of duff consumption in plantations is that duff load is typically not very high to begin with due to earlier site preparation practices and the lack of time for it to develop. Instead of actively decreasing duff loads, therefore, winter burns may function more to limit its future development by maintaining low litter loads which are the supply of future duff loads.\u003c/p\u003e\n\u003cp\u003eCrown damage was generally low across plots, although some did experience amounts of scorch (i.e. \u0026gt;50%) that may be considered undesirable. While plots with more ponderosa pine had more crown scorch, this species has also been observed to be capable of surviving extensive scorch well compared to other species (Harrington 1993). Given that scorch damage occurs low on the crown, where branches are less productive in terms of contributing to growth (Sprugel et al. 1991), it may be that these levels of scorch do not negatively influence long-term growth. If crown scorch is undesirable for other reasons such as aesthetic quality, then managing for fewer ponderosa pine would seem to be a logical implication. This, however, would likely be trading scorch for mortality since ponderosa pine in young stands are relatively good survivors of scorch (York et al. In Press a). Lowering shrub cover in locations with particularly high amounts of shrubs could be a reasonable way to reduce crown scorch. Our results, however, suggest that only the lowest of management tolerance for crown scorch may justify reducing shrubs prior to burning. It is more likely that managers with some tolerance for scorch would view our results as a way to cost-effectively reduce shrub cover with fire, as opposed to using costly mechanical or chemical treatments before applying fire. Monitoring the rate and extent of shrub recovery, as well as describing shrub dynamics during follow-up burns, will be a relevant topic to explore in the future.\u003c/p\u003e\n\u003ch2\u003eWinter burn windows occur but are sporadic and fleeting\u003c/h2\u003e\n\u003cp\u003ePeriods during the winter at this location when both weather and air quality conditions allow for feasible burns do not occur during long windows. Rather, they occur during \u0026ldquo;winks\u0026rdquo; of time that can occur in any month on any given year. Developing weather prescription ranges during the winter period is complicated by the fact that by the time fuel realistically dries out enough to conduct a burn, a winter storm will soon reset fuel into saturated conditions. While consideration for local weather and fuel conditions is always a dominant factor in deciding when to burn (Biswell 1989), it may be even more important for winter burns. At higher elevations or latitudes than our site, more drying time would be needed given higher amounts of snowfall per storm. At rain-dominated lower elevations or latitudes, less time would be needed. In our experience at this site, a minimum of ten days of drying time is needed following winter storms. This varies considerably depending on the details of the fuel structure (e.g. ponderosa pine needles versus black oak leaves) and topography (e.g. south versus west facing slopes), but it has been a dependable rule of thumb.\u003c/p\u003e\n\u003cp\u003eThe fleeting nature of winter burn windows requires a specifically-designed approach to preparations for burning. Most importantly, it requires being nimble. If multiple days of in-prescription conditions are needed to justify burning, then the opportunity has likely already passed. For large agencies focused on increasing temporary staffing during summer wildfire seasons, this is a challenge. However, large landowners and agencies have the advantage of being able to hedge bets operationally across a wide range of conditions that occur in the winter. For example, preparing pile burn projects at higher elevations can keep personnel active until lower elevation sites become available for broadcast burning. Regardless of the exact approach, winter burning means having personnel hired and available throughout the winter period. For private landowners with smaller ownership sizes, winter burning may come easier than for large programs, or it may be the only feasible option to burn at all. In California, permitting during the fall period can restrict burning, even when conditions are ideal (York et al. 2020). The removal or lessening of the permitting constraint during the winter period is thus a significant advantage. Importantly, our burns demonstrate a lower complexity of winter burns, as each 8 ha stand was burned with a four person crew.