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Article

Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil

by
Kleiperry F. Ferreira
1,
Jhonathan O. Silva
2,
Pablo Cuevas-Reyes
3,
Luiz Alberto Dolabela Falcão
1 and
Mário M. Espírito-Santo
1,*
1
Laboratório de Ecologia Evolutiva, DBG/CCBS, Universidade Estadual de Montes Claros (Unimontes), Montes Claros 39401-089, MG, Brazil
2
Laboratório de Ecologia Básica e Aplicada, Colegiado de Ecologia, Universidade Federal do Vale do São Francisco (UNIVASF), Senhor do Bonfim 48970-000, BA, Brazil
3
Laboratorio de Ecología de Interacciones Bióticas, Universidad Michoacana de San Nicolás de Hidalgo, Francisco J. Mújica S/N Col. Felicitas del Río, Ciudad Universitaria, Morelia 58030, Michoacán, Mexico
*
Author to whom correspondence should be addressed.
Forests 2024, 15(10), 1700; https://doi.org/10.3390/f15101700
Submission received: 13 August 2024 / Revised: 13 September 2024 / Accepted: 23 September 2024 / Published: 26 September 2024
(This article belongs to the Section Forest Soil)
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Abstract

:
The structure and diversity of tropical vegetation are shaped by biotic and abiotic factors, which function as environmental filters affecting plant performance on different spatial and temporal scales. We compared soil (Ferrasols and Cambisols) conditions, vegetation structure and leaf traits (e.g., specific leaf area, polyphenols, and chlorophyll contents a/b and in total) in the early, intermediate and late successional stages of a tropical dry forest (TDF) in southeastern Brazil. For this purpose, we measured leaf traits of the most abundant species in the same 18 plots (50 × 20 m/six per successional stage) in 2009 and 2018. Our prediction is that tree species growing in early forests have a greater investment in conservative traits related to chemical defenses and tolerance to desiccation compared to late-stage tree species. We observed contrasting results when comparing the chronosequence differences in leaf traits both in 2009 and 2018 and the temporal changes along this period. Specific leaf area was lower than expected for all successional stages, while polyphenol content increased over time, contrary to other studies in TDFs. These results suggest that contrasting environmental factors such as soil conditions and light availability are responsible of the observed pattern. Total chlorophyll content did not change significantly, while the a/b chlorophyll ratio doubled in all successional stages, contrary to our prediction. Therefore, we suggest that the conservative–acquisitive spectrum in successional gradients of TDFs should be further investigated with time-series data for a better understanding of plant community assemblages.

1. Introduction

Vegetation structure and diversity are affected by both biotic and abiotic factors, which operate as environmental filters on different spatial and temporal scales [1,2,3]. Therefore, it is expected that the morpho-physiological plant traits associated with establishment, growth, survival and reproduction in different environments are strongly related to abiotic variables [4], constraining the occurrence and abundance of plant species across different communities [5,6,7]. For example, under harsher and more stressful environmental conditions, stronger selective pressures can result in lower variabilities in functional traits within a plant community [6,8,9]. Thus, environmental filters may shape assemblages consisting of plant species with similar functional traits [9,10]. Indeed, several studies quantifying the morphological or physiological traits associated with plant performance have shown the importance of the environmental filters driving plant diversity patterns on a local scale [3,5].
Because of the dynamics of land use and cover in tropical regions, agricultural areas are frequently abandoned, generating a landscape dominated by secondary forests at different successional stages [11,12,13]. This natural regeneration involves the gradual recolonization of disturbed areas by different species, modulated by changes in abiotic conditions such as increases in soil nutrient levels [14,15,16] and in forest structural complexity (e.g., greater plant height, basal area and canopy cover) [9,17]. Understanding such a process is fundamental to establish reliable indicators to distinguish successional stages of a given forest type, which is important for the enforcement of environmental policies on land use regulation [18] and to determine the adequacy of natural regeneration as an option in restoration programs [19,20].
Theories related to ecological succession in tropical regions were developed predominantly for tropical rain forests [17,21,22], but important differences in mechanisms driving natural regeneration have been documented in tropical dry forests (TDFs) [23,24,25,26]. For example, in tropical rain forests, light availability is the main environmental filter determining the changes in the successional processes and species coexistence [22]. In contrast, water availability and temperature are the most important in TDFs [27]. Based on this, Lebrija-Trejos et al. [22] proposed different patterns for the variation in plant functional traits during succession in tropical rain and dry forests within the leaf economics spectrum framework [28]. They hypothesized that, in rain forests, pioneer species have acquisitive leaf traits and late successional plants have conservative leaf traits associated with greater capacity of light uptake. On the other hand, TDF species would vary from conservative to acquisitive leaf traits in relation to water use [6,7,25]. However, studies testing this hypothesis used mostly chronosequences (i.e., space-for-time substitution) and reported mixed results [7,23,25]. Thus, long-term temporal data are necessary to more accurately determining successional trends on leaf traits and ecosystem functioning in tropical forests.
In this study, we determined the changes in chemical soil parameters, vegetation structure and leaf functional traits in a Brazilian TDF using the following two complementary approaches: (i) a chronosequence consisting of plots from the early, intermediate and late successional stages; and (ii) a temporal comparison of functional trait changes after nine years of succession (2009–2018) in the same plots. Specifically, the following hypotheses were tested (i) regardless of the sampling period (2009 or 2018), the soil resource availability and structural vegetation traits (height, basal area, density and Holdridge Complexity Index (HCI)) increase over the chronosequence; (ii) in the chronosequences, pioneer species in early successional forests have greater investment in conservative traits, related to chemical defense and tolerance to desiccation (i.e., higher levels of phenolic compounds and sclerophylly), whereas tree species in the intermediate and late stages will have greater investment in acquisitive traits, exhibiting greater specific leaf area (SLA) and chlorophyll content; (iii) after nine years of succession, we expect an increase in soil resource availability and vegetation structure for all successional stages; finally, (iv) leaf traits of tree species in all three successional stages will be different due to temporal changes in species composition or phenotypic plasticity (i.e., the production of multiple phenotypes from a single genotype as a consequence of variations in environmental conditions [29]). These changes will be more evident for the early stage, since the abiotic and biotic changes in this environment are usually faster. Thus, after nine years of succession, we expect that trees will change from an investment in conservative strategies, expressed in lower SLAs and higher levels of polyphenols (defense), to an investment in acquisitive strategies, exhibiting higher SLAs and chlorophyll contents (a, b, ratio a/b, and total).

