Long-Term Impact of Forest Fragmentation on Tree Functional Diversity, Trait Composition and Aboveground Biomass.

Long-Term Impact of Forest Fragmentation on Tree Functional Diversity, Trait Composition and Aboveground Biomass. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Long-Term Impact of Forest Fragmentation on Tree Functional Diversity, Trait Composition and Aboveground Biomass. Érica Rievrs Borges, Marcela Venelli Pyles, Marcelo Leandro Bueno, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-47498/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Community composition on fragmented forest remnants has been shown to diverge from their natural successional path, revealing an alternative state which has been known as retrogressive succession . Here we show the long-term monitoring of a small forest fragment concerning community structure, species richness, functional diversity and composition throughout 17 years to offer a means to generalize community performance under the impacts of forest fragmentation. Results: The adult tree community showed shifts on its functional composition: reduction in species’ maximum height and percentage of simple leaves, indicating an investment in resource-acquisitive and disturbance adapted traits. However, we also found a gradual increase in wood density throughout the years for the adult community. For the overall community, functional composition analysis indicated a gradual reduction in the percentage of simple leaves and a significant increase in aboveground biomass. The decrease in Hmax and simple leaves, especially for the adult tree community, are mostly related do microenvironmental conditions caused by edge effects, as desiccation and tree canopy damage. Conclusion: Our results show that natural regeneration is being negatively affected, except for aboveground biomass. Although these findings could have resulted from a transient dynamic, they constitute a warning to future conservation policies around the ecological integrity of small forest fragments. Forestry Behavioral Ecology forest fragmentation community dynamics aboveground biomass functional traits disturbance Figures Figure 1 Figure 2 Background The high rates of forest degradation and fragmentation across tropical rain forests have led to a scenario where most forest remnants are reduced in size and inserted in a human transformed matrix (Magnano et al. 2014; Matos et al. 2016; Valdés et al., 2019 ; Zambrano et al., 2020 ). These fragmented remnants are usually surrounded by pastures and arable lands beyond other human settlements (Valdés et al. 2019 ). Consequently, fragmentation promotes the reduction of forested areas, isolation of remnants and creation of edges (Kupfer et al. 2006 ; Matos et al. 2016), and threatens the long-term persistence of species (Turner and Corlett 1996 ; Pereira et al. 2010 ; Rands et al. 2010 ), as well as the goods and services provided by those ecosystems (Foley et al. 2007 ; Zambrano et al. 2020 ). Small and isolated fragments are exposed to a variety of edge effects (hotter and drier microclimate, increased light intensity, wind turbulence) and immigration of alien species (Murcia 1995 ; Turner and Corlett 1996 ; Laurance et al. 2006b , a ; Haddad et al. 2015 ). Thus, the sustainability of tree communities under such environmental pressures has motivated studies on the mechanisms by which species coexistence is maintained in a local-scale habitat. The reduction of forest remnants’ area drives biodiversity loss directly through the removal of species and indirectly by changes in environmental conditions at forest edges which favour the performance of early successional species (Laurance et al. 2002 , 2006a ; Tabarelli and Santos 2008; Lôbo et al. 2011 ). Community composition from fragmented forests has been shown to diverge from their natural successional path, revealing an alternative state which has been known as retrogressive succession (Santos et al. 2008 ; Tabarelli et al. 2008a , 2012 ; Magnago et al. 2014 ; Rocha-santos et al. 2016 ; Ewers et al. 2017 ). This altered successional trajectory is characterized by increased rates of mortality (Lindenmayer et al. 2012 ), species-poor communities with a proliferation of disturbance-adapted tree species (Laurance et al., 2006b ; Tabarelli and Lopes 2008), loss of large trees (due to physiological stress and wind turbulence; Oliveira et al., 2008 , increase of small-seeded softwood species (Laurance et al. 2006b ), lower biomass storage potential (Van Der Sande et al. 2016 ) and impacts phylogenetic diversity (Matos et al. 2016; Zambrano et al. 2020 ). Overall, the shift in successional trajectories caused in a great part by the edge creation promotes a functional simplification of plant communities affecting late-successional trees (Suding et al. 2008 ). Species respond to changes in environmental conditions through their functional traits, represented by plant community functional composition (defined by community weighted trait means, CWMs) and functional diversity (i.e. the distribution of traits within a community) (Diaz and Cabido 2001 ; Candido et al. 2020 ). Functional traits represent physiological trade-offs in plant strategies (Wright et al. 2004 ; Reich 2014 ), being greatly useful to summarize changes in community composition in response to selective pressures and in relation to forest succession trajectories (Poorter et al., 2019 ). Therefore, long-term monitoring of functional diversity and composition throughout time offer a means to generalize community performance under the impacts of forest fragmentation. Habitat fragmentation can lead to major changes on species community composition, yet little is known about its long term impacts, especially on functional traits composition and aboveground biomass (Laurance et al. 2006b ; Santos et al. 2008 ; Magnano et al. 2014). Old-growth forest fragments surrounded by human-modified landscapes offer an excellent opportunity to examine the long-term effects of habitat fragmentation on diversity, functional composition and ecosystem services potential of such small forest remnants. Thus, understanding how these effects influence forest regeneration and successional processes over time is of crucial importance to ensure forest persistence in fragmented landscapes. Here we investigated how species richness, functional diversity and composition and aboveground biomass change over 17 years in a small tropical forest remnant surrounded by human-modified landscapes. We hypothesized that the tree community will show altered succession trajectory signs such as i) high mortality rate (superior to the recruitment rate); ii) decrease in species diversity and aboveground biomass throughout time and iii) shifts in functional composition toward a prevalence of species with highly colonization ability and early-successional traits (abiotic dispersion, small seed size, lower stature and wood density and compound leaves) throughout time. Finally, we also tested for shifts in functional composition within the adult and juvenile trees, as different forest stratum face different environmental constraints. Materials And Methods Study area The study was conducted in the southeast region of Minas Gerais, Brazil (21°13’11” S − 44°58’15” W), on a remnant of the endangered Brazilian Atlantic Rain Forest, classified as Semideciduous Seasonal Forests (IBGE 2012 ). The mean altitude is 884 meters. The regional climate is classified as Cwb (Mesothermic climate of Köppen), defined by dry winters and mild summers. Mean annual rainfall and temperature are 1493 mm and 19.3ºC, respectively (Vilela and Ramalho 1979 ). Soil types in the region are primarily argisols and nitosols (Machado and Oliveira-Filho 2010 ). Fragment area is currently around 4.0 hectares (ha) and it is surrounded by a low biomass landscape matrix, composed mostly by coffee plantations, pastures and a small dawn drainage canal. Data collection Data for this study was derived from 17 years (2000 to 2017) of forest survey from the 29 permanent plots of 0.4 ha each (20 m x 20 m), a total of 1.16 ha sampled (data were obtained from ForestPlots.net; Lopez-Gonzalez et al. 2009 ; Lopez-Gonzalez et al. 2011 ). All trees with a diameter at breast height (DBH) ≥ 5 cm were identified to species level. All plots were first measured in 2000, re-measured in 2005, 2011 and 2017, when surviving, dead and recruiting (individuals that met the inclusion criterion) were evaluated. Species identities were checked for nomenclatural synonyms using the online tool Taxonomic Name Resolution Service (TNRS) ver. 3.2 (Boyle et al. 2013 ). To analyze the changes in species richness of the trees between the four surveys (2000, 2005, 2011 and 2017), we computed species rarefaction curves using the platform R (R Core Development Team 2018 ) and the vegan package (Oksanen et al. 2015 ). Demographic metrics The changes in the tree community over time were determined for each plot by calculating the following mean annual abundance rates: Mortality (M = (1 - [(N0 - Nm) / N0] 1 / t) * 100) and recruitment (R = (1 - (1 - Nr / Nt) 1 / t)*100, whereas biomass dynamics were described by basal area loss (P = (1 - ((AB0 -(ABm + ABd)) / AB0) 1 / t) *100) and gain (G = (1- (1 - (ABr + ABg) / ABt) 1 / t) * 100), where: t is the time elapsed between censuses; N0 and Nt the initial and final tree counts; Nm and Nr are dead trees and recruit counts; AB0 and ABt are the initial and final tree basal area; ABm is the basal area of dead trees; ABr is basal area of recruits; and ABd and ABg are the increment and decrement in the basal area from the surviving trees (Sheil et al. 2000 ; Sheil and May, 1996 ). Functional traits The functional traits considered in the study are related to natural successional trajectories of forests (Van Der Sande et al. 2016 ), species stress resistance, and species standing biomass ( Attri et al. 2015; Lohbeck et al. 2015 ; Poorter et al. 2015): Maximum height, wood density, leaf type, seed size and dispersal mode were obtained following standardized protocols (Cornelissen et al. 2003 ; Pérez-Harguindeguy et al. 2013 ). Species maximum height (Hmax, m) is an indicator of the adult stature of species, potentially related to the species longevity and life-history strategy (King et al. 2006 ), and was calculated as the 95th-percentile height of all trees of the species. Species wood density (WD, g.cm-3) represents biomass per wood volume constructed and was obtained from the Global Wood Density Database (filtered by Tropical South America, Zanne et al. 2009 ). For the species with WD not available, we used mean values for the WD of the genus or family. The leaf type (LT, categorical data) reflects the species heat balance. In other words, leaf size profoundly affects a variety of biological water and energy processes and has particularly important implications for understanding the adaption strategy of plants to environmental changes (Wang et al. 2019 ). All species were categorized: compound and simple leaves. Seed size (SS, categorical data), although usually related to the competitive vigour of the seedlings (Kitagima 2007 ), is also an important life-history trait for trees, correlated to a suite of morphological and physiological traits of pioneer species (small seeds) and shade-tolerant species (large seeds) (Poorter and Rose 2005 ; Osuri and Sankaran 2016 ). Qualitative data for species SS were obtained from herbarium specimens, and the species were classified as small seeds