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Article

Growth Rate, Tree Rings, and Wood Anatomy of a Tropical Cloud Forest Tree Invader

by
Guadalupe Williams-Linera
1,
Milton H. Díaz-Toribio
2 and
Guillermo Angeles
1,*
1
Functional Ecology, Instituto de Ecología, A.C., Xalapa 91073, Veracruz, Mexico
2
Botanic Garden, Instituto de Ecología, A.C., Xalapa 91073, Veracruz, Mexico
*
Author to whom correspondence should be addressed.
Forests 2025, 16(2), 258; https://doi.org/10.3390/f16020258
Submission received: 25 November 2024 / Revised: 18 January 2025 / Accepted: 28 January 2025 / Published: 30 January 2025
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
The presence of shade-tolerant tree invaders has been recently noted in tropical and temperate forest understories. Maximum growth rate is an important trait for exotic trees becoming invaders in a forest. This study aimed to determine the growth rate of Eriobotrya japonica in a secondary cloud forest in central Veracruz, Mexico. The objectives of this study were to determine wood density, tree ring boundaries and number, and their relationship to diameter at breast height (DBH) and climate data. Tree ring counts were obtained using Python-based software with subsequent visual validation. Growth rates were measured using diametric tape, dendrometric bands, and the pinning method. The number of rings increased with DBH, presenting values ranging from 14 to 27. Tree rings were delimited by fibers that were five times narrower in the ring limit zone than in the intra-ring zone. The growth ring delimitation zones were formed when vascular cambium activity stalled during the relatively dry-cold season (January–February). The growth rate of E. japonica was statistically similar (ca. 0.2 mm yr−1) regardless of the method employed to measure it. Relative growth rate was low (0.02 cm cm−1 yr−1). Wood density (0.76 g cm−3) values lay within the upper values for mature forest trees. Eriobotrya japonica is a potential invader, with traits such as high wood density and a relatively low growth rate, which are characteristic of the shade-tolerant tree species.

