Influence of Increasing Fires on Mixed Conifer Stand Dynamics in the U.S. Southwest

Abstract

(1) Stand-replacing fires may threaten the continued stability of mixed conifer forests in the U.S. Southwest. Increasing fire frequency and severity have made post-fire forest recovery trajectories uncertain for many coniferous species, potentially leading to long-term shifts in forest structure and composition. (2) The purpose of this study was to examine post-fire stand dynamics over a 10-year period, using a network of permanent plots established prior to wildfire events across Arizona and New Mexico. We assessed changes in overstory composition, regeneration, and fuel loading across different fire severities. (3) High severity fire caused near-total overstory mortality, with little to no conifer regeneration and abundant sprouting hardwood regeneration. Lower severity fire was more favorable to fire-tolerant conifer species; however, mortality among mature trees was high, and fire-intolerant conifers were either diminished or extirpated completely. (4) In high severity fires, changes in overstory and understory structure and composition may be long-lasting. Additionally, increased fuel loads following high severity fire suggests a heightened risk of reburns, potentially perpetuating ecotype conversion. Our findings highlight the need for active management strategies, including reforestation and fuel reduction treatments, to support forest resilience for mixed conifer ecosystems in the US Southwest and similar forest types in other regions in the face of ongoing climate and fire regime changes.

1. Introduction

Stand-replacing fires have steadily increased over the past several decades in the mixed conifer forests of the U.S. Southwest [1,2]. European settlement of the region has led to over a century of fire exclusion and suppression [3]. This relative lack of natural and human-caused fire has contributed to an increase in fuel loading and continuity [4], making the fires that do take place larger and more intense [2]. In addition, land use changes, such as extensive grazing and decades of poor forest management practices, have contributed to changes in forest structure and composition, allowing forests to become denser and more susceptible to stand-replacing events [5,6,7]. Anthropogenic ignitions now account for 84% of wildfires, an increase that has greatly expanded area burned and extended fire seasons [8]. In lower-elevation forests, fire exclusion has led to an increase in both fire-intolerant species and ladder fuels [9]. Increased fuel connectivity could increase the possibility of crown fires and lead to fire spread into adjacent forests [2]. Additionally, the impact of climate change is expected to play an increasing role in wildfires and post-fire ecosystem dynamics [2,10,11,12]. Research has linked climate change to increased fire severity, size, and frequency [10,13]. Under a warming climate it is expected that there will be elevated tree mortality from wildfires, drought, and other climate-related stress [14,15]. After a fire, the effects of drought exacerbated by climate change may also push some species beyond their climatic envelope for regeneration [11,16,17]. Studies have predicted climate-driven range shifts in several key species in mixed conifer ecosystems in the U.S. Southwest [18,19,20,21].

High severity burns generally reset a forest to an early successional state [22]. The first years proceeding a fire are crucial in determining post-disturbance trajectories [23]. In this reorganization phase, tree survival and establishment may set development trajectories towards long-lasting structural and compositional changes [23]. Conifers are often dependent on fire refugia (remaining live overstory) as a seed source for post-fire regeneration [24]. After a fire in which most or all overstory is killed, a lack of seed source and competition from sprouting hardwoods may lead to a substantial reduction in regeneration of these species [24,25,26]. High severity fires have also been correlated with higher severity reburns due to high overstory mortality contributing to an abundance of fuel-loading and changes in vegetation type following the first fire event [27,28]. Repeated burning may limit or prohibit the establishment of seedlings entirely [29,30,31].

Combined, post-fire factors can potentially lead to a permanent forest type conversion to sprouting hardwood species or a non-forested state [12,23,32]. Such post-fire trends have already been documented in mixed conifer forests across the U.S. Southwest and are predicted to increase in both frequency and scale [16,33,34].

Relatively few studies have focused on the post-fire stand dynamics of mixed conifer forests in the U.S. Southwest [24,27,31,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53]. By using an existing network of permanent monitoring plots, our study is unique in that we can compare pre-fire plot data with multiple post-fire remeasures across a 10-year time window to identify fine-scale changes and trends. An understanding of these post-fire changes occurring on this landscape can identify patterns that may be helpful to land managers, both for mitigation of the detrimental impacts of stand-replacing fires and for facilitating post-fire ecosystem restoration and reforestation. Using a long-term permanent plot network, our objective was to evaluate the effects of wildfire over a 10-year time scale on the post-fire stand dynamics of mixed conifer forests in the U.S. Southwest.

Specifically, we sought to answer the following questions:

(a)

What changes occurred in overstory structure and composition immediately following wildfire, and in the subsequent 10 years of recovery, and how are these changes influenced by pre-fire stand structure, composition, and burn severity?

(b)

What changes in fuel loading occurred over time and across different fire severities, and how do long-term fuel loading trends compare with pre-fire levels?

(c)

Does tree regeneration density and species composition differ significantly from pre-fire levels at different burn severities?

2. Materials and Methods

2.1. Study Area

In the U.S. Southwest, forests generally occur along gradients, transitioning from pinyon–juniper woodlands to ponderosa pine dominated forests, and to warm/dry and then cool/wet mixed conifer ecosystems, according to elevational and moisture requirements [54,55]. Warm/dry mixed conifer forests generally occur at higher elevation than ponderosa pine forests, often along south-facing aspects within a rain shadow or on ridge tops [54,55,56]. These systems are historically adapted to a frequent low-severity fire regime, with fire return intervals of <35 years and stand-replacing events historically rare [42,54]. Ponderosa pine is normally a dominant overstory component, but species composition may also include shade-tolerant conifers and a variety of hardwood species [54,55]. Cool/wet mixed conifer forests are characterized by shade-tolerant species and a more closed canopy structure compared with their warm/dry mixed conifer counterparts [56]. These forests are more often located at higher elevations, north-facing slopes, and on the windward side of mountains or in valley bottoms where there is generally more moisture [56]. Fire regimes in these ecosystems have historically been mixed severity with occasional stand-replacing events and return intervals of 35–100+ years. There is evidence to suggest that both mixed conifer ecotypes are now at increased risk of high severity fires [1,2,56,57].

A permanent plot network was installed in cool/wet and warm/dry mixed conifer stands across Arizona and New Mexico between 2010 and 2013, primarily to monitor the spread and severity of white pine blister rust, a disease caused by the fungal pathogen Cronartium ribicola J.C. Fisch on southwestern white pine (Pinus strobiformis Engelm.) [58]. Based on U.S. Forest Service stand examinations and other inventory data, stands were initially selected for inclusion based on a minimum combined basal area of southwestern white pine exceeding 6.9 m² ha⁻¹ [58], and one 20 × 50 m plot was installed in each stand. After plot installation, six fires occurred in the study area, burning an estimated total of 404,126 ha and 30 plots (Figure 1, Table S1). Burned plots were located in the Apache–Sitgreaves, Gila, Coronado, and Santa Fe National Forests. A total of 3 plots were inaccessible following the fire and were not included in this study (final n = 27). These fires across the plot network offered an opportunity to study post-fire ecosystem dynamics.