2. Materials and Methods

2.1. Study Area

This study was conducted at the Mata Seca State Park (MSSP), located in northern Minas Gerais state, Brazil. The MSSP was created in 2000 after the expropriation of farms on which extensive livestock and irrigated agriculture were the main economic activities. This park covers 15,466 ha in the Valley of the São Francisco River, between 14°97′02″–14°53′08″ S and 43°97′02″–44°00′05″ W. The original and currently predominant vegetation is the TDF, dominated by dry-deciduous plant species [30] and growing on flat and fertile soils [14,31]. According to Coelho et al. [32], seven soil types are found in the MSSP, but Ferrasols and Cambisols compose 57% of the area. Approximately 1525 ha of the MSSP are covered by abandoned pastures, which are regenerating to early successional forests, and the remaining area is characterized by a mosaic of TDFs in both secondary and old-growth forests [14]. The region’s climate is tropical with dry winters (Aw, according to Köppen’s classification, Alvares et al. [33]), characterized by the severe dry season from May to October. The average temperature in the region is 24 °C [34] and the average precipitation is 818 ± 242 mm (data from the Manga weather station, approximately 10 km from the MSSP).
In 2006, we established a chronosequence consisting of nine plots of 50 × 20 m in early, intermediate and late forest areas (three plots in each stage), where all trees with diameter at breast height (DBH) ≥ 5 cm were measured annually until 2017. Successional stages were determined considering both the vertical (i.e., number of vegetation strata) and horizontal (i.e., tree density) structure of the vegetation, as well as the time elapsed since the disturbance and the type of previous land use [35]. Thus, in 2009, the early successional stage was composed mainly of sparse patches of woody vegetation, shrubs and herbs, with a single vertical layer formed by a discontinuous canopy of approximately 4 m in height. In 2018, these early-stage plots developed into taller forests, approximately 7 m in height, with a less representative shrub–herb layer. This area was used as pasture for at least 20 years and abandoned in 2000, with the creation of the MSSP. The intermediate successional stage was characterized by the following two vertical strata: the first composed of fast-growing trees up to 10–12 m high, forming a closed canopy, with some emerging trees up to 15 m; the second stratum composed of a dense understory, with the dominance of lianas and juvenile trees. This area was used as pasture through an undefined period and abandoned in the late 1960s (for 40–45 years regenerating in 2009). Finally, the late successional stage had the following three strata: the first stratum consisted of tall trees forming a closed canopy up to 20 m high; the second stratum was formed by a sparse understory with reduced light penetration and low density of juvenile trees and lianas; and the third stratum possessed typical understory shrub and herbaceous species. There are no clear-cut records in this area for at least 50 years, although low-intensity selective logging and free-ranging cattle occurred throughout the whole area until the MSSP was created in 2000 [35]. No changes were observed in the stratification of the intermediate and late stages from 2009 to 2018.

2.2. Sampling Design

2.2.1. Characterization of Biotic and Abiotic Changes

For each plot, we calculated the average tree height and basal area, species richness and density of individuals in 2009 and 2017 (the last plot re-census; for methodological details see [35]). In addition, we calculated the Holdridge Complexity Index (HCI) for each plot, using the following formula: HCI = (height × stem density × basal area × number of species)/1000 [36]. The HCI considers only arboreal individuals with DBH > 10 cm, but we used a modified version of the index because we sampled individuals with DBH ≥ 5 cm.
Chemical analysis of the soil was also carried out on three samples 0–20 cm deep for each plot, in 2009 and 2018. Analyses of the soil samples from 2009 were carried out at EMBRAPA Solos [14] whereas the samples from 2018 were conducted at the soil analysis laboratory of the Institute of Agricultural Sciences (ICA) at the Federal University of Minas Gerais (UFMG), following the same EMBRAPA protocols used for the samples from 2009. The environmental change associated with successional development was evaluated by measuring photosynthetically active radiation (PAR) in April 2009 and 2018, using a sensor (Smart Sensor S-LIA-M003, Onset Company, College Park, MD, USA) connected to an understory microclimate station installed in a plot of each successional stage.

2.2.2. Tree Species and Leaf Sampling

Based on the plot structure and composition data, five tree species with the highest importance value (IV) in each plot were selected (see [5,7]). The IV expresses, numerically, the importance of a given species in the community (in this case, the successional stage), being determined through the sum of its values of relative density, frequency and dominance, expressed as a percentage [37]. Thus, the species sampled were not necessarily the same per plot. In total, we sampled 13 tree species from 7 families in 2009 and 24 species from 16 families in 2018 (see Table 1). For all species, three individuals were marked in each plot (i.e., 15 individuals per plot) and leaf functional traits were evaluated. All measurements were performed in the middle of the rainy season (January 2009 and 2018).
The protocols used to obtain the leaf functional traits were defined by Faccion et al. [7]. To determine the leaf functional traits, 10 expanded leaves without visible damage by herbivores and pathogens were collected from each of the 15 individuals previously marked per plot. Of the total number of leaves collected (N = 10) per individual, five were used to calculate the specific leaf area (SLA) dividing the total leaf area by dry weight. The leaf area was calculated using the software ImageJ 1.52 [38]. Afterwards leaves were oven-dried for 48 h at 70 °C [39] and weighed using an analytical balance. The other five leaves were used to determine the polyphenol content (μmol/cm2) (Dualex® Dual Excitation, prototype CNRS-LURE, Paris, France) and for chlorophyll extraction (see below). For statistical analysis, each variable was the average per individual for all sampled species.

2.2.3. Leaf Polyphenol and Chlorophyll Contents

Five leaves were used for nondestructive evaluation of the polyphenols present in the leaf epidermis. The measurements were made in the field with the aid of a dual excitation fluorimeter (Dualex® Dual Excitation, prototype CNRS-LURE, France) [40,41]. These same leaves were used to determine the chlorophyll content. All leaves were wrapped in foil and then stored in a polystyrene box with ice and taken to the laboratory, where they were frozen at −18 °C for analysis, following a method adapted from Hiscox and Israelstam [42,43,44]. For full methodological details on polyphenol and chlorophyll quantification, see Faccion et al. [7].

2.3. Statistical Analyses

2.3.1. Characterization of Biotic and Abiotic Changes

The vegetation’s structural parameters (i.e., HCI, species richness, density of individuals, average tree height and basal area) (response variables) were compared between years (2009 and 2018) and among successional stages (explanatory variables) using generalized linear models (GLMs; one for each response variable), with pos-hoc comparison of means by contrast analyses [45]. The residuals of the adjusted models were analyzed to assess the adequacy of the error distribution [46]. These analyses were performed using the software R 3.6.0 [47].
To determine the differences in plant species composition among successional stages and years, a nonmetric multidimensional scaling analysis (NMDS; [48]) was performed using the Bray–Curtis Dissimilarity Index. The complete floristic composition data per plot, obtained in 2009 and 2018, were included instead of only the species used for the analysis of leaf characteristics. To test the significance of the separation of the plots obtained through NMDS, we used a similarity analysis (ANOSIM). NMDS and ANOSIM analyses were performed using the software Past 3.23 [48].
The chemical parameters of the soil were also compared among the successional stages and years (2009 and 2018) using GLMs (one for each response variable). The same procedures performed in the analyses of the vegetation’s structural parameters were used. The PAR values were not compared statistically, since there were data available only for one sensor per successional stage. Thus, only the PAR average was calculated for January 2009 and 2018 for each successional stage.

2.3.2. Leaf Functional Traits

Chlorophyll contents, polyphenols and SLA (response variables) were compared among the successional stages and years (explanatory variables) using GLMs (one model for each response variable), following the same procedures used for previous analyses. In addition, a principal component analysis (PCA) was performed using the average values of all leaf traits. Because of the large discrepancies among the values of each variable, the logarithm of the average values per species in each plot was calculated to normalize the data. To compare the three successional stages among years (explanatory variable) in functional terms, the scores for axis 1 generated by the PCA (response variable) were compared using a GLM. The PCA was conducted using the Past software [48].

