Interspecific Responses to Fire in a Mixed Forest Reveal Differences in Seasonal Growth
by
, ,
Jesús Efrén Gutiérrez-Gutiérrez
1, 
José Alexis Martínez-Rivas
1,2
,
Andrea Cecilia Acosta-Hernández
1,
Felipa de Jesús Rodríguez-Flores
3,4 and
Marín Pompa-García
1,*
1
Laboratorio de Dendroecología, Facultad de Ciencias Forestales y Ambientales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd. Durango S/N Col. Valle del Sur, Durango 34120, Mexico
2
Programa Institucional de Doctorado en Ciencias Agropecuarias y Forestales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd. Durango S/N Col. Valle del Sur, Durango 34120, Mexico
3
Ingeniería Ambiental y Sustentabilidad, Universidad Politécnica de Durango, Carr. Mexico. Km 9.5, Dolores Hidalgo, Durango 78321, Mexico
4
Facultad de Ciencias Forestales y Ambientales, Universidad Juárez del Estado de Durango, Río Papaloapan y Blvd. Durango, S/N Col. Valle del Sur, Durango 34120, Mexico
*
Author to whom correspondence should be addressed.
Forests 2025, 16(4), 633; https://doi.org/10.3390/f16040633
Submission received: 28 February 2025
/
Revised: 2 April 2025
/
Accepted: 3 April 2025
/
Published: 5 April 2025
(This article belongs to the Special Issue Forest Responses to Fires)
Abstract
:Despite recurring episodes of fire exacerbated by climate change, post-fire dynamics in trees remain to be fully understood. In a mixed forest in northern Mexico that experiences frequent fires, we aimed to determine how tree growth responds to surface fire by examining earlywood (EW) and latewood (LW) responsiveness, as well as their connection with canopy activity, using UAV-acquired NDVI data. We compared EW and LW growth from mini cores of burned and unburned trees (n = 100) across four species, correlating this with NDVI data from 33 UAV monthly flights at the individual tree level from 2021 to 2023. Our results identified Quercus durifolia Seemen as the species that presented the highest growth following exposure to surface fire. Arbutus arizonica (A. Gray) Sarg. was the species most affected by fire in terms of EW production immediately after burning but showed benefits in subsequent summers. Juniperus deppeana Steud. demonstrated adaptive plasticity by responding more quickly to fire, with notable growth in EW. Pinus engelmannii Carrière responded in 2023, and its NDVI was associated to the least extent with seasonal growth. Thus, there is an evident seasonal response in trees subjected to low-intensity fire, which can act to shape the stand habitat. However, there is a divergence in response between broadleaf and evergreen species that could be attributed to fire-adaptive traits and hydraulic strategies. Although combining the tree-ring data with the NDVI served to improve our understanding of the effects of fire, further research is required.
1. Introduction
Given the increasing frequency of forest fires (i.e., the Pyrocene age [1]), forests are changing in composition, structure, and functionality. When these disturbances combine with hydroclimatic variations, they also impact the phenological cycles of forest species, affecting the timing and rate of radial growth [2]. In the presence of fire, tree growth responds negatively depending on the severity and type of fire, activating various eco-physiological mechanisms that regulate competition and the distribution of nutrients, water, and light.
Although the post-fire radial growth of trees has traditionally been documented through growth rates [3], dendroecology has recently benefited from the availability of spectral remote sensors that can record processes invisible to the human eye [4]. In particular, the use of drone technology has enabled the tracking of the dynamics of vegetation in detail and near real-time, improving the spatial, spectral, and temporal correspondence of the data obtained. Linking canopy monitoring via remote sensors using the Normalized Difference Vegetation Index (hereafter, NDVI) and tree growth rings significantly enhances our understanding of the connections between primary (i.e., canopy phenology) and secondary (i.e., radial growth) growth in trees.
Our understanding of the potential interactions between canopy activity and wood formation remains incomplete and the topic is the subject of debate [5]. For instance, Gallardo et al. [6] suggest a strong dependence between the NDVI of canopies and tree-ring width. Conversely, studies by Brehaut and Danby [7] and Mašek et al. [5] found no such significant associations. These connections have mostly been analyzed with approximate spatial resolution [8], meaning that the models require further adjustments since the sensors capture the total canopy cover of the stand rather than individual tree responses [9]. It is, therefore, advisable to analyze the individual tree as the basic unit of analysis to strengthen the explanatory capacities between the temporality of EW and LW [3,10] and the spectral capacity of the NDVI to which the tree is environmentally exposed while being subjected to fire.