\u003c/p\u003e\n\u003cp\u003eStriplin et al. (2020) also considered burn window opportunities in the Sierra Nevada, and documented that winter burn windows existed in conifer forests that were drier but also colder and with precipitation much more dominated by snow compared to our site. Whereas Striplin et al. (2020) found approximately half of winter days to be in prescription in terms of fuel and weather conditions, we found it to be much lower (7%). There are several reasons for this difference. First, our study used a weather station that was located near the burn locations and thus avoided low humidity readings that can come from exposure to dry winds on ridge tops. Second, unlike in Striplin et al. (2020) which did not apply any minimum drying out time, this study applied a minimum period of 10 days following the last precipitation. Finally, this study applied a prescription that was specific to winter burns and was not a general prescription applied year-round. Whereas Striplin et al. (2020) labeled a burn day as one where any hour during the day had humidity less than 50% and 10-hour moisture less than 20%, we required burn days to have at least three hours at less than 45% relative humidity in order to be realistic in terms of burn operation efficiency. We did not include dropping below a maximum 10-hour fuel moisture, which has been observed to be underestimated when using RAWS stations (Estes et al. 2012). The experience-derived winter burning prescription developed at BFRS requires on-site 10-hour fuels to be less than 12%, considerably lower than the 20% maximum used at the higher elevation site. We point out these differences to underscore the importance of deriving specific winter burn prescriptions at a local level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBurns at BFRS have been allowed on all fair and most marginal burn days. Additionally, exceptions have been given to conduct small winter burns on no-burn days when a justification is provided. This experience explains our different methodology in labelling days as burn versus no-burn days compared to Striplin et al. (2020), who did not include any marginal or even fair burn days as acceptable air quality burn days. Despite our inclusive approach to labelling days as acceptable in terms of air quality, the number of days on which air quality restricted burning was surprisingly high. Although it is often listed as a constraint (e.g. Quinn-Davidson and Varner 2012), air quality has recently been described by practitioners across the western United States as not being a major impediment except in some locales (Schultz et al. 2019). Our analysis suggests that air quality is a significant constraint in this region during the winter period. That air quality was more of a constraint toward the end of the winter period as it transitions to the spring season does not conform to the expectation that the fall / early winter season has atmospheric conditions that are less conducive to burning (Cahill et al. 1996). \u0026nbsp;The net effect was a smoothing-out on the frequency of burning opportunities across the winter period (Fig. 3). \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions And Management Implications","content":" \u003cp\u003eThe potential logistic, economic, and risk-avoidance advantages of winter burning should be appealing for managers who want to both insulate economic investments and also protect future ecosystem services gains that can come from developing mixed-species plantations. Until results from winter burning trials are evaluated, however, it remains a concept with appeal but lacking in proof. Here, we contribute evidence that winter burning can be feasible and effective for common prescribed burn objectives, albeit with limitations.\u003c/p\u003e \u003cp\u003eAs with numerous other forest types throughout the western United States, there is an urgency to increase the use of prescribed fire in mixed conifer forests. Yet actual implementation of broadcast burning lags far behind desired goals. In the 2016\u0026ndash;2017 fiscal year, the most recent period for which information is available, state agency burning occurred on less than 5,700 hectares across the state (Brown et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This could be a vast overestimate of broadcast burning on forestlands, since it includes both pile burning and grassland burning. Winter burning, especially in young stands managed with pyrosilvicultural treatments prior to burning, may be an overlooked opportunity to lower wildfire severity in structures that are otherwise particularly vulnerable. In the context of young stands managed for timber with even-aged management, prescribed fires could be used multiple times in the lifespan of a managed stand. At our study site, long-term tracking of mean annual volume increments suggest that approximately 100 years is an optimal rotation age in terms of maximizing yield (BFRS, unpublished