Do the distribution patterns of plant functional traits change during early secondary succession in tropical montane cloud forests?

Acta Oecologica 95 (2019) 26–35

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Do the distribution patterns of plant functional traits change during early secondary succession in tropical montane cloud forests?

T

Guadalupe Hernández-Vargasa, Yareni Perronia, Juan Carlos López-Acostab, Juan Carlos Noa-Carrazanaa, Lázaro Rafael Sánchez-Velásqueza,∗ a

Instituto de Biotecnología y Ecología Aplicada (INBIOTECA), Universidad Veracruzana, Av. de las Culturas Veracruzanas No. 101, Col. Emiliano Zapata, C.P. 91090, Xalapa, Veracruz, 10870, Mexico b Centro de Investigaciones Tropicales (CITRO), Universidad Veracruzana, Calle José María Morelos No. 44, Zona Centro, C.P. 91000, Apartado Postal 525, Xalapa, Veracruz, 12648, Mexico

A R T I C LE I N FO

A B S T R A C T

Keywords: Cloud forest Environmental filtering Functional convergence Mexico Oreomunnea mexicana

Several ecological processes intervene in the assembling of plant communities, such as environmental filtering and biotic interactions (e.g., competition, facilitation). The analysis of the distribution of the functional traits of plants helps to identify which of these processes is involved in the succession of a plant community assembly. We analyzed the distribution patterns of two groups of functional traits in a chronosequence spanning the first 40 years of secondary succession (SS) of a plant community of the tropical montane cloud forest (TMCF). One group of traits (leaf area, leaf thickness, leaf phosphorous and leaf nitrogen) is associated to the resource-acquisition strategy, and the second group to the conservation strategy (wood density, leaf dry mass per area unit, leaf density and leaf carbon). For each group, two multi-trait indices were quantified, namely functional divergence and dispersion. The number of individuals per plot was randomized in order to produce a null model and analyze the simulated index-distribution patterns. Then, the standardized size effect (SES) was calculated. Our results showed that both, divergence and dispersion, decreased towards the end of the successional gradient, leading to convergent patterns in the conservation traits group. The SES acquired negative values, which suggests that convergent patterns may be driven by environmental filtering. Functional diversity showed a tendency to decrease even when a species turnover between successional stages had occurred (ANOSIM, R Global = 0.64, P = 0.01). The dominance of Oreomunnea mexicana, at the end of the chronosequence, may have a particularly important role in the observed functional convergence.

1. Introduction The description and understanding of the assembly mechanisms that provide a structure to plant communities have been a topic of particular interest in the field of community ecology for a long time. One of the ways to attempt the discernment of such mechanisms is the analysis of the distribution patterns of the functional traits of plants. Functional traits include the morphologic, physiologic or phenologic aspects of plants that influence the ecosystem's functioning (Díaz and Cabido, 2001; Violle et al., 2007). It is known that the variation of functional traits provides information on how the environmental factors influence changes in the ecologic specialization strategies of plants, in terms of resource use, acquisition and/or conservation (Díaz et al., 2004). Thus, in analyzing the distribution patterns of the functional traits of species, the use of gradients (e.g., ecological succession, light, water or

∗

nutrients) could establish a relationship with the assembling mechanisms involved in the structuring of plant communities (Keddy, 1992; Cornwell et al., 2006). The process of secondary plant succession (SS) represents a longterm directional change in the floristic composition of a community after disturbance (Chazdon, 2008). It implies that abiotic factors, solar radiation, temperature, humidity, and soil nutrients, among many others, change over time (Guariguata and Ostertag, 2001). Different mechanisms during the SS (e.g., facilitation, tolerance, competition, and environmental filtering) have been proposed to explain plant replacement over time (Connell and Slatyer, 1977; Keddy, 1992) and, consequently, the formation of plant assemblies. This successional process is a complex environmental gradient with marked contrasts between the initial, intermediate and late stages, and promotes intense restrictions and opportunities for the establishment of plants with

Corresponding author. E-mail address: [email protected] (L.R. Sánchez-Velásquez).

https://doi.org/10.1016/j.actao.2019.01.003 Received 8 August 2018; Received in revised form 5 December 2018; Accepted 2 January 2019 1146-609X/ © 2019 Elsevier Masson SAS. All rights reserved.