Increasingly warmer climate conditions are leading to frequent and severe droughts in megadiverse extratropical ecosystems [3]. Conifers and hardwood trees dominate the forest stands in these systems, which has raised concerns in the scientific community regarding ecosystem functioning [8]. The mixed forests of northern Mexico are subjected to frequent and exacerbated fire events, which are in turn related to drought episodes [4]. Despite their significant commercial and ecological multifunctionality [2], the mechanisms underlying seasonal responses to fire in these forests remain unclear. For instance, in this biodiverse region, the interaction between fire regimes and indigenous knowledge is considered strategic for sustainable forest management [11]. Thus, given their greater resilience compared to monoculture forests, the convergence of species in these forests offers an ideal natural laboratory for investigating how trees cope with fire disturbances.
In this study, we address whether trees facing surface fire are seasonally responsive in terms of LW and EW radial growth and explore the potential of analyzing canopy activity from UAV-acquired NDVI data to elucidate these responses. Our specific aims were (i) to compare the EW and LW growth between burned and unburned coexisting trees and (ii) to analyze the relationships between seasonal growth (EW, LW) and NDVI data at the individual tree level. We expect differentiated interspecific tree responses modulated by the impact of fire.
2. Materials and Methods
2.1. Study Area and Tree-Ring and NDVI Data
The study area is located within the Tarahumara Sierra of Mexico (Figure 1) and corresponds to an uneven-aged natural forest (27.149167° N, 107.111389° W; 2400 m above sea level). The vegetation is dominated by the genera Pinus and Quercus, mixed with Juniperus and Arbutus. Leptosol soils featuring stoniness and a clayey texture prevail in this moderately sloped site. The climate is predominantly semi-cold and semi-humid, with long summers, monsoon rains, and winter precipitation [2].
On 14 March 2024, we selected 50 trees of different species that were previously subjected to fire and another 50 unburned (i.e., control) individuals adjacent to the burned site. The phenotypic characteristics of the trees were similar in terms of basal diameter (DBH) and height (A. arizonica [≤1.5 cm and ≤0.3 m, respectively], J. deppeana [≤4.2 cm and ≤0.1 m, respectively], P. engelmannii [≤0.7 cm and ≤0.6 m, respectively], and Q. durifolia [≤1.8 cm and ≤2.4 m, respectively]) (Table 1). The prescribed burn was carried out on 23 March 2021 with the help of CONAFOR personnel (see details of the prescribed burn in Table A1).
A mini core of ~1.5 cm was extracted from the trunk of each tree at a height of 1.3 m as a strategy to obtain at least three years of growth rings from the 2021–2023 period (Figure A1). These mini cores were mounted on wooden frames, dried, progressively sanded, and dated [12]. Finally, the total ring width (TRW), earlywood width (EW), and latewood width (LW) were measured with a Velmex tree-ring measuring system (Velmex Inc., Bloomfield, NY, USA), with a precision of ±0.01 mm. Abrupt changes in the tissue structure were visually detected to discern the limits of the EW and LW, following the methodology proposed by Cherubini et al. [13].
To further our understanding of the trees’ response to fire during the 2021–2023 period, we analyzed the relationships of EW and LW to monthly NDVI data at the individual tree level obtained with a DJI Phantom 4 multispectral quadcopter drone (Innovation Technology Co., Shenzhen, China). A total of 33 monthly flight missions were conducted at approximately noon on clear days between April 2021 and December 2023. The resulting images were processed and analyzed using digital photogrammetry, producing multispectral orthophotos (of image size 1600 × 1300 pixels). For each canopy tree, we calculated NDVI = (NIR − R)/(NIR + R) at the level of the individual tree canopy, where NIR and R are near-infrared and red values, respectively. To reduce reflectance errors, we standardized the NDVI values at the same time to strengthen their consistency [4].
2.2. Statistical Analysis
To test for significant differences in radial growth in EW and LW between burned and unburned trees from 2021 to 2023, an analysis of variance (ANOVA) was conducted among the four species. For significant differences found in growth between the different factors (species and site), a Bonferroni post hoc test was performed to verify multiple comparisons. All data processing and visualization of results were conducted using the free statistical software R Studio, Version 3.0.1 [14]. Lastly, we conducted a Pearson correlation analysis between the seasonal ring width measurements (EW and LW) and the monthly NDVI values per species.
3. Results
From 2021 to 2023 in the control site, A. arizonica had the highest seasonal EW growth, while Q. durifolia had the lowest. However, for LW, Q. durifolia presented the highest averages and J. deppeana the lowest. In the burned trees, J. deppeana had the highest EW and Q. durifolia the lowest, while Q. durifolia, followed by P. engelmannii, once again surpassed A. arizonica and J. deppeana in terms of LW (Table 2).
The ANOVA for TRW (Table A2) from 2021 to 2023 indicated marked and important differences among the species, while no difference was found in burned versus unburned trees.
Seasonally, for EW, we found differences between burned and control trees, including a significant effect per species and year of growth. However, the interactions between site and species, as well as between site and year, indicated that the factor site did not influence the observed temporal trends. The interaction between species and year highlights the fact that the responses of the different species vary temporally. Finally, the three-way interaction between site, species, and year showed that the combined effect of these factors did not generate significant differences in the response variable (Figure 2 and Table A2).