species (seed length 1.6 cm), following Tabarelli and Peres ( 2002 ) and Santos et al. ( 2008 ). Dispersal mode (DM, categorical data) is an indicator of the ability of plants to colonize habitats and is especially important in fragmented landscapes because they can improve predictions of dispersal probability and seed bank composition (Kraft et al. 2015 ). All species were categorized: biotic dispersion and abiotic dispersion. Community weighted mean traits and functional diversity indices Community weighted mean (CWM) trait values (Maximum height, wood density, leaf type, seed size and dispersal mode) and functional diversity indices were calculated per plot and per inventory (2000, 2005, 2011 and 2017) for the overall community, juveniles (5 cm ≤ DBH < 10 cm) and adults (DBH ≥ 10 cm). Both were weighted by species abundance to equally weight recruiting and dying trees on changes in CWM values (van der Sande et al. 2016 ). Adult survivors respond slowly to disturbance and tend to make a large contribution to community biomass because they are large (Chazdon et al. 2007 ), while recruits (smaller individuals) respond fast to disturbance and contribute more to abundance (Van Breugel et al. 2007 ). For functional diversity, we checked the functional richness (FRic), an indicator of the species volume occupying the niche space of a community (Villéger et al. 2008 ) which measures the extent to which the trait space is filled. The functional richness and the CWM values were calculated using Gower's distance (that allows mixed traits types: continuous, ordinal and categorical). The functional analyses were calculated using the 'FD' package in R (Laliberté et al. 2015). Biomass estimates For every tree with DBH ≥ 5 cm, the aboveground biomass (AGB) was calculated using the allometric formula of Chave et al. 2014: AGB = exp[-1.803-0.976E + 0.976ln(DBH) + 2.673ln(DBH)-0.0299[ln(DBH)] 2 ] and the parameters, DBH (cm), species wood density (WD, g cm − 3 ) and E, which is a measure of environmental stress (retrieved from Rejou-Mechain and Chave, 2014 ). Statistical analysis We used generalized linear mixed models (GLMM) to assess the differences in species richness, functional diversity and composition (SR, FRic, CWM Hmax, CWM WD, CWM LT, CWM SS and CWM DM) and aboveground biomass among the four forest inventories (2000, 2005, 2011 and 2017). Measures from the plot from the different years of monitoring were considered as a random factor, to account the lack of independence within the plots among the four inventories (Bates et al. 2014a ). When necessary, data were log10 or square root transformed prior to analysis to meet the assumptions of normality and homoscedasticity. Tukey post-hoc tests were used to assess the differences between the forest surveys (2000, 2005, 2011 and 2017). To test for potential spatial nonindependence of plots, we assessed the degree of spatial autocorrelation in our linear mixed model residuals using the Moran’s I test. A p -value < 0.05 would indicate that the model residuals show spatial autocorrelation, which was not found for any of our models (Kissling and Carl 2008 ) (Table S1). All analyses, figures and graphs were performed using the platform R (R Core Development Team 2018 ) and the following packages: multcomp (Bretz et al. 2015 ), lme4 (Bates et al. 2014b ), lmerTest (Kuznetsova et al. 2016 ), MuMIn (Barton 2016 ), and ggplot2 (Wickham and Chang 2016 ). Results We found that the annual mortality in the tree community was higher than recruitment for all forest inventories, except for the last one, resulting in a net decrease in total tree abundance (Table 1 ). In 2000, 1275 individuals were recorded consisting of 151 species within 46 families (Table S2). In 2005, 1234 individuals were recorded, a reduction caused by the higher mortality rate (3.28% year − 1 ) in comparison with the recruitment rate (2.65% year − 1 ) for the survey interval. A similar pattern was found for 2011 when the lower recruitment rate (2.86% year − 1 ) and high mortality rate (4.00% year − 1 ) resulted in a reduction of tree abundance (1150). In 2017 there was a small increase in abundance (1153 individuals), a result from the slightly higher recruitment rate (2.48% year − 1 ) in comparison with the mortality rate (2.43% year − 1 ). Overall, from 2000 and 2017, we found a decrease in 9.56% in tree abundance (Table 1 ). The higher rates of annual basal area loss compared to the gain rates in the basal area in the community within the first three intervals resulted in a loss of basal area (34.01 m 2 , 32.81 m 2 , and 31.64 m 2 for 2000, 2005 and 2011, respectively). However, a strong increase in the total basal area was recorded in 2017 (39.40 m 2 ), resulting in a positive net change of 13.68% between 2000 and 2017 (Table 1 ). Indeed, we found an increase in AGB for the last survey, resulting in a net gain in 18.61% in biomass (Table 2 ). The small reduction found in species richness was not significant (Table 2 , Table S3), as also suggested by the rarefaction curves as there is an overlap of the standard deviation of the four richness curves (Fig. 1). Species functional richness showed a small but not significant reduction (Table 2 ). Table 1 Tree community dynamics components of a Semideciduous Forest in Lavras, Minas Gerais State, Brazil. Survey interval 2000–2005 2005–2011 2011–2017 Number of plots 29 29 29 Number of trees: Initial 1275 1234 1150 Final 1234 1150 1153 Survivals 1079 966 992 Deads 196 268 158 Recruits 155 184 161 Mortality rate (% year − 1 ) 3.28 4.00 2.43 Recruitment rate (% year − 1 ) 2.65 2.86 2.48 Basal Area: Initial 34.01 32.81 31.64 Final 32.81 31.64 39.40 BA loss rate (% year − 1 ) 2.67 3.03 2.52 BA gain rate (% year − 1 ) 1.97 2.44 6.03 Dead (m 2 ) 3.78 4.69 -3.56 Recruits (m 2 ) 0.72 0.70 2.13 Survival (m 2 ) 1.87 2.81 9.19 BA Basal Area Table 2 Species richness, functional richness and aboveground biomass per plot for the overall community fitted from generalized linear mixed models. 2000 2005 2011 2017 Species richness 22.06 a 21.44 ab 20.13 b 20.48 ab Functional richness 20.62 a 20.56 a 20.53 a 20.16 a Aboveground biomass (Mg/ha) 255.05 b 249.94 b 242.02 b 313.36 a Different letters indicate significant differences among mean values ( p < 0.05) based on pairwise comparisons in mixed linear models (Tukey’s HSD). For the overall community, functional composition analysis indicated a gradual reduction in the percentage of simple leaves (Fig. 2, Table S4). The adult tree community showed a reduction in Hmax and the percentage of simple leaves, indicating an investment in resource-acquisitive and disturbance adapted traits. However, we also found a gradual increase in wood density through the years for the adult community (Fig. 2). The juvenile tree community did not show any shifts in functional trait composition. Discussion This study has provided relevant insights into the successional trajectory effects of fragmentation on the tree community of a small forest remnant surrounded by human-modified landscapes. Under 17 years of community monitoring, the dynamics' analysis revealed a continuous reduction in the overall tree abundance and shifts in trait composition favouring adult species with resource-acquisitive, pioneer life-strategies (reduction in tree maximum height and in species with simple leaves and a slight increase in the percentage of small seeds). However, for the adult tree community (DBH > 10 cm), we found a significant increase in wood density. Indeed, the last monitoring interval (2011–2017) showed a strong increase in total basal area and a significant increase in aboveground biomass (reaching higher values than found for the first survey). Based on these findings, our hypothesis was partly accepted, as natural succession is being negatively affected, except for aboveground biomass. Other studies in Neotropical small forests have shown tree communities dominated by a small set generalist species in forest fragments with one to 10 hectares (Laurance 2001 ; Laurance et al. 2006b ). After analyzing the long-term dynamics of a forest fragment with 4 ha, we found a decrease in Hmax and simple leaves for the adult tree community and a decrease in simple leaves for the overall tree community. During succession, pioneer species are expected to be replaced by shade-tolerant ones, with the community gradually accumulating species diversity and ecological functions (Guariguata and Ostertag 2001 ). This trajectory is changed when strong environmental filters lead to the selection of species that share the adaptation strategies required to colonize and survive in a changed post-disturbance habitat (Tabarelli et al. 2008). In disturbed habitats, the establishment of late-successional species is compromised until the stressful conditions had reduced to an acceptable state (Lebrija-Trejos et al. 2010 ). Edge creation, e.g., promotes microclimatic and structural changes within the forest with severe consequences to tree communities (Murcia 1995 ), such as desiccation which act as a climate filter favouring mostly generalist pioneer species (Pierce et al. 2017 ). Leaf type reflects adaptations to reduced water availability and therefore a gradual increase in species with simple leaves is expected in the course of succession towards more mature and undisturbed forests (Wright et al. 2017 ; Gei et al. 2018 ). Consequently, we found a gradual increase of trees with compound leaves, a strategy to prevent the excessive water loss as plants with pinnate or bipinnate leaves can release individual leaflets (and not complete leaves) during severe water stress, increasing heat dissipation and regulating temperature (Wright et al. 2017 ; Gei et al. 2018 ). When considering the adult tree community, we also found a decrease in species Hmax. The reduction of adult trees’ Hmax is also expected in communities under disturbance (Carreño-Rocabado et al. 2012). The tree canopy damage allows trees to get more access to light, which no longer consists of a limiting-resource (Ruiz-jaen and Potvin 2011 ). These results suggest the presence of strong post-fragmentation edge effects, leading to an increase in pioneer species while shade-tolerant species are negatively impacted (Zambrano et al. 2020 ). As a consequence, adult tree species may not invest in increasing their height as much as other life strategies (King et al. 2006 ). Besides, it is also expected a great loss of large trees due to physiological stress and wind turbulence (Oliveira et al., 2008 , also see Laurance et al., 1988 , 2000 ). The increase in wood density throughout time in the adult tree community can also be related to disturbed habitats as it guarantees trunk resistance to physical damages by herbivores, pathogens and woody debris (Van Gelder et al. 2006 ; Mccarthy-Neumann and Kobe 2008) and also an indication of hydraulic safety (Sterck et al. 2006 ). Previous studies have shown the lack of wood density variation with fragmentation and fragment forest size, even for secondary forests (Magnago et al. 2014 ; Liu et al. 2019 ). However, since wood densities of species can be affected by environmental changes due to fragmentation (Laurance et al. 2006a , b ; Nock et al. 2009 ), the average wood densities we used from the literature (Chave et al. 2009; Zanne et al. 2009 ) would miss any such variation. Our functional diversity analyses could be refined in future analyses using wood densities derived from in situ measurements, and also, when herbarium collections permit, fruit