3. Results

3.1. Characterization of Biotic and Abiotic Changes

In both 2009 and 2018, Fabaceae and Bignoniaceae were the most representative families. The most common species sampled in 2009 and 2018 were Combretum duarteanum, Handroanthus chrysotrichus, H. ochraceus and Astronium urundeuva. However, the species with highest IV changed along the chronosequence, as follows: in the early successional stage, H. ochraceus, Machaerium acutifolium and A. urundeuva were the dominant species sampled in both 2009 and 2018. On the other hand, Combretum duarteanum, Commiphora leptophloeos and Tabebuia reticulata dominated in the intermediate and late successional stages (see Table 1). Average tree height and basal area, species richness and the Holdridge Complexity Index (HCI) increased along the chronosequences (Table 2). Tree density increased along the chronosequence in 2009 but not in 2018, when the intermediate stage exhibited the lowest density (Table 2). The structural parameters did not change between 2009 and 2018, except for the HCI which decreased over the studied period in the intermediate and late stages (Table 2). The NMDS analysis based on the floristic composition indicated that there were no temporal differences for each successional stage (p > 0.05; Figure S1).
The mean values of PAR in the understory for 2009 were higher in the early and intermediate stages than in the late successional stage. The PAR decreased 44% in the early stage from 2009 to 2018, indicating a relatively rapid canopy closure compared to the intermediate (13.6%) and late stages, where PAR values remained practically constant along the study period (Figure S2).
We detected significant differences in several chemical soil parameters over the chronosequences in both 2009 and 2018 (p < 0.05), mostly between the intermediate and the early/late stages (Table 3). Soils from the intermediate stage were more acidic and had lower values of K, Ca and Mg. Significant temporal changes in soil chemistry were observed for p levels and the contents of organic matter and organic carbon (Table 3).

3.2. Leaf Functional Traits

The chronosequence trends for leaf functional traits were not consistent in 2009 and 2018, except for the total and a/b chlorophyll contents (Figure 1). For the SLA, the highest values were observed in the late stage in 2009 and in the intermediate stage in 2018 (Figure 1A). We observed a gradual decrease in polyphenol content along the chronosequence in 2009, but in 2018 the highest levels of these secondary compounds were detected in the intermediate stage (Figure 1B). The ratio between chlorophylls a/b did not differ among successional stages, neither in 2009 nor 2018, but the total chlorophyll content was lower in the intermediate stage in both years, although this difference was not statistically significant in 2018 (Figure 1C,D).
On the other hand, consistent temporal changes in leaf functional traits were detected from 2009 to 2018, except for polyphenol content. The SLA decreased in all successional stages, although the temporal difference was not statistically significant for the intermediate stage (Figure 1A). The ratio between chlorophylls a/b almost doubled during the study period in all stages, although the total chlorophyll content did not change significantly (but tended to increase in all stages; Figure 1C,D). Polyphenol content increased from 2009 to 2018 in the late stage and decreased in the early stage, with no statistically significant difference in the intermediate stage (Figure 1B).
The principal component analysis of the leaf traits indicated a clear separation between years (2009 and 2018), regardless of the successional stage (Figure 2). The first PCA axis explained 67.9% of the variation, while the second PCA axis explained 23.2%. The first axis was positively correlated with the ratio between chlorophylls a/b and polyphenol content, and negatively correlated with the SLA (Figure 2). The second axis correlated positively with the ratio between chlorophylls a/b and SLA, and negatively with polyphenol content (Table S1). For 2009, it was also possible to notice a separation between late-stage plots and early/intermediate stage plots (Figure 2). There was a statistically significant difference in the scores of the first axis of the PCA between 2009 and 2018 (GLM, p < 0.05).

4. Discussion

We observed contrasting successional patterns for leaf functional traits when using the following three different comparisons: a chronosequence in 2009 and 2018 and the actual temporal changes that occurred in the forest over this nine-year period. The predicted successional pattern was only observed for SLA (increased) and polyphenols (decreased) for the chronosequence in 2009 (see Table 4), and for chlorophylls a/b (increase) from 2009 to 2018. This lack of consistency is likely related to the fact that the temporal changes in forest structure and abiotic conditions (light and soil parameters) followed an unexpected successional pathway due to stochastic climatic events causing high tree mortality in the intermediate and late stages. These results reveal the limitations of the space-for-time substitution approach in assessing the regeneration of ecosystem functioning.

4.1. Temporal Changes in Biotic and Abiotic Conditions

The environmental conditions of the three successional stages changed along nine years of natural regeneration, as demonstrated by the chemical soil parameters, as well as by the PAR. Although there were no differences in the general compositions of the plant species in the successional stages between 2009 and 2018, the number of species sampled in each year was quite different (13 vs. 24). While the five plant species with the highest IV were the same (see Table 1), the IV values decreased over time, indicating a decrease in species dominance. In addition, these results suggest an increase in beta diversity in all successional stages. In 2009, the 15 individuals sampled in the three plots of the early stage belonged to seven species, increasing to ten species in 2018. A similar pattern was found for the intermediate (six to nine) and late (six to eleven) stages. However, the most striking results were recorded for the structure of the successional stages.
Contrary to our expectations, the average tree species richness decreased in all successional stages. The basal area, height and Holdridge Complexity Index (HCI) increased in the early successional stage but decreased in the more advanced successional stages. According to several studies, structural complexity generally increases along forest natural regeneration [10,14,22,49]. This unexpected result is probably related to the long period of drought that occurred between 2011 and 2016, aggravated by the occurrence of the El Niño Southern Oscillation (ENSO, 2015−2016) [50]. In fact, recent studies on tropical forests in Brazil [51] and Panama [52] have shown the negative impacts of the ENSO from 2015−2016, considered the most intense in the last 50 years [53]. These studies showed that drought-induced mortality is more common in the seedling stage and in large trees [51,52]. In addition, in February 2011, a strong windstorm was recorded in the studied plots, causing tree fall and extensive canopy opening (MM Espírito-Santo, pers. obs.). Despite these structural changes, the PAR values decreased in the early and intermediate stages and remained stable in the late (see Figure S2), indicating canopy closure from 2009 to 2018. However, the results of the PAR must be interpreted with caution, since they represent a measurement of a single area of one plot per successional stage, not expressing all the variability of light distribution in the forest canopy.
In general, the soil chemical analyses indicated that the soil fertility was lower in the intermediate stage, both in 2009 and 2018. The soil types of the intermediate and late plots were the same (red, eutrophic Ferrasols; [32]); thus, it is likely that the marked difference in the soils among successional stages is a consequence of a more intense previous land use in the intermediate successional stage before 1972, although the land use history in the intermediate plots was not completely clarified [14]. Despite the lack of a clear successional gradient in soil fertility in the chronosequences, the temporal increases in organic matter and organic carbon suggest that soils of all successional stages have become more fertile. In a comprehensive analysis of soil resistance and recovery along tropical dry and wet forest succession, Van der Sande et al. [16] found that changes in soil traits are strongly dependent on local conditions, such as clay properties (i.e., high- or low-activity clay). In sites such as the MSSP, with low-activity clay and low annual rainfall, successional changes in soil parameters are usually less marked, although bulk density is expected to decrease, and C and N contents generally increase as forests recover [16]. It is possible that the increase in organic matter and organic C observed in our study is a consequence of increasing litterfall along succession, as well as its decomposition by a more abundant and diverse macrofauna [54]. Soil organic matter and C have been reported to increase along tropical forest succession in other studies, a pattern that is driven by the interaction of multiple factors, such as plant functional diversity, microbial regulation of enzymatic activity, soil bulk density, N and P contents and PH [55,56].