The ANOVA results (Table A2) for LW show that the effect of species was highly significant, while the effects of site and year had no notable impact. However, the effect of species varied by site since the responses of the different species changed significantly over time. Finally, the interactions between site and year, as well as the three-way interaction between site, species, and year, were not significant (Figure 2 and Table A2).
In terms of the growth of EW in deciduous trees, Q. durifolia and A. arizonica showed significant differences over the three years, with the former presenting a consistent pattern of higher growth in the burned site across all three years and significant differences between years (Figure 2A and Table A3), while the latter consistently showed higher growth in the control site. On the other hand, the conifers J. deppeana and P. engelmannii presented a synchronized temporal pattern. Significant differences were observed in 2021 and 2023, while none were detected in 2022, indicating that similar growth had occurred under both treatments. Quercus durifolia showed higher LW growth in burned trees over the three years, while A. arizonica presented higher growth in the control trees, with significant differences detected between years. In the case of J. deppeana, higher growth was observed in the control trees in 2021, while in 2022 and 2023, growth was significantly greater in the burned sites. For P. engelmannii, significant differences were found in 2021 and 2022, with higher growth found in the control sites in all three years. In 2023, however, higher growth was observed in the burned sites (Table A4).
The information in Figure 3 shows the oscillatory dynamics of NDVI and the formation of EW and LW. Bypassing an in-depth comparison between burned and unburned trees (addressed later), a similar trend was observed, which differed marginally in magnitude and timing. The highest NDVI values for A. arizonica and Q. durifolia occurred in late summer and early autumn during LW formation, while the lowest values in these species occurred at the end of spring, when EW was formed. The conifers P. engelmannii and J. deppeana reached their peak at the end of winter, extending to early spring with the beginning of EW formation. Synchronously, the lowest green index values occurred in late summer and early autumn, when LW formation began.
In short, the graphical correspondence between the NDVI and the formation of EW and LW occurred inversely between the deciduous trees and conifers. While Q. durifolia and A. arizonica seemed to match their NDVI with LW formation during summer-autumn, J. deppeana and P. engelmannii showed their correspondence with EW in winter-spring, although this aspect requires further analysis. According to Pompa-García et al. [2], A. arizonica and Q. durifolia exhibit similar behavior in terms of the timing of seasonal wood formation. The formation of EW begins in spring, with peak cell production occurring in May, while the formation of LW cells begins in summer, reaching maximum production in July. For J. deppeana and P. engelmannii, the formation of EW began in early spring, with peak production in August-September, while LW formation began as early as July and reached its peak production in autumn.
The correlation analysis revealed the interspecific differences between burned and control trees. Arbutus arizonica only showed negative correlations with the burned trees. The NDVI values during the application of the burn (i.e., early spring), which were seasonally replicated in the following year, negatively affected EW production. However, positive NDVI values in the subsequent summers strengthened growth in this species. LW production was negatively associated with the NDVI values in the following spring for the unburned trees, while no notable association was found in burned trees (Figure 4).
Quercus durifolia had greater positive associations between NDVI and EW in burned trees than in unburned trees. This was particularly true in the fire-exposed trees during the late summer and early autumn following the burn, while in the control trees, this occurred in the subsequent winter season. The LW was only negatively associated with NDVI at the start of the autumn immediately after the burn in the control trees, with no negative associations found in the burned trees (Figure 4).
The EW of J. deppeana showed a negative association with NDVI, mostly in the control site during the winter and spring following the burn, while this negative effect was accentuated in the burned site during the late autumn and early winter, almost 30 months after the burn had been applied. The NDVI–LW relationship presented a similar pattern to that of EW at the control sites but of greater magnitude. No significant association was found at the burned sites (Figure 4).
The EW of P. engelmannii only had a slight negative association with the NDVI of the final summer of the studied period for burned trees and no association for the control trees. The LW only showed a negative association at the beginning of autumn immediately after the burn in control trees, while there was no relationship apparent in the burned sites (Figure 4).
4. Discussion
4.1. Post-Fire Seasonal Radial Growth–NDVI Relationships
By integrating tree-ring and spectral data of coexisting species in a stand with similar microsite environmental conditions, we demonstrated that post-fire seasonal radial growth responses exhibited species-specific patterns. The divergence in responses between broadleaf and conifers appears to be attributable to the temporal linkage between EW, LW, and NDVI, including fire-adaptive traits and hydraulic strategies.