and seed sizes as continuous rather than categorical variables. The gain in basal area in the community indicates their species’ high productivity (Finegan et al. 2015 ). Indeed, the higher basal area is translated in gains in biomass, which showed a significant increase through time. The values for AGB found in this study are comparable to large undisturbed rainforests (Pyles et al. 2018 ). Previous studies have reported that fragmentation has no significant effect on biomass, with the productivity of the shade-intolerant trees having a high role in the accumulation of biomass (Magnago et al. 2014 ; Liu et al. 2019 ). Proximity to edge and small fragment size favours a turnover with trees typical from late-successional stages being replaced by pioneer and especially the short-lived species (Nascimento and Laurance 2004 ; Tabarelli et al. 2004 ; Laurance et al. 2006a ; Michalski et al. 2007 ; Santos et al. 2012 ). Consequently, forest regeneration potential might be lower in these fragments, with detrimental consequences for tree diversity and regeneration dynamics in the long term. However, the process of forest succession can take many years of centuries, therefore longer monitoring is needed to check whether the conditions found in the studied fragment matches the retrogressive succession hypothesis or is rather a transient, regeneration dynamic in human-modified landscapes that might lead to advanced successional stages (Guariguata and Ostertag 2001 ; Letcher and Chazdon 2009 ; Ewers et al. 2016 ; Muscarella et al. 2016 ). Conclusion This study provides important contributions to the influence of fragmentation on the successional trajectory of tree communities on small forest remnants surrounded by human-modified landscapes. Based on our findings, natural succession is being negatively affected, except for aboveground biomass. The decrease in Hmax and simple leaves, especially for the adult tree community, are mostly related do microenvironmental conditions caused by edge effects, as desiccation and tree canopy damage. Although those findings could have resulted from a transient dynamic, they constitute a warning to future conservation policies around the ecological integrity of small and isolated forest fragments. Abbreviations DBH: Diameter at breast height; Hmax: Species maximum height; WD: Species wood density; LT: Leaf type; SS: Seed size; DM: Dispersal mode; CWM: Community weighted mean; FRic: Functional richness; AGB: Aboveground biomass. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets generated during and/or analysed during the current study are available in the ForestPlots repository, www.florestplots.net. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), the Federal University of Lavras (UFLA), the Foundation for the Support of Studies in Minas Gerais (FAPEMIG) and the Brazilian National Council for Scientific and Technological Development (CNPq). Authors' contributions Study concept and design: Borges E, Pyles, M. Analysis and interpretation of data: Borges E, Pyles, M. Drafting of the manuscript: Borges E, Bueno, M. Critical revision of the manuscript for important intellectual content: Pyles, M, Bueno, M. Statistical analysis: Borges E, Pyles, M. Obtained funding: Borges E, Fontes M, Santos R. Study supervision: Oliveira-Filho A, Bueno, M. The author(s) read and approved the final manuscript. Acknowledgements Not applicable References Barton K (2016) Package ‘ MuMIn ’ Bates D, Mächler M, Bolker B, Walker (2014a) Fitting Linear Mixed-Effects Models using lme4. Cran Vignette 1–51. https://doi.org/10.18637/jss.v067.i01 Bates D, Maechler M, Bolker B, et al (2014b) Package ‘lme4’ Boyle B, Hopkins N, Lu Z, et al (2013) The taxonomic name resolution service: An online tool for automated standardization of plant names. BMC Bioinformatics 14:. https://doi.org/10.1186/1471-2105-14-16 Bretz F, Westfall P, Heiberger RM, et al (2015) Package ‘ multcomp ’ Candido HMN, Miazaki Â, Pontara V, et al (2020) Colonization-related functional traits of plants in a 50-hectare plot of secondary tropical forest. Acta Bot Brasilica 34:1–8. https://doi.org/10.1590/0102-33062019abb0087 Carreño-rocabado G, Peña-claros M, Bongers F, et al (2012) Effects of disturbance intensity on species and functional diversity in a tropical forest. 1453–1463. https://doi.org/10.1111/j.1365-2745.2012.02015.x Chave J et al. (2015) Improved allometric models to estimate the aboveground biomass of tropical trees. 3177–3190. https://doi.org/10.1111/gcb.12629 Chazdon RL, Letcher SG, Van Breugel M, et al (2007) Rates of change in tree communities of secondary Neotropical forests following major disturbances. Philos Trans R Soc B Biol Sci 362:273–289. https://doi.org/10.1098/rstb.2006.1990 Cornelissen JHC, Lavorel S, Garnier E, et al (2003) A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust J Bot 51:335–380. https://doi.org/10.1071/BT02124 Diaz S, Cabido M (2001) Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol Evol 16:646–655 Ewers R, Andrade A, Laurance S, et al (2016) Predicted trajectories of tree community change in Amazonian rainforest fragments. Ecography (Cop) 40:26–35 Ewers RM, Andrade A, Laurance SG, et al (2017) Predicted trajectories of tree community change in Amazonian rainforest fragments. Ecography (Cop) 40:26–35. https://doi.org/10.1111/ecog.02585 Finegan B et al. (2015) Does functional trait diversity predict above-ground biomass and productivity of tropical forests ? Testing three alternative hypotheses. 191–201. https://doi.org/10.1111/1365-2745.12346 Foley JA, Asner GP, Costa MH, et al (2007) Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front Ecol Environ 5:25–32 Gei M, Rozendaal DMA, Poorter L, et al (2018) Legume abundance along successional and rainfall gradients in Neotropical forests. Nat Ecol Evol 2:1104–1111. https://doi.org/10.1038/s41559-018-0559-6 Guariguata MR, Ostertag R (2001) Neotropical secondary forest succession : changes in structural and functional characteristics. 148: Haddad NM, Brudvig LA, Clobert J, et al (2015) Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci Adv 1:1–10. https://doi.org/10.1126/sciadv.1500052 IBGE (2012) Manual Técnico da Vegetação Brasileira King DA, Davies SJ, Tan S, Noor NSM (2006) The role of wood density and stem support costs in the growth and mortality of tropical trees. J Ecol 94:670–680. https://doi.org/10.1111/j.1365-2745.2006.01112.x Kissling, W. D., & Carl G (2008) Spatial autocorrelation and the selection of simultaneous autoregressive models. Glob Ecol Biogeogr 17:59–71 Kitagima K (2007) Seed and seedling ecology. In: Pugnaire FI VF (ed) Functional plant ecology, 2nd edn. CRC Press, New York, pp 549–880 Kraft NJB, Adler PB, Godoy O, et al (2015) Community assembly, coexistence and the environmental filtering metaphor. Funct Ecol 29:592–599. https://doi.org/10.1111/1365-2435.12345 Kupfer JA, Malanson GP, Franklin SB (2006) Not seeing the ocean for the islands: The mediating influence of matrix-based processes on forest fragmentation effects. Glob Ecol Biogeogr 15:8–20. https://doi.org/10.1111/j.1466-822X.2006.00204.x Kuznetsova A, Brockhoff PB, Christensen RHB (2016) Package ‘ lmerTest ’ Laliberté E, Legendre P SB (2015) Measuring functional diversity (FD) from multiple traits, and other tools for functional ecology Laurance W, Lovejoy T, Vansconcelos H, et al (2002) Ecosystem Decay of Amazonian Forest Fragments: a 22-Year Investigation. Conserv Biol 16:605–618. https://doi.org/10.1214/aos/1059655905 Laurance WF (2001) Fragmentation and plant communities: synthesis and implications for landscape management. In: R.O. Jr., Gascon, C., Lovejoy, T.E., Mesquita RCG (ed) Lessons from Amazonia: The Ecology and Conservation of a Fragmented Forest. Yale University Press, New Haven, pp 158–168 Laurance WF, Delamônica P, Laurance S, et al (2000) Rainforest fragmentation kills big trees. Nature 404:2000 Laurance WF, Ferreira L V, Merona JMR, et al (1988) Society for Conservation Biology Effects of Forest Fragmentation on Recruitment Patterns in Amazonian Tree Communities Linked references are available on JSTOR for this article : Efects of Forest Fragmentation on Recruitment Patterns in Amazonian Tree Com. Conserv Biol 12:460–464 Laurance WF, Nascimento HEM, Laurance SG, et al (2006a) Rain forest fragmentation and the proliferation of successional trees. Ecology 87:469–482. https://doi.org/10.1890/05-0064 Laurance WF, Nascimento HEM, Laurance SG, et al (2006b) Rapid decay of tree-community composition in Amazonian forest fragments. Proc Natl Acad Sci U S A 103:19010–19014. https://doi.org/10.1073/pnas.0609048103 Lebrija-Trejos EE, Perez-Garcia EA, Meave JA, et al (2010) Functional traits and environmental filtering drive community assembly in a species-rich tropical system. Ecology 91:386–398 Letcher SG, Chazdon RL (2009) Rapid Recovery of Biomass , Species Richness , and Species Composition in a Forest Chronosequence in Northeastern Costa Rica. 41:608–617 Lindenmayer DB, Laurance WF, Franklin JF (2012) Ecology: Global decline in large old trees. Science (80- ) 338:1305–1306. https://doi.org/10.1126/science.1231070 Liu J, Coomes DA, Hu G, et al (2019) Larger fragments have more late-successional species of woody plants than smaller fragments after 50 years of secondary succession Lôbo D, Leão T, Melo FPL, et al (2011) Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers Distrib 17:287–296. https://doi.org/10.1111/j.1472-4642.2010.00739.x Lohbeck M, Lebrija-Trejos E, Martínez-Ramos M, et al (2015) Functional trait strategies of trees in dry and wet tropical forests are similar but differ in their consequences for succession. PLoS One 10:1–15. https://doi.org/10.1371/journal.pone.0123741 Lopez-Gonzalez, G., Lewis, S.L., Burkitt, M., Baker T.R. and Phillips O. (2009) ForestPlots.net Database. www.forestplots.net. Date of extraction [15/09/2019] Lopez-Gonzalez, G., Lewis, S.L., Burkitt, M. and Phillips OL (2011) ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J Veg Sci 22:610–613. https://doi.org/10.1111/j.1654-1103.2011.01312.x Machado ELM, Oliveira-Filho AT de (2010) Spatial patterns of tree community dynamics are detectable in a small (4 ha) and disturbed fragment of the Brazilian Atlantic forest. Acta Bot Brasilica 24:250–261. https://doi.org/10.1590/s0102-33062010000100027 Magnago LFS, Edwards DP, Edwards FA, et al (2014) Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J Ecol 102:475–485. https://doi.org/10.1111/1365-2745.12206 Mccarthy-neumann S, Kobe RK (2008) Tolerance of Soil Pathogens Co-Varies with Shade Tolerance across Species of Tropical Tree Seedlings. Ecology 89:1883–1892 Michalski F, Nishi I, Peres CA (2007) Disturbance-Mediated Drift in Tree Functional Groups in Amazonian Forest Fragments. Biotropica 39:691–701 Murcia C (1995) Edge effects in fragmented forests: implications for conservation. Trends Ecol Evol 10:58–62. https://doi.org/10.1016/S0169-5347(00)88977-6 Muscarella R, Uriarte M, Aide TM, et al (2016) Functional convergence and phylogenetic divergence during secondary succession of subtropical wet forests in Puerto Rico. J Veg Sci 27:283–294. https://doi.org/10.1111/jvs.12354 Nascimento H, Laurance WF (2004) Biomass dynamics in amazonian forest fragments. Ecol Appl 14:127–138 Nock CA, Geihofer D, Grabner M, Baker PJ (2009) Wood density and its radial variation in six canopy tree species differing in shade-tolerance in western Thailand. Annals 104:297–306. https://doi.org/10.1093/aob/mcp118 Oksanen J, F. Guillaume Blanchet RK, Legendre P, et al (2015) Package ‘vegan.’ R Packag version 23-4 Oliveira MA, Santos AMM, Tabarelli M (2008) Profound impoverishment of the large-tree stand in a hyper-fragmented landscape of the Atlantic forest. For Ecol Manage 256:1910–1917. https://doi.org/10.1016/j.foreco.2008.07.014 Osuri & Sankaran MS (2016) Seed size predicts community composition and carbon storage potential of tree communities in rainforest fragments in India’s Western Ghats. https://doi.org/10.1111/1365-2664.12626 Pereira HM, Scharlemann JPW, Fernandez-manjarrés JF, Araújo MB (2010) Scenarios for Global Biodiversity. Science (80- ) 330:1496–1501. https://doi.org/10.1126/science.1196624 Pérez-Harguindeguy N, Diaz S, Garnier E, et al (2013) New Handbook for standardized measurment of plant functional traits worldwide. Aust J Bot 61:167–234. https://doi.org/http://dx.doi.org/10.1071/BT12225 Pierce S, Negreiros D, Cerabolini BEL, et al (2017) A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct Ecol 31:444–457. https://doi.org/10.1111/1365-2435.12722 Poorter L et al (2019) Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat Ecol Evol 3:928–934 Poorter L, Rose SA (2005) Light-dependent changes in the relationship between seed mass and seedling traits: A meta-analysis for rain forest tree species. Oecologia 142:378–387. https://doi.org/10.1007/s00442-004-1732-y Pyles M V, Prado-Junior JA, Magnago LFS, et al (2018) Loss of biodiversity and shifts in aboveground biomass drivers in tropical rainforests with different disturbance histories. Biodivers Conserv 3215–3231. https://doi.org/10.1007/s10531-018-1598-7 R Core Development Team (2018) A language and environment for statistical computing. 1 Rands MRW, Adams WM, Bennun L, et al (2010) Biodiversity Conservation: Challenges Beyond 2010. Science (80- ) 329:1298–1304 Reich PB (2014) The world-wide “fast-slow” plant economics spectrum: A traits manifesto. J Ecol 102:275–301. https://doi.org/10.1111/1365-2745.12211 Rejou-Mechain M, Chave J (2014) Manipulate raster and netCDF datasets. http://chave.ups-tlse.fr/pantropical_allometry/readlayers.r Rocha-santos L, Pessoa MS, Cassano CR, et al (2016) The shrinkage of a forest : Landscape-scale deforestation leading to overall changes in local forest structure. BIOC 196:1–9. https://doi.org/10.1016/j.biocon.2016.01.028 Ruiz-jaen MC, Potvin C (2011) Can we predict carbon stocks in tropical ecosystems from tree diversity ? Comparing species and functional diversity in a plantation and a natural forest. New 189:978–987 Santos BA, Peres CA, Oliveira MA, et al (2008) Drastic erosion in functional attributes of tree assemblages in Atlantic forest fragments of northeastern Brazil. Biol Conserv 141:249–260. https://doi.org/10.1016/j.biocon.2007.09.018 Santos G, Santos B, Nascimento HEM, Tabarelli M (2012) Contrasting Demographic Structure of Short- and Long-lived Pioneer Tree Species on Amazonian Forest Edges. Biotropica 0:1–8 Sheil, D.; Jennings, S.; Savill P (2000) Long-term permanent plot observations of vegetation dynamics in Budongo, a Ugandan rain forest. J Trop Ecol 16:765–800 Sheil, D. & May R (1996) Mortality and recruitment rat evaluations in heterogeneous tropical forests. J Ecol 84:91–100 Sterck F, Van Gelder HA, Poorter L (2006) Mechanical branch constraints contribute to life-history variation across tree species in a Bolivian forest. J Ecol 94:1192–1200. https://doi.org/10.1111/j.1365-2745.2006.01162.x Suding KN, Lavorel S, Chapin FS, et al (2008) Scaling environmental change through the community-level: A trait-based response-and-effect framework for plants. Glob Chang Biol 14:1125–1140. https://doi.org/10.1111/j.1365-2486.2008.01557.x Tabarelli M, Cardoso, Maria Silva DA, Gascon C (2004) Forest fragmentation, synergisms and the impoverishment of neotropical forests. Biodi 13:1419–1425 Tabarelli M, Lopes A V., Peres CA (2008a) Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40:657–661. https://doi.org/10.1111/j.1744-7429.2008.00454.x Tabarelli M, Lopes A V, Peres CA (2008b) Edge-effects Drive Tropical Forest Fragments Towards an Early-Successional System. 40:657–661 Tabarelli M, Peres C a (2002) Abiotic and vertebrate seed dispersal in the Brazilian Atlantic forest: implications for forest regeneration. Biol Conserv 106:165–176. https://doi.org/10.1016/S0006-3207(01)00243-9 Tabarelli M, Peres CA, Melo FPL (2012) The “few winners and many losers” paradigm revisited: Emerging prospects for tropical forest biodiversity. Biol Conserv 155:136–140. https://doi.org/10.1016/j.biocon.2012.06.020 Turner IM, Corlett RT (1996) The conservation value of small, isolated fragments of lowland tropical rain forest. Trends Ecol Evol 11:330–333. https://doi.org/10.1016/0169-5347(96)10046-X Valdés A, Lenoir J, Frenne P De, et al (2019) High ecosystem service delivery potential of small woodlands in agricultural landscapes. J Appl Ecol 1–13. https://doi.org/10.1111/1365-2664.13537 Van Breugel M, Bongers F, Martínez-Ramos M (2007) Species dynamics during early secondary forest succession: Recruitment, mortality and species turnover. Biotropica 39:610–619. https://doi.org/10.1111/j.1744-7429.2007.00316.x Van Der Sande M, Arets EJMM, Peña-Claros M, et al (2016) Old- growth Neotropical forests are shifting in species and trait composition. Ecol Monogr 86:228–243. https://doi.org/10.1890/15-1815.1 Van Gelder HA, Poorter L, Sterck FJ (2006) Wood mechanics , allometry , and life-history variation in a tropical rain forest tree community. New Phytol 171:367–378 Vilela, E. A. & Ramalho MAP (1979) Análise das temperaturas e precipitações pluviométricas de Lavras, Minas Gerais. Ciência e Prática 3:71-79. Villéger S, Mason NWH, Mouillot D (2008) New multidimensional functional diversity indices for a multifaced framework in functional ecology Dynamique de la diversité fonctionnelle des communautés de poissons ( Lagune de Terminos , Mexique ). Ecology. https://doi.org/10.1890/07-1206.1 Wang C, He J, Zhao TH, et al (2019) The smaller the leaf is, the faster the leaf water loses in a temperate forest. Front Plant Sci 10:1–12. https://doi.org/10.3389/fpls.2019.00058 Wickham H, Chang W (2016) Package ‘ggplot2’ Wright IJ, Dong N, Maire V, et al (2017) Global climatic drivers of leaf size. Science (80- ) 357: Wright IJ, Reich PB, Westoby M, et al (2004) The worldwide leaf economics spectrum. Nature 428:821–827. https://doi.org/10.1038/nature02403 Zambrano J, Garzon-Lopez CX, Yeager L, et al (2020) Investigating the direct and indirect effects of forest fragmentation on plant functional diversity. PLoS One in press:1–16. https://doi.org/10.1371/journal.pone.0235210 Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC CJ (2009) Data from: Towards a worldwide wood economics spectrum. In: Dryad Digit. Repos. https://doi.org/10.5061/dryad.234/1. Accessed 11 Jun 2017 Supplementary Files supplementary.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-47498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":1123588,"identity":"4cf7e52d-8e7e-408d-bb33-7a45c46be01a","order_by":0,"name":"Érica Rievrs Borges","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFACxgYgIQFisTF8AJHspGhhnAEimUmwj42ZB0QR0sIvfbjxw889FnIGt48/e2zza5s8HzMD44ePObi1SPYlNkv2PJMwNjiXY26c23fbsI2ZgVly5jbcWgzOMLYx8ByQSJzZw8MmndtzmxGohY2ZF48We6AWxj8HJOpn9rA/k7bsuW1PUIsBD9BYoC0J/DwMZtIMP24nEtQicYaxWVrmgIRhPw+PuWFvw+3kNmbGZrx+4e9hf/jxzYE6eTYe9mcPfvy5bTu/vfngh494tKACYFAwQCOXaPCHFMWjYBSMglEwUgAABvpIQg7YkZEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-7751-6265","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Érica","middleName":"Rievrs","lastName":"Borges","suffix":""},{"id":1123589,"identity":"1c5bb77f-7fff-4601-ac7c-474c3abba3e5","order_by":1,"name":"Marcela Venelli Pyles","email":"","orcid":"","institution":"Universidade Federal de Lavras","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcela","middleName":"Venelli","lastName":"Pyles","suffix":""},{"id":1123590,"identity":"04145fff-fee4-4ad2-bd50-a71da379521f","order_by":2,"name":"Marcelo Leandro Bueno","email":"","orcid":"","institution":"Universidade Estadual de Mato Grosso do Sul","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marcelo","middleName":"Leandro","lastName":"Bueno","suffix":""},{"id":1123591,"identity":"a54cba91-298d-4d05-acf3-956aca27dca0","order_by":3,"name":"Rubens Manoel dos Santos","email":"","orcid":"","institution":"Universidade Federal de Lavras","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rubens","middleName":"Manoel dos","lastName":"Santos","suffix":""},{"id":1123592,"identity":"8d908b43-d68d-4e37-9365-3a0e8ed48cfa","order_by":4,"name":"Marco Aurélio Leite Fontes","email":"","orcid":"","institution":"Universidade Federal de Lavras","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Aurélio Leite","lastName":"Fontes","suffix":""},{"id":1123593,"identity":"c92c5b9f-6bff-4c99-b497-59fd432ebc5c","order_by":5,"name":"Ary Teixeira de Oliveira-Filho","email":"","orcid":"","institution":"Universidade Federal de Minas Gerais","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ary","middleName":"Teixeira","lastName":"de Oliveira-Filho","suffix":""}],"badges":[],"createdAt":"2020-07-22 11:03:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-47498/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-47498/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1777164,"identity":"1d926500-a005-4bcf-aabe-ce3005ea6264","added_by":"auto","created_at":"2020-08-04 15:22:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216503,"visible":true,"origin":"","legend":"Species rarefaction curves for species richness in a semidesciduous tropical forest tree community at Lavras, Minas Gerais, southeast Brazil in 2000, 2005, 2010 and 2017.","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-47498/v1/Fig1.jpg"},{"id":1777165,"identity":"bb7121b1-4b42-453a-be38-f9f07d0de583","added_by":"auto","created_at":"2020-08-04 15:22:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":574682,"visible":true,"origin":"","legend":"Community trait composition for the four years of forest inventories (2000, 2005, 2011 and 2017) for the overall community, adult tree community and juveniles. Different letters indicate significant differences among mean values (p \u003c 0.05) based on pairwise comparisons in mixed linear models (Tukey’s HSD).","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-47498/v1/Fig2.jpg"},{"id":13567504,"identity":"f6de5432-552a-4bf1-8cbf-d8ff83d8374b","added_by":"auto","created_at":"2021-09-17 03:33:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":513359,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-47498/v1/da14a03b-0c76-43c1-b8e4-a25bd6db7d0b.pdf"},{"id":1777167,"identity":"00e85f8d-c688-4753-820c-e5374cf5b761","added_by":"auto","created_at":"2020-08-04 15:22:02","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":395264,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.doc","url":"https://assets-eu.researchsquare.com/files/rs-47498/v1/supplementary.doc"}],"financialInterests":"","formattedTitle":"\u003cp\u003eLong-Term Impact of Forest Fragmentation on Tree Functional Diversity, Trait Composition and Aboveground Biomass.