1. Introduction

Invasions by tree species have been relatively overlooked compared to those of herbaceous plants [1]. Furthermore, invasive woody plants are commonly characterized by trait strategies associated with high resource demands and site disturbances. Forest invaders confined to disturbed sites, gaps, or forest edges follow those strategies and are shade-intolerant, fast-growing, and require open canopies. On the other hand, although it is generally assumed that undisturbed forests are resistant to invasion, the presence of shade-tolerant woody invader species has been consistently reported in tropical and temperate low-light forest understories [2,3,4,5]. These species combine several key traits characteristic of the super invader tree phenotype, including rapid growth when light is abundant, establishment and high survivorship under shade, persistence following canopy closure, and high fecundity [5]. Martin et al. [2] documented that, in temperate and tropical forests, at least 68 shrub and tree species are shade-tolerant invasives, and these shade-tolerant exotics can invade deeply shaded forests. Fridley et al. [5] found evidence of the super invader phenotype in ca. 20 woody forest species (in temperate forests of North America, Europe, and in tropical and subtropical forests of Australia, the Caribbean, Brazil, and East Asia, among other places) and documented examples of woody invaders that are shade-tolerant in their invasive but not native range. Shade tolerance is an ecological concept that refers to the capacity of a given plant to tolerate low light levels (review in ref. [6]). Thus, the invasion of forests by shade-tolerant exotics is a slower process than that of the establishment of exotic species in disturbed or open ecosystems, but the long-term effects are likely to be just as pervasive [2].
In any case, maximum growth rate is an important trait for non-native species, in terms of becoming invaders in tropical and temperate forests [5,7]. Several methods have been used to determine growth rate, including diametric tape measurements, dendrometer bands, and the pinning method. The micro-coring and pinning methods, which enable observation of cellular activity processes, are considered the most reliable techniques to monitor wood formation during a growing season [8,9]. Annual tree growth rate can be related to the presence and periodicity of tree ring number and ring wood anatomy.
In general, tree rings exhibit greater contrast when trees grow in environments where climate is influenced by seasonal weather variations, producing differential growth rates [10]. In tropical montane forests in Ethiopia, the mean annual DBH relative growth rate was higher during a humid year than during a drier year [11]. While some researchers have proposed that growth rate can be correlated with functional traits, numerous empirical studies indicate that the correlation between growth and traits is not consistent (see review in ref. [12]). For instance, the trait of wood density has been related to growth in large plants but a weak or no correlation has also been reported [13,14]. Tree growth ring anatomy and the relationship between dendrometric traits (e.g., DBH, height) and the number of growth rings have been studied to support effective forest management focused on invasive and non-native trees [15].
This study focused on Eriobotrya japonica, a subtropical tree with the potential to become an invader in the lower montane forests of central Veracruz, Mexico [16,17]. Eriobotrya japonica is one of the most economically important fruit crops, with high edible, medical, as well as ornamental values in China [18]. It has long been recognized as a medicinal plant, producing numerous compounds in extracts of leaves, flowers, and even seeds that are used to treat many diseases from allergies to cancer, as found in a search in the Web of Science Core Collection (5 November 2024), using the search term Eriobotrya japonica. However, using the terms wood anatomy, tree growth, tree rings, or invader, the search results were less than 10 publications. Eriobotrya japonica has been mentioned as an invader with a low occurrence frequency in a few localities (e.g., 5 out of 22,994 invaded plots worldwide [1]; recorded in one out of nine islands in the Caribbean [19]).
The anatomy of growth-ring boundaries has been defined according to vessel size. To describe growth-ring boundaries, abrupt changes in radial fiber diameter have been characterized (e.g., refs. [20,21]). In seasonal semi-deciduous forest in SE Brazil, Lisi et al. [20] concluded that marginal parenchyma was the most common and identifiable characteristic that defined a growth ring boundary. In a drier area, the Chaco plain, the growth ring boundary was indicated by marginal parenchyma, in contrast to that of montane forest trees, which showed thicker fibers at the limit of the ring [22].
This study aimed to determine the growth rate of E. japonica individuals growing in a secondary forest (Figure 1). As part of a resprouting experiment [17], we noted that tree rings were visually clear, so we then asked whether these tree rings were annual and whether growth rate and wood density differed among DBH classes. Thus, the specific objectives of this study were (1) to determine the wood anatomical boundaries of the tree rings, (2) to count tree rings and relate them to DBH and climate data, and (3) to compare growth rates estimated through the direct measurement of DBH increase using diametric tape, dendrometric bands, and the pinning method. In this paper, we hypothesize that growth rate, tree rings, and wood anatomy will be important traits with which to define the phenotype of a super invader tree. Super invader trees are non-native trees that are shade-tolerant, which can remain under the canopy for long periods and grow quickly as soon as they receive sunlight. Studying these traits can provide valuable insights for forest management and conservation strategies.

2. Materials and Methods

The study site is located in the Cloud Forest Sanctuary reserve (19°30′49.1″ N, 96°56′14.2″ W, 1300 m asl) at INECOL, Mexico. Mean air temperature is 18 °C and total annual precipitation is 1600 mm. The climate is mild and humid with three well-defined seasons: a relatively dry-cold season (November–March—the “nortes” season), a dry-warm season (April and May), and a rainy-warm season (June–October—the hurricane season) (Table 1). Monthly climate data are sourced from the closest meteorological station located less than 1 km from the study site. Wood data were obtained from a 1 ha permanent plot in the secondary forest section of the reserve with ca. 50% canopy openness (Figure 1). In October 2020, a total of 36 trees were cut with a hand saw at a height of 50 cm above ground level, with the intent of experimentally producing stumps to study resprouting [17]. The diameter of the trees was measured at 1.3 m above ground level (DBH). These trees were categorized into seven diameter classes based on their DBH: class 1 (2–2.9 cm), class 2 (3–4.9 cm), class 3 (5–6.9 cm), class 4 (7–8.9 cm), class 5 (9–10.9 cm), class 6 (11–12.9 cm), and class 7 (13–16.8 cm). The trunk portion above 50–60 cm in height was used to obtain wood samples for anatomical analysis and wood density measurements. Stem discs were cut from those portions for tree ring analysis. Photographs of each stump were taken and the orientation was recorded using a compass, with the east side of each disc marked for reference. A random selection of 17 stumps, representing all seven DBH classes, was used to produce stem discs for further analysis. The discs are deposited in the Wood collection of INECOL (Xiloteca Faustino Miranda).