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Fig. 1. Panels A and B show changes in the index values of functional divergence (FDiv), where the solid circle represents the observed, and the void circle the simulated, acquisition strategy (i.e., leaf area, leaf thickness, leaf phosphorous and leaf nitrogen). Panels C and D show the standardized effect size (SES) variation in relation to the years of abandonment and the number of species. Negative SES values indicate that the observed niche overlap is less than expected by chance, while positive SES values indicate that the observed niche overlap is greater than expected by chance.

resource-use strategy of plants in a successional gradient (represented by a chronosequence of 40 years of plant succession). Two different groups of functional traits were used, each one of them adapted to the existing environmental conditions at the two extremes of the successional gradient (Bazzaz, 1979). The first group, ‘acquisition strategy’, is adapted to the early stages of SS, whereas the second group, ‘conservation strategy’, is adapted to the late stages of SS. A trade-off exists between these two strategies (Poorter et al., 2009; Reich, 2014). This procedure would allow us to observe whether the distribution patterns of plant functional traits show tendencies that reveal the assembling mechanisms involved in SS in the TMCF. In this study, we proposed the following hypothesis: If during the SS of the TMCF, a stress gradient is found in relation to various soil properties (such as an increase in acidity and a decrease in the storage of soil phosphorous), we can expect that the functional traits associated to the resource-acquisition and resource-conservation strategies will show a convergence pattern. If the stress gradient does not occur, then the functional traits associated to the acquisition strategy will show a divergent pattern, promoted by biotic interaction.

different strategies for resource use and acquisition. Based on the analysis of plant functional traits, SS studies have shown that environmental factors are associated to strategic changes in several ways. In tropical humid forests, light has been identified as the main factor directing species replacement during SS (Chazdon, 2008; Selaya et al., 2008; Schönbeck et al., 2015). Light favors the establishment of plants with functional traits associated to a rapid resource acquisition. Moreover, it has been proposed that during SS, both differentiation and an increase in functional space occur, leading to a divergent distribution pattern of functional traits (Lohbeck et al., 2013). Conversely, in dry tropical forests, it is humidity that controls the replacement of plant species, with strategies directed towards resource conservation (Lohbeck et al., 2013, 2015). Convergence patterns are observed in such cases, where both a reduction of functional space and an aggregation and overlapping of the functional traits of plants occur during SS (Weiher and Keddy, 1995; Webb et al., 2002). This suggests that in stressed environments, a convergence pattern will be present, while in environments with a highly competitive and facilitation interaction, divergent distribution patterns will occur. Thus, the distribution patterns of functional traits may be the result of various environmental controls and mechanisms operating at a wide variety of scales, which hinders both generalization and studies comparison (Reich et al., 2003). The tropical montane cloud forest (TMCF) is a system that physiognomically, as well as functionally, differs from the rest of tropical forests (Bruijnzeel and Veneklass, 1998). In terms of nutrients, the TMCF is characterized by slow organic matter decomposition, high underground biomass inversion, and a poor quality of leaf litter (Bruijnzeel and Veneklass, 1998; Tanner et al., 1998; Dalling et al., 2016). Many of the TMCFs are probably co-limited by multiple nutrients (Dalling et al., 2016), particularly phosphorous (P), one of the nutrients that usually limit productivity in this ecosystem (Tanner et al., 1998). Empirical studies have shown that some of the soil properties decrease during SS, namely pH, P, or nitrogen (N) (Bautista-Cruz and del Castillo, 2005; Dalling et al., 2016 and references therein). This suggests that, as opposed to what happens in humid tropical forests, soil properties in the TMCF act as a filter for plants with functional traits adapted to resource conservation (Andersen et al., 2012). The purpose of this study was to explore the changes in the