Our study contained a mix of warm/dry and cool/wet mixed conifer sites. We defined warm/dry mixed conifer forests as comprising 40%–90% ponderosa pine (Pinus ponderosa Dougl. ex Laws) before fire exclusion (1879) [31]. Trees that were present before fire exclusion were defined as conifers >40 cm diameter based on studies of pre-settlement forest structures [31,59,60,61]. We chose pre-settlement characteristics as a baseline for classification due to the substantial changes that have taken place in these ecosystems since Euro-American settlement, potentially impacting structure, composition, and fire regimes [7,59,62]. We classified 24 of our plots as cool/wet mixed conifer and 3 plots as warm/dry mixed conifer, and their overstory and understory characteristics reflect their historic structure (Tables S2 and S3). Soil types were mainly volcanic across the plot network, but sedimentary and metamorphic parent materials were also present [58]. Climate conditions in the study area were characterized by monsoonal climate patterns, with dry periods throughout the summer and fall [63]. The sites were at a mean elevation of 2706 m with cool summers, cold winters, and a year-round average relative humidity of 54% (

Table 1. An overview of site and climatic characteristics (n = 27 plots). Values are represented as mean (standard error) when appropriate and separated into ecotype (see Methods). Asp = aspect, Slp = slope, Elev = elevation, C/W = cool/wet mixed conifer ecotype (see Methods), W/D = warm/dry ecotype, TRMI = topographic relative moisture index [64], MAT = mean annual temperature, MWMT = mean warmest month temperature, MCMT = mean coldest month temperature, MAP = mean annual precipitation, MSP = May to September precipitation, RH = relative humidity, HL = heat load [65], PAR = potential annual radiation [65]. Decadal climate data representing years 2011–2020 obtained from ClimateNA (https://climatena.ca/ (accessed on 30 May 2025)).

Ecotype	Asp (°)	Slp (°)	Elev (m)	TRMI (Index)	MAT (°C)	MWMT (°C)	MCMT (°C)	RH (%)	MAP (mm)	MSP (mm)	HL (Index)	PAR (Mj cm2 yr−1)
C/W	185.5 (20.1)	23.8 (2.7)	2706.4 (39.8)	26.3 (2)	8.4 (0.2)	17.1 (0.2)	0.2 (0.2)	57 (1)	723.2 (33.5)	368.8 (21.9)	0.8 (0)	1.3 (0)
W/D	207.7 (37)	32 (14)	2705 (110.6)	22 (4.7)	7.1 (0.7)	16.2 (1.2)	−1.2 (0.4)	52 (0.7)	668.3 (5.7)	332.3 (49.6)	0.7 (0.1)	1.3 (0.1)
All plots	188 (18.2)	24.7 (2.8)	2706.2 (36.7)	25.9 (1.8)	8.3 (0.2)	17 (0.2)	0.1 (0.2)	56.4 (0.9)	717.1 (29.9)	364.7 (19.9)	0.8 (0)	1.3 (0)

and Table S4).

Study sites exhibited diverse pre-fire overstory characteristics (Table S5). Southwestern white pine and Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) were overstory components on all plots. Southwestern white pine is known to hybridize with limber pine (Pinus flexilis James) [66]. For the purposes of our research, we treat the two as the same species. White fir (Abies concolor (Gord & Glenda.) Lindl. ex Hilbar.) and ponderosa pine were also prevalent in the overstory, each found on 21 plots (78%). Hardwood species, such as quaking aspen (Populus tremuloides Michx.) and Gambel oak (Quercus gambelii Nutt.), were present on 56% and 19% of plots, respectively. We observed fire-intolerant species in smaller numbers, including blue spruce (Picea pungens Engelm.), Engelmann spruce (Picea engelmannii Parry ex Engelm.), corkbark fir (Abies lasiocarpa var. arizonica (Merriam) Lemm.), and Rocky Mountain bristlecone pine (Pinus aristata Engelm.). New Mexico locust (Robinia neomexicana A. Gray), while having a significant pre-fire prominence in the understory, was not present as an overstory species due to our overstory cutoff criterion (no stems >12.7 cm DBH were present). For the purposes of this study, when considering changes in overstory, we analyzed fire-intolerant conifers and hardwoods (quaking aspen, Gambel oak, Engelmann spruce, blue spruce, corkbark fir, and Rocky Mountain bristlecone pine) as a group. Although white fir is generally fire-intolerant at smaller diameters, we analyzed it separately due to its prevalence in the plot network and differing post-fire mortality rates when compared with other fire-intolerant species [67]. Additionally, we sometimes grouped fire-tolerant species together for use as a model covariate; for instance, this grouping included southwestern white pine, Douglas fir, and ponderosa pine.

2.2. Data Collection

Looney and Waring (2012) [58] provided detailed measurement protocols, which we summarize here. Our plots each covered a 0.10 ha (20 × 50 m) overstory plot, with three nested and sampled 10 × 10 m and 5 × 5 m subplots. We also utilized five 1 × 1 m subplots along a 50 m-long transect at the plot center line to estimate canopy and understory cover by life form. We recorded data for overstory trees (>12.7 cm DBH), including status (live or dead), DBH (cm), and species. We measured live southwestern white pine in greater detail, noting strata, crown class, total tree height (m), crown height (m), and white pine blister rust presence/absence. Other damaging agents were noted when present. Regeneration (seedlings and saplings <12.7 cm DBH) was tallied by species and size class in the nested subplots.

We collected ground fuels data using a modified Brown’s transect, with three 16.7 m transects from the plot center point [68]. We counted fuels on each transect using the following size classes: 1 h (0 to 0.63 cm), 10 h (0.63 to 2.54 cm), and 100 h (2.54 to 7.62 cm) fuels. Individual 1000 h fuels (>7.62 cm) were recorded along with their diameter and decay class. Decay class was recorded on a scale of 1–5, with 1–3 being sound or undecayed and 4–5 being decayed [69]. We recorded litter and duff depths at 4 points on each transect. A 1.1 m-radius vegetation plot was established at 10.7 m on each transect which estimated the percent live fuel cover, which we categorized as litter, rock, bare soil, grass, forbs, and shrubs.