Quercus durifolia grew more in absolute terms compared to the other trees during the study period, particularly when subjected to fire, with LW growth in this species prominent above that of the other species. This species presents notable resilience due to its comparatively efficient resource use in the face of stress. The underlying mechanisms of fire tolerance remain the subject of ongoing research [15]; however, the evidence suggests the crucial role of wood density in LW, which is associated with greater resistance to embolism, given the thick cell walls that impede cell collapse. This species can thus resist episodes of water stress and tree deciduousness when fire dries out the soil moisture [16]. In other words, Q. durifolia follows an opportunistic and perhaps anisohydric strategy, which allows it to photosynthesize and form structural carbohydrates as a result of the high photosynthesis rates it presents per unit leaf area. According to Álvarez-Yépiz et al. [17], oaks have an ecophysiological advantage over conifers since they reduce the cost of foliage production to maximize carbon gain, shedding their foliage when soil moisture decreases and evapotranspiration rates rise. Despite this anisohydric strategy, Q. durifolia may be susceptible to carbon starvation and decay under conditions of elevated soil evaporation rates resulting from increased temperatures and heat stress [2].
The positive relationships of NDVI in winter and autumn with the EW of Q. durifolia confirm the growth capacity of this species. Its extensive root system, regenerative stump sprouting, greater leaf thickness, and smaller stomata also represent opportunistic adaptive strategies to deal with the presence of fire, increasing xylem resistance to cavitation and leaf senescence [18].
Arbutus arizonica exhibited pronounced susceptibility to fire in terms of EW growth when exposed to fire. This can be explained by hydraulic dysfunction induced by heat stress, a process that provokes the depletion of non-structural carbohydrates [19]. Although this species could potentially leverage its resprouting capability to cope with climate change, this advantage also implies a continuity of the understory stratum, which involves a high fire risk, as well as the high cost of allocating resources to vegetative growth and the consequent competition for resources [20]. This was evident in the control trees, where the spring NDVI was found to affect LW production (Figure 4).
At least in the short term, our results contrast with those found by Quevedo et al. [21] and González-Pelayo et al. [22], who describe this species as resilient to fire. However, the ability to benefit from NDVI in subsequent summers, as reflected in its EW production (Figure 4), could be considered a step toward resilience. The positive associations of NDVI also suggest that the leaves that persist until the next season may constitute a mechanism to compensate for the radial growth.
Juniperus deppeana temporarily presented variable post-fire resilience strategies, in which EW responded favorably to the immediate disturbance, but LW stood out in terms of its growth after the year in which the fire was applied. Indeed, EW has been reported to present higher plasticity than LW [10]. It is known that EW forms the majority of the growth ring and is responsible for transporting nutrients to new leaves at the beginning of the growth season [23]. There is also a strong correlation between EW and forest vigor. This species demonstrated an adaptive capacity, with notable EW growth as a response to the immediate fire, and can adjust its nutrient distribution without risking the resources necessary to modify its physiology [24]. One trait that may have contributed to its resilience was tree height since the crowns avoided significant damage from the applied surface fire. High branch conductivity suggests a compensatory mechanism to transport water to the leaves under regimes of fire stress. It is also known that, when crowns limit their photosynthetic rates, cytokinins in the root stimulate the resprouting response [25]. This species is capable of repopulating post-fire areas, with a high tolerance for marginal conditions [26] (Table A2). However, severe and frequent fires can put it at risk [27]. Juniperus deppeana has exhibited an ecological dynamic influenced by fire [25], showing traits of plasticity and efficient water use, especially in young individuals that respond to rapid environmental fluctuations with high photosynthetic rates [28]. Its fire resistance is also attributed to its thick bark, lignotuber, and epicormic buds [29].
Finally, P. engelmannii showed a low response to the prescribed burn, indicating its greater tolerance to fire, at least in the short term. Its response was recorded as a delayed effect of the fire and occurred up to 2023. This slight impact is attributed to the resistance of its thick bark, as well as the low intensity of the fire, which did not significantly damage its cambial activity or tree crowns, including their potential water reserve. However, in more intense fires, it has been found that pines reduce their basal area even when their crowns are undamaged [10]. In other words, the hydraulic disruption induced by heat-induced cavitation can cause failures in physiological functions that act to reduce photosynthetic activity [30], affecting the EW/LW proportions that influence wood density [31].
The inclusion of NDVI data acquired by drones has been advantageous in explaining the complex impacts of fire on radial growth. The relationship revealed in the results suggests an undeniable connection between the aerial part of the tree and its trunk. However, this connection is temporally and physically misaligned and can vary between species. The foliar activity recorded in the leaves implies that the phytobiomass stored in the trunk features a certain temporal delay [32], even though light is not a limiting factor at our study site (but see Lusk et al. [33]).