2. Materials and methods 2.1. Study area The research was carried out in the central region of the state of Veracruz, Mexico, on the eastern slope of the Volcano Cofre de Perote (extreme coordinates: 19° 30′ 13″ and 19° 27′ 34″ N; 97° 01′ 59″ and 96° 59′ 29″ W; Fig. A1), at an altitude of 1420 to 1756 m a.s.l. The mean annual temperature and rainfall are 19 °C and 1500 mm, respectively (SMN, 2017); January being the month with the minimum rainfall in the year (41.2 mm), and June the month with the maximum rainfall (288.1 mm). The soil type is Andosol (WRB, 2007), which may be deep or superficial, with abundant organic matter, high water retention, low evaporation and a pH between 4 and 6 (Rzedowski, 1978). However, reports from the study area indicate a pH ranging from 3.2 to 4.8 in a successional gradient (Hernández-Vargas et al., personal observation). The vegetation type in the area is known as tropical montane cloud forest (TMCF; Hamilton et al., 1995), and the Mexican equivalent is known as bosque mesófilo de montaña (sensu Rzedowski, 1978). The high 27

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from each a-priori selected tree or shrub (i.e., the largest and most vigorous), at a height of 1.3 m from the trunk base with a Pressley increment metric borer (Mora Sweden, caliber 0.4 mm). In those cases in which the borer could not be used, a transversal cut approximately 2 mm long was performed on the stem. In order to avoid loss of moisture, samples were placed in plastic containers and processed within the first 12 h of collection. During that time, samples were stored at a temperature of 4 °C. The volume of the wood sample was estimated using the displacement technique, based on the 1 g = 1 cm3 equivalence, and samples were then placed in a drying oven at 60 °C until a constant weight was reached. Eight leaf traits and one wood trait were registered and classified a priori into two groups, according to their links with a given plant strategy. The first group, representing the acquisition strategy, was formed by leaf area, leaf thickness and leaf P and N. Plants with high values for these traits adapt to a rapid resource-acquisition strategy (Reich, 2014). The second group, representing the conservation strategy, was formed by wood density, leaf dry mass per area unit (LMA), leaf density and leaf C. Plants with high values for these traits adapt to a resource-conservation strategy.

heterogeneity and beta diversity of the TMCF has hindered a clear distinction between high and low forest. However, Williams-Linera et al. (2013) suggest a classification of this vegetation into two main groups: a) low elevation montane forest, with less plant and beta diversity, and a similar floristic composition, and b) high elevation montane forest, with more diversity and more species exchange. From the latter group stands out a community that differs from the plant communities of its group due to its low species diversity and number of individuals per species. This community is dominated by the tropical species Oreomunnea mexicana (Junglandaceae). Our chronosequence study sites were located in this plant community. In all cases, before being abandoned the land had been used for the cultivation of maize under the non-irrigated regime. A non-irrigated crop depends on the rainfall occurring during the maize production cycle. 2.2. Sampling design Environmental changes during SS were inferred using the chronosequence approach (Walker et al., 2010), a useful method to represent the successional dynamics of plant communities by selecting a series of sites with similar biophysical conditions and a similar management history, but with different time of agricultural inactivity (abandonment) for natural recuperation. The successional trajectory of the plant chronosequence used in this study consisted of three different successional stages. Stage I included plots with ages between 7 and 10 years of abandonment (n = 3), stage II plots with 17–20 years (n = 3), and stage III plots with 30–40 years (n = 3). In each site, plots were spaced 100–800 m apart from each other (see Fig. A1). Using the information provided by their owners and managers, the age and successional stage of each plot were determined. Only the plots with the desired time of abandonment and land management were selected. For each successional stage, three plots of 20 × 20 m were selected, in which a survey of all trees and shrubs with diameters ≥5 cm at breast height (1.3 m) was conducted. The species, basal area and height of each individual were registered, and with this data the amount of species and individuals was calculated. Tree height was estimated with a digital clinometer (Haglöf Sweden). The species were identified at the XAL herbarium of the National Ecology Institute (INECOL AC).