data). In fully-stocked plantations with increasing leaf areas, fuel loads will recover quickly following prescribed burns, suggesting a frequent prescribed burning frequency in order to maintain low fuel loads (York et al. In Press b). Each subsequent burn ostensibly will have less canopy damage, as trees get larger and lower portions of crowns that are scorched are no longer present. One approach to burn timing may be to conduct burns following commercial thins, thus taking advantage of lower canopy densities to dry fuel and also to consume activity fuel associated with harvesting. This would likely translate to three or four burns prior to rotation age, depending on the type of thinning regime that is applied.\u003c/p\u003e \u003cp\u003eOn forestlands not managed with a timber objective, avoiding or staying below certain levels of mortality may not be as relevant. Instead fire damage and tree mortality may be desirable if it is relied upon as the primary density management tool. However, even with a more broad range of acceptable outcomes following prescribed fires, federal mixed conifer lands also lag far behind compared to desired use of fire (North et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For landowners who place the reintroduction of fire as a high priority, the winter period may represent an overlooked opportunity to advance overall burn program objectives.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAR: Autoregresssive\u003c/p\u003e\n\u003cp\u003eBFRS: Blodgett Forest Research Station\u003c/p\u003e\n\u003cp\u003eCARB: California Air Resources Board\u003c/p\u003e\n\u003cp\u003eLASSO: Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eLOOVC: Leave One Out Cross Validation\u003c/p\u003e\n\u003cp\u003ePCVS: Percent Crown Volume Scorch\u003c/p\u003e\n\u003cp\u003eRAWS: Remote Automated Weather Station\u003c/p\u003e\n\u003cp\u003eUS: United States\u003c/p\u003e\n\u003cp\u003eUSDA: United States Department of Agriculture\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve the use of any animal or human data or tissue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not contain data from any individual person.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge funding support from the SPARx-Cal (Smart Practices and Architectures for Rx fires) project under the University of California Laboratory Fees Research Program funded by the UC Office of the President (UCOP), grant ID LFR-20-653572, entitled\u0026nbsp;\u0026rsquo;Transforming Prescribed Fire Practices for California.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRY was the burn boss, supervised data collection, conceptualized the study, and wrote the manuscript\u003c/p\u003e\n\u003cp\u003eJL conducted analysis and edited the manuscript\u003c/p\u003e\n\u003cp\u003eKR collected data, conducted analysis and edited the manuscript\u003c/p\u003e\n\u003cp\u003eJR helped conceptualize the study and edited the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge A. Roughton and H. Noble for assistance with burning. B. Collins provided a helpful review of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAgee, JK. 1993. Fire Ecology of Pacific Northwest Forests. Island Press, Covelo, CA, USA, p. 493.\u003c/p\u003e\n\u003cp\u003eBaldwin, H, W Stewart, S Sommarstrom. In Press. Reforesting California. In: Stewart, W. (ed). Reforestation Practices for Conifers in California. Davis, CA. University of California Agriculture and Natural Resources. \u003ca href=\"https://www.fvmc.org/\"\u003ehttps://www.fvmc.org/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eBanerjee, T, W Heilman, S Goodrick, JK Heirs, and R Linn. 2020. Effects of canopy midstory management and fuel moisture on wildfire behavior. Nature Scientific Reports 10: 17312\u003c/p\u003e\n\u003cp\u003eBellows, RS, AC Thomson, KJ Helmstedt, RA York, and MD Potts. 2016. Damage and mortality patterns in young mixed conifer plantations following prescribed fires in the Sierra Nevada, California. Forest Ecology and Management 376: 193-204\u003c/p\u003e\n\u003cp\u003eBiswell, HH 1989. Prescribed Burning In California Wildlands Vegetation Management. University of California Press, Berkeley, CA, USA pp. 44\u003c/p\u003e\n\u003cp\u003eBrown, JK 1974. Handbook for inventorying downed woody material. USDA Forest Service, General Technical Report INT-16.\u003c/p\u003e\n\u003cp\u003eBrown, EG, J Laird, and K Pimlott. 2018. California\u0026rsquo;s Forest and Rangelands: 2017 Assessment. http://frap.fire.ca.gov/assessment2017\u003c/p\u003e\n\u003cp\u003eBusse, M, and R Gerrard. 