We remeasured plots at 1-, 5- and 10-year intervals post-fire (hereafter y1, y5, and y10). First-year post-fire measurements included quantifying burn severity through the composite burn index (CBI), an index combining data on char height, crown scorch, ground cover, vegetation, and substrates collected within one year post-fire [70]. Although CBI is typically measured within 1–3 years of a fire, we did not remeasure our plots on the Owl and Whitewater–Baldy fires (n = 2) until 5- and 10-years later. We estimated burn severity in these fires based on the bole char height on living trees, fire-related mortality, and available burn severity data [70,71]. We classified fires with a CBI greater than or equal to 2.5 as high severity and below 2.5 as low–moderate severity (hereafter H and L-M, respectively) (Figures S1 and S2). Burned plots spanned a range of post-fire outcomes, with 13 plots experiencing full stand replacement (Figure 1, Table S1).

2.3. Calculations

We performed all calculations and analyses using R Statistical Software version 4.4.2 [72]. We calculated overstory summary statistics by 10 cm diameter class and by species for each time step at the plot level. Calculations included mean live basal area per hectare (BA; m² ha⁻¹), mean live and dead trees per hectare (TPH; trees ha⁻¹), and quadratic mean diameter (QMD; cm). Dead trees per hectare was calculated as trees alive pre-fire that were confirmed to have died, and it was confined to post-fire time steps. Percent change was calculated for each of these metrics across all time steps.

We calculated total fuel load in Mg ha⁻¹ using protocols developed by Looney (2012) [73]. We determined specific gravity values and average square diameters using data from Sackett (1980) [74], supplemented from several other sources [75,76]. We weighted the average specific gravity values by live species composition. Values for sound (decay class 1–3) 1000 h fuels were based on a 12% moisture content, and heavily decayed (decay class 4–5) 1000 h fuels were assumed to have a specific gravity of 0.30 [73,75]. Due to a lack of studies within the U.S. Southwest, we applied data for litter and duff characteristics in Sierra Nevada mixed conifer forests, fitting several local species with the nearest appropriate analogs [73,77]. Fuel loading was averaged at the plot level across transects, and statistics were calculated for the percent composition of total fuel load by fuel type, as well as percent changes over time. We combined fuel types into the following three categories: litter and duff (hereafter litter/duff), 1-, 10-, and 100 h fuels (hereafter small fuels), and 1000 hr sound and decayed (hereafter 1000 hr). Regeneration (combined seedling and sapling tally) was summed at the plot level by species and used to calculate TPH.

We used a modified relative importance index to evaluate changes in overstory species composition across all plots [78].

We calculated relative importance as follows:

$$Relative dominance={\displaystyle \frac{B{A}_{species}}{B{A}_{plot}}}\times 100$$

$$Relative density={\displaystyle \frac{TP{H}_{species}}{TP{H}_{plot}}}\times 100$$

$$Relative importance=Relative dominance+Relative density$$

where:

TPH_species = TPH of a given species, TPH_plot = TPH of a given plot.

BA_species = BA of a given species, BA_plot = BA of a given plot.

We also calculated potential annual direct radiation and heat load to assess the relationship among slope, aspect, and temperature on regeneration at the site-level using equations from McCune and Keon, 2002 [65]. We calculated a topographic relative moisture index (TRMI), combining slope, aspect, elevation, and topographic position to estimate soil moisture conditions [64].

2.4. Statistical Analyses

We employed linear mixed-effects models to analyze data from all wildfire events (n = 27 plots), providing a more comprehensive view of the post-fire ecosystem dynamics that is less sensitive to unbalanced samples [79]. We fitted models to post-fire data, incorporating relevant pre-fire baseline metrics as covariates. Variables were selected a priori based on subject matter expertise, data availability, and the results of a correlation analysis using a calculated Spearman’s correlation coefficient from the HMISC package in R (Table S6) [80]. Variables were added or removed iteratively, and interaction effects were added when biologically appropriate among significant main effect terms [81]. Candidate models were compared based on a likelihood-ratio test and the Akaike information criterion (AIC) (Table S7) [82]. During variable selection, we used the Performance R package to calculate a variance inflation factor to assess for multicollinearity [83]. Plot was set as a random intercept in all models to address the dependence of repeated measures. Residuals in final models were tested for goodness-of-fit and model assumptions with simulated residual tests using functions in the DHARMa R package [84]. We chose not to cross-validate models with hold-out samples as this may not offer any additional information that model fit statistics do not already provide [85].

We identified influential outliers in the models using the DHARMa package [84]. We decided to retain or remove outliers based on biological significance and a review of field data and notes. One plot was removed from our regeneration analyses based on high counts of unidentified conifer germinants. We were unable to analyze fuel loading or regeneration in one plot due to a post-fire salvage harvest. Trace values (<2.5%) on ground cover and vegetation plots were standardized to 1.25% in order to calculate mean values. Due to a lack of post-fire regeneration and 100% overstory mortality, we excluded Rocky Mountain bristlecone pine, corkbark fir, Engelmann spruce, and blue spruce from regeneration analyses, and grouped these with fire-intolerant species for overstory analyses. Salix spp. was present on 1 plot as part of the regeneration post-fire and was not noted in the overstory; this was also excluded from regeneration analyses.

We utilized paired nonparametric analyses and summary statistics to measure changes between pre- and post-fire measurement years in the Wallow and Signal fires, which were measured consistently at 1-, 5-, and 10-years post-fire (n = 19). Due to the skewed distribution of the data, we utilized a Wilcoxon signed-rank test (hereafter WCX), a non-parametric equivalent of the repeated measures ANOVA [86]. At each time step we also performed a Kruskal–Wallis (hereafter K-W) test, with the response variable in question tested against fire severity. For further post hoc contrasts on the mixed-effects models, we used the emmeans R package [87] to examine significant trends and interactions through pairwise comparisons, using the Bonferroni correction to adjust for multiple comparisons.

Overstory structure and composition: We fit a logistic mixed-effects regression model with the lme4 package in R [88] to assess overstory mortality among species over time. ‘Species’, ‘CBI’, ‘time’, and ‘diameter class’ were included as model covariates among other potentially significant factors, and tree status (live or dead) was selected as the response, with only trees that were alive pre-fire included (Table S7). Time was coded as a continuous variable to correspond with the predicted trajectory of mortality. Overstory analyses were confined to L-M severity plots due to the near-100% mortality on H severity plots. To assess changes in the overstory diameter class distribution over time, we employed a Kolmogorov–Smirnov (hereafter K-S) test. This nonparametric test is designed to evaluate whether two samples are drawn from the same distribution [89]. Diameter distributions of dead trees were also compared pairwise between species at y1, y5, and y10 using a K-S test. Changes in live TPH, BA, QMD, and relative importance were assessed among species and diameter classes between measurement years using a WCX test. We utilized two-tailed tests for QMD and relative importance and right-tailed tests for BA and TPH since these values could only decrease through mortality and our study timeframe was not long enough for significant recruitment into the overstory. At each time step, we also utilized a K-W test on BA and TPH versus severity. Fuel Loading: Changes in fuel loading over time were analyzed by fitting a mixed-effects regression model on a Tweedie distribution using the glmmTMB R package with total calculated fuel load (Mg ha⁻¹) as the response variable [90]. Time (coded as a factor due to the variable direction of fuel load trends between measurement years) and severity were selected as predictors a priori (Table S7). We used a WCX test to analyze the magnitude of change from pre- to post-fire measurement years, subset by burn severity and fuel type. We also tested individual fuel type categories using a WCX test between all measurement years. A K-W test was performed comparing each fuel category against fire severity at each time step.