4.2. Chronosequence Variations in Leaf Traits

The variations in leaf traits among successional stages were not consistent between the chronosequences of 2009 and 2018. Our hypothesis was partially corroborated for the chronosequence in 2009, with SLA and polyphenols varying according to the conservative–acquisitive spectrum (see Table 4). Lower chlorophyll values were expected at early-successional species because the leaves are more exposed to solar radiation [5,10]. In the presence of intense light, chlorophyll degradation rates by photo-oxidation are higher than its synthesis rates [57]. As discussed by Alves et al. [6] and Faccion et al. [7], the lowest total chlorophyll values in the intermediate successional stage may be related to the relatively low soil nutrient levels observed in this stage, as nitrogen participates in the constitution of this pigment [58,59].
However, the same pattern was not observed for the chronosequence in 2018, when none of the leaf traits varied as predicted by our proposed hypothesis. In this year, no successional differences were observed for chlorophyll levels, despite the soil nutrient content was still lower at the intermediate successional stage. Furthermore, higher SLA and polyphenol contents were observed in the intermediate successional stage in 2018 due to a decrease in the average values of these traits in the early and late successional stages (i.e., these variables did not exhibit significant temporal changes in the intermediate successional stage). Such an inconsistent pattern in the chronosequence in 2018 is clearly observed in the PCA biplots (see Figure 2), where the partial separation of plots from different stages in 2009 (left side) was shuffled in 2018 (right side), likely a consequence of an unexpected successional trajectory caused by stochastic events along the study period, as explained below.

4.3. Temporal Differences in Leaf Traits

In general, the temporal changes in leaf traits between 2009 and 2018 were more striking than the changes observed along the chronosequences in each year. However, many of these changes did not corroborate the hypothesis that we proposed in the framework of the conservative–acquisitive spectrum of leaf economy during succession in TDFs [24]. The decrease in SLA in all successional stages (although the difference was not statistically significant for the intermediate stage) over time was the opposite of our expectation. The small increase observed in total chlorophyll, although not statistically significant, indicates a trend that is contrary to the prediction of our hypothesis, but it is partially consistent with that observed in the chronosequences (i.e., no difference in 2018). The most remarkable change was observed in the ratio between chlorophylls a/b, which doubled in all stages, also contradicting our predictions. In TDFs, where the canopy is naturally less closed than in tropical rain forests, strong differences in light availability in the understory are not expected along the successional gradient [23,25,26]. Despite the nonsignificant temporal differences in total chlorophyll, the sharp increase in the ratio between chlorophylls a/b suggests an increase in light incidence in all successional stages. Chlorophyll b captures light at different wavelengths and transfers it to chlorophyll a, a physiological strategy to increase the light capture efficiency in shaded environments [60,61]. Thus, the decrease in the level of this pigment may reflect canopy opening for all stages along time, instead of canopy closure that would occur at least for the early and intermediate stages.
It is important to highlight the lack of consistency between the changes in leaf traits observed along the chronosequence (whose pattern was different in each year) and over the nine years of the present study. The only reasonably consistent pattern between the two approaches was observed for total chlorophyll (i.e., no difference). Despite being an extremely useful approach in studies on ecological succession, chronosequences are based on measurements of specific parameters and processes and its premises are often not confirmed in time-series studies [62]. In the case of the present study, the temporal changes in forest structure observed during the nine years of succession contradicted our expectations (i.e., decrease in complexity) for the more advanced success ional stages due to the windstorm in 2011 and the long sequence of dry years whose effects had a different intensity across stages. These results reinforce the need for long-term studies for a better understanding of the natural regeneration of ecosystem functioning in tropical forests.
The consistent separation of plots from 2009 and 2018 in the ordination analysis indicate that all successional stages functionally changed over the study period. Plant functional traits generally changed from acquisitive to conservative characteristics (i.e., general decreases in SLA and in the ratio between chlorophylls a/b; increase in polyphenols in the advanced stages), probably as a consequence of the combined effects of temporal changes in species composition (13 species in 2009 and 24 in 2018, considering all stages) and phenotypic plasticity as a response to the decreasing forest structural complexity in intermediate and late stages.

5. Conclusions

Few studies have accompanied successional changes in functional plant traits over time to corroborate the results obtained from chronosequences. The present study showed an inconsistency between the two approaches, which may be related to stochastic events that occurred over the nine-year interval between the samples. In 2009, the chronosequence partially corroborated the hypothesis of conservative–acquisitive changes according to the leaf economics spectrum throughout TDF succession. However, the unexpected decrease in structural complexity in advanced successional stages over time “blurred” these differences in 2018. Over time, the intermediate and late successional stages apparently became habitats that supported plants with more conservative characteristics in 2018 than in 2009. Therefore, the conservative–acquisitive spectrum in regenerating TDFs should be further investigated preferentially with temporal data, as the intensity and direction of environmental filters are likely to change over time, especially with the possibility of the increasing frequency of extreme events due to climate change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15101700/s1, Figure S1. Non-Metric Multidimensional Scaling (NMDS) and ANOSIM analysis comparing the species composition of the three successional stages in 2009 and 2018. Filled symbols represent the plots in 2009 and the empty symbols represent plots in 2018. Circles: early stage; triangles: intermediate stage; squares: late stage. No temporal differences were detected for any successional stage (p > 0.05). Figure S2. Average values (µm) of photosynthetically active radiation (PAR) (Smart Sensor S-LIA-M003) positioned in the understory of one plot per successional stage. Values were obtained for the April 2009 and 2018. Table S1. Scores of the first two axes from principal component analysis (PCA) conducted with leaf functional traits obtained in 2009 and 2018 in three successional stages (early, intermediate and late) in a Brazilian tropical dry forest. Asterisks indicate significant (p < 0.05) Pearson correlations between leaf functional traits and PCA scores.

Author Contributions

Conceptualization, K.F.F.; Methodology, K.F.F., J.O.S., L.A.D.F. and M.M.E.-S.; Software, L.A.D.F.; Validation, M.M.E.-S.; Formal analysis, J.O.S., P.C.-R. and L.A.D.F.; Data curation, J.O.S., P.C.-R. and L.A.D.F.; Writing—original draft, K.F.F.; Writing—review & editing, K.F.F., J.O.S. and P.C.-R.; Visualization, P.C.-R.; Supervision, M.M.E.-S.; Project administration, M.M.E.-S. All authors have read and agreed to the published version of the manuscript.

Funding

National Council for Scientific and Technological Development (CNPq—308623/2021-5), FAPEMIG (APQ-03020-22), Interamerican Institute for Global Change Research (IAI-CRN 3025).

Data Availability Statement

Data are contained within the article and supplementary materials.