2.4. Data analysis Divergence indices (FDiv; Villéger et al., 2008) and functional dispersion indices (FDis; Laliberté and Legendre, 2010) were used to explore changes in the resource-use strategy and the distribution patterns of plants. FDiv and FDis indices were selected for their various advantages, for example, they analyze several functional traits at the same time; they are independent from species richness; they take into account species abundance (Laliberté and Legendre, 2010); and they have proved to be robust in the identification of the convergent and divergent distribution patterns of the functional traits of plants (Aiba et al., 2013). Both indices were calculated for the two a-priori groups of traits, that is, the group of traits associated to a rapid acquisition strategy, and the group of traits associated to a resource conservation strategy. For the calculation of FDiv and FDis, the dbFD function of the FD package (Distance-Based Functional Diversity Indices) was used for R (R Development Core Team, 2010). This function works with principal coordinate analysis (PCoA) producing PCoA axes, which are then used as “attributes” for the calculation of FD (Laliberté and Legendre, 2010). To evaluate changes in plant composition among successional stages, a similarity analysis was used (ANOSIM, originally developed by Clarke, 1993). ANOSIM performs tests for significant differences among two or more groups of multivariate samples. Values of the Bray-Curtis dissimilarity matrix among all the groups are ranked (the most similar having rank 1). The method then compares similarities among groups with similarities within the group. For this, the statistic R, based on the difference of mean ranks among groups and within groups, is calculated. R varies in the interval −1 to 1, the value of 0 indicating a completely random grouping, and no difference among groups. The statistical significance of observed R is assessed by a randomization test that randomly permutes the samples among the groups to obtain the empirical distribution of R under the null-model. This is done for all the groups simultaneously in a global test and then as pairwise comparisons among groups, whenever significant differences are found (P ≤ 0.05). To evaluate the potential effect of changes in the number of individuals on the distribution patterns of the functional traits throughout the SS, a randomization of individual number values was carried out. A thousand randomizations were performed (with replacement) to obtain an average null model. For each simulated model, the functional diversity indices FDiv and FDis were recalculated, so as to obtain a simulated distribution of the functional traits under analysis. The type of randomization performed has the advantage of retaining all the processes that produced the observed data, except for those that affect the

2.3. Quantification of functional traits The leaf samples used in this study were collected from the upper part of the tree crown. For each successional stage (n = 3), six individuals of each registered species were selected to obtain the mean values of the leaf traits indicated below. Leaf area (mm2) was calculated by measuring four leaves (excluding the petiole) from each individual, using the Leaf Area Measurement software (Version 1.3). The leaf area of species with compound leaves was calculated by adding the area of all the leaflets. Leaf thickness (mm) was measured by means of a digital Vernier with a resolution of 0.01 mm (Stainless Hardened), avoiding contact with the primary and secondary leaf veins during measurement. When it was not possible to avoid the veins, the leaf was torn in order to appropriately carry out the measurement. Leaf density was obtained from the volume/dry weight ratio (g cm−3). Leaf dry mass per area unit (LMA) was determined from the dry weight/leaf area ratio (g mm−2). Leaf carbon (C) and leaf N (mg g−1 dry mass) were determined using a sample of 0.1 g of dry leaf by means of an elementary analyzer (TruSpec, Leco, Michigan, USA). Leaf P (mg g−1 of dry mass) was determined using a sample of 0.5 mg of dry leaf pretreated to humid digestion using nitric and perchloric acids. The method employed was the vanadate-molybdate (yellow) treatment, using a spectrometer with an absorbance of 470 nm. All leaf nutrient concentrations were determined by using leaf samples previously dried at 70 °C for 48 h, and then milled and sieved at 0.85 mm. Finally, wood density (mg m−3) was calculated using the dry weight/volume ratio. A wood core sample was extracted 28

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this index (Table B.1). In this case, only two plots showed positive SES (Table B.1), which indicates that the observed niche overlap is higher that would be expected by chance.

number of individuals per species (since the number of species remains constant). In order to identify the direction and magnitude of the differences between the observed distribution and the simulated distribution, the standardized effect size (SES) was calculated, that is, the relative deviation of each observed distribution (Iobs) of its simulated distribution (Isim) (Gotelli and Entsminger, 2001), thus:

3.2. Resource-conservation strategy Functional divergence (FDiv) showed a tendency to decrease with the number of years of abandonment (P = 0.06) and with the number of species (P = 0.05; Fig. 3a and b). SES values did not show significant changes in either case, and values were negative (Fig. 3c and d). Four of the nine plots under analysis showed values under the simulated values (Table B.2). In the case of FDis, this index showed a unimodal pattern (Fig. 4b), whereby the highest values were concentrated in seven to 10 species, and then decreased. An outlier value of FDis was eliminated from the analysis, in order to allow for a better adjustment of the model (see Table B.2). The ANOSIM revealed differences in plant composition among successional stages (R Global = 0.64, P = 0.01).