The post-fire growth responses identified fire as a filter that promotes habitat partitioning [17,34], favoring the coexistence of conifers and broadleaf trees. However, we recognize that there is still a need to characterize the functional response of each species in sites that represent their optimum habitat in the study area. The divergence between isohydric and anisohydric plants [35,36] could also explain our results. For example, Q. durifolia, an anisohydric species, maintains its functions but is vulnerable to extreme drought, whereas A. arizonica, J. deppeana, and P. engelmannii, as isohydric species, have proven to be more sensitive to fire stress, which halts their growth, although their recovery rates have been faster than that of the oaks [35]. In this fire-prone ecosystem, trees can exhibit a variety of ecological strategies and adaptive traits, such as sprouting abilities, thick bark as an insulating layer, and deep roots for improved access to nutrients. For instance, the evergreen species studied here could show a decrease in radial growth due to high demand for carbohydrates during a longer growth season, while broadleaf species present a dependence on reserves assimilated during the previous year to ensure resource acquisition [2].
4.2. Limitations
The results are only conclusive for the first three years of analysis and many factors remain to be evaluated, including the intensity and seasonality of the fire [37], as well as the biometric attributes of the trees. The effect of fire on tree growth appears to be more indirect, being more closely related to the prevailing conditions after the disturbance. It is, therefore, highly advisable to extend the monitoring period beyond the time horizon of this study and incorporate different intensities and timings of fire. It has been reported that species burned in early spring have a comparative advantage over those burned later in the year due to increased carbohydrate (mainly starches) storage [37]. The temporal correspondence between radial growth and crown activity remains a subject of discussion, since this decoupling has been observed in similar studies [38]. Although our results showed post-fire responses in the short term, potential legacy effects (e.g., decay, mortality, and loss of productivity) could occur as a result of warmer and drier scenarios. For instance, tree-ring growth relies mostly on carbon accumulation many months after the fire [4]. Under our approach, however, it is challenging to predict full-term patterns, and further research must therefore be undertaken. This should include the coupling of anatomical studies with dendrochronology [23] as well as climate relationships.
5. Conclusions
The trees subjected to a prescribed low-intensity burn responded seasonally differently from those that were unburned. This was evident in the relationships between the earlywood (EW), latewood (LW), and NDVI data. The favorable response of Q. durifolia to surface fire is highlighted, while A. arizonica showed high vulnerability to fire. The divergence in tree responses appears to identify fire as a filter that can promote species composition. However, further experimentation is required to assess the effects of fire intensity and timing, as well as its association with climate data in this region, which has high biodiversity and is subject to seasonal hydroclimatic variability.
Author Contributions
Conceptualization, M.P.-G.; methodology, M.P.-G.; formal analysis, J.A.M.-R., A.C.A.-H., J.E.G.-G., and F.d.J.R.-F.; writing—original draft preparation, M.P.-G., J.A.M.-R., A.C.A.-H., J.E.G.-G., and F.d.J.R.-F.; writing—review and editing, M.P.-G.; project administration, M.P.-G. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
Dataset available on request from the authors.
Acknowledgments
We thank DendroRed (http://dendrored.ujed.mx; accessed 27 February 2025). We also acknowledge Ejido Papajichi, Dirección Forestal of the municipality of Guachochi, CONAFOR Chihuahua, and the Dirección Forestal of the Chihuahua State Government. Thanks also go to Juan Pablo Crespo Antia for statistical advice. This work was conducted within the framework of Academic Mobility supported by the UJED “Postdoctoral and Sabbatical Stays for Consolidation of Researchers Teams”. We acknowledge the support given by UQAC at Chicoutimi and thank Sergio Rossi for his helpful assistance.
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A
Figure A1.
Dated samples of individuals of the studied species (A) P. engelmannii, (B) J. deppeana, (C) A. arizonica, and (D) Q. durifolia.
Figure A1.
Dated samples of individuals of the studied species (A) P. engelmannii, (B) J. deppeana, (C) A. arizonica, and (D) Q. durifolia.
Table A1.
Burn parameters.
| Variable | Parameters |
|---|---|
| Green material load (ton/ha) | 18.5 ton/ha estimated following the methodology of Caballero-Cruz et al. [39] |
| Ignition material load (ton/ha) | Leaf litter and dry organic matter in the soil: 2.5 ton/ha estimated following the methodology of Caballero-Cruz et al. [39] |
| Relative humidity of the fuels | According to the CNA, the relative humidity of the fuels is 26%. |
The burn treatment corresponded to a low-intensity prescribed burn with estimated flame and scorch heights of ≤2.2 m and ≤0.35 m, respectively.
Table A2.