Acknowledgments

We are grateful for the logistical support of the State Forestry Institute (IEF) of Minas Gerais during the fieldwork in Mata Seca State Park.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. PePeña-Claros, M.; Poorter, L.; Alarcón, A.; Blate, G.; Choque, U.; Fredericksen, T.S.; Justiniano, M.J.; Leaño, C.; Licona, J.C.; Pariona, W.; et al. Soil effects on forest structure and diversity in a moist and a dry tropical forest. Biotropica 2012, 44, 276–283. [Google Scholar] [CrossRef]
  2. Arruda, D.M.; Schaefer, C.E.G.R.; Correa, G.R.; Rodrigues, P.M.S.; Duque-Brasil, R.; Ferreira, W.G., Jr.; Oliveira-Filho, A.T. Landforms and soil attributes determines the vegetation structure in the Brazilian semiarid. Folia Geobot. 2015, 50, 175–184. [Google Scholar] [CrossRef]
  3. Silva, J.O.; Souza-Silva, H.; Rodrigues, P.M.S.; Cuevas-Reyes, P.; Espírito-Santo, M.M. Soil resource availability, plant defense, and herbivory along a successional gradient in a tropical dry forest. Plant Ecol. 2021, 222, 625–637. [Google Scholar] [CrossRef]
  4. Vaca-Sánchez, M.S.; Maldonado-López, Y.; Oyama, K.; Delgado, G.; Aguilar-Peralta, J.S.; Borges, M.A.Z.; de Faria, M.L.; Fagundes, M.; López-Maldonado, M.C.; Cuevas-Reyes, P. Changes in herbivory patterns and insect herbivore assemblages associated to canopy of Quercus laurina: Importance of oak species diversity and foliar chemical defense. Trees 2022, 37, 699–715. [Google Scholar] [CrossRef]
  5. Alvarez-Añorve, M.Y.; Quesada, M.; Sánchez-Azofeifa, G.A.; Avila-Cabadilla, L.D.; Gamon, J.A. Functional regeneration and spectral reflectance of trees during succession in a highly diverse tropical dry forest ecosystem. Am. J. Botany 2012, 99, 816–826. [Google Scholar] [CrossRef]
  6. Alves, A.M.; Espírito-Santo, M.M.; Silva, J.O.; Faccion, G.; Sanchez-Azofeifa, A.; Ferreira, K.F. Successional and intraspecific variations in leaf traits, spectral reflectance indices and herbivory in a Brazilian tropical dry forest. Front. For. Glob. Chang. 2021, 4, 780299. [Google Scholar] [CrossRef]
  7. Faccion, G.; Alves, A.M.; Espírito-Santo, M.M.; Silva, J.O.; Sanchez-Azofeia, G.A.; Ferreira, K.F. Intra- and interspecific variations on plant functional traits along a successional gradient in a Brazilian tropical dry forest. Flora 2021, 279, 151–815. [Google Scholar] [CrossRef]
  8. Violle, C.; Enquist, B.J.; McGill, B.J.; Jiang, L.; Albert, C.H.; Hulshof, C.; Jung, V.; Messier, J. The return of the variance: Intraspecific variability in community ecology. Trends Ecol. Evol. 2012, 27, 244–252. [Google Scholar] [CrossRef]
  9. Fonseca, M.B.; Silva, J.O.; Falcão, L.A.D.; Dupin, M.G.V.; Melo, G.A.; Espírito-Santo, M.M. Leaf damage and functional traits along a successional gradient in Brazilian tropical dry forests. Plant Ecol. 2018, 219, 403–415. [Google Scholar] [CrossRef]
  10. Lebrija-Trejos, E.; Pérez-García, E.A.; Meave, J.A.; Bongers, F.; Poorter, L. Functional traits and environmental filtering drive community assembly in a species-rich tropical system. Ecology 2010, 91, 386–398. [Google Scholar] [CrossRef]
  11. Sanaphre-Villanueva, L.; Dupuy, J.M.; Andrade, J.L.; Reyes-García, C.; Jackson, P.C.; Paz, H. Patterns of plant functional variation and specialization along secondary succession and topography in a tropical dry forest. Environ. Res. Lett. 2017, 12, 055004. [Google Scholar] [CrossRef]
  12. Mantero, G.; Morresi, D.; Marzano, R.; Motta, R.; Mladenoff, D.J.; Garbarino, M. The influence of land abandonment on forest disturbance regimes: A global review. Landsc. Ecol. 2020, 35, 2723–2744. [Google Scholar] [CrossRef]
  13. Jakovac, C.C.; Junqueira, A.B.; Crouzeilles, R.; Peña-Claros, M.; Mesquita, R.C.G.; Bongers, F. The role of land-use history in driving successional pathways and its implications for the restoration of tropical forests. Biol. Rev. 2021, 96, 1114–1134. [Google Scholar] [CrossRef] [PubMed]
  14. Espírito-Santo, M.M.; Olívio-Leite, L.; Neves, F.S.; Nunes, Y.R.F.; Zazá-Borges, M.A.; Falcão, L.A.D.; Fonseca-Pezzini, F.; Louro-Berbara, R.; Maia-Valério, H.; Fernandes, G.W.; et al. Tropical dry forests of northern Minas Gerais, Brazil: Diversity, conservation status, and natural regeneration. In Tropical Dry Forests in the Americas: Ecology, Conservation, and Management; Sánchez-Azofeifa, A., Powers, J.S., Fernandes, G.W., Quesada, M., Eds.; CRC Press: Boca Raton, FL, USA, 2014; pp. 69–81. [Google Scholar]
  15. Ayala-Orozco, B.; Gavito, M.E.; Mora, F.; Siddique, I.; Balvanera, P. Resilience of soil properties to land-use change in a tropical dry forest ecosystem. Land Degrad. Dev. 2018, 29, 315–325. [Google Scholar] [CrossRef]
  16. van der Sande, M.T.; Powers, J.S.; Kuyper, T.W.; Norden, N.; Salgado-Negret, B.; de Almeida, J.S.; Bongers, F.; Delgado, D.; Dent, D.H.; Derroire, G.; et al. Soil resistance and recovery during neotropical forest succession. Philos. Trans. R. Soc. B 2023, 378, 20210074. [Google Scholar] [CrossRef]
  17. Poorter, L.; Amissah, L.; Bongers, F.; Hordijk, I.; Kok, J.; Laurance, S.G.; van der Sande, M.T. Successional theories. Biol. Rev. 2023, 98, 2049–2077. [Google Scholar] [CrossRef]
  18. Rosenfield, M.F.; Jakovac, C.C.; Vieira, D.L.M.; Poorter, L.; Brancalion, P.H.S.; Vieira, I.C.G.; Almeida, D.R.A.; Massoca, P.; Schietti, J.; Albernaz, A.L.M.; et al. Ecological integrity of tropical secondary forests: Concepts and indicators. Biol. Rev. 2022, 98, 662–676. [Google Scholar] [CrossRef]
  19. Chazdon, R.L.; Lindenmayer, D.; Guariguata, M.R.; Crouzeilles, R.; Benayas, J.M.R.; Chavero, E.L. Fostering natural forest regeneration on former agricultural land through economic and policy interventions. Environ. Res. Lett. 2020, 15, 043002. [Google Scholar] [CrossRef]
  20. Crouzeilles, R.; Beyer, H.L.; Monteiro, L.M.; Feltran-Barbieri, R.; Pessôa, A.C.; Barros, F.S.; Strassburg, B.B. Achieving cost-effective landscape-scale forest restoration through targeted natural regeneration. Conserv. Lett. 2020, 13, e12709. [Google Scholar] [CrossRef]
  21. Chazdon, R. Chance and determinism in tropical forest succession. In Tropical Forest Community Ecology; Carson, W., Schnitzer, F., Eds.; Wiley-Blackwell: Sussex, UK, 2008; pp. 384–408. [Google Scholar]