Understory response: Significant zero-inflation and overdispersion in our regeneration count data were identified using tests from the DHARMa R package [84]. We used a zero-inflated negative binomial mixed-effects model framework with the glmmTMB R package [90]. A zero-inflation formula to account for excess zeroes was selected based on model covariates that had a significant relationship with regeneration count as fixed-effects. Hardwood and conifer species were modeled separately to account for differing regeneration strategies (Table S7). Differences between pre- and post-fire regeneration TPH were assessed longitudinally in both L-M and H severity fires using a WCX test.

3. Results

Pre-fire plot overstory was composed primarily of southwestern white pine, Douglas fir, ponderosa pine, and white fir. Quaking aspen was present on 56% of plots, Engelmann spruce on 22%, and Gambel oak on 19%. Blue spruce and corkbark fir were present on 11% of plots and Rocky Mountain bristlecone pine was found on 1 plot (Table S5). Following fire, across the plot network we observed a complete extirpation of Engelmann spruce, blue spruce, corkbark fir, and Rocky Mountain bristlecone pine from the overstory, with no regeneration of these species identified up to y10. Gambel oak was removed entirely as an overstory component, and mature quaking aspen was seen only on three plots that burned at L-M severity or in unburned patches, and only at y1. Of the 16 plots that burned at H severity, all but 4 experienced full overstory mortality by y1. Surviving overstory components in those plots were minimal—25 trees in total, 12 of which would later die. Of the 11 plots that burned at L-M severity, live overstory components were present through the most recent remeasure, although significant immediate mortality and decline over time were observed in all plots. We also observed significantly higher post-fire fuel loading in H severity plots, and regeneration dynamics that favored sprouting hardwood species.

3.1. Overstory Structure and Composition

In the Wallow and Signal Mountain fires (n = 19 plots), H severity burning reduced overstory by >99% in TPH and BA, a significantly different outcome from L-M plots (p < 0.01). L-M severity plots experienced an average 66% reduction in live TPH and a 55% reduction in live BA by y10 (p < 0.01) (Figure 2A,B). The TPH mortality rate among most species groups followed a similar trajectory, with substantial mortality at y1 followed by steady and continued mortality through y10. By y10 we noted a 49% loss of ponderosa pine, a 57% loss of southwestern white pine, a 71% loss of Douglas fir, a 77% of loss white fir, and a 100% loss of fire-intolerant species (p < 0.05; Figure 2A).

There was an immediate post-fire decline in the relative importance of white fir and other fire-intolerant species, alongside an increase in that of ponderosa pine (Figure 2C). Southwestern white pine increased in relative importance at y1, followed by a consistent decrease through y10. The relative importance of Douglas fir was 21% lower than pre-fire by y10. Changes in relative importance were not statistically significant between measurement years or significantly different from pre-fire values (p > 0.05). Changes in QMD were not statistically significant (p > 0.05); southwestern white pine QMD decreased consistently through y10, while other species groups’ QMD remained relatively stable or increased slightly after y5 (Figure 2C,D).

The final overstory mortality model (R² = 0.42) found higher CBI to be the strongest predictors of reduced survivorship on L-M severity plots (

Table 2. Terms included in the mixed-effects overstory mortality model and their significance and effect size (odds-ratio). Bolded p-values indicate statistical significance (p < 0.05). CBI = composite burn index, d-class = diameter class. Reference levels for factors are as follows: species = Douglas fir, d-class = 18 cm.

Predictors	Odds Ratios	p-Value
(Intercept)	33.83	<0.001
Time	0.87	<0.001
CBI	0.06	<0.001
Pre-fire fire-tolerant basal area	1.04	0.037
Southwestern white pine	0.75	0.359
White fir	0.53	0.091
Ponderosa pine	1.53	0.301
Fire-intolerant species	0.39	0.152
28 cm d-class	2.40	<0.006
38 cm d-class	5.56	<0.001
48 cm d-class	5.91	<0.001
58 cm d-class	7.28	0.001
68+ cm d-class	3.49	0.057

and Table S7). Time since fire was also a significant predictor of reduced survivorship. Predictions based on diameter class were variable. Survival was generally predicted to be lowest in the 18, 28, and 68+ cm diameter classes; however, only pairwise contrasts against the 18 cm diameter class were statistically significant. Pre-fire fuel loading explained little variance in post-fire survival and was not included in the final model (p > 0.05). All plots in the warm/dry ecotype burned at H severity and were not included in the L-M model subset. Pre-fire fire tolerant BA was a significant predictor of increased survival; however, the effect size was small. The model did not predict a significant effect of species (p = 0.11).

Diameter distributions were significantly altered at y1 (p = 0.01), but no further significant changes were identified between y1 and y10 (p > 0.90) (Figure 3). Significant TPH and BA changes were identified in the 18 and 28 cm diameter classes between each measurement year. Mortality rates varied by diameter classes, with the largest proportional mortality in the 18, 28, and 68+ cm diameter classes (

Table 3. Average TPH (trees ha⁻¹) in overstory diameter classes by species pre-fire and 10-years post-fire on low–moderate severity plots in the Wallow and Signal Mountain fires (n = 8 plots). Pct change represents the percent change between pre-fire and 10 years post-fire TPH values. Significant (p < 0.05) changes from pre-fire values in the “All species” group were identified using a WCX test and are denoted by an asterisk (*). The standard error of the mean is denoted in parentheses.