Results of the analysis of variance for EW and LW widths.
| Width | Var | Df | Sum Sq | Mean Sq | F Value | Pr(>F) |
| Site | 1 | 0.94 | 0.94 | 4.65 | 0.031 | |
| Species | 3 | 42.77 | 14.25 | 70.52 | <2 × 10−16 | |
| Year | 1 | 23.18 | 23.17 | 114.66 | <2 × 10−16 | |
| EW | Site:Species | 3 | 1.58 | 0.52 | 2.59 | 0.051 |
| Site:Year | 1 | 0.06 | 0.06 | 0.32 | 0.573 | |
| Species:Year | 3 | 12.86 | 4.28 | 21.21 | 4.70 × 10−16 | |
| Site:Species:Year | 3 | 0.85 | 0.28 | 1.39 | 0.242 | |
| Residuals | 583 | 117.85 | 0.20 | |||
| Site | 1 | 0.49 | 0.49 | 2.43 | 0.118 | |
| Species | 3 | 198.8 | 66.27 | 333.10 | <2 × 10−16 | |
| Year | 1 | 0.39 | 0.39 | 1.95 | 0.162 | |
| LW | Site:Species | 3 | 3.33 | 1.11 | 5.57 | 0.000 |
| Site:Year | 1 | 0.26 | 0.26 | 1.29 | 0.256 | |
| Species:Year | 3 | 7.15 | 2.38 | 11.97 | 1.28 × 10−16 | |
| Site:Species:Year | 3 | 0.47 | 0.16 | 0.78 | 0.501 | |
| Residuals | 584 | 116.18 | 0.2 |
Table A3.
Results of the mean comparison tests of EW and LW between the two sites and per species and year.
Table A3.
Results of the mean comparison tests of EW and LW between the two sites and per species and year.
| Var | Species | Year | Treatment | n1 | n2 | statistic | df | p-Value | Conf.low | Conf.high | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021 | 132 | 132 | 15.21 | 245.00 | 3.2 × 10−37 | 13.3639 | 17.0510 | ||||
| A. arizonica | 2022 | Control site | Burned site | 132 | 132 | 5.08 | 185.88 | 9.3 × 10−07 | 4.4605 | 5.6934 | |
| 2023 | 132 | 132 | 10.63 | 142.60 | 8.4 × 10−20 | 9.3371 | 11.9237 | ||||
| 2021 | 363 | 330 | 10.02 | 632.78 | 5.1 × 10−22 | 9.2683 | 10.7625 | ||||
| J. deppeana | 2022 | Control site | Burned site | 363 | 330 | 0.06 | 662.06 | 9.6 × 10−1 | 0.0526 | 0.0610 | |
| 2023 | 363 | 330 | 4.06 | 517.14 | 5.6 × 10−05 | 3.7597 | 4.3661 | ||||
| EW | 2021 | 793 | 858 | 5.15 | 1561.43 | 3.0 × 10−07 | 4.8990 | 5.3960 | |||
| P. engelmannii | 2022 | Control site | Burned site | 793 | 858 | −0.04 | 1628.08 | 9.7 × 10−1 | −0.0383 | −0.0422 | |
| 2023 | 793 | 858 | −9.21 | 1625.73 | 9.4 × 10−20 | −8.7692 | −9.6588 | ||||
| 2021 | 330 | 330 | −7.29 | 583.39 | 1.0 × 10−12 | −6.7308 | −7.8451 | ||||
| Q. durifolia | 2022 | Control site | Burned site | 330 | 330 | −7.34 | 410.57 | 1.1 × 10−12 | −6.7823 | −7.9063 | |
| 2023 | 330 | 330 | −5.76 | 657.81 | 1.3 × 10−08 | −5.3173 | −6.1973 | ||||
| 2021 | 132 | 132 | 3.27 | 173.55 | 1.3 × 10−03 | 2.8712 | 3.6652 | ||||
| A. arizonica | 2022 | Control site | Burned site | 132 | 132 | 21.92 | 231.61 | 2.0 × 10−58 | 19.2648 | 24.5817 | |
| 2023 | 132 | 132 | 31.67 | 261.34 | 1.9 × 10−91 | 27.8343 | 35.5110 | ||||
| 2021 | 363 | 330 | 13.02 | 672.21 | 9.8 × 10−35 | 12.0517 | 13.9944 | ||||
| J. deppeana | 2022 | Control site | Burned site | 363 | 330 | −6.61 | 668.62 | 7.9 × 10−11 | −6.1160 | −7.1019 | |
| 2023 | 363 | 330 | −7.76 | 626.30 | 3.4 × 10−14 | −7.1836 | −8.3417 | ||||
| LW | 2021 | 793 | 858 | 2.51 | 1641.64 | 1.2 × 10−02 | 2.3906 | 2.6331 | |||
| P. engelmannii | 2022 | Control site | Burned site | 793 | 858 | 4.32 | 1587.98 | 1.7 × 10−05 | 4.1127 | 4.5299 | |
| 2023 | 793 | 858 | −6.62 | 1334.42 | 5.1 × 10−11 | −6.3042 | −6.9439 | ||||
| 2021 | 330 | 330 | −8.32 | 428.91 | 1.2 × 10−15 | −7.6838 | −8.9569 | ||||
| Q. durifolia | 2022 | Control site | Burned site | 330 | 330 | −5.84 | 657.99 | 8.1 × 10−09 | −5.3956 | −6.2887 | |
| 2023 | 330 | 330 | −3.03 | 653.31 | 2.5 × 10−03 | −2.8026 | −3.2664 | ||||
Table A4.