\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eThe high rates of forest degradation and fragmentation across tropical rain forests have led to a scenario where most forest remnants are reduced in size and inserted in a human transformed matrix (Magnano et al. 2014; Matos et al. 2016; Vald\u0026eacute;s et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zambrano et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These fragmented remnants are usually surrounded by pastures and arable lands beyond other human settlements (Vald\u0026eacute;s et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, fragmentation promotes the reduction of forested areas, isolation of remnants and creation of edges (Kupfer et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Matos et al. 2016), and threatens the long-term persistence of species (Turner and Corlett \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Pereira et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rands et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), as well as the goods and services provided by those ecosystems (Foley et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zambrano et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Small and isolated fragments are exposed to a variety of edge effects (hotter and drier microclimate, increased light intensity, wind turbulence) and immigration of alien species (Murcia \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Turner and Corlett \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Laurance et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003ea\u003c/span\u003e; Haddad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Thus, the sustainability of tree communities under such environmental pressures has motivated studies on the mechanisms by which species coexistence is maintained in a local-scale habitat.\u003c/p\u003e \u003cp\u003eThe reduction of forest remnants\u0026rsquo; area drives biodiversity loss directly through the removal of species and indirectly by changes in environmental conditions at forest edges which favour the performance of early successional species (Laurance et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e; Tabarelli and Santos 2008; L\u0026ocirc;bo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Community composition from fragmented forests has been shown to diverge from their natural successional path, revealing an alternative state which has been known as \u003cem\u003eretrogressive succession\u003c/em\u003e (Santos et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tabarelli et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Magnago et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rocha-santos et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ewers et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This altered successional trajectory is characterized by increased rates of mortality (Lindenmayer et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), species-poor communities with a proliferation of disturbance-adapted tree species (Laurance et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e; Tabarelli and Lopes 2008), loss of large trees (due to physiological stress and wind turbulence; Oliveira et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, increase of small-seeded softwood species (Laurance et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e), lower biomass storage potential (Van Der Sande et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and impacts phylogenetic diversity (Matos et al. 2016; Zambrano et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Overall, the shift in successional trajectories caused in a great part by the edge creation promotes a functional simplification of plant communities affecting late-successional trees (Suding et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecies respond to changes in environmental conditions through their functional traits, represented by plant community functional composition (defined by community weighted trait means, CWMs) and functional diversity (i.e. the distribution of traits within a community) (Diaz and Cabido \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Candido et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Functional traits represent physiological trade-offs in plant strategies (Wright et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Reich \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), being greatly useful to summarize changes in community composition in response to selective pressures and in relation to forest succession trajectories (Poorter et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, long-term monitoring of functional diversity and composition throughout time offer a means to generalize community performance under the impacts of forest fragmentation.\u003c/p\u003e \u003cp\u003eHabitat fragmentation can lead to major changes on species community composition, yet little is known about its long term impacts, especially on functional traits composition and aboveground biomass (Laurance et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Magnano et al. 2014). Old-growth forest fragments surrounded by human-modified landscapes offer an excellent opportunity to examine the long-term effects of habitat fragmentation on diversity, functional composition and ecosystem services potential of such small forest remnants. Thus, understanding how these effects influence forest regeneration and successional processes over time is of crucial importance to ensure forest persistence in fragmented landscapes.\u003c/p\u003e \u003cp\u003eHere we investigated how species richness, functional diversity and composition and aboveground biomass change over 17\u0026nbsp;years in a small tropical forest remnant surrounded by human-modified landscapes. We hypothesized that the tree community will show altered succession trajectory signs such as i) high mortality rate (superior to the recruitment rate); ii) decrease in species diversity and aboveground biomass throughout time and iii) shifts in functional composition toward a prevalence of species with highly colonization ability and early-successional traits (abiotic dispersion, small seed size, lower stature and wood density and compound leaves) throughout time. Finally, we also tested for shifts in functional composition within the adult and juvenile trees, as different forest stratum face different environmental constraints.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the southeast region of Minas Gerais, Brazil (21\u0026deg;13\u0026rsquo;11\u0026rdquo; S \u0026minus;\u0026thinsp;44\u0026deg;58\u0026rsquo;15\u0026rdquo; W), on a remnant of the endangered Brazilian Atlantic Rain Forest, classified as Semideciduous Seasonal Forests (IBGE \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The mean altitude is 884 meters. The regional climate is classified as Cwb (Mesothermic climate of K\u0026ouml;ppen), defined by dry winters and mild summers. Mean annual rainfall and temperature are 1493\u0026nbsp;mm and 19.3\u0026ordm;C, respectively (Vilela and Ramalho \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Soil types in the region are primarily argisols and nitosols (Machado and Oliveira-Filho \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fragment area is currently around 4.0 hectares (ha) and it is surrounded by a low biomass landscape matrix, composed mostly by coffee plantations, pastures and a small dawn drainage canal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData for this study was derived from 17\u0026nbsp;years (2000 to 2017) of forest survey from the 29 permanent plots of 0.4\u0026nbsp;ha each (20\u0026nbsp;m x 20\u0026nbsp;m), a total of 1.16\u0026nbsp;ha sampled (data were obtained from ForestPlots.net; Lopez-Gonzalez et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lopez-Gonzalez et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). All trees with a diameter at breast height (DBH)\u0026thinsp;\u0026ge;\u0026thinsp;5\u0026nbsp;cm were identified to species level. All plots were first measured in 2000, re-measured in 2005, 2011 and 2017, when surviving, dead and recruiting (individuals that met the inclusion criterion) were evaluated. Species identities were checked for nomenclatural synonyms using the online tool Taxonomic Name Resolution Service (TNRS) ver. 3.2 (Boyle et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo analyze the changes in species richness of the trees between the four surveys (2000, 2005, 2011 and 2017), we computed species rarefaction curves using the platform R (R Core Development Team \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the vegan package (Oksanen et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDemographic metrics\u003c/h2\u003e \u003cp\u003eThe changes in the tree community over time were determined for each plot by calculating the following mean annual abundance rates: Mortality (M = (1 - [(N0 - Nm) / N0] 1 / t) * 100) and recruitment (R = (1 - (1 - Nr / Nt) 1 / t)*100, whereas biomass dynamics were described by basal area loss (P = (1 - ((AB0 -(ABm\u0026thinsp;+\u0026thinsp;ABd)) / AB0) 1 / t) *100) and gain (G = (1- (1 - (ABr\u0026thinsp;+\u0026thinsp;ABg) / ABt) 1 / t) * 100), where: t is the time elapsed between censuses; N0 and Nt the initial and final tree counts; Nm and Nr are dead trees and recruit counts; AB0 and ABt are the initial and final tree basal area; ABm is the basal area of dead trees; ABr is basal area of recruits; and ABd and ABg are the increment and decrement in the basal area from the surviving trees (Sheil et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sheil and May, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional traits\u003c/h2\u003e \u003cp\u003eThe functional traits considered in the study are related to natural successional trajectories of forests (Van Der Sande et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), species stress resistance, and species standing biomass ( Attri et al. 2015; Lohbeck et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Poorter et al. 2015): Maximum height, wood density, leaf type, seed size and dispersal mode were obtained following standardized protocols (Cornelissen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; P\u0026eacute;rez-Harguindeguy et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecies maximum height (Hmax, m) is an indicator of the adult stature of species, potentially related to the species longevity and life-history strategy (King et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and was calculated as the 95th-percentile height of all trees of the species. Species wood density (WD, g.cm-3) represents biomass per wood volume constructed and was obtained from the Global Wood Density Database (filtered by Tropical South America, Zanne et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For the species with WD not available, we used mean values for the WD of the genus or family. The leaf type (LT, categorical data) reflects the species heat balance. In other words, leaf size profoundly affects a variety of biological water and energy processes and has particularly important implications for understanding the adaption strategy of plants to environmental changes (Wang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All species were categorized: compound and simple leaves. Seed size (SS, categorical data), although usually related to the competitive vigour of the seedlings (Kitagima \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), is also an important life-history trait for trees, correlated to a suite of morphological and physiological traits of pioneer species (small seeds) and shade-tolerant species (large seeds) (Poorter and Rose \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Osuri and Sankaran \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Qualitative