2.1. Tree Rings

To use tree rings as a proxy for determining annual growth, we first established the formation of rings on an annual basis through anatomical analysis to confirm both the presence and distinctiveness of the rings. Subsequently, the rings were counted and correlated with annual climatic variability to establish the relationship between climate factors and radial growth. If the tree rings were confirmed to be annual, their formation would be influenced by climatic conditions, thereby allowing us to assess the growth rate of the tree in response to these environmental factors.

2.1.1. Anatomical Boundaries of the Tree Rings

Anatomical measurements of fibers were conducted to define the boundaries of the tree rings. Stem segments 3–5 cm in diameter were obtained with a saw, subdivided into quarters, and fixed in FAA (formalin/acetic acid/70% ethanol, 5:5:90 per volume) for 48 h. After washing the fixative, the stem segments were dehydrated in an ethanol concentration series, up to 100%, and embedded in epoxy resin (ECOPOXY, UV poxy kit, Xalapa-Enríquez, Mexico) following the manufacturer’s instructions. After heat-curing the resin in an oven at 58 °C, the epoxy-embedded wood segments were fixed to wood blocks measuring 0.5 cm per side and attached to a microtome clamp. Sections of 5–6 μm in thickness were obtained using a metal knife, soaking the sample in a mixture of equal volumes of glycerin, water, and 70% ethanol (GAA) as a lubricant. These sections were then stained with an aqueous safranin (0.01%) solution for 20 min, washed, air-dried, and mounted with synthetic resin dissolved in xylenes. The growth rings are delimited by a compact band of fibers that is easily observed under a stereoscope (Nikon Eclipse E600, Nikon Corporation, Tokyo, Japan). The dimensions of the fibers at the intra-ring and ring limit area were measured using a compound microscope (Leica MZ8, Leica Microsystems, Wetzlar, Germany) and using the Leica software (LAS EZ Imaging v. 3.4, Germany). We measured 50 paired limiting-ring and intra-ring diameters.

2.1.2. Counting Tree Rings

Following the anatomical definition of the presence of tree ring boundaries, we developed a computer method to quantify the tree rings, assuming that one circle of early wood and a unique circle of late wood represented a ring. The tree discs were sanded, and digital images were taken using a 12-megapixel smartphone camera for each disc such that the pith was located at one end of the image while the cambium/bark was located at the opposite end. Four images were taken for each sample disc, one for each cardinal direction. To detect tree rings using the Python programing language (v. 3.10), a script was written to automate the estimation of the ring boundaries as previously anatomically defined. The script operates by reading a given image, converting it from RGB to grayscale, and analyzing a line of pixel data, at the center of the image, ranging from pith to cambium/bark. In a grayscale image, the pixel value represents the pixel’s brightness, ranging from 0 to 255, where 0 represents black and 255 is white. A ring was considered to exist where the pixel intensity is too bright (white), which, in terms of the pixel data, can be considered as a local maximum. Specifically, by counting the total local maxima, it was possible to estimate the number of rings for a specific tree disc in each of the four cardinal directions (Figure 2). The process of finding local maxima was repeated for 10 adjacent lines of pixels towards the left of the original center line and 10 adjacent lines towards the right. After performing all the iterations for a particular image, the program estimates the average number of rings, maximum and minimum number of rings, standard deviation (SD), and standard error (SE). We also visually quantified the number of rings to validate the accuracy of the program results.