Results of the mean comparison tests of EW and LW between the two sites and per species, including levels of confidence (ns = not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
Table A4.
Results of the mean comparison tests of EW and LW between the two sites and per species, including levels of confidence (ns = not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001).
| Var | Site | Group1 | Group2 | Estimate | se | df | Conf.Low | Conf.High | Statistic | p | p.Adj | p.Adj.Signif |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EW | Burned site | A. arizonica | J. deppeana | 0.043 | 0.135 | 292 | −0.224 | 0.309 | 0.316 | 0.753 | 1 | ns |
| A. arizonica | Q. durifolia | 0.379 | 0.135 | 292 | 0.113 | 0.646 | 2.800 | 0.005 | 0.033 | * | ||
| A. arizonica | P. engelmannii | 0.055 | 0.123 | 292 | −0.187 | 0.297 | 0.451 | 0.653 | 1 | ns | ||
| J. deppeana | Q. durifolia | 0.336 | 0.102 | 292 | 0.135 | 0.538 | 3.286 | 0.001 | 0.007 | ** | ||
| J. deppeana | P. engelmannii | 0.013 | 0.085 | 292 | −0.155 | 0.180 | 0.149 | 0.882 | 1 | ns | ||
| Q. durifolia | P. engelmannii | −0.324 | 0.085 | 292 | −0.491 | −0.156 | −3.801 | 0.000 | 0.001 | ** | ||
| Control site | A. arizonica | J. deppeana | 0.353 | 0.134 | 292 | 0.090 | 0.616 | 2.638 | 0.009 | 0.053 | ns | |
| A. arizonica | Q. durifolia | 0.913 | 0.135 | 292 | 0.647 | 1.180 | 6.744 | 8.25 × 10−11 | 4.95 × 10−10 | **** | ||
| A. arizonica | P. engelmannii | 0.545 | 0.123 | 292 | 0.303 | 0.788 | 4.424 | 1.37 × 10−05 | 8.22 × 10−05 | **** | ||
| J. deppeana | Q. durifolia | 0.561 | 0.100 | 292 | 0.364 | 0.757 | 5.606 | 4.79 × 10−08 | 2.87 × 10−07 | **** | ||
| J. deppeana | P. engelmannii | 0.193 | 0.083 | 292 | 0.030 | 0.356 | 2.328 | 0.021 | 0.124 | ns | ||
| Q. durifolia | P. engelmannii | −0.368 | 0.086 | 292 | −0.536 | −0.199 | −4.296 | 2.37 × 10−05 | 0.000 | *** | ||
| LW | Burned site | A. arizonica | J. deppeana | 0.007 | 0.163 | 292 | −0.314 | 0.328 | 0.044 | 0.965 | 1 | ns |
| A. arizonica | Q. durifolia | −1.970 | 0.163 | 292 | −2.291 | −1.649 | −12.087 | 1.52 × 10−27 | 9.09 × 10−27 | **** | ||
| A. arizonica | P. engelmannii | −0.149 | 0.148 | 292 | −0.440 | 0.142 | −1.006 | 0.315 | 1 | ns | ||
| J. deppeana | Q. durifolia | −1.977 | 0.123 | 292 | −2.219 | −1.735 | −16.049 | 5.44 × 10−42 | 3.27 × 10−41 | **** | ||
| J. deppeana | P. engelmannii | −0.156 | 0.103 | 292 | −0.358 | 0.046 | −1.523 | 0.129 | 0.773 | ns | ||
| Q. durifolia | P. engelmannii | 1.821 | 0.103 | 292 | 1.619 | 2.023 | 17.765 | 2.23 × 10−48 | 1.34 × 10−47 | **** | ||
| Control site | A. arizonica | J. deppeana | 0.034 | 0.161 | 292 | −0.283 | 0.351 | 0.211 | 0.833 | 1 | ns | |
| A. arizonica | Q. durifolia | −1.479 | 0.163 | 292 | −1.799 | −1.158 | −9.074 | 1.75 × 10−17 | 1.05 × 10−16 | **** | ||
| A. arizonica | P. engelmannii | −0.130 | 0.148 | 292 | −0.422 | 0.162 | −0.874 | 0.383 | 1 | ns | ||
| J. deppeana | Q. durifolia | −1.513 | 0.120 | 292 | −1.750 | −1.276 | −12.568 | 2.99 × 10−29 | 1.79 × 10−28 | **** | ||
| J. deppeana | P. engelmannii | −0.164 | 0.100 | 292 | −0.360 | 0.032 | −1.642 | 0.102 | 0.610 | ns | ||
| Q. durifolia | P. engelmannii | 1.349 | 0.103 | 292 | 1.146 | 1.552 | 13.089 | 4.08 × 10−31 | 2.45 × 10−30 | **** |
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Figure 1.