  22. Lebrija-Trejos, E.; Meave, J.A.; Poorter, L.; Pérez-García, E.A.; Bongers, F. Pathways, mechanisms and predictability of vegetation change during tropical dry forest succession. Perspect. Plant Ecol. 2010, 12, 267–275. [Google Scholar] [CrossRef]
  23. Lohbeck, M.; Poorter, L.; Lebrija-Trejos, E.; Martínez-Ramos, M.; Meave, J.A.; Paz, H.; Pérez-García, E.A.; Romero-Pérez, I.E.; Tauro, A.; Bongers, F. Successional changes in functional composition contrast for dry and wet tropical forest. Ecology 2013, 94, 1211–1216. [Google Scholar] [CrossRef] [PubMed]
  24. Lohbeck, M.; Lebrija-Trejos, E.; Martínez- Ramos, M.; Meave, J.A.; Poorter, L.; Bongers, F. Functional trait strategies of trees in dry and wet tropical forests are similar but differ in their consequences for succession. PLoS ONE 2015, 10, 0123741. [Google Scholar] [CrossRef] [PubMed]
  25. Poorter, L.; Rozendaal, D.M.A.; Bongers, F.; de Almeida-Cortez, J.S.; Zambrano, A.M.A.; Álvarez, F.S.; Andrade, J.L.; Villa, L.F.A.; Balvanera, P.; Becknell, J.M.; et al. Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat. Ecol. Evol. 2019, 5, eaau3114. [Google Scholar] [CrossRef] [PubMed]
  26. Poorter, L.; Craven, D.; Jakovac, C.C.; van der Sande, M.T.; Amissah, L.; Bongers, F.; Chazdon, R.L.; Farrior, C.E.; Kambach, S.; Meave, J.A.; et al. Multidimensional tropical forest recovery. Science 2021, 374, 1370–1376. [Google Scholar] [CrossRef]
  27. Lebrija-Trejos, E.; Bongers, F.; Pérez-García, E.A.; Meave, J.A. Successional change and resilience of a very dry tropical deciduous forest following shifting agriculture. Biotropica 2008, 40, 422–431. [Google Scholar] [CrossRef]
  28. Wright, I.J.; Reich, P.B.; Westoby, M.; Ackerly, D.D.; Baruch, Z.; Bongers, F.; Villar, R. The worldwide leaf economics spectrum. Nature 2004, 428, 821–827. [Google Scholar] [CrossRef]
  29. Miner, B.G.; Sultan, S.E.; Morgan, S.G.; Padilla, D.K.; Relyea, R.A. Ecological consequences of phenotypic plasticity. Trends Ecol. Evol. 2005, 20, 685–692. [Google Scholar] [CrossRef]
  30. Pezzini, F.F.; Ranieri, B.D.; Brandão, D.O.; Fernandes, G.W.; Quesada, M.; Espírito-Santo, M.M.; Jacobi, C.M. Changes in tree phenology along natural regeneration in a seasonally dry tropical forest. Plant Biosyst. 2014, 148, 965–974. [Google Scholar] [CrossRef]
  31. Vasques, G.M.; Coelho, M.R.; Dart, R.O.; Oliveira, R.P.; Teixeira, W.G. Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil. Pesqui Agropecu. Bras. 2016, 5, 1371–1385. [Google Scholar] [CrossRef]
  32. Coelho, M.R.; Dart, R.D.O.; Vasques, G.D.M.; Teixeira, W.G.; Oliveira, R.P.; Brefin, M.; Berbara, R. Levantamento Pedológico Semi-Detalhado (1: 30.000) do Parque Estadual da Mata Seca, Município de Manga-MG; Boletim de Pesquisa e Desenvolvimento 217; Embrapa Solos: Rio de Janeiro, Brazil, 2013; pp. 1–264. [Google Scholar]
  33. Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.L.M.; Sparovek, G. Koppen’s climate classification map for Brazil. Meteorol. Z. 2013, 22, 711–728. [Google Scholar] [CrossRef]
  34. Antunes, F.Z. Caracterização Climática—Caatinga do Estado de Minas Gerais. Info. Agropec. 1994, 17, 15–19. [Google Scholar]
  35. Madeira, B.G.; Espírito-Santo, M.M.; D’Angelo-Neto, S.; Nunes, Y.R.F.; Sánchez-Azofeifa, G.A.; Fernandes, G.W.; Quesada, M. Changes in tree and liana communities along a successional gradient in a tropical dry forest in south-eastern Brazil. For. Ecol Manag. 2009, 201, 291–304. [Google Scholar] [CrossRef]
  36. Holdridge, L.R.; Grenke, W.C.; Hatheway, W.H.; Liang, T.; Tosi, J.A.J.R. Forest Environments in Tropical Life Zones: A Pilot Study; Pergamon Press: New York, NY, USA, 1971. [Google Scholar]
  37. Müeller-Dombois, D.; Ellenberg, H.A. Aims and Methods of Vegetation Ecology; John Wiley: New York, NY, USA, 1974. [Google Scholar]
  38. Rasband, W.S. ImageJ; U.S. National Institutes of Health: Bethesda, MD, USA, 2006. Available online: http://rsb.info.nih.gov/ij (accessed on 26 January 2019).
  39. Cornelissen, J.H.C.; Lavorel, S.; Garnier, E.; Díaz, S.; Buchmann, N.; Gurvich, D.E.; Poorter, H. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 2003, 51, 335–380. [Google Scholar] [CrossRef]
  40. Cartelat, A.; Cerovic, Z.; Goulas, Y.; Meyer, S.; Lelarge, C.; Prioul, J.-L.; Barbottin, A.; Jeuffroy, M.-H.; Gate, P.; Agati, G.; et al. Optically assessed contents of leaf polyphenolics and chlorophyll as indicators of nitrogen deficiency in wheat (Triticum aestivum L.). Field Crop. Res. 2005, 91, 35–49. [Google Scholar] [CrossRef]
  41. Goulas, Y.; Cerovic, Z.G.; Cartelat, A.; Moya, I. Dualex: A new instrument for field measurements of epidermal UV-absorbance by chlorophyll fluorescence. Appl. Opt. 2004, 43, 4488–4496. [Google Scholar] [CrossRef]
  42. Hiscox, J.; Israelstam, G.F. A method for the extraction of chlorophyll from leaf tissue without maceration. Canad. J. Bot. 1979, 57, 1332–1334. [Google Scholar] [CrossRef]
  43. Mackinney, G. Absorption of light by chlorophyll solutions. J. Biol. Chem. 1941, 140, 315–322. [Google Scholar] [CrossRef]
  44. Arnon, D.I. Copper Enzymes in Isolated Chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiol. 1949, 24, 1–15. Available online: https://www.jstor.org/stable/4258165 (accessed on 1 May 2019). [CrossRef]
  45. Reis-Jr, R.; Oliveira, M.L.; Borges, G.R.A. RT4Bio: R Tools for Biologists. 2015. Available online: https://sourceforge.net/projects/rt4bio/ (accessed on 26 June 2020).
  46. Crawley, M.J. Statistical Computing—An Introduction to Data Analysis Using S-Plus; John Wiley & Sons: London, UK, 2007. [Google Scholar]
  47. R Development Core Team R. A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2018; ISBN 3-900051-07-0. Available online: http://www.R-project.org. (accessed on 12 June 2019).
  48. Hammer, Ø.; Harper, D.; Ryan, P.D. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontol. Electron. 2001, 4, 1–9. Available online: https://palaeo-electronica.org/2001_1/past/issue1_01.htm (accessed on 21 April 2019).