2.2. Wood Density

Wood density is the oven-dry mass divided by the green volume [14]. From the discs collected from above 50 cm (50–60 cm) in height, wood samples were cut into 1 × 1 × 1 cm cubes. Samples were taken from the sapwood (laburnum-outermost wood) and heartwood (duramen). The wood sample cubes were measured with a caliper for length, width, and height, then oven-dried at 100 °C for ca. 72 h until a constant weight was obtained, which was then measured on an analytical balance.

2.3. Pinning Method

The pinning method was used to date the onset and cessation of cambial activity, as well as the transition from earlywood to latewood, following the protocol of Seo et al. [23]. In January 2022, pinning was performed on four trees, with each tree marked in four orientations, producing 16 pinning points (Figure 3). The wounded areas were marked with red circles corresponding to the pinning locations and excised using a chisel at the due time. Eight samples were collected one year later and the other eight a year and a half after wounding. The excised cambial regions were fixed in 0.5% formalin at 4 °C for 24 h, then washed and trimmed, preserving the cambial zone.
Samples were dehydrated in an ethanol series, embedded in BOPP solution, and sectioned to 6–8 µm in thickness. Sections were stained with 0.05% safranin for 20 min, washed, dehydrated, and mounted in synthetic resin. Under a compound microscope (Leica MZ8), the pin scar was identifiable, and the wood produced after wounding was measured in millimeters from the cambial zone to the scar line.

2.4. Tree Growth Rate

At 1.3 m above ground level, a permanent line was marked in six trees to measure DBH and dendrometric bands were installed above the line to measure variations in stem circumference and the mean level of stem radial growth. Simultaneously, DBH was measured with a diametric tape and dendrometer bands were read at 0, 8, 16, and 24 mo. The diametric growth rate was calculated in cm yr−1, and relative growth rate (RGR) was calculated using the formula:
RGR = ln (d2) – ln (d1)/t2 − t1
where d2 and d1 are the final and initial diameter (cm) and t2 − t1 is time (in years) between both measurements.

2.5. Statistical Analysis

Prior to running statistical tests, data normality was assessed using Shapiro–Wilk’s test, p > 0.05. Regression analysis was carried out between wood density and DBH. Differences between fibers’ diameter delimiting tree rings were tested with Student’s t-test, using a significance level of p < 0.05. DBH and number of tree rings were related using Sperman’s ρ correlation coefficients. We used two-way ANOVAs to determine differences in the number of tree rings among DBH classes and the orientation of the counted tree rings. Annual DBH increases estimated using dendrometric bands, diametric tape, and pinning were compared using one-way ANOVAs. When significant differences were detected, we used Tukey HDS. Reported values are means ± standard error (SE). Data were analyzed using JMP (v 10.0.0, SAS Institute, Cary, NV, USA).

3. Results

3.1. Wood Density

Overall, wood density was 0.76 ± 0.01) g cm−3. On average, the heartwood was 0.76 g cm−3, whereas the sapwood was 0.74 g cm−3, with no significant differences (t12 = 0.62, p > 0.05). The regression analyses indicated no significant relationship between wood density and DBH (R2 = 0.107, F1,24 = 2.866, p = 0.103).

3.2. Tree Rings

Our results indicate that E. japonica presents distinct growth rings. Anatomically, the growth rings were delimited by fibers with reduced radial expansion. The fibers at the ring limit displayed a radial diameter that was significantly smaller (mean = 2.86 ± 0.14 µm) than the fibers formed in the intra-ring zone (13.53 ± 0.72 µm; t = −14.43, df = 49, p < 0.0001). Although there were some vessels towards the limit of the ring that showed reduced diameter, the main marker of the ring limit was the reduced radial diameter of the fibers (Figure 3).
The DBH values of the sampled trees in the secondary forest ranged between 5 and 16 cm. The smallest DBH class had 14 rings on average, whereas the largest class presented 27 rings on average, with estimated counts of a maximum of 47 rings. Differences in size classes were significant (F = 5.64, p = 0.0004; Figure 4), but no significant differences in orientation were detected (F = 2.37, p = 0.089). The number of rings tended to increase with DBH (Figure 4c). There was a significant correlation between the number of estimated rings and DBH (ρ = 0.56, p < 0.0001).