2020. Thinning and burning effects on long-term litter accumulation and function in young ponderosa pine forests. Forest Science 66(6): 761-769\u003c/p\u003e\n\u003cp\u003eCahill, TA, JJ Carroll, D Campbell, and TE Gill. 1996. Air quality. In: Sierra Nevada Ecosystem Project, Final Report to Congress, vol. II, Assessments and Scientific Basis for Management Options. University of California Davis, Centers for Water and Wildland Resources, pp. 1227\u0026ndash;1260.\u003c/p\u003e\n\u003cp\u003eEstes, BL, EE Knapp, CN Skinner, and FCC Uzoh. 2012. Seasonal variation in surface fuel moisture between unthinned and thinned mixed conifer forest, northern California, USA. International Journal of Wildland Fire 21: 428-435\u003c/p\u003e\n\u003cp\u003eHan, S-K., H-S Han, DS Page-Dumroese, and LR Johnson. 2009. Soil compaction associated with cut-to-length and whole-tree harvesting of a coniferous forest. Canadian Journal of Forest Research 39: 976-989.\u003c/p\u003e\n\u003cp\u003eHarrington, MG 1993. Predicting \u003cem\u003ePinus ponderosa \u003c/em\u003emortality from dormant season and growing season fire injury. International Journal of Wildland Fire 3(2): 65-72\u003c/p\u003e\n\u003cp\u003eHartsough, BR, S Abrams, RJ Barbour, ES Drews, JD McIver, JJ Moghaddas, and SL Stephens. 2008. The economics of alternative fuel reduction treatments in western United States dry forests: Financial and policy implications from the National Fire and Fire Surrogate Study. \u003cem\u003eForest Policy and Economics\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(6), 344\u0026ndash;354. \u003ca href=\"https://doi.org/10.1016/J.FORPOL.2008.02.001\"\u003ehttps://doi.org/10.1016/J.FORPOL.2008.02.001\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHille, MG and SL Stephens. 2005. Mixed conifer forest duff consumption during prescribed fires: Tree crown impacts. Forest Science 51(5): 417-424\u003c/p\u003e\n\u003cp\u003eHolmes, E, E Ward, M Scheuerell, and K Willis. 2020. MARSS: Multivariate autoregressive state-space modeling. R package version 3.11.3 https://cran.r-project.org/web/packages/MARSS/MARSS.pdf\u003c/p\u003e\n\u003cp\u003eHuang, X., AD Hall, and N Berg. 2018. Anthropogenic warming impacts on today\u0026rsquo;s Sierra Nevada snowpack and flood risk. Geophysical Research Letters 45(12): 6215-6222\u003c/p\u003e\n\u003cp\u003eKeeley, JE and HD Safford. 2016. Fire as an Ecosystem Process. In: Mooney, H. and E. Zavaleta (eds). Ecosystems of California. Oakland, CA, USA. University of California Press pp. 27-45.\u003c/p\u003e\n\u003cp\u003eKobziar, LN, JR McBride, and SL Stephens. 2009. The efficacy of fire and fuels reduction treatments in a Sierra Nevada pine plantation. International Journal of Wildland Fire 20(8): 932-945\u003c/p\u003e\n\u003cp\u003eKnapp, EE, JE Keeley, EA Ballenger, and TJ Brennan. 2005. Fuel reduction and coarse woody debris dynamics with early season and late season prescribed fire in a Sierra Nevada mixed conifer forest. Forest Ecology and Management 208: 383-397\u003c/p\u003e\n\u003cp\u003eKnapp, EE and JE Keeley. 2006. Heterogeneity in fire severity within early season and late season prescribed burns in a mixed-conifer forest. International Journal of Wildland Fire 15: 37-45\u003c/p\u003e\n\u003cp\u003eKnapp, EE, CN Skinner, MP North, and BE Estes. 2013. Long-term overstory and understory change following logging and fire exclusion in a Sierra Nevada mixed-conifer forest. Forest Ecology and Management 310: 903-914.\u003c/p\u003e\n\u003cp\u003eKobziar, LN, JR McBride, and S Stephens. 2009. The efficacy of fire and fuels reduction treatments in a Sierra Nevada pine plantation. International Journal of Wildland Fire 18: 791-801\u003c/p\u003e\n\u003cp\u003eLevine, JI, BC Collins, RA York, DE Foster, DL Fry, and SL Stephens. 2020. Forest stand and site characteristics influence fuel consumption in repeat prescribed burns. International Journal of Wildland Fire 29(2): 148-159\u003c/p\u003e\n\u003cp\u003eLyons-Tinsley, C, and DL Peterson. 2012. Surface fuel treatments in young, regenerating stands affect wildfire severity in a mixed conifer forest, eastside Cascade Range, Washington, USA. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e270\u003c/em\u003e, 117\u0026ndash;125. \u003ca href=\"https://doi.org/10.1016/j.foreco.2011.04.016\"\u003ehttps://doi.org/10.1016/j.foreco.2011.04.016\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eMcLeod, AI. 2011. Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.2. \u003ca href=\"https://CRAN.R-project.org/package=Kendall\"\u003ehttps://CRAN.R-project.org/package=Kendall\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eMitchell, RJ, JK Heirs, J O\u0026rsquo;Brien, and G Starr. 2009. Ecological forestry in the southeast: Understanding ecology of fuels. Journal of Forestry December: 391-397\u003c/p\u003e\n\u003cp\u003eNorth, M, BM Collins, and S Stephens. 