  49. Becknell, J.M.; Powers, J.S. Stand age and soils as drivers of plant functional traits and aboveground biomass in secondary tropical dry forest. Can. J. For. Res. 2014, 44, 604–613. [Google Scholar] [CrossRef]
  50. Bretfeld, M.; Ewers, B.E.; Hall, J.S. Plant water use responses along secondary forest succession during the 2015-2016 El Niño drought in Panama. New Phytol. 2018, 219, 885–899. [Google Scholar] [CrossRef] [PubMed]
  51. Rowland, L.; da Costa, A.C.L.; Galbraith, D.R.; Oliveira, R.S.; Binks, O.J.; Oliveira, A.A.R.; Pullen, A.M.; Doughty, C.E.; Metcalfe, D.B.; Vasconcelos, S.S.; et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 2015, 528, 119–122. [Google Scholar] [CrossRef] [PubMed]
  52. Meakem, V.; Tepley, A.J.; Gonzalez-Akre, E.B.; Herrmann, V.; Muller-Landau, H.C.; Wright, S.J.; Hubbell, S.P.; Condit, R.; Anderson-Teixeira, K.J. Role of tree size in moist tropical forest carbon cycling and water deficit responses. New Phytol. 2017, 47, 38–48. [Google Scholar] [CrossRef] [PubMed]
  53. Bennett, A.C.; de Sousa, T.R.; Monteagudo-Mendoza, A.; Esquivel-Muelbert, A.; Morandi, P.S.; de Souza, F.C.; Castro, W.; Duque, L.F.; Llampazo, G.F.; dos Santos, R.M.; et al. Sensitivity of South American tropical forests to an extreme climate anomaly. Nat. Clim. Chang. 2023, 13, 967–974. [Google Scholar] [CrossRef]
  54. Barros, E.; Curmi, P.; Hallaire, V.; Chauvel, A.; Lavelle, P. The role of macrofauna in the transformation and reversibility of soil structure of an oxisol in the process of forest to pasture conversion. Geoderma 2001, 100, 193–213. [Google Scholar] [CrossRef]
  55. Satdichanh, M.; Dossa, G.G.; Yan, K.; Tomlinson, K.W.; Barton, K.E.; Crow, S.E.; Harrison, R.D. Drivers of soil organic carbon stock during tropical forest succession. J. Ecol. 2023, 111, 1722–1734. [Google Scholar] [CrossRef]
  56. Xing, G.; Wang, X.; Jiang, Y.; Yang, H.; Mai, S.; Xu, W.; Long, W. Variations and influencing factors of soil organic carbon during the tropical forest succession from plantation to secondary and old–growth forest. Front. Ecol. Evol. 2023, 10, 1104369. [Google Scholar] [CrossRef]
  57. Kramer, T.; and Kozlowski, T. Physiology of Woody Plants; Academic Press: New York, NY, USA, 1979. [Google Scholar]
  58. Bojović, B.; Marković, A. Correlation between nitrogen and chlorophyll content in wheat (Triticum aestivum L.). Kragujevac J. Sci. 2009, 31, 69–74. [Google Scholar]
  59. Nogueira, P.D.M.; Sena Júnior, D.G.; Ragagnin, V.A. Clorofila foliar e nodulação em soja adubada com nitrogênio em cobertura. Global Sci. Technol. 2010, 3, 117–124. [Google Scholar]
  60. Gonçalves, C.J.F.; Marenco, A.R.; Vieira, G. Concentration of photosynthetic pigments and chlorophyll fluorescence of Mahogany and Tonka bean under two light environments. Rev. Bras. Fisiol. Veg. 2001, 13, 149–157. [Google Scholar] [CrossRef]
  61. Li, Y.; Liu, C.; Zhang, J.; Yang, H.; Xu, L.; Wang, Q.; He, N. Variation in leaf chlorophyll concentration from tropical to cold-temperate forests: Association with gross primary productivity. Ecol. Indic. 2018, 85, 383–389. [Google Scholar] [CrossRef]
  62. Johnson, E.A.; Miyanishi, K. Testing the assumptions of chronosequences in succession. Ecol. Lett. 2008, 11, 419–431. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Leaf functional traits of tree species along a chronosequence in a tropical dry forest of Brazil. (A) Specific leaf area; (B) Polyphenol content; (C) ratio between the content of chlorophylls a and b; (D) total chlorophyll content. Letters a, b, c on the bars = statistically significant differences among successional stages in 2009; letters d, e = differences among stages in 2018. * Statistically significant differences between years for each stage. Mean ± SE.
Figure 1. Leaf functional traits of tree species along a chronosequence in a tropical dry forest of Brazil. (A) Specific leaf area; (B) Polyphenol content; (C) ratio between the content of chlorophylls a and b; (D) total chlorophyll content. Letters a, b, c on the bars = statistically significant differences among successional stages in 2009; letters d, e = differences among stages in 2018. * Statistically significant differences between years for each stage. Mean ± SE.
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Figure 2. Ordering of leaf functional traits of tree species in early, intermediate and late successional stages in 2009 and 2018 (n = three plots per stage) through principal component analysis (PCA). The variables that explained most of the variation in each axis are indicated.
Figure 2. Ordering of leaf functional traits of tree species in early, intermediate and late successional stages in 2009 and 2018 (n = three plots per stage) through principal component analysis (PCA). The variables that explained most of the variation in each axis are indicated.
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Table 1. Tree species with the highest importance value (IV) in each successional stage in 2009 and 2018 in the Mata Seca State Park, Brazil. See text for further details on the characterization of each successional stage.
Table 1. Tree species with the highest importance value (IV) in each successional stage in 2009 and 2018 in the Mata Seca State Park, Brazil. See text for further details on the characterization of each successional stage.
Tree SpeciesFamilySuccessional Stage
IVEarlyIntermediateLate
2009
Astronium urundeuva (M.Allemão) Engl.Anacardiaceae32.54X
Handroanthus chrysotrichus (Mart. ex DC.)Bignoniaceae26.63 X
Tabebuia reticulata A.H. GentryBignoniaceae22.13 XX
Combretum duarteanum Loefl.Combretaceae20.92 XX
Commiphora leptophloeos (Mart.) J.B.GillettBurseraceae11.97 XX
Senna spectabilis (DC.) H.S.Irwin & BarnebyFabaceae 9.49X
Cenostigma pluviosum (DC.) Gagnon & G.P.LewisFabaceae 9.43XXX
Terminalia fagifolia L.Combretaceae9.31 XX
Handroanthus ochraceus (Cham.) MattosBignoniaceae8.60X
Machaerium acutifolium Vogel.Fabaceae 5.59X
Mimosa tenuiflora (Wild.) Poir.Fabaceae 4.47X
Senegalia polyphylla (DC.) Britton & RoseFabaceae3.87X
Spondias tuberosa ArrudaAnacardiaceae3.77 X
2018
Astronium urundeuva (M.Allemão) Engl.Anacardiaceae31.44X X