SES = (Iobs – Isim)/Isdsim Where Isim is the mean and Isdsim is the standard deviation of the null values. The interpretation of SES was done according to Mason et al. (2012), where positive values indicate that the observed FD is higher than expected in the simulated distribution. In contrast, negative SES values indicate that functional diversity is smaller than expected in relation to simulated distribution. This in turn suggests that more abundant species tend to have similar functional traits, as opposed to what would be expected by chance, which suggests a more intense environmental filtering (Cornwell et al., 2006; Mouchet et al., 2010). All data analyses were performed using the statistical program R (R Development Core Team, 2010).

4. Discussion Our resource-conservation traits results were mainly a decrease in functional divergence (FDiv) as SS progressed (years of abandonment), and in relation to the number of species. In contrast, the acquisition strategy showed a weak negative relation (P < 0.07) between FDis and the number of years of abandonment, and the conservation strategy showed a unimodal relationship with the number of species. A discussion of these results in the light of the functional diversity theory follows.

3. Results 3.1. Resource-acquisition strategy No significant changes were observed in FDiv values for both this strategy and the SES, in relation to the time of abandonment and the number of species (Fig. 1a and d). Four of the nine plots under analysis showed FDiv values under the simulated ones, and all SES values turned out to be negative (Table B.1; Fig. 1c and d). That is, the observed niche overlap is lower than would be expected by chance. Functional dispersion (FDis) showed a slight tendency to decrease (P = 0.07) with the number of years of abandonment (Fig. 2a). However, SES values did not show any significant change with the number of years of abandonment, or with the number of species (Fig. 2c and d). Seven of the nine plots showed values under the simulated values for

4.1. Acquisition strategy (leaf area, thickness, P and leaf N) Only the FDis showed a (slight) tendency to decrease with the years of site abandonment (Fig. 2a). This convergence pattern might be associated to an increase in environmental stress at the late stages of SS. Changes in soil properties towards nutrient limitation and an increase in acidity have been documented for the TMCF (Bruijnzeel and Fig. 2. Panels A and B show changes in the index values of functional dispersion (FDis), where the solid circle represents the observed, and the void circle the simulated, acquisition strategy (i.e., leaf area, leaf thickness, leaf phosphorous and leaf nitrogen). Panels C and D show the standardized effect size (SES) variation in relation to the years of abandonment and the number of species. Negative SES indicate that the observed niche overlap is less than expected by chance, while positive SES indicate that the observed niche overlap is greater than expected by chance. The continuous line is the statistical model adjusted to the trajectory of the index value. The adjusted model for A was a linear one ( y = a + bx ).

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Fig. 3. Panels A and B show changes in the index values of functional divergence (FDiv), where the solid circle represents the observed, and the void circle the simulated, conservation strategy (i.e., wood density, LMA, leaf density and leaf carbon). Panels C and D show the standardized effect size (SES) variations in relation to the years of abandonment and the number of species. Negative SES indicate that the observed niche overlap is less than expected by chance, while positive SES indicate that the observed niche overlap is greater than expected by chance. The continuous line is the statistical model adjusted to the trajectory of the index value. The adjusted model for A and B was a linear one ( y = a + bx ).

Fig. 4. Panels A and B show changes in the index values of functional dispersion (FDis), where the solid circle represents the observed, and the void circle the simulated, conservation strategy (i.e., wood density, LMA, leaf density and leaf carbon). Panels C and D show the standardized effect size (SES) variations in relation to the years of abandonment and the number of species. Negative SES values indicate that the observed niche overlap is less than expected by chance, while positive SES values indicate that the observed niche overlap is greater than expected by chance. The continuous line is the statistical model adjusted to the trajectory of the index value. The adjusted model for B was a Ricker curve ( y = axe−bx ) .

results on the convergence of functions in the acquisition strategy, we suggest that environmental restrictions might reduce the functional space available. This might contribute to reduce the number of plants with high values of functional traits associated to rapid resource acquisition. We do know that during SS an increase in soil acidity occurs (Bautista and del Castillo, 2005), which might promote a decrease in P and Mo solubility in the soil, as well as changes in microbial populations and their processes (Binkley et al., 1989; Paul and Clark, 1996), and might indirectly affect, as a result, the strategies by plants to