Species	Time	10 cm Diameter Class
		18 cm	28 cm	38 cm	48 cm	58 cm	68+ cm
All species	Pre-fire	181 (46.6)	101 (13.8)	56 (11.8)	31 (4)	23 (2.5)	23 (6.3)
	10-yr	33 (11.6) *	35 (10.4) *	35 (7.8) *	18 (3.1)	13 (4.1)	6 (2.4)
	Pct change	−82.1%	−65.4%	−37.8%	−44.0%	−44.4%	−73.3%
Douglas fir	Pre-fire	102 (26.1)	31 (3.4)	33 (13.1)	20 (4.1)	13 (3.3)	13 (3.3)
	10-yr	10 (4.4)	13 (3.6)	18 (11.1)	13 (4.8)	13 (3.3)	3 (2.5)
	Pct change	−90.2%	−59.1%	−46.2%	−37.5%	0.0%	−81.3%
Fire-intolerant	Pre-fire	25 (11.9)	17 (6.7)	15 (5)	10 (0)	0 (0)	0 (0)
	10-yr	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
	Pct change	−100%	−100%	−100%	--	--	--
Ponderosa pine	Pre-fire	42 (20.6)	23 (7.5)	15 (2.2)	13 (2.5)	10 (0)	0 (0)
	10-yr	12 (7.3)	10 (7.1)	12 (3.1)	13 (2.5)	6 (2.4)	0 (0)
	Pct change	−71.4%	−55.6%	−22.2%	0.0%	−40.0%	--
White fir	Pre-fire	44 (10.3)	27 (8.9)	13 (3.3)	20 (10)	10 (0)	0 (0)
	10-yr	2 (1.7)	10 (6.9)	7 (3.3)	3 (2.5)	5 (5)	0 (0)
	Pct change	−96.2%	−63.2%	−50.0%	−87.5%	−50.0%	--
Southwestern white pine	Pre-fire	44 (6.1)	33 (6.7)	20 (4.6)	17 (3.3)	14 (2.4)	25 (15)
	10-yr	17 (6.4)	10 (2.7)	15 (4.2)	8 (4.8)	4 (2.4)	7 (3.3)
	Pct change	−61.3%	−69.2%	−25.0%	−55.0%	−71.4%	−73.3%

; Figure S3). Comparing dead tree TPH by diameter class at the species level, the diameter distribution of fire-killed southwestern white pine was significantly different from Douglas fir at all post-fire measurement years, and from ponderosa pine at y5 and y10. No other significant differences were detected between species. Among conifer species, mortality varied by diameter class (Table 3). Douglas fir had the lowest proportional mortality in the 48 and 58 cm diameter classes, ponderosa pine and southwestern white pine in the 38 and 48 cm classes, and white fir in the 38 and 58 cm classes.

3.2. Fuel Loading

By y10 total fuel loads had increased over pre-fire fuel loads by 82% on L-M and 163% on H severity plots (p = 0.15 and 0.02, respectively) (Figure 4). At y10, total fuel load on H severity plots was 2.5x that on L-M severity plots (p < 0.01). Changes in fuel loading also represented changes in fuel composition. Among H severity burns, there were significant increases in 1000 h fuels from pre-fire values by y10, alongside decreases in litter and duff (p < 0.05). In L-M severity burns changes from pre-fire among fuel types were not significant; however, between y5 and y10 both small fuels and 1000 h fuels increased significantly (p < 0.05). At y10, 1000 h fuels and total fuel load were significantly higher on plots that burned at an H severity than on plots that burned at an L-M severity (p < 0.05). The final fuel load model (R² = 0.51) predicted significant increases in fuel loading over time on H severity plots (p < 0.01) (

Table 4. Terms included in the mixed-effects fuel load model, and their significance and effect size (model estimate). Bolded p-values indicate statistical significance (p < 0.05). Reference levels for factors are as follows: time = 1-year post-fire. ‘x’ denotes interaction of terms.

Predictors	Estimate	p=
(Intercept)	62.16	<0.001
Time (y5)	3.13	<0.001
Time (y10)	6.70	<0.001
Severity (L-M)	1.04	0.916
Pre-fire fire tolerant BA	0.99	0.436
Fire-intolerant live BA	1.03	0.240
Pre-fire plot QMD	1.00	0.997
Time (5) x Severity (L-M)	0.42	0.045
Time (10) x Severity (L-M)	0.35	0.024

and Table S7). Other factors, such as pre-fire stand structure and species composition, contributed to the overall model fit but were not significant predictors of fuel loading (p > 0.20). Ecotype and pre-fire fuel load did not explain significant variance and were excluded from the final model (p = 0.56).

3.3. Understory Response

The immediate post-fire landscape saw increases in hardwood regeneration alongside decreases in conifer regeneration across all burn severities. On H severity plots, an initial pulse of post-fire conifer germination was followed by high mortality. By y10, southwestern white pine and white fir were completely absent, and Douglas fir was diminished from pre-fire levels and present on 11% of plots (p = 0.02, 0.04 and 0.18, respectively). Ponderosa pine increased in average density by y10, but this was primarily driven by large regeneration counts on three plots and was not significant (p = 0.59). Quaking aspen increased significantly in density at y1, along with non-significant increases in New Mexico locust and Gambel oak (p = 0.02, 0.06, 1.0, respectively). These large increases in quaking aspen and Gambel oak were followed by later declines through self-thinning (p = 0.01 and 0.27), while New Mexico locust continued to increase through y10 (p < 0.01) (Figure 5A,C). With the exception of ponderosa pine and quaking aspen, hardwoods were found on more plots between y1 and y10, while conifers were found on fewer plots (

Table 5. Percentage of plots containing live regeneration by species, severity, and time since fire in the Wallow and Signal Mountain fires (n = 9 high severity and 8 low–moderate severity plots).

	Severity
Species	Low–Moderate		High
	1 yr	10 yr	1 yr	10 yr
Quaking aspen	75%	50%	78%	67%
Gambel oak	50%	13%	33%	44%
New Mexico locust	38%	50%	56%	67%
Southwestern white pine	38%	50%	11%	0%
Douglas fir	63%	63%	22%	11%
White fir	25%	38%	22%	0%
Ponderosa pine	25%	50%	11%	22%

).

On L-M severity plots hardwood species followed similar trends at a lesser magnitude, with a significant increase in quaking aspen density immediately post-fire followed by a significant decline, and a significant increase in New Mexico locust between y5 and y10 (p < 0.05) (Figure 5B). Douglas fir was significantly reduced from its pre-fire density (p = 0.03), while southwestern white pine and ponderosa pine densities increased (Figure 5D). Between y1 and y10, conifer species maintained or increased their presence across the plot network, while hardwood species excluding New Mexico locust decreased (Table 5).

The hardwood mixed-effects model (R² = 0.48) predicted significantly higher regeneration on high severity plots (

Table 6. Terms included in the fitted mixed-effects hardwood regeneration model and their significance. Model estimates are reported as an incidence rate ratio, where values >1 indicate a positive effect on regeneration and <1 indicate a negative effect, with the distance from 1 indicating the magnitude. Bolded p-values indicate statistical significance (p < 0.05). Reference levels for factors are as follows: severity = high, species = New Mexico locust. ‘x’ denotes interaction of terms.