Handroanthus chrysotrichus (Mart. ex DC.)Bignoniaceae24.45 X
Tabebuia reticulata A.H. GentryBignoniaceae21.32 XX
Combretum duarteanum LoeflCombretaceae19.12 XX
Commiphora leptophloeus (Mart.) J.B.GillettBurseraceae10.77 XX
Terminalia fagifolia L.Combretaceae8.87 XX
Handroanthus ochraceus (Cham.) MattosBignoniaceae7.99X
Handroanthus spongiosus (Rizzini) S. GroseBignoniaceae7.69 X
Cenostigma pluviosum (DC.) Gagnon & G.P.LewisFabaceae7.47X X
Enterolobium contortisiliquum (Vell.) MorongFabaceae7.21X
Aspidosperma parvifolium A.DC.Apocynaceae6.78X
Cochlospermum vitifolium (Wild.) Spreng.Bixaceae5.71 X
Coccoloba schwackeana LindauPolygonaceae5.29 X
Machaerium acutifolium Vogel.Fabaceae4.99X
Leutzelburgia andradelimae H.C.LimaFabaceae4.83 X
Manihot anomala Mill.Euphorbiaceae4.32 X
Randia armata (Sw.) DC.Rubiaceae4.21X
Acosmium lentiscifolium SchottFabaceae3.98X
Aralia warmegiana (Marchal) J.WenAraliaceae 3.91 X
Cordia incognita Gottschling & J.S.MillCordiaceae3.87 X
Pereskia bahiensis GürkeCactaceae3.76 X
Platymiscium floribundum VogelFabaceae3.45X
Sapium glandulosum (L.) MorongEuphorbiaceae3.32 X
Schinopsis brasiliensis Engl.Anacardiaceae3.12X
Table 2. Forest structural parameters (mean ± standard error) in three successional stages of a Brazilian tropical dry forest in 2009 and 2018. HCI = Holdridge Complexity Index.
Table 2. Forest structural parameters (mean ± standard error) in three successional stages of a Brazilian tropical dry forest in 2009 and 2018. HCI = Holdridge Complexity Index.
ParameterEarly Intermediate Late
YearYearYear
20092018Change20092018Change20092018Change
Basal area (m2 ha−1)0.50 ± 0.09 a0.68 ± 0.16 d+36%1.73 ± 0.16 b1.23 ± 0.12 e−28%2.56 ± 0.17 c2.15 ± 0.17 f−16%
Height (m)6.07 ± 0.39 a7.34 ± 0.77 d+20%8.92 ± 0.22 b8.51 ± 0.50 d−4%10.0 ± 0.64 c9.49 ± 0.29 e−5%
Density (ha−0.1)47.6 ± 0.77 a57.5 ± 10.5 d+20%50.4 ± 0.58 a44.1 ± 3.54 e−12%62.3 ± 0.42 b56.9 ± 3.01 d−8%
Species richness12.3 ± 1.22 a10.3 ± 1.22 d−16%22.2 ± 1.19 b19.0 ± 1.21 e−14%21.8 ± 1.40 b19.3 ± 1.30 e−15%
HCI2.42 ± 0.88 a3.94 ± 1.38 d+62%17.9 ± 3.53 b8.64 ± 1.08 e−51% *35.3 ± 5.02 c22.5 ± 2.58 f−36% *
Letters a, b, c = statistically significant differences among successional stages in 2009; letters d, e, f = statistically significant differences among stages in 2018. * Statistically significant differences between years for each stage.
Table 3. Chemical soil parameters (mean ± standard error) of three successional stages in a Brazilian tropical dry forest in 2009 and 2018.
Table 3. Chemical soil parameters (mean ± standard error) of three successional stages in a Brazilian tropical dry forest in 2009 and 2018.
ParametersEarly Intermediate Late
YearYearYear
200920182009201820092018
Chemical Change Change Change
pH6.8 ± 0.11 a6.36 ± 0.14 d−6.0%5.3 ± 0.05 b5.16 ± 0.0 e−2.0%6.63 ± 0.16 a6.36 ± 0.03 d−4.0%
P Meh4.0 ± 0.57 a1.0 ± 0.07 d−75% *3.0 ± 0.00 a1.0 ± 0.07 d−66% *3.0 ± 0.44 a1.0 ± 0.33 d−200% *
K143.0 ± 18.9 a96.0 ± 7.96 d−32%64.0 ± 2.6 b53.0 ± 1.6 e−17%126.0 ± 12.8 a108.0 ± 9.13 d−14%
Ca8.0 ± 0.18 a8.0 ± 0.78 d0%3.0 ± 0.58 b3.0 ± 0.24 e0%8.0 ± 0.50 a8.0 ± 0.37 d0%
Mg2.0 ± 0.38 a3.0 ± 0.76 d+50%1.0 ± 0.03 b1.0 ± 0.08 e0%2.0 ± 0.15 a3.0 ± 0.09 d+50%
Organic matter3.0 ± 0.09 a7.09 ± 0.07 d+133% *3.0 ± 0.02 a5.0 ± 0.06 d+66% *3.0 ± 0.05 a6.0 ± 0.06 d+100% *
Organic C2.0 ± 0.05 a4.0 ± 0.04 d+100% *1.0 ± 0.12 a3.0 ± 0.03 d+200% *3.0 ± 0.06 a4.0 ± 0.39 d+33%
Letters a, b = statistically significant differences among successional stages in 2009; letters d, e = statistically significant differences among successional stages in 2018. * Statistically significant differences between years for each stage.
Table 4. Expected and observed patterns for vegetation structure, light and soil conditions and leaf functional traits using a chronosequence (early, intermediate and late stages) approach in 2009 and 2018, and temporal data from 2009 to 2018 in a Brazilian tropical dry forest. HCI = Holdridge Complexity Index; PAR = photosynthetically active radiation; WHC = water-holding capacity. Green indicates that the proposed pattern was corroborated.
Table 4. Expected and observed patterns for vegetation structure, light and soil conditions and leaf functional traits using a chronosequence (early, intermediate and late stages) approach in 2009 and 2018, and temporal data from 2009 to 2018 in a Brazilian tropical dry forest. HCI = Holdridge Complexity Index; PAR = photosynthetically active radiation; WHC = water-holding capacity. Green indicates that the proposed pattern was corroborated.
ParameterExpectedObserved Trend
Chronosequence 2009Chronosequence 2018Temporal (2009–2018)
Biotic and abiotic changes
HCIIncreaseIncreaseIncreaseDecreases in the intermediate and late
PARDecreaseDecreaseHigher in the intermediateDecrease
Soil nutrientsIncreaseLower in the intermediateLower in the intermediateDecrease in P and increase in organic matter
Soil WHCIncreaseHigher in the intermediateHigher in the intermediateDecrease in the early and late
Leaf functional traits
SLAIncreaseIncreaseHigher in the intermediateDecrease
PolyphenolsDecreaseDecreaseHigher in the intermediateDecrease in early, increases in intermediate and late
Total chlorophyllIncreaseLower in the intermediateNo significant differencesNo significant differences
Chlorophylls a/bIncreaseNo significant differencesNo significant differencesIncrease
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MDPI and ACS Style

Ferreira, K.F.; Silva, J.O.; Cuevas-Reyes, P.; Falcão, L.A.D.; Espírito-Santo, M.M. Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil. Forests 2024, 15, 1700. https://doi.org/10.3390/f15101700

AMA Style

Ferreira KF, Silva JO, Cuevas-Reyes P, Falcão LAD, Espírito-Santo MM. Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil. Forests. 2024; 15(10):1700. https://doi.org/10.3390/f15101700

Chicago/Turabian Style

Ferreira, Kleiperry F., Jhonathan O. Silva, Pablo Cuevas-Reyes, Luiz Alberto Dolabela Falcão, and Mário M. Espírito-Santo. 2024. "Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil" Forests 15, no. 10: 1700. https://doi.org/10.3390/f15101700

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