2012. Using fire to increase the scale, benefits, and future maintenance of fuels treatments. Journal of Forestry 110(7): 392-401\u003c/p\u003e\n\u003cp\u003eNorth, M, Werner, CM, Safford, HD, Walsh, D, Tompkins, RE, Coppoletta, M, Latimer, AM, \u0026hellip; Estes, BL 2019. Tamm Review: Reforestation for resilience in dry western U.S. forests. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e432\u003c/em\u003e: 209\u0026ndash;224. \u003ca href=\"https://doi.org/10.1016/j.foreco.2018.09.007\"\u003ehttps://doi.org/10.1016/j.foreco.2018.09.007\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eReiner, AL, NM Valliant, and SN Dailey. 2012. Mastication and prescribed fire influences on tree mortality and predicted fire behavior in ponderosa pine. Western Journal of Applied Forestry 27(1): 36-41\u003c/p\u003e\n\u003cp\u003eSchultz, CA, SM McCaffrey, and HR Huber-Stearns. 2019. Policy barriers and opportunities for prescribed fire application in the western United States. International Journal of Wildland Fire 28: 874-884\u003c/p\u003e\n\u003cp\u003eSeymour RS, AS White, and PG deMaynadier. 2002. Natural disturbance regimes in northeastern North America \u0026ndash; evaluating silvicultural systems using natural scales and frequencies. Forest Ecology and Management \u003cstrong\u003e155\u003c/strong\u003e: 357-367.\u003c/p\u003e\n\u003cp\u003eSprugel, DG, TM Hinckley, and W Schaap 1991. The theory and practice of branch autonomy. Annu. Rev. Ecol. Syst. \u003cstrong\u003e22\u003c/strong\u003e(1): 309\u0026ndash;334. doi:10.1146/annurev.es.22.110191.001521.\u003c/p\u003e\n\u003cp\u003eStarrs, CF, V Butsic, C Stephens, and W Stewart. 2018. The impact of ownership, firefighting, and reserve status on fire probability in California. Environmental Research Letters 13: 1-11\u003c/p\u003e\n\u003cp\u003eStephens, CW and RA York. 2017. An evaluation of stand age as a factor of mastication efficiency and effectiveness in the Central Sierra Nevada, California. Northwest Science 91(4): 389-398. \u003ca href=\"https://doi.org/10.3955/046.091.0408\"\u003ehttps://doi.org/10.3955/046.091.0408\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eStephens, SL and JM Moghaddas. 2005. Experimental fuel treatment impacts on forest structure, potential fire behavior, and predicted tree mortality in a California mixed conifer forest. Forest Ecology and Management 215: 21-36\u003c/p\u003e\n\u003cp\u003eStephens, SL, RC Heald, and JJ Moghaddas. 2005. Silvicultural and reserve impacts on potential fire behavior and forest conservation: Twenty-five years of experience from Sierra Nevada mixed conifer forests. Biological Conservation 125: 369-379\u003c/p\u003e\n\u003cp\u003eStephens, SL, BM Collins, and G Roller. 2012. Fuel treatment longevity in a Sierra Nevada mixed conifer forest. Forest Ecology and Management 285: 204-212\u003c/p\u003e\n\u003cp\u003eStewart, W, R Standiford, S Kocher, J Webster. In Press. In: Stewart, WC (ed) Reforestation Practices for Conifers in California. Davis, CA, University of California Agriculture and Natural Resources. \u003ca href=\"https://www.fvmc.org/\"\u003ehttps://www.fvmc.org/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eStriplin, R, SA McAfee, HD Safford, and MJ Papa. 2020. Retrospective analysis of burn windows for fire and fuels management: an example from the Lake Tahoe Basin, California, USA. Fire Ecology 16:13\u003c/p\u003e\n\u003cp\u003eThompson, J. R., Spies, T. A., \u0026amp; Olsen, K. A. (2011). Canopy damage to conifer plantations within a large mixed-severity wildfire varies with stand age. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e262\u003c/em\u003e, 355\u0026ndash;360. \u003ca href=\"https://doi.org/10.1016/j.foreco.2011.04.001\"\u003ehttps://doi.org/10.1016/j.foreco.2011.04.001\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eTibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (methodological). Wiley. 58 (1): 267\u0026ndash;88.\u003c/p\u003e\n\u003cp\u003eUSDA Forest Service, Forest Inventory and Analysis Program, Mon Mar 01 17:58:28 GMT 2021. Forest Inventory EVALIDator web-application Version 1.8.0.01. St. Paul, MN: U.S. Department of Agriculture, Forest Service, Northern Research Station. [Available only on internet:\u0026nbsp;\u003ca href=\"https://gcc02.safelinks.protection.outlook.com/?url=http%3A%2F%2Fapps.fs.usda.gov%2FEvalidator%2Fevalidator.jsp\u0026amp;data=04%7C01%7CJoe.Restaino%40fire.ca.gov%7C02f65061b2de463b761b08d8dcf0dfaf%7C447a4ca05405454dad68c98a520261f8%7C1%7C0%7C637502274551203712%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000\u0026amp;sdata=2tNVB%2FXtfK57%2FNhqA7%2FzTQETopF180v8r69XjEQvxbQ%3D\u0026amp;reserved=0\"\u003ehttp://apps.fs.usda.gov/Evalidator/evalidator.jsp\u003c/a\u003e]\u003c/p\u003e\n\u003cp\u003evan Wagtendonk JW, Benedict JM, Sydoriak WM (1998) Fuel bed characteristics of Sierra Nevada conifers. \u003cem\u003eWestern\u003c/em\u003e\u003cem\u003eJournal of Applied Forestry \u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 73\u0026ndash;84.