3.3. Pinning Method

Following the pinning method in E. japonica (Figure 5a,b), we observed young xylem growth from the wounded pin area to the vascular cambium (Figure 5c). Wood anatomical observations in samples extracted at 12 and 20 months after inflicting the wound indicated the presence of tree rings and that rings were annual (Figure 6a,b). We found that annual tree rings were related to climate, since wood formation stalled during the relatively dry and cold season (January–February) when reduced rainfall and minimum temperatures were recorded (Table 1 and Figure 6b).

3.4. Growth Rate

The measured diameter growth of E. japonica was similar using diametric tape, dendrometric bands, and the pinning method (Table 2; p > 0.05). We also found that the RGR of E. japonica was low, at around 0.02 cm cm−1 yr −1 (Table 2).

4. Discussion

The growth rate of trees of E. japonica under shaded secondary cloud forest was relatively low, while the density of the wood was relatively high compared with trees growing in the same environment. These traits are disparate from the prevailing model, which proposes that exotic tree invaders should be shade-intolerant, fast-growing, and present low wood density [7]. In addition, the tree rings of E. japonica are well delimited and appear to be annual.

4.1. Wood Density

The wood density of E. japonica (0.76 ± 0.01 g cm−3) was within the upper values recorded for tree species in the local mature forest in central Veracruz, Mexico (e.g., Q. germana, 0.67; Quercus lancifolia, 0.76; Q. sartorii, 0.62; Carpinus tropicalis, 0.60; and Oreomunnea mexicana, 0.59 g cm−3; [24]). The wood densities of trees classified as non-pioneer, pioneer, and exotic species differed significantly. Interestingly, some exotic tree species planted in shaded coffee plantations also presented high wood density (e.g., Citrus sinensis, 0.74 g cm−3) [24]. At the global scale, wood density ranges from 0.1 to 1.5 g cm−3, and Chave et al. [14] report that the mean wood density varies from ca. 0.5 g cm−3 in temperate regions to over 0.7 g cm−3 in subtropical environments [14]. Eriobotrya japonica is a subtropical species, and higher wood density has been reported in India (0.880 g cm−3), while other species of Eriobotrya also present high wood density values (0.730 in southeast tropical Asia to 0.779 g cm−3 in China) (Global Wood Density Database, [25]).
In general, long-lived primary species tend to have high wood density, while pioneers have low wood density [13]. The relationship between wood density and growth rate has been of great interest, but the association is often weak or even nonexistent [13,14]. Moreover, the association between high wood density and lower risk of trunk breakage, xylem implosion, resistance to decay, or pathogen invasion may not be casual but could instead reflect a correlated selection for other traits of value to long-lived trees [26].

4.2. Pinning Method

Eriobotrya japonica tree rings were clearly delimited. The fibers at the ring limit zone displayed a radial diameter that was five times smaller than the size of the fibers located at the intermediate ring zone, making it easy to distinguish the tree rings. The annual ring formation was corroborated using the pinning method and monthly climate data record from the nearby meteorological station. This comparison allowed observation of the fact that the limit of a tree ring was formed in January–February, the cool and relatively dry season of the year. This period is characterized by relatively low rainfall and the lowest minimum temperature, although air humidity remains high due to frequent fogs and cold northerly weather fronts (“nortes”). Lisi et al. [20] also reported that the cambial marking method was reliable in terms of defining the annual nature of tree ring formation. Several authors have reported that climate factors strongly influence tree growth; for instance, lower precipitation during the winter strongly reduces the rate of radial growth in tropical forest trees [27], although other factors may influence tropical tree growth and wood formation [28].