data for species SS were obtained from herbarium specimens, and the species were classified as small seeds species (seed length\u0026thinsp;\u0026lt;\u0026thinsp;1.5\u0026nbsp;cm) and large seeds species (seed length\u0026thinsp;\u0026gt;\u0026thinsp;1.6\u0026nbsp;cm), following Tabarelli and Peres (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and Santos et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Dispersal mode (DM, categorical data) is an indicator of the ability of plants to colonize habitats and is especially important in fragmented landscapes because they can improve predictions of dispersal probability and seed bank composition (Kraft et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All species were categorized: biotic dispersion and abiotic dispersion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCommunity weighted mean traits and functional diversity indices\u003c/h2\u003e \u003cp\u003eCommunity weighted mean (CWM) trait values (Maximum height, wood density, leaf type, seed size and dispersal mode) and functional diversity indices were calculated per plot and per inventory (2000, 2005, 2011 and 2017) for the overall community, juveniles (5\u0026nbsp;cm\u0026thinsp;\u0026le;\u0026thinsp;DBH\u0026thinsp;\u0026lt;\u0026thinsp;10\u0026nbsp;cm) and adults (DBH\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026nbsp;cm). Both were weighted by species abundance to equally weight recruiting and dying trees on changes in CWM values (van der Sande et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Adult survivors respond slowly to disturbance and tend to make a large contribution to community biomass because they are large (Chazdon et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), while recruits (smaller individuals) respond fast to disturbance and contribute more to abundance (Van Breugel et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor functional diversity, we checked the functional richness (FRic), an indicator of the species volume occupying the niche space of a community (Vill\u0026eacute;ger et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) which measures the extent to which the trait space is filled. The functional richness and the CWM values were calculated using Gower's distance (that allows mixed traits types: continuous, ordinal and categorical). The functional analyses were calculated using the 'FD' package in R (Lalibert\u0026eacute; et al. 2015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBiomass estimates\u003c/h2\u003e \u003cp\u003eFor every tree with DBH\u0026thinsp;\u0026ge;\u0026thinsp;5\u0026nbsp;cm, the aboveground biomass (AGB) was calculated using the allometric formula of Chave et al. 2014: AGB\u0026thinsp;=\u0026thinsp;exp[-1.803-0.976E\u0026thinsp;+\u0026thinsp;0.976ln(DBH)\u0026thinsp;+\u0026thinsp;2.673ln(DBH)-0.0299[ln(DBH)]\u003csup\u003e2\u003c/sup\u003e] and the parameters, DBH (cm), species wood density (WD, g cm\u0026thinsp;\u0026minus;\u0026thinsp;\u003csup\u003e3\u003c/sup\u003e) and E, which is a measure of environmental stress (retrieved from Rejou-Mechain and Chave, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used generalized linear mixed models (GLMM) to assess the differences in species richness, functional diversity and composition (SR, FRic, CWM Hmax, CWM WD, CWM LT, CWM SS and CWM DM) and aboveground biomass among the four forest inventories (2000, 2005, 2011 and 2017). Measures from the plot from the different years of monitoring were considered as a random factor, to account the lack of independence within the plots among the four inventories (Bates et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen necessary, data were log10 or square root transformed prior to analysis to meet the assumptions of normality and homoscedasticity. Tukey post-hoc tests were used to assess the differences between the forest surveys (2000, 2005, 2011 and 2017).\u003c/p\u003e \u003cp\u003eTo test for potential spatial nonindependence of plots, we assessed the degree of spatial autocorrelation in our linear mixed model residuals using the Moran\u0026rsquo;s I test. A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 would indicate that the model residuals show spatial autocorrelation, which was not found for any of our models (Kissling and Carl \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) (Table S1).\u003c/p\u003e \u003cp\u003eAll analyses, figures and graphs were performed using the platform R (R Core Development Team \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the following packages: multcomp (Bretz et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), lme4 (Bates et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e), lmerTest (Kuznetsova et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), MuMIn (Barton \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and ggplot2 (Wickham and Chang \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003eWe found that the annual mortality in the tree community was higher than recruitment for all forest inventories, except for the last one, resulting in a net decrease in total tree abundance (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2000, 1275 individuals were recorded consisting of 151 species within 46 families (Table S2). In 2005, 1234 individuals were recorded, a reduction caused by the higher mortality rate (3.28% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in comparison with the recruitment rate (2.65% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the survey interval. A similar pattern was found for 2011 when the lower recruitment rate (2.86% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and high mortality rate (4.00% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) resulted in a reduction of tree abundance (1150). In 2017 there was a small increase in abundance (1153 individuals), a result from the slightly higher recruitment rate (2.48% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in comparison with the mortality rate (2.43% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Overall, from 2000 and 2017, we found a decrease in 9.56% in tree abundance (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe higher rates of annual basal area loss compared to the gain rates in the basal area in the community within the first three intervals resulted in a loss of basal area (34.01\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e, 32.81\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e, and 31.64\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e for 2000, 2005 and 2011, respectively). However, a strong increase in the total basal area was recorded in 2017 (39.40\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e), resulting in a positive net change of 13.68% between 2000 and 2017 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Indeed, we found an increase in AGB for the last survey, resulting in a net gain in 18.61% in biomass (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe small reduction found in species richness was not significant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S3), as also suggested by the rarefaction curves as there is an overlap of the standard deviation of the four richness curves (Fig.\u0026nbsp;1). Species functional richness showed a small but not significant reduction (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTree community dynamics components of a Semideciduous Forest in Lavras, Minas Gerais State, Brazil.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSurvey interval\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2000\u0026ndash;2005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2005\u0026ndash;2011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2011\u0026ndash;2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of plots\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of trees:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInitial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1150\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1153\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurvivals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e992\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeads\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecruits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMortality rate (% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecruitment rate (% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBasal Area:\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInitial\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFinal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.40\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBA loss rate (% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBA gain rate (% year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.03\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDead (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecruits (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurvival (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003cem\u003eBA\u003c/em\u003e Basal Area\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSpecies richness, functional richness and aboveground biomass per plot for the overall community fitted from generalized linear mixed models.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2000\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2005\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2011\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpecies richness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.06\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.44\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.13\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.48\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFunctional richness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.62\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.56\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAboveground biomass (Mg/ha)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e255.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e249.94\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e242.02\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e313.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eDifferent letters indicate significant differences among mean values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) based on pairwise comparisons in mixed linear models (Tukey\u0026rsquo;s HSD).\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the overall community, functional composition analysis indicated a gradual reduction in the percentage of simple leaves (Fig.\u0026nbsp;2, Table S4). The adult tree community showed a reduction in Hmax and the percentage of simple leaves, indicating an investment in resource-acquisitive and disturbance adapted traits. However, we also found a gradual increase in wood density through the years for the adult community (Fig.\u0026nbsp;2). The juvenile tree community did not show any shifts in functional trait composition.