(A) Application of a prescribed burn on 23 March 2021, with the support of CONAFOR personnel in the study area; (B) Aerial perspective of stand during the burning treatment; (C) Location of the study area in Mexico and (D) climograph of the study area.
Figure 1.
(A) Application of a prescribed burn on 23 March 2021, with the support of CONAFOR personnel in the study area; (B) Aerial perspective of stand during the burning treatment; (C) Location of the study area in Mexico and (D) climograph of the study area.
Figure 2.
(A) Seasonal EW and (B) LW growth distribution throughout the studied period. The black dots mean outliers.
Figure 2.
(A) Seasonal EW and (B) LW growth distribution throughout the studied period. The black dots mean outliers.
Figure 3.
NDVI dynamic of NDVI for the studied species.
Figure 4.
Pearson’s correlations of (A) EW and (B) LW monthly NDVI values for the studied species in relation to burned and control (unburned) trees. Significant correlations are marked with an asterisk (*) at p < 0.05.
Figure 4.
Pearson’s correlations of (A) EW and (B) LW monthly NDVI values for the studied species in relation to burned and control (unburned) trees. Significant correlations are marked with an asterisk (*) at p < 0.05.
Table 1.
Descriptive dasometric statistics of the sampled trees.
| Site | Species | No. of Trees | DBH (cm) Mean ± SD | TH (m) Mean ± SD |
|---|---|---|---|---|
| Control (unburned) | A. arizonica | 5 | 14.6 ± 4.9 | 5.8 ± 1.1 |
| J. deppeana | 10 | 13.4 ± 3.5 | 4.7 ± 1.1 | |
| P. engelmannii | 25 | 15.7 ± 8.5 | 8.1 ± 3.8 | |
| Q. durifolia | 10 | 41.4 ± 4.1 | 11.7 ± 2.1 | |
| Burned | A. arizonica | 5 | 15.9 ± 2.5 | 5.5 ± 1.6 |
| J. deppeana | 10 | 10.3 ± 1.8 | 4.8 ± 0.7 | |
| P. engelmannii | 25 | 15.1 ± 6.0 | 8.6 ± 2.9 | |
| Q. durifolia | 10 | 39.6 ± 6.2 | 14.1 ± 1.5 |
DBH = diameter at breast height, TH = total height (m), and SD = standard deviation.
Table 2.
Average growth of earlywood (EW) and latewood (LW) per site and species from 2021–2023.
| Site | Species | No. of Trees | EW and LW Width Mean ± SD (mm) | |
|---|---|---|---|---|
| EW | LW | |||
| Control (unburned) | A. arizonica | 5 | 1.275 ± 0.62 | 0.072 ± 0.03 |
| J. deppeana | 10 | 0.795 ± 0.47 | 0.043 ± 0.01 | |
| P. engelmannii | 25 | 0.677 ± 0.34 | 0.210 ± 0.13 | |
| Q. durifolia | 10 | 0.300 ± 0.16 | 1.557 ± 0.84 | |
| Burned | A. arizonica | 5 | 0.655 ± 0.44 | 0.051 ± 0.02 |
| J. deppeana | 10 | 0.757 ± 0.65 | 0.046 ± 0.02 | |
| P. engelmannii | 25 | 0.690 ± 0.33 | 0.207 ± 0.13 | |
| Q. durifolia | 10 | 0.370 ± 0.11 | 2.022 ± 1.22 | |
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Gutiérrez-Gutiérrez, J.E.; Martínez-Rivas, J.A.; Acosta-Hernández, A.C.; Rodríguez-Flores, F.d.J.; Pompa-García, M. Interspecific Responses to Fire in a Mixed Forest Reveal Differences in Seasonal Growth. Forests 2025, 16, 633. https://doi.org/10.3390/f16040633
AMA Style
Gutiérrez-Gutiérrez JE, Martínez-Rivas JA, Acosta-Hernández AC, Rodríguez-Flores FdJ, Pompa-García M. Interspecific Responses to Fire in a Mixed Forest Reveal Differences in Seasonal Growth. Forests. 2025; 16(4):633. https://doi.org/10.3390/f16040633
Chicago/Turabian StyleGutiérrez-Gutiérrez, Jesús Efrén, José Alexis Martínez-Rivas, Andrea Cecilia Acosta-Hernández, Felipa de Jesús Rodríguez-Flores, and Marín Pompa-García. 2025. "Interspecific Responses to Fire in a Mixed Forest Reveal Differences in Seasonal Growth" Forests 16, no. 4: 633. https://doi.org/10.3390/f16040633
APA StyleGutiérrez-Gutiérrez, J. E., Martínez-Rivas, J. A., Acosta-Hernández, A. C., Rodríguez-Flores, F. d. J., & Pompa-García, M. (2025). Interspecific Responses to Fire in a Mixed Forest Reveal Differences in Seasonal Growth. Forests, 16(4), 633. https://doi.org/10.3390/f16040633
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