Predictors	Incidence Rate Ratio	p=
Count Model
(Intercept)	29.07	<0.001
Severity (L-M)	0.14	<0.001
Quaking aspen	9.15	<0.001
Gambel oak	3.11	0.021
Time (y5)	2.39	0.032
Time (y10)	6.39	<0.001
Slope	1.05	0.018
Live TPH (of a species)	1.02	0.025
Time (y5) x Quaking aspen	0.23	0.005
Time (y5) x Gambel oak	0.25	0.022
Time (y10) x Quaking aspen	0.05	<0.001
Time (y10) x Gambel oak	0.07	<0.001
Zero-Inflation Model
(Intercept)	0.9	0.729
Quaking aspen	0.49	0.062
Gambel oak	1.86	0.1
Severity (L-M)	1.32	0.387

and Table S7). Time post-fire was a significant predictor of decreased regeneration for quaking aspen and increased regeneration for New Mexico locust. Post-fire live overstory TPH of a given species was a predictor of a small increase in regeneration, however pre-fire presence or density of a species were not significant predictors (p > 0.05). Steeper slope was also a significant predictor of increased regeneration; however, the effect size was small. There were no significant terms in the zero-inflation model. Potential annual radiation and heat load did not contribute significantly to model fit for either the conifer or hardwood regeneration models and were not included in the final model (p = 1.0).

In the conifer mixed-effects model (R² = 0.54), severity and time were not significant predictors of regeneration counts. The live post-fire overstory TPH of a species predicted greater regeneration, and higher topographic relative moisture index (TRMI) predicted less regeneration, although the effect sizes of these were small (

Table 7. Terms included in the fitted mixed-effects conifer regeneration model and their significance. Model estimates are reported as an incidence rate ratio, where values >1 indicate a positive effect on regeneration and <1 indicate a negative effect, with the distance from 1 indicating the magnitude. Bolded p-values indicate statistical significance (p < 0.05). TRMI = topographic relative moisture index. Reference levels for factors are as follows: severity = high, species = ponderosa pine.

Predictors	Incidence Rate Ratio	p=
Count Model
(Intercept)	91.59	<0.001
Severity (L-M)	0.39	0.136
Time (y5)	1.17	0.58
Time (y10)	1.25	0.474
Douglas fir	0.86	0.621
Southwestern white pine	0.19	<0.001
White fir	1.19	0.659
TRMI	0.91	0.002
Live TPH (of a given species)	1.01	0.009
Zero-Inflation Model
(Intercept)	12.94	<0.001
Douglas fir	0.32	0.04
Southwestern white pine	0.49	0.241
White fir	1.11	0.847
Severity (L-M)	0.07	<0.001

and Table S7). Southwestern white pine was predicted to have the lowest regeneration count of all species in pairwise contrasts (p < 0.01). In the zero-inflation model, severity was a significant predictor of zero-regeneration counts, and additionally all individual species were predicted to have more zero-regeneration counts in H severity burns (p < 0.01). Douglas fir was predicted to have fewer zero-regeneration counts than ponderosa pine and white fir; however, these were not significant in post hoc tests when adjusting for multiple comparisons (p > 0.05).

4. Discussion

In the U.S. Southwest, a growing body of research has suggested that mixed conifer ecosystems may no longer be able to recover to their historical structure and composition following wildfire [2,16,35,47,91]. Research points to changing fire regimes and an increase in stand-replacing fires in both warm/dry and cool/wet mixed conifer ecosystems [1,2,13]. In an era marked by shifting baselines, understanding the dynamics of post-fire mortality and recovery is crucial for informing management strategies that may help sustain mixed conifer forests in the U.S. Southwest into the future [46,47].

We found evidence of substantial divergence from pre-fire conditions in (a) overstory structure and composition, (b) fuel loading, and (c) regeneration density and composition, suggesting long-term changes in the future structure and composition of stands recovering from high severity fires. Our findings are supported by other studies suggesting that high severity fires can act as ecological tipping points, leading to fundamental shifts in stand structure and composition [12,32,46,92]. The near-complete loss of overstory conifers in high severity burns, coupled with limited conifer regeneration, suggests that many of these sites may be transitioning toward hardwood-dominated ecosystems. Increasing fuel loads in these areas further elevates the risk of future high severity fires, reinforcing these shifts. In contrast, L-M severity burns allowed for greater survival of fire-tolerant conifers, although there were still evident shifts that persisted to y10 and may have the potential to alter future stand trajectories (Figures S1 and S2).

4.1. Overstory Structure and Composition

Mortality was substantial across all plots, but near-total in plots that burned at a high severity. On L-M severity plots, fire-adapted overstory species were favored and less-adapted species were extirpated entirely. Continuing significant post-fire mortality was observed through y10. As supported by other research, the mixed-effects overstory mortality model predicted ponderosa pine as the most likely species to survive, followed by Douglas fir and southwestern white pine [93]. The lack of significant differences between species in the model likely suggests that mature fire-tolerant conifers can survive L-M severity fires to a similar extent [94], which is also supported by the consistent small-tree mortality we observed across all species. The lack of significant variation in overstory mortality that can be explained by pre-fire density or fuel loading is likely limited by the small sample size (n = 11 plots) used by the model. The small number of plots in the warm/dry ecotype in our study also limited our interpretation of the effects of ecotype on overstory mortality. And while higher stand density generally contributes to post-fire mortality through increased severity and crown fire behavior [3], the small effect size in our model suggests that the effects of this on L-M plots in our study were minimal.

White fir, along with other fire- and drought-intolerant species, has encroached into lower elevation forests since fire exclusion [7,31]. This may explain the decline we observed in the post-fire relative importance of white fir and other fire-intolerant species, alongside minor fluctuations in the relative importance of fire-tolerant species. The long-term resilience of fire-tolerant species will likely depend on the severity and return interval of future fires. Frequent, low severity fire generally benefits the fire-tolerant survivors (i.e., ponderosa pine, Douglas fir, southwestern white pine), while high severity events with high overstory mortality initially benefit species with rapid colonization and sprouting regeneration (i.e., quaking aspen, New Mexico locust, Gambel oak) [32,95,96].

Our findings of elevated post-fire mortality rates in the smallest and largest diameter classes among fire-tolerant conifer species agree with studies describing a “U-shaped” mortality curve [97,98]. While post-fire mortality of smaller stems is common due to thinner bark and a lower crown base [99], mortality of large trees could be due to reduced vigor with age or a buildup of duff at the base causing smoldering and injury to root tissues [98]. Bark beetle attacks are also a common cause of mortality in large trees that may otherwise have survived [46,100]. Significant differences between species in diameter class mortality were likely due to their differing fire-resistant traits and other unique characteristics of species [50,101,102]. In the case of southwestern white pine, the confirmed presence of white pine blister rust on two plots that burned at L-M severity also brings into question whether the deleterious effects of the disease might have an additive effect with fire injury and other stressors to contribute to mortality [103]. The significance and large effect size of CBI in the mixed-effects overstory model further underscores the potential, even in L-M severity fires, of injuries sustained by fire to contribute to mortality [98,101,102]. A higher CBI generally corresponds with higher bole char on standing trees, a higher percentages of crown scorch, torching behavior, and consumption of soil substrate [70], which are all factors linked to tree mortality [101,102]. It seems likely that elevated large tree mortality could be due to a combination of prior stressors, fire-related injuries, environmental factors, and bark beetle attacks post-fire [46,98,102,104].