4.3. Tree Rings and Age

Recently, computer-aided methods have been developed to detect and visualize growth ring boundaries [29]. Tree ring quantification has been approached using different techniques, some commercial (e.g., WinDendro) and others more particular (e.g., refs. [9,30]). Subah et al. [9] acknowledge that manual analysis of tree rings is a laborious task and requires a domain area expert. These authors therefore developed a program to analyze tree ring count and width. Hietz [30] developed software that links image analysis, Excel, and statistical analysis for applications of tree ring analysis. In our study, we used an effective method with a Python Script-computer followed by visual corroboration to count the number of rings in the discs. Our method has several advantages such as an average inference time of under 100 ms per image (height ≈ 1000 pixels) and it does not perform image scaling, meaning that it can analyze any image regardless of its dimensions. Another advantage is that the images had heights ranging from 1000 to over 3000 pixels. Additionally, the method uses open-source python libraries and is computationally cheap, meaning that it can perform without the need for a Graphics Processing Unit and can potentially be incorporated on mobile devices in the form of an App or website.
This process enabled us to be confident that the reported number of rings in the samples was accurate. Assuming the rings to be annual, the estimated age of the individuals indicated that the old trees were approximately 37 years old. This age coincided with the age of the secondary forest, the area of which was sampled in 1980 and reported as being a field of herbaceous plants with no saplings or trees.

4.4. Tree Growth

Tree growth was measured using different techniques. However, we did not find significant differences between methods. Other authors have reported differences when comparing the wounding method (modification of the pinning method) to the use of band-dendrometers [8,9]. Since bands are affected by physical shrinkage of the trunks during the dry season and trunk swelling following precipitation, we conclude that, for detailed information on radial growth, the wounding method is recommended [8,31].
The growth rate in DBH of E. japonica trees in secondary forest (ca. 0.163 cm/yr) was relatively low in comparison to that of the nearby natural forest, where the average tree growth rate was 0.29 cm/yr. The RGR was also low, but the tree growth was in the reported range of slow growth values in other forests. Similar growth rates have been reported in the southern Appalachian Mountains, USA, for trees of 5–15 cm in DBH (mean growth rate = 0.157 cm yr−1). However, shade tolerance classification showed no relationship to growth rate [32]. Growth values were also similar in a neotropical forest in southeast Brazil, where late secondary tree species showed a lower radial increase compared to pioneer and secondary-stage tree species [20]. Tree growth in a tropical semi-deciduous moist forest of Central Africa was variable (0.26 + 0.44 cm yr−1; [33]), while another invasive tree in the Azores, Portugal, Pittosporum undulatum, presented higher values (0.38 cm yr−1; [34]) than those found for E. japonica.
The growth rate in diameter of E. japonica seedlings differed between individuals in the forest understory (0.49 cm yr−1) and those in a light gap (0.77 cm yr−1, [16]). In any case, the growth rate of seedlings was higher than in adults. The differences in growth rate between adults and seedlings agree with reports on changes in relative growth rate and traits with ontogenetic stage since the seedlings grew faster than the adult trees [12].

5. Conclusions

In conclusion, Eriobotrya japonica represents an emerging invasive species within the lower montane cloud forest in central Veracruz, Mexico. Our findings, based on earlier studies [16,17] and an integrated analysis of tree growth, wood development, and wood anatomical characteristics, reveal that E. japonica exhibits high wood density and a relatively low growth rate—traits typical of shade-tolerant tree species. This enables the species to adopt a “sit-and-wait” strategy, persisting under the low-light conditions of undisturbed, closed-canopy forests and rapidly exploiting disturbances to establish dominance [5]. As many tree invasions remain in early stages, with significant “invasion debts” linked to recent plantings or horticultural escapes [1,7], studying E. japonica during this critical phase of forest invasiveness provides essential insights into invasion mechanisms and informs the development of preventive management strategies to protect the regional cloud forest.