Veneklass, 1998; Tanner et al., 1998; Dalling et al., 2016). However inconclusive our results may have proved, due to the fact that only the FDis showed this negative association (P < 0.07; Fig. 2a), the patterns shown by this index were the expected ones. Decrease in functional diversity is a pattern not frequently observed during SS, particularly in tropical forests. Research on the changes in functional diversity during SS has shown that an increase in divergence promoted by biotic interactions may occur during the successional process (Lohbeck et al., 2012, 2013). In spite of the limitations of our

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(Cornwell et al., 2006; Mason et al., 2012). However, we did not find sufficient evidence that this actually occurred. Even so, in this first approach, biotic interactions, an important process in the assembly of plant communities during SS in the TMCF, cannot be discarded. Other mechanisms would probably be detected if the successional gradient (or the sample size) were to be expanded. We carried out randomization with the intention of testing whether a change in species dominance along the chronosequence modifies the observed distribution patterns, which did not happen. Apparently, changes in dominance are not linked to a decrease in functional diversity during SS. However, a different result would be obtained if we were to increase the size of the sample, and the perception of the processes that are relevant to a plant community assembly would also be different (Chase, 2014).

tolerate this condition. However, we reckon a more thorough pH measurement would be needed to evaluate the direct effect on plant strategy changes, P availability, and N:P and C:N relations (Townsend et al., 2007; Ordóñez et al., 2009). 4.2. Conservation strategy (wood density, leaf dry mass per area unit [LMA], density and leaf C) In line with our hypothesis, the observed FDiv for the functional traits associated to the conservation strategy showed a negative relationship with both the number of years of abandonment and the number of plant species (Fig. 3a and b). Similarly, FDis values exhibited a unimodal distribution with the number of plant species (Fig. 4b). These results suggest that plant communities at the late stages of the chronosequence have similar functional traits associated to the conservation strategy. Species replacement during SS was confirmed by the ANOSIM (R Global = 0.64, P = 0.01). As we expected, the convergence of functions persisted during this process. Some experimental studies have shown that plants converge in their functional traits towards the end of SS, as unfavorable environmental conditions become more evident (Li and Shipley, 2017), even when species turnover has occurred (Fukami et al., 2005). These observations support the theory that the environment operates as a filter, favoring those plants with variations in their functional traits, which allows them to tolerate certain environmental conditions, but that this filter operates over the variation of functional traits, and not over the species themselves (Keddy, 1992). Given the combination of traits we analyzed, our results may be influenced by the presence of morpho-physiological adaptations by plants as a response to stress conditions in the soil of a TMCF community. The dominance of Oreomunnea mexicana (60.4%, or relative abundance; Table C.1) at the end of the chronosequence may have a particularly important effect on the observed functional convergence. It has been suggested that O. mexicana associates with ectomycorrhizae, which may represent a competitive advantage in such environments. For example, Connell and Lowman (1989) have proposed some mechanisms that may promote the dominance of one species in highly diverse ecosystems, such as mycorrhizal associations. In this scenario, the species having such associations would have competitive advantage over those that do not (see Connell and Lowman, 1989). Although in O. mexicana ectomycorrhizal associations have been found (Corrales et al., 2015), the existing evidence is insufficient to indicate that this mechanism effectively contributes to the successful monodominance of this species in the TMCF (Corrales et al., 2016). Our results suggest that an increase in the number of species is not positively related to an increase in functional diversity. Díaz et al. (2001) suggest that a positive linear relationship between number of species and functional diversity only occurs where species are either uniformly or randomly distributed, and when there is an increase in the functional space cover. However, this scenario is not commonly found in nature. Though our results are inconclusive, many observational studies in various ecosystems suggest that different distribution patterns of functional traits contribute simultaneously to the assembly process of plant communities (Spasojevic and Suding, 2012; Mason et al., 2012; Raevel et al., 2012; Lohbeck et al., 2013), which supports the idea that species richness is not an appropriate substitute for functional diversity.