Ecological function is often closely linked to structural complexity [105], and shifts in the diameter distribution hold implications for forest health and resilience. Large trees are important stand elements, providing habitat, contributing to genetic diversity, increasing resilience against future fires, and offering esthetic benefits [106,107]. Endangered species, such as the Mexican spotted owl (Strix occidentalis Xántus de Vésey, 1860), prefer late-seral forests characterized by an interlocking canopy of large trees [36]. Small-diameter trees are also critical in supporting a future overstory. This is particularly important when managing forest health issues such as white pine blister rust, where a high ratio of small to large trees is necessary to offset additional mortality caused by the disease [108]. Small-diameter trees may also provide an important forage and nesting habitat for species such as the house wren (Troglodytes aedon Vieillot, 1809) or red-faced warbler (Cardellina rubrifrons Giraud, 1841) [109].

4.2. Fuel Loading

According to our results, increases in fuel loads were best predicted by burn severity. This has been noted by other researchers as a theme among high severity fires, which may contribute to the possibility of a high severity reburn through the rapid buildup of fuels [27,110]. Repeated high severity fires over a short interval pose challenges for forest ecosystem recovery due to the removal of mature seed sources, the death of seedlings and saplings, and the creation of a harsh growing environment [16,34]. Much of the increase in fuel load was driven by the 1000 h size class. The burning of heavy fuels in a second fire can cause disproportionate levels of root heating and mortality among live overstory trees [111,112], and may contribute to crowning and torching fire behavior [113]. We also observed significant consumption of the litter and duff layer, which has increased in many stands in the U.S. Southwest since fire exclusion [114]. Thick duff layers can contribute to smoldering combustion, potentially affecting soil properties such as water retention, nutrient content, and productivity [115,116,117]. Litter and duff also reduces moisture availability for seedling growth [118]. On the other hand, exposed bare mineral soil is generally the best substrate for conifer seedling establishment [119,120]. A reduction in litter and duff layers may help with seedling establishment, provided a seed source is present [120,121,122].

4.3. Understory Response

Species that were extirpated from the overstory were also missing from the post-fire understory. On high severity plots, we observed a large post-fire increase in sprouting hardwood regeneration alongside a lack of regeneration for coniferous species. When conifer regeneration was present after high severity burns, it is likely that conifer refugia (live overstory) were present nearby [43,123]. This is supported by the inclusion of live overstory TPH as a significant term in the mixed-effects model. The effect of refugia may differ by species. Douglas fir has lighter seeds than ponderosa pine and southwestern white pine, which may allow it to disperse over longer distances via wind [123,124]. In contrast, southwestern white pine regeneration was only present in L-M severity plots with residual live southwestern white pine overstory. The closely related limber pine can be an effective early-seral colonizer in part due to avian seed dispersal; it is likely that southwestern white pine lacks these relationships to the degree that allow it to effectively colonize large burned patches [124,125]. A lack of white fir regeneration in high severity patches may be explained by its primarily gravity-driven seed dispersal and relative fire intolerance [67,126].

Ponderosa pine seedlings were found in greater abundance by y10 than pre-fire on both high and L-M severity plots. Studies have predicted a decline in ponderosa pine regeneration under unfavorable climatic conditions, particularly in harsh post-disturbance environments or more xeric sites [19,127]. In contrast, some studies found ponderosa pine in greater abundance in post-fire environments on north-facing slopes and higher elevations with greater moisture availability [43,128,129]. Given that much of the post-fire ponderosa pine regeneration in our study occurred on cool/wet mixed conifer sites, it is possible that, where seed is readily available, ponderosa pine may find a new niche that is less climatically limiting [43,128].

The inclusion of TRMI as a significant term in the conifer regeneration model may be explained in part by its strong positive correlation with CBI in our correlational analyses, its negative correlations with elevation, or its positive correlation with maximum temperatures. In general, sites with a higher TRMI tend to burn hotter, are at lower elevations, and have hotter summers. Similarly, the inclusion of slope in the hardwood model may be explained by it being a component of heat load and potential annual radiation [65], although neither of these variables were significant. It should be noted that in correlational analyses, slope had a small but positive correlation with heat load and a weak negative correlation with potential annual radiation. Site and climate clearly play an important role in regeneration dynamics following a fire [26,128], however our study was not prepared to address this question due to a small sample size and lack of experimental controls.

We found evidence of self-thinning among quaking aspen and Gambel oak, a common trait among these species upon reaching high densities [130,131]. While New Mexico locust density continued to increase in all plots where it was present through y10, we hypothesize that it will soon reach a state of density-dependent mortality under increased competition. Some research suggests that New Mexico locust may remain abundant on a site for 10–20 years, until it begins to be out-competed by conifers, provided a seed source is present [132,133]. Other research suggests that New Mexico locust may become a dominant part of an oak-scrub-juniper ecosystem [19,37]. Regarding the long-term viability of hardwood species on these sites, both aspen and Gambel oak may benefit from a mixed-severity fire regime both through their recruitment methods and the easing of browsing pressure [134]. Gambel oak is well-adapted to low soil moisture and thrives in a post-disturbance environment, making an increase in abundance under a warming climate likely [135]. Gambel oak has been documented to survive in frequent-fire regimes [136] and to thrive in post-fire environments, potentially becoming a dominant component of future stands [45]. Aspen is less tolerant of drought and is already in decline on many sites due to fire exclusion [137,138]. Linkages between high severity fires and increased aspen regeneration are well-researched [31,42,62,134]. Increases in the frequency of high severity fire events under climate change may help aspen expand to higher-elevation sites as climate change and other factors remove it from lower elevations [139,140,141]. There is evidence to suggest the possibility of a permanent hardwood conversion in these systems [23,32,43,44]. On high severity plots in our study, changes in overstory and regeneration dynamics and lack of conifer seed sources may support this eventual outcome.

4.4. Comparison of Findings

While it is important to view stand-replacing fires through the lens of historical fire regimes [42,142], it is also critical to recognize that anthropogenic changes may make these historic regimes and ecosystems untenable in many places [91,143]. Across our plot network, 59% of plots burned at high severity. This is comparable to a study by Roccaforte (2013) [39] on the Wallow fire which assessed 50% of mixed conifer stands burning at high severity, representing roughly 16,000 ha. When viewed at a landscape-scale, this represents a large proportion of forest lacking the structural and compositional complexity and ecosystem functions of mature stands [36,54,105]. Other studies in mixed conifer ecosystems across the US Southwest have also noted large patches of high severity fire and their effects on overstory mortality and tree regeneration (for example, see: [2,12,47,144]. The shift towards hardwood dominance and potential for ecotype conversion has been widely observed and predicted (see: [16,32,44,46]), as have changes in fuel load and their potential implications (see: [110,145,146]). Our study contributes to this growing body of research on changing fire regimes.