Author Contributions

Conceptualization, G.W.-L. and M.H.D.-T.; methodology, G.W.-L., G.A. and M.H.D.-T.; formal analysis, G.W.-L.; investigation, G.W.-L., G.A. and M.H.D.-T.; data curation, G.W.-L.; visualization, G.W.-L.; writing—original draft, G.W.-L.; writing—review and editing, G.W.-L., G.A. and M.H.D.-T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We thank Javier Tolome Romero, Carolina Madero Vega and Mariana Quetzalli Vizcaino-Bravo for assistance in the field and laboratory work and Manuel Ortiz, Octavio Rivera, Joel López and Genaro Justo for help with wood collection and woodwork processing discs and samples. We are grateful to Eric Williams-Linera for developing the Python script to count tree rings.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of the study site in central Veracruz, Mexico. The polygon is the tropical cloud forest nature reserve of the Instituto de Ecología (INECOL), indicating the areas of secondary and mature forest. Trees of Eriobotrya japonica were sampled in the secondary forest.
Figure 1. Map of the study site in central Veracruz, Mexico. The polygon is the tropical cloud forest nature reserve of the Instituto de Ecología (INECOL), indicating the areas of secondary and mature forest. Trees of Eriobotrya japonica were sampled in the secondary forest.
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Figure 2. Diagrammatic representation of the Python script to detect tree rings where individual columns of pixels at the center of the image are analyzed by locating local maxima, which were defined as the location of a particular ring.
Figure 2. Diagrammatic representation of the Python script to detect tree rings where individual columns of pixels at the center of the image are analyzed by locating local maxima, which were defined as the location of a particular ring.
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Figure 3. (a) Cross-section through an annual growth ring showing the expanded fibers (black arrows) and the compressed fibers delimiting the ring (white arrows). Scale bar = 25 μm. (b) Radial diameter (µm) of the fibers in the intra-ring and ring limit areas in growth rings of Eriobotrya japonica. The crossbar within the box marks the median, the length of the box represents the interquartile range of distribution, the lower and upper fences indicate the 10th and 90th percentiles, respectively, and dots represent atypical values.
Figure 3. (a) Cross-section through an annual growth ring showing the expanded fibers (black arrows) and the compressed fibers delimiting the ring (white arrows). Scale bar = 25 μm. (b) Radial diameter (µm) of the fibers in the intra-ring and ring limit areas in growth rings of Eriobotrya japonica. The crossbar within the box marks the median, the length of the box represents the interquartile range of distribution, the lower and upper fences indicate the 10th and 90th percentiles, respectively, and dots represent atypical values.
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Figure 4. (a) Tree disc of Eriobotrya japonica, (b) boxplots of number of tree rings estimated in trees of different DBH classes (see text) in a secondary cloud forest of Veracruz, Mexico. The crossbar within the box marks the median, the length of the box represents the interquartile range of distribution, the lower and upper fences indicate the 10th and 90th percentiles, respectively, and the dot represents an atypical value. Different superscript letters indicate significant differences (Tukey’s test, α = 0.05). (c) Estimated number of rings in the sampled individuals and the Spearman’s ρ correlation coefficient.
Figure 4. (a) Tree disc of Eriobotrya japonica, (b) boxplots of number of tree rings estimated in trees of different DBH classes (see text) in a secondary cloud forest of Veracruz, Mexico. The crossbar within the box marks the median, the length of the box represents the interquartile range of distribution, the lower and upper fences indicate the 10th and 90th percentiles, respectively, and the dot represents an atypical value. Different superscript letters indicate significant differences (Tukey’s test, α = 0.05). (c) Estimated number of rings in the sampled individuals and the Spearman’s ρ correlation coefficient.