4.4. Final remarks The use of multi-traits indices made it possible to establish some of the relations that might be taking place during SS. We propose that the abiotic conditions, particularly those linked to soil fertility (environmental filters at a fine scale, according to de Bello et al., 2013) may throw some light on the assembly mechanisms involved in the structuring of plant communities in the TMCF. However, our results must be taken with reserve, since the site replicas are relatively few, a limiting feature of most ecological studies. We hope that, in combination with other studies, our results will contribute to better understand the processes of ecological succession, and the patterns and tendencies of the attributes analyzed here. Declarations of interest None. All authors have approved the final article. Author contribution Guadalupe Hernández-Vargas contributed ideas for the research project and the sampling design, carried out field samplings, carried out the statistical analysis, and wrote the manuscript. Yareni Perroni contributed ideas for the research project and the sampling design, carried out the samplings, carried out the statistical analysis, and wrote the manuscript. Juan Carlos López-Acosta contributed ideas for the research project and the sampling design. Juan Carlos Noa-Carrazana contributed ideas for the research project and the sampling design. Lázaro Rafael Sánchez-Velásquez, contributed ideas for the research project and the sampling design, carried out the statistical analysis, and coordinated and wrote the manuscript. Acknowledgements The authors of this article wish to thank the plot owners for their permission to carry out this research, and for having kindly provided the necessary information on the management history of each site. Guadalupe Hernández-Vargas thanks Rogelio Lara and Rafael Ortega for their support during fieldwork, especially for their highly valued participation in the collection of leaves from the highest trees. We also thank Sara Ibarra for helping us in the use of R to carry out the null model, and Óscar Muñoz for the elaboration of the location map of the study area. This manuscript has substantially improved thanks to all the comments by two anonymous referees. Finally, G. Hernández-Vargas thanks the National Council on Science and Technology (Conacyt), México for the scholarship granted for her doctoral studies (ID 251817), as well as the project SEP-CONACYT CB‒2010‒01‒156053.

4.3. Deviation of randomized values SES values did not show significant changes either with the number of years of site abandonment or with the number of species. In all cases, a negative relationship was observed. This is usually interpreted as a reduction in functional space and an increase in the functional similarities of species in plant communities during the assembly process, promoted by a series of abiotic restrictions (environmental filtering)

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Appendix

Appendix A.1. Location map of the study area. Symbols represent the secondary succession stage to which the plot belongs.

Appendix B.1 Observed values, expected values and standardized effect size (SES) of the functional divergence and dispersion (FDiv and FDis) for the acquisition strategy (i.e., leaf area, leaf thickness, leaf phosphorous and leaf nitrogen). SES was calculated to determine the direction and intensity of the variation between the statistically observed and the simulated (Gotelli and McCabe, 2002). Y.A. is years since abandonment. Values in bold indicate that the observed value is below the simulated value by chance. Plot

Y.A.

FDiv observed

FDiv simulated

SES

FDis observed

FDis simulated

SES

1 2 3 4 5 6 7 8 9

10 17 30 8 19 35 7 20 40

0.5760871 0.8885284 0.6320227 0.6673933 0.9252088 0.5507886 0.6030536 0.7599701 0.7627411

0.6700857 0.6839905 0.6259347 0.6824126 0.7865732 0.722611 0.79334 0.1245456 0.7558115

−3.368039 −3.749552 −3.900081 −4.773624 −6.491506 −6.391522 −6.080922 −5.502431 −7.235816

1.871958 0.6990472 1.2352451 2.2255859 0.9960415 0.4912803 0.5645281 0.7851495 0.9529545

2.357291 1.251191 1.348918 1.897695 1.004862 1.03068 1.087693 1.049845 0.8216079

−0.6511626 −1.239399 −0.254634 0.7187364 −0.05135848 −2.3989 −1.789633 −1.474967 0.6809994

Appendix B.2 Observed values, expected values and standardized effect size (SES) of the functional divergence and dispersion (FDiv and FDis) for the conservation strategy (i.e., wood density, LMA, leaf density and leaf carbon). SES was calculated to determine the direction and intensity of the variation between the statistically observed and the simulated (Gotelli and McCabe, 2002). Y.A. is years since abandonment. Values in bold indicate that the observed value is below the simulated value by chance. *Outlier removed from analysis. Plot

Y.A.