Our results offer a long-term perspective in the U.S. Southwest, with applicability to managers of mixed conifer fire-dependent ecosystems across the Western US and beyond facing similar challenges [10,11,13]. Relatively few long-term studies have taken place in post-fire mixed conifer forests in the U.S. Southwest [24,27,31,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53]. Of these, many were based on remote sensing data or simulations [27,35,36,37,38,39,40,41]. Few field studies included pre-fire plot data [5,50,53,147], extended to 10-years post-fire [46,48,50,51,147], included revisits to field sites [46,48], or utilized an in situ measure of burn severity [52,53]. Given the prevalence of cool/wet mixed conifer stands in our study, our results are unlikely to add to the body of research on warm/dry mixed conifer ecotypes [7,54,55,121]. While stand-replacing events are not within the historic norm of most warm/dry mixed conifer sites, a mixed-severity fire regime has been documented in the past in some locations [42,54,55]. In less-researched cool/wet mixed conifer forests, successional trajectories are believed to be highly variable [54,55,148] and evidence suggests a history of mixed-severity fire creating a mosaic of high severity burned patches across the landscape [39,55,57,149]. In these ecosystems, our study may fill a knowledge gap. Further extension of our findings to mixed conifer across the Western US [4,143,149,150], mixed woods, and boreal forests [151,152], or even comparable fire regimes and ecosystems worldwide [126,153,154] can be performed with caution.

4.5. Management Implications

Ensuring conifers have the potential to re-establish, particularly species functionally extirpated or less well-adapted for seed dispersal, may require active intervention. Significant climate-driven shifts have been predicted for southwestern white pine, ponderosa pine, quaking aspen, and Douglas fir [18,19,20,21]. Reforestation may be crucial after stand-replacing events, particularly in the context of climate change [26,155]. Identifying climate-resilient seed sources, expanding nursery operations, and prioritizing action at a strategic level will be necessary to successfully reforest the increased burned area anticipated [156]. Common garden experiments have shown promise in identifying drought- and heat-resistant traits in several key species, but more work is needed [157,158,159]. Overstory refugia should be protected from additional post-fire mortality factors (e.g., bark beetles) to enhance natural regeneration following fires [160]. The loss of mature fire-tolerant overstory trees is also concerning for managers looking to maintain a resilient stand structure [106,107], protect important disease-resistant genetic resources [108,161], or provide habitat for wildlife or other ecosystem services [36,109]. Active management prioritizing the protection of these trees may be necessary if managing for old-growth attributes [162].

The negative ecological effects of fire exclusion [3] indicate that the best strategy to sustain healthy forests may be to encourage and guide fire rather than suppress it [57,163]. Managers seeking to reduce high-severity fire hazard and/or restore historical structures should embrace L-M fire effects, as these landscapes have evolved with fire and fire exclusion is a relatively recent deviation from the norm [3,54,55]. L-M severity wildfires or prescribed burns following a fuel treatment have been shown to produce forest structures that are resilient against future high severity fires [45,51,57,145]. They also have the potential to improve native understory cover [48,51] and wildlife habitat [57,164]. As we saw in our data, these fires can also remove encroaching fire-intolerant species like white fir that contribute to ladder fuels and alter stand structure and composition [7,31,54,62]. Fuel reduction treatments and efforts to restore stand structures and historic fire regimes can also lower the potential for large high severity fires and increase stand resilience against the effects of climate change [2,54,91,147]. Due to increased fuel loads, high severity fires are more likely to be followed by a high severity reburn [27,28]. Salvage logging and prescribed fire both offer opportunities to reduce fuel loads and should be applied carefully with consideration to their broader ecological context [165,166]. Particularly in the early stages of stand development, a reburn has the potential to alter recovery trajectories and solidify long-term changes in stand structure and composition [23,41,145]. We recommend silvicultural prescriptions that provide for reduced fire hazard while maintaining heterogeneity in structure and species composition alongside the use of L-M severity fire. These treatments are likely to create more resilient stands that will better withstand future disturbances and the changing climate [54,55,167].

5. Conclusions

Long-term developments in stand dynamics are often determined in the years immediately following a fire, known as reorganization [23]. In the 10 years post-fire, we observed that the future of these stands was mostly dependent on burn severity. In L-M severity fires, changes to the overstory were primarily structural, with compositional changes limited to the reduction and removal of some fire-intolerant species. The loss of mature trees, while ecologically important [36,106,107], will likely be offset over time through regeneration and the recruitment of smaller stems released from competition [22]. Although notable, changes in fuel loading were not significant and the regeneration environment was not substantially different from pre-fire by year 10. Given time, it is likely that these systems will return to something resembling their pre-fire state, although some species may be missing. In contrast, high severity fires represented a return to early successional systems [22], with evidence of significant changes in both structure and composition suggesting potentially long-lasting effects [23,32,44]. Overstory mortality was near-total; absent of a seed source, conifer regeneration was generally supplanted by resprouting hardwood, and conifers may not be able to re-colonize due to a lack of overstory refugia [23,32,43,123]. Increases in fuel load signaled an increased risk of a high severity reburn [27,28], potentially entrenching these changes in species composition [23,41,145]. While we did not see evidence for transition from forested to non-forested states, these changes from conifer- to hardwood-dominated ecosystems have the potential to be ‘locked-in’ for the foreseeable future [23].

Our study identified several gaps in the current body of research. Cool/wet mixed conifer ecosystems in the U.S. Southwest have received relatively little research attention. Some studies suggest cool/wet mixed conifer forests have been less affected by fire exclusion and may still be within their historic range of variability [39,55,168]. Others suggest this may no longer be the case [1,5,42]. Research on the effects of L-M severity fires on regeneration in mixed conifer forests has also been limited, and results have been inconclusive or contradictory on to the effect of burn severity [26,49,125,126,127,129].

Future research should focus on increasing our understanding of historic and current mixed conifer structure and composition in the context of changing climate and fire regimes. Research is also needed to further elucidate the role of abiotic factors and overstory refugia in post-fire regeneration establishment and success. Such research would assist in developing more refined management recommendations, including active mature forest management (e.g., fire hazard reduction treatments) and post-fire reforestation planning. Finally, forest stand dynamics over longer time scales (multiple decades) may not align with shorter-term results, highlighting the importance of continued remeasurements of permanent plot networks [143].

Long-term successional trajectories in southwestern mixed conifer forests are poorly understood due to the complexity of these systems and occur across long temporal scales, from decades to centuries [55]. While this study offers some insights, more research is needed into these valuable ecosystems to manage them in an uncertain future.