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Figure 5. The pinning method is used in an individual of Eriobotrya japonica. (a) Pinning at 1.3 m height above the ground, (b) sample extracted one year later, and (c) cross-section indicating the wounded area and the young growth formation after wounding, from the blue arrow to the vascular cambium (orange arrows). Ph: secondary phloem; Xi: secondary xylem.
Figure 5. The pinning method is used in an individual of Eriobotrya japonica. (a) Pinning at 1.3 m height above the ground, (b) sample extracted one year later, and (c) cross-section indicating the wounded area and the young growth formation after wounding, from the blue arrow to the vascular cambium (orange arrows). Ph: secondary phloem; Xi: secondary xylem.
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Figure 6. (a) Pinning wound in January 2022 (blue arrow), young growth, and a ring (green arrow), formed after wounding of the vascular cambium. Sample harvested in October 2023. (b) Annual precipitation (bars) and maximum (red dots) and minimum (blue dots) temperatures recorded monthly at the nearest (ca. 500 m in distance) meteorological station, indicating that the tree rings of E. japonica are annual. The circled areas refer to the relatively dry-cold season. Ph: secondary phloem; Xi: secondary xylem.
Figure 6. (a) Pinning wound in January 2022 (blue arrow), young growth, and a ring (green arrow), formed after wounding of the vascular cambium. Sample harvested in October 2023. (b) Annual precipitation (bars) and maximum (red dots) and minimum (blue dots) temperatures recorded monthly at the nearest (ca. 500 m in distance) meteorological station, indicating that the tree rings of E. japonica are annual. The circled areas refer to the relatively dry-cold season. Ph: secondary phloem; Xi: secondary xylem.
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Table 1. Seasonality in the tropical cloud forest of central Veracruz, Mexico. Values are average during the study period. Climate data are from the closest meteorological station. Values are means ± SE.
Table 1. Seasonality in the tropical cloud forest of central Veracruz, Mexico. Values are average during the study period. Climate data are from the closest meteorological station. Values are means ± SE.
Seasons
Relatively Dry–ColdDry–WarmRainy–Warm
MonthsNovember–MarchApril–MayJune–October
Precipitation (mm)75 ± 1363 ± 19229 ± 46
Maximum temperature (°C)20.9 ± 0.626.6 ± 0.826.1 ± 0.7
Minimum temperature (°C)10.9 ± 0.613.7 ± 0.315.2 ± 0.8
Table 2. Growth of the individuals of Eriobotrya japonica, measured using A. diameter tape, B. dendrometer bands, and C. the pinning method and expressed as the mean annual diameter growth (cm/yr) and relative growth rate (cm/cm/yr). Values are means and 1 SE in parentheses. Shared superscript letters indicate no significant differences (p > 0.05). RGR was not calculated for the pinning method because we had only measurements for 18 months (not for 12 months).
Table 2. Growth of the individuals of Eriobotrya japonica, measured using A. diameter tape, B. dendrometer bands, and C. the pinning method and expressed as the mean annual diameter growth (cm/yr) and relative growth rate (cm/cm/yr). Values are means and 1 SE in parentheses. Shared superscript letters indicate no significant differences (p > 0.05). RGR was not calculated for the pinning method because we had only measurements for 18 months (not for 12 months).
Annual Growth (cm yr−1)RGR (cm cm−1 yr−1)
A. Diameter tape0.156 ± 0.056 a0.0198 ± 0.0070 a
B. Dendrometer0.179 ± 0.059 a0.0250 ± 0.0073 a
C. Pinning method0.153 ± 0.048 a
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Williams-Linera, G.; Díaz-Toribio, M.H.; Angeles, G. Growth Rate, Tree Rings, and Wood Anatomy of a Tropical Cloud Forest Tree Invader. Forests 2025, 16, 258. https://doi.org/10.3390/f16020258

AMA Style

Williams-Linera G, Díaz-Toribio MH, Angeles G. Growth Rate, Tree Rings, and Wood Anatomy of a Tropical Cloud Forest Tree Invader. Forests. 2025; 16(2):258. https://doi.org/10.3390/f16020258

Chicago/Turabian Style

Williams-Linera, Guadalupe, Milton H. Díaz-Toribio, and Guillermo Angeles. 2025. "Growth Rate, Tree Rings, and Wood Anatomy of a Tropical Cloud Forest Tree Invader" Forests 16, no. 2: 258. https://doi.org/10.3390/f16020258

APA Style

Williams-Linera, G., Díaz-Toribio, M. H., & Angeles, G. (2025). Growth Rate, Tree Rings, and Wood Anatomy of a Tropical Cloud Forest Tree Invader. Forests, 16(2), 258. https://doi.org/10.3390/f16020258

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