FDiv observed

FDiv simulated

SES

FDis observed

FDis simulated

SES

1 2 3 4 5 6 7 8 9

10 17 30 8 19 35 7 20 40

0.8665977 0.9632732 0.5025129 0.6731957 0.8152727 0.3872318 0.9451532 0.361163 0.5852379

0.8214252 0.7961792 0.6467015 0.6731767 0.7629036 0.757295 0.8014994 0.6724506 0.6749766

−11.32215 −5.090949 −4.898808 −4.914083 −5.934359 −5.308913 −5.627469 −4.659079 −5.282129

1.4048506 0.4147758 1.0694427 2.3482424 0.6906 0.961808 0.4745457 0.7242747 0.991927

1.133613 0.8892372 1.594496 1.659003 0.9165669 1.55108 0.6761152 1.29779* 1.277748

1.081126 −2.141592 −0.680323 1.030486 −1.222613 −1.473624 −1.138505 −1.739678 −0.633145

32

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Appendix C.1 Distribution of relative abundance per species at each successional stage. Species

Successional stage

Euphorbiaceae Alchornea latifolia Klotzsch Annonaceae Annona cherimola Miller Euphorbiaceae Bernardia interrupta Müll. Arg. Brunelliaceae Brunellia mexicana Standl. Lauraceae Cinnamomum effusum (Meisn.) Kosterm. Clethraceae Clethra mexicana D.C. Asteraceae Eupatorium L. Chloranthaceae Hedyosmum mexicanum Cordem. Tiliaceae Heliocarpus L. Leguminosae Inga hintonii Sandwith Leucaena leucocephala (Lam.) de Wit Verbenaceae Lippia myriocephala Schltdl. & Cham. Hamamelidaceae Liquidambar styraciflua L. Magnoliaceae Magnolia schiedeana Schlecht. Melastomataceae Miconia chrysoneura Triana Miconia glaberrima Naudin Miconia mexicana Naudin Juglandaceae Oreomunnea mexicana (Standl.) J.-F.Leroy Araliaceae Oreopanax liebmannii Marchal Oreopanax xalapensis Decne. & Planch. Rubiaceae Palicourea padifolia (Roem. & Schult.) C.M.Taylor & Lorence Escalloniaceae Phyllonoma laticuspis Engl. Piperaceae Piper auritum Sieber ex Kunth Piper hispidum M.Martens & Galeotti Rosaceae Prunus brachybotrya Zucc. Prunus capuli Cav. Fagaceae Quercus corrugata Hook. Quercus cortesii Liebm. Quercus laurina Bonpl. Quercus salicifolia Hort. ex Steud. Quercus L. Myrsinaceae Rapanea myricoides (Schltdl.) Lundell Actinidiaceae Saurauia leucocarpa Schltdl. Saurauia pedunculata Hook. Solanaceae Solanum americanum Mill. Solanum aphyodendron S.Knapp Solanum diphyllum Forssk. Solanum nigricans M.Martens & Galeotti Styracaceae Styrax glabrescens Benth. Ulmaceae Trema micrantha Blume Staphyleaceae Turpinia insignis Tul. Turpinia occidentalis G.Don

I

II

III

0.71

5.00

6.54

–

–

0.93

–

0.61

–

0.71

–

–

–

0.61

–

12.14

2.44

–

–

–

1.87

37.14

1.83

0.93

–

–

–

– –

– –

0.93 0.93

8.57

–

–

–

1.22

0.93

–

1.22

1.87

– 2.86 –

– 0.61 1.22

0.93 0.93 0.93

–

60.40

50.50

– –

– 0.61

1.87 –

2.14

–

–

–

0.61

–

0.71 5.71

– –

– –

– –

4.30 –

2.80 1.87

– – – – –

3.66 0.61 – 4.30 1.22

1.87 5.61 1.87 2.80 –

2.14

0.61

0.93

– 1.43

– –

0.93 –

0.71 1.43 7.14 8.60

– – – –

– – – –

3.60

5.50

1.87

2.86

–

–

– –

0.61 –

0.93 0.93

(continued on next page) 33

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G. Hernández-Vargas et al.

Appendix C.1 (continued) Species

Successional stage I

II

III

–

0.61

–

Ericaceae Vaccinium leucanthum Schltdl. Asteraceae Vernonia deppeana Less. Rutaceae Zanthoxylum melanostictum Schltdl. & Cham.

0.71

–

–

–

–

7.50

%

100

100

100

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