\u003c/p\u003e\n\u003cp\u003eWestfall, JA and CW Woodall. 2007. Measurement repeatability of a large-scale inventory of forest fuels. Forest Ecology and Management 253: 171-176\u003c/p\u003e\n\u003cp\u003eWooley, T, KC Shaw, LM Ganio, and S Fitzgerald. 2012. A review of logistic regression models used to predict post-fire tree mortality of western North American conifers. International Journal of Wildland Fire 21(1): 1-35\u003c/p\u003e\n\u003cp\u003eYork, RA, H Noble, L Quinn-Davidson, and JJ Battles. In Press. Pyrosilviculture: Combining prescribed fire with gap-based silviculture in mixed-conifer forests of the Sierra Nevada. Canadian Journal of Forest Research.\u003c/p\u003e\n\u003cp\u003eYork, RA, J Levine, D Foster, S Stephens, and BM Collins. In Press. Silviculture can facilitate repeat prescribed burn programs. California Agriculture\u003c/p\u003e\n\u003cp\u003eYork, RA, A Roughton, R Tompkins, and S Kocher. 2020. Burn permits need to facilitate \u0026ndash; not prevent- \u0026ldquo;good fire\u0026rdquo; in California. California Agriculture 74(2): 62-66\u003c/p\u003e\n\u003cp\u003eZald, H, and C Dunn. 2018. Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape. Ecological Applications \u003cstrong\u003e28\u003c/strong\u003e(4): 1068-1080.\u003c/p\u003e\n\u003cp\u003eZhang, J, KA Finley, and EE Knapp. 2020. Resilience of a ponderosa pine plantation to a backfiring operation during a mid-summer wildfire. International Journal of Wildland Fire 28(12): 981-992\u003c/p\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":"Pyrosilviculture, plantations, winter burning","lastPublishedDoi":"10.21203/rs.3.rs-423745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-423745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eYoung, planted forests are particularly vulnerable to wildfire. High severity effects in planted forests translate to the loss of previous reforestation investments and the loss of future ecosystem service gains. We conducted prescribed burns in three\u0026thinsp;~\u0026thinsp;35 year-old mixed conifer plantations during February in order to demonstrate the effectiveness of winter burning, which is not common in the Sierra Nevada, California.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOn average, 59% of fine fuels were consumed and the fires reduced shrub cover by 94%. The average percent of crown volume that was damaged was 25%, with no mortality observed in overstory trees one year following the fires. A plot-level analysis of the factors of fire effects did not find strong predictors of fuel consumption. Shrub cover was reduced dramatically, regardless of the specific structure that existed in plots. We found a positive relationship between crown damage and the two variables of \u003cem\u003ePinus ponderosa\u003c/em\u003e relative basal area and shrub cover. But these were not particularly strong predictors. An analysis of the weather conditions that have occurred at this site over the past 20 years indicated that there has consistently been opportunities to conduct winter burns. Windows of time are short, typically one or two days, and may occur at any time during the winter season.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study demonstrates that winter burning can be an important piece of broader strategies to reduce wildfire severity in the Sierra Nevada. Preparing forest structures so that they can be feasible to burn and also preparing burn programs so that they can be nimble enough to burn opportunistically during short windows during the winter are key strategies.\u003c/p\u003e","manuscriptTitle":"Opportunities for winter prescribed burning in mixed conifer plantations of the Sierra Nevada","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-04 14:25:13","doi":"10.21203/rs.3.rs-423745/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-05-12T13:05:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-30T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-18T00:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2021-04-13T10:58:13+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":"af81344b-63f8-48f3-96e1-c6d93c815b3b","owner":[],"postedDate":"May 4th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":4058273,"name":"Ecological Modeling"},{"id":4058274,"name":"Environmental Policy"},{"id":4058275,"name":"Forestry"}],"tags":[],"updatedAt":"2021-09-14T18:24:30+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-04 14:25:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-423745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-423745","identity":"rs-423745","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}