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eThis study has provided relevant insights into the successional trajectory effects of fragmentation on the tree community of a small forest remnant surrounded by human-modified landscapes. Under 17\u0026nbsp;years of community monitoring, the dynamics' analysis revealed a continuous reduction in the overall tree abundance and shifts in trait composition favouring adult species with resource-acquisitive, pioneer life-strategies (reduction in tree maximum height and in species with simple leaves and a slight increase in the percentage of small seeds). However, for the adult tree community (DBH\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026nbsp;cm), we found a significant increase in wood density. Indeed, the last monitoring interval (2011\u0026ndash;2017) showed a strong increase in total basal area and a significant increase in aboveground biomass (reaching higher values than found for the first survey). Based on these findings, our hypothesis was partly accepted, as natural succession is being negatively affected, except for aboveground biomass.\u003c/p\u003e \u003cp\u003eOther studies in Neotropical small forests have shown tree communities dominated by a small set generalist species in forest fragments with one to 10 hectares (Laurance \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Laurance et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006b\u003c/span\u003e). After analyzing the long-term dynamics of a forest fragment with 4\u0026nbsp;ha, we found a decrease in Hmax and simple leaves for the adult tree community and a decrease in simple leaves for the overall tree community. During succession, pioneer species are expected to be replaced by shade-tolerant ones, with the community gradually accumulating species diversity and ecological functions (Guariguata and Ostertag \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This trajectory is changed when strong environmental filters lead to the selection of species that share the adaptation strategies required to colonize and survive in a changed post-disturbance habitat (Tabarelli et al. 2008).\u003c/p\u003e \u003cp\u003eIn disturbed habitats, the establishment of late-successional species is compromised until the stressful conditions had reduced to an acceptable state (Lebrija-Trejos et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Edge creation, e.g., promotes microclimatic and structural changes within the forest with severe consequences to tree communities (Murcia \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), such as desiccation which act as a climate filter favouring mostly generalist pioneer species (Pierce et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Leaf type reflects adaptations to reduced water availability and therefore a gradual increase in species with simple leaves is expected in the course of succession towards more mature and undisturbed forests (Wright et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gei et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, we found a gradual increase of trees with compound leaves, a strategy to prevent the excessive water loss as plants with pinnate or bipinnate leaves can release individual leaflets (and not complete leaves) during severe water stress, increasing heat dissipation and regulating temperature (Wright et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gei et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen considering the adult tree community, we also found a decrease in species Hmax. The reduction of adult trees\u0026rsquo; Hmax is also expected in communities under disturbance (Carre\u0026ntilde;o-Rocabado et al. 2012). The tree canopy damage allows trees to get more access to light, which no longer consists of a limiting-resource (Ruiz-jaen and Potvin \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These results suggest the presence of strong post-fragmentation edge effects, leading to an increase in pioneer species while shade-tolerant species are negatively impacted (Zambrano et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a consequence, adult tree species may not invest in increasing their height as much as other life strategies (King et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Besides, it is also expected a great loss of large trees due to physiological stress and wind turbulence (Oliveira et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, also see Laurance et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1988\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increase in wood density throughout time in the adult tree community can also be related to disturbed habitats as it guarantees trunk resistance to physical damages by herbivores, pathogens and woody debris (Van Gelder et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mccarthy-Neumann and Kobe 2008) and also an indication of hydraulic safety (Sterck et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Previous studies have shown the lack of wood density variation with fragmentation and fragment forest size, even for secondary forests (Magnago et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, since wood densities of species can be affected by environmental changes due to fragmentation (Laurance et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Nock et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), the average wood densities we used from the literature (Chave et al. 2009; Zanne et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) would miss any such variation. Our functional diversity analyses could be refined in future analyses using wood densities derived from \u003cem\u003ein situ\u003c/em\u003e measurements, and also, when herbarium collections permit, fruit and seed sizes as continuous rather than categorical variables.\u003c/p\u003e \u003cp\u003eThe gain in basal area in the community indicates their species\u0026rsquo; high productivity (Finegan et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Indeed, the higher basal area is translated in gains in biomass, which showed a significant increase through time. The values for AGB found in this study are comparable to large undisturbed rainforests (Pyles et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Previous studies have reported that fragmentation has no significant effect on biomass, with the productivity of the shade-intolerant trees having a high role in the accumulation of biomass (Magnago et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProximity to edge and small fragment size favours a turnover with trees typical from late-successional stages being replaced by pioneer and especially the short-lived species (Nascimento and Laurance \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tabarelli et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Laurance et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e; Michalski et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Santos et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consequently, forest regeneration potential might be lower in these fragments, with detrimental consequences for tree diversity and regeneration dynamics in the long term. However, the process of forest succession can take many years of centuries, therefore longer monitoring is needed to check whether the conditions found in the studied fragment matches the retrogressive succession hypothesis or is rather a transient, regeneration dynamic in human-modified landscapes that might lead to advanced successional stages (Guariguata and Ostertag \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Letcher and Chazdon \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ewers et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Muscarella et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eThis study provides important contributions to the influence of fragmentation on the successional trajectory of tree communities on small forest remnants surrounded by human-modified landscapes. Based on our findings, natural succession is being negatively affected, except for aboveground biomass. The decrease in Hmax and simple leaves, especially for the adult tree community, are mostly related do microenvironmental conditions caused by edge effects, as desiccation and tree canopy damage. Although those findings could have resulted from a transient dynamic, they constitute a warning to future conservation policies around the ecological integrity of small and isolated forest fragments.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eDBH: Diameter at breast height; Hmax: Species maximum height; WD: Species wood density; LT: Leaf type; SS: Seed size; DM: Dispersal mode; CWM: Community weighted mean; FRic: Functional richness; AGB: Aboveground biomass.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the ForestPlots repository, www.florestplots.net.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Brazilian Coordination for the Improvement of Higher Education Personnel (CAPES), the Federal University of Lavras (UFLA), the Foundation for the Support of Studies in Minas Gerais (FAPEMIG) and the Brazilian National Council for Scientific and Technological Development (CNPq).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Borges E, Pyles, M. Analysis and interpretation of data: Borges E, Pyles, M. Drafting of the manuscript: Borges E, Bueno, M. Critical revision of the manuscript for important intellectual content: Pyles, M, Bueno, M. Statistical analysis: Borges E, Pyles, M. Obtained funding: Borges E, Fontes M, Santos R. Study supervision: Oliveira-Filho A, Bueno, M. The author(s) read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarton K (2016) Package \u0026lsquo; MuMIn \u0026rsquo;\u003c/li\u003e\n\u003cli\u003eBates D, M\u0026auml;chler M, Bolker B, Walker (2014a) Fitting Linear Mixed-Effects Models using lme4. Cran Vignette 1\u0026ndash;51. https://doi.org/10.18637/jss.v067.i01\u003c/li\u003e\n\u003cli\u003eBates D, Maechler M, Bolker B, et al (2014b) Package \u0026lsquo;lme4\u0026rsquo;\u003c/li\u003e\n\u003cli\u003eBoyle B, Hopkins N, Lu Z, et al (2013) The taxonomic name resolution service: An online tool for automated standardization of plant names. BMC Bioinformatics 14:. https://doi.org/10.1186/1471-2105-14-16\u003c/li\u003e\n\u003cli\u003eBretz F, Westfall P, Heiberger RM, et al (2015) Package \u0026lsquo; multcomp \u0026rsquo;\u003c/li\u003e\n\u003cli\u003eCandido HMN, Miazaki \u0026Acirc;, Pontara V, et al (2020) Colonization-related functional traits of plants in a 50-hectare plot of secondary tropical forest. Acta Bot Brasilica 34:1\u0026ndash;8. https://doi.org/10.1590/0102-33062019abb0087\u003c/li\u003e\n\u003cli\u003eCarre\u0026ntilde;o-rocabado G, Pe\u0026ntilde;a-claros M, Bongers F, et al (2012) Effects of disturbance intensity on species and functional diversity in a tropical forest. 1453\u0026ndash;1463. https://doi.org/10.1111/j.1365-2745.2012.02015.x\u003c/li\u003e\n\u003cli\u003eChave J et al. (2015) Improved allometric models to estimate the aboveground biomass of tropical trees. 3177\u0026ndash;3190. https://doi.org/10.1111/gcb.12629\u003c/li\u003e\n\u003cli\u003eChazdon RL, Letcher SG, Van Breugel M, et al (2007) Rates of change in tree communities of secondary Neotropical forests following major disturbances. Philos Trans R Soc B Biol Sci 362:273\u0026ndash;289. https://doi.org/10.1098/rstb.2006.1990\u003c/li\u003e\n\u003cli\u003eCornelissen JHC, Lavorel S, Garnier E, et al (2003) A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust J Bot 51:335\u0026ndash;380. https://doi.org/10.1071/BT02124\u003c/li\u003e\n\u003cli\u003eDiaz S, Cabido M (2001) Vive la diff\u0026eacute;rence: plant functional diversity matters to ecosystem processes. Trends Ecol Evol 16:646\u0026ndash;655\u003c/li\u003e\n\u003cli\u003eEwers R, Andrade A, Laurance S, et al (2016) Predicted trajectories of tree community change in Amazonian rainforest fragments. Ecography (Cop) 40:26\u0026ndash;35\u003c/li\u003e\n\u003cli\u003eEwers RM, Andrade A, Laurance SG, et al (2017) Predicted trajectories of tree community change in Amazonian rainforest fragments. Ecography (Cop) 40:26\u0026ndash;35. https://doi.org/10.1111/ecog.02585\u003c/li\u003e\n\u003cli\u003eFinegan B et al. (2015) Does functional trait diversity predict above-ground biomass and productivity of tropical forests ? Testing three alternative hypotheses. 191\u0026ndash;201. https://doi.org/10.1111/1365-2745.12346\u003c/li\u003e\n\u003cli\u003eFoley JA, Asner GP, Costa MH, et al (2007) Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Front Ecol Environ 5:25\u0026ndash;32\u003c/li\u003e\n\u003cli\u003eGei M, Rozendaal DMA, Poorter L, et al (2018) Legume abundance along successional and rainfall gradients in Neotropical forests. Nat Ecol Evol 2:1104\u0026ndash;1111. https://doi.org/10.1038/s41559-018-0559-6\u003c/li\u003e\n\u003cli\u003eGuariguata MR, Ostertag R (2001) Neotropical secondary forest succession : changes in structural and functional characteristics. 148:\u003c/li\u003e\n\u003cli\u003eHaddad NM, Brudvig LA, Clobert J, et al (2015) Habitat fragmentation and its lasting impact on Earth\u0026rsquo;s ecosystems. Sci Adv 1:1\u0026ndash;10. https://doi.org/10.1126/sciadv.1500052\u003c/li\u003e\n\u003cli\u003eIBGE (2012) Manual T\u0026eacute;cnico da Vegeta\u0026ccedil;\u0026atilde;o Brasileira\u003c/li\u003e\n\u003cli\u003eKing DA, Davies SJ, Tan S, Noor NSM (2006) The role of wood density and stem support costs in the growth and mortality of tropical trees. J Ecol 94:670\u0026ndash;680. https://doi.org/10.1111/j.1365-2745.2006.01112.x\u003c/li\u003e\n\u003cli\u003eKissling, W. D., \u0026amp; Carl G (2008) Spatial autocorrelation and the selection of simultaneous autoregressive models. Glob Ecol Biogeogr 17:59\u0026ndash;71\u003c/li\u003e\n\u003cli\u003eKitagima K (2007) Seed and seedling ecology. In: Pugnaire FI VF (ed) Functional plant ecology, 2nd edn. CRC Press, New York, pp 549\u0026ndash;880\u003c/li\u003e\n\u003cli\u003eKraft NJB, Adler PB, Godoy O, et al (2015) Community assembly, coexistence and the environmental filtering metaphor. Funct Ecol 29:592\u0026ndash;599. https://doi.org/10.1111/1365-2435.12345\u003c/li\u003e\n\u003cli\u003eKupfer JA, Malanson GP, Franklin SB (2006) Not seeing the ocean for the islands: The mediating influence of matrix-based processes on forest fragmentation effects. Glob Ecol Biogeogr 15:8\u0026ndash;20. https://doi.org/10.1111/j.1466-822X.2006.00204.x\u003c/li\u003e\n\u003cli\u003eKuznetsova A, Brockhoff PB, Christensen RHB (2016) Package \u0026lsquo; lmerTest \u0026rsquo;\u003c/li\u003e\n\u003cli\u003eLalibert\u0026eacute; E, Legendre P SB (2015) Measuring functional diversity (FD) from multiple traits, and other tools for functional ecology\u003c/li\u003e\n\u003cli\u003eLaurance W, Lovejoy T, Vansconcelos H, et al (2002) Ecosystem Decay of Amazonian Forest Fragments: a 22-Year Investigation. Conserv Biol 16:605\u0026ndash;618. https://doi.org/10.1214/aos/1059655905\u003c/li\u003e\n\u003cli\u003eLaurance WF (2001) Fragmentation and plant communities: synthesis and implications for landscape management. In: R.O. Jr., Gascon, C., Lovejoy, T.E., Mesquita RCG (ed) Lessons from Amazonia: The Ecology and Conservation of a Fragmented Forest. Yale University Press, New Haven, pp 158\u0026ndash;168\u003c/li\u003e\n\u003cli\u003eLaurance WF, Delam\u0026ocirc;nica P, Laurance S, et al (2000) Rainforest fragmentation kills big trees. Nature 404:2000\u003c/li\u003e\n\u003cli\u003eLaurance WF, Ferreira L V, Merona JMR, et al (1988) Society for Conservation Biology Effects of Forest Fragmentation on Recruitment Patterns in Amazonian Tree Communities Linked references are available on JSTOR for this article : Efects of Forest Fragmentation on Recruitment Patterns in Amazonian Tree Com. Conserv Biol 12:460\u0026ndash;464\u003c/li\u003e\n\u003cli\u003eLaurance WF, Nascimento HEM, Laurance SG, et al (2006a) Rain forest fragmentation and the proliferation of successional trees. Ecology 87:469\u0026ndash;482. https://doi.org/10.1890/05-0064\u003c/li\u003e\n\u003cli\u003eLaurance WF, Nascimento HEM, Laurance SG, et al (2006b) Rapid decay of tree-community composition in Amazonian forest fragments. Proc Natl Acad Sci U S A 103:19010\u0026ndash;19014. https://doi.org/10.1073/pnas.0609048103\u003c/li\u003e\n\u003cli\u003eLebrija-Trejos EE, Perez-Garcia EA, Meave JA, et al (2010) Functional traits and environmental filtering drive community assembly in a species-rich tropical system. Ecology 91:386\u0026ndash;398\u003c/li\u003e\n\u003cli\u003eLetcher SG, Chazdon RL (2009) Rapid Recovery of Biomass , Species Richness , and Species Composition in a Forest Chronosequence in Northeastern Costa Rica. 41:608\u0026ndash;617\u003c/li\u003e\n\u003cli\u003eLindenmayer DB, Laurance WF, Franklin JF (2012) Ecology: Global decline in large old trees. Science (80- ) 338:1305\u0026ndash;1306. https://doi.org/10.1126/science.1231070\u003c/li\u003e\n\u003cli\u003eLiu J, Coomes DA, Hu G, et al (2019) Larger fragments have more late-successional species of woody plants than smaller fragments after 50 years of secondary succession\u003c/li\u003e\n\u003cli\u003eL\u0026ocirc;bo D, Le\u0026atilde;o T, Melo FPL, et al (2011) Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers Distrib 17:287\u0026ndash;296. https://doi.org/10.1111/j.1472-4642.2010.00739.x\u003c/li\u003e\n\u003cli\u003eLohbeck M, Lebrija-Trejos E, Mart\u0026iacute;nez-Ramos M, et al (2015) Functional trait strategies of trees in dry and wet tropical forests are similar but differ in their consequences for succession. PLoS One 10:1\u0026ndash;15. https://doi.org/10.1371/journal.pone.0123741\u003c/li\u003e\n\u003cli\u003eLopez-Gonzalez, G., Lewis, S.L., Burkitt, M., Baker T.R. and Phillips O. (2009) ForestPlots.net Database. www.forestplots.net. Date of extraction [15/09/2019]\u003c/li\u003e\n\u003cli\u003eLopez-Gonzalez, G., Lewis, S.L., Burkitt, M. and Phillips OL (2011) ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J Veg Sci 22:610\u0026ndash;613. https://doi.org/10.1111/j.1654-1103.2011.01312.x\u003c/li\u003e\n\u003cli\u003eMachado ELM, Oliveira-Filho AT de (2010) Spatial patterns of tree community dynamics are detectable in a small (4 ha) and disturbed fragment of the Brazilian Atlantic forest. Acta Bot Brasilica 24:250\u0026ndash;261. https://doi.org/10.1590/s0102-33062010000100027\u003c/li\u003e\n\u003cli\u003eMagnago LFS, Edwards DP, Edwards FA, et al (2014) Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J Ecol 102:475\u0026ndash;485. https://doi.org/10.1111/1365-2745.12206\u003c/li\u003e\n\u003cli\u003eMccarthy-neumann S, Kobe RK (2008) Tolerance of Soil Pathogens Co-Varies with Shade Tolerance across Species of Tropical Tree Seedlings. Ecology 89:1883\u0026ndash;1892\u003c/li\u003e\n\u003cli\u003eMichalski F, Nishi I, Peres CA (2007) Disturbance-Mediated Drift in Tree Functional Groups in Amazonian Forest Fragments. Biotropica 39:691\u0026ndash;701\u003c/li\u003e\n\u003cli\u003eMurcia C (1995) Edge effects in fragmented forests: implications for conservation. Trends Ecol Evol 10:58\u0026ndash;62. https://doi.org/10.1016/S0169-5347(00)88977-6\u003c/li\u003e\n\u003cli\u003eMuscarella R, Uriarte M, Aide TM, et al (2016) Functional convergence and phylogenetic divergence during secondary succession of subtropical wet forests in Puerto Rico. J Veg Sci 27:283\u0026ndash;294. https://doi.org/10.1111/jvs.12354\u003c/li\u003e\n\u003cli\u003eNascimento H, Laurance WF (2004) Biomass dynamics in amazonian forest fragments. Ecol Appl 14:127\u0026ndash;138\u003c/li\u003e\n\u003cli\u003eNock CA, Geihofer D, Grabner M, Baker PJ (2009) Wood density and its radial variation in six canopy tree species differing in shade-tolerance in western Thailand. Annals 104:297\u0026ndash;306. https://doi.org/10.1093/aob/mcp118\u003c/li\u003e\n\u003cli\u003eOksanen J, F. Guillaume Blanchet RK, Legendre P, et al (2015) Package \u0026lsquo;vegan.\u0026rsquo; R Packag version 23-4\u003c/li\u003e\n\u003cli\u003eOliveira MA, Santos AMM, Tabarelli M (2008) Profound impoverishment of the large-tree stand in a hyper-fragmented landscape of the Atlantic forest. For Ecol Manage 256:1910\u0026ndash;1917. https://doi.org/10.1016/j.foreco.2008.07.014\u003c/li\u003e\n\u003cli\u003eOsuri \u0026amp; Sankaran MS (2016) Seed size predicts community composition and carbon storage potential of tree communities in rainforest fragments in India\u0026rsquo;s Western Ghats. https://doi.org/10.1111/1365-2664.12626\u003c/li\u003e\n\u003cli\u003ePereira HM, Scharlemann JPW, Fernandez-manjarr\u0026eacute;s JF, Ara\u0026uacute;jo MB (2010) Scenarios for Global Biodiversity. Science (80- ) 330:1496\u0026ndash;1501. https://doi.org/10.1126/science.1196624\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Harguindeguy N, Diaz S, Garnier E, et al (2013) New Handbook for standardized measurment of plant functional traits worldwide. Aust J Bot 61:167\u0026ndash;234. https://doi.org/http://dx.doi.org/10.1071/BT12225\u003c/li\u003e\n\u003cli\u003ePierce S, Negreiros D, Cerabolini BEL, et al (2017) A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct Ecol 31:444\u0026ndash;457. https://doi.org/10.1111/1365-2435.12722\u003c/li\u003e\n\u003cli\u003ePoorter L et al (2019) Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat Ecol Evol 3:928\u0026ndash;934\u003c/li\u003e\n\u003cli\u003ePoorter L, Rose SA (2005) Light-dependent changes in the relationship between seed mass and seedling traits: A meta-analysis for rain forest tree species. Oecologia 142:378\u0026ndash;387. https://doi.org/10.1007/s00442-004-1732-y\u003c/li\u003e\n\u003cli\u003ePyles M V, Prado-Junior JA, Magnago LFS, et al (2018) Loss of biodiversity and shifts in aboveground biomass drivers in tropical rainforests with different disturbance histories. Biodivers Conserv 3215\u0026ndash;3231. https://doi.org/10.1007/s10531-018-1598-7\u003c/li\u003e\n\u003cli\u003eR Core Development Team (2018) A language and environment for statistical computing. 1\u003c/li\u003e\n\u003cli\u003eRands MRW, Adams WM, Bennun L, et al (2010) Biodiversity Conservation: Challenges Beyond 2010. Science (80- ) 329:1298\u0026ndash;1304\u003c/li\u003e\n\u003cli\u003eReich PB (2014) The world-wide \u0026ldquo;fast-slow\u0026rdquo; plant economics spectrum: A traits manifesto. J Ecol 102:275\u0026ndash;301. https://doi.org/10.1111/1365-2745.12211\u003c/li\u003e\n\u003cli\u003eRejou-Mechain M, Chave J (2014) Manipulate raster and netCDF datasets. http://chave.ups-tlse.fr/pantropical_allometry/readlayers.r\u003c/li\u003e\n\u003cli\u003eRocha-santos L, Pessoa MS, Cassano CR, et al (2016) The shrinkage of a forest : Landscape-scale deforestation leading to overall changes in local forest structure. BIOC 196:1\u0026ndash;9. https://doi.org/10.1016/j.biocon.2016.01.028\u003c/li\u003e\n\u003cli\u003eRuiz-jaen MC, Potvin C (2011) Can we predict carbon stocks in tropical ecosystems from tree diversity ? Comparing species and functional diversity in a plantation and a natural forest. New 189:978\u0026ndash;987\u003c/li\u003e\n\u003cli\u003eSantos BA, Peres CA, Oliveira MA, et al (2008) Drastic erosion in functional attributes of tree assemblages in Atlantic forest fragments of northeastern Brazil. Biol Conserv 141:249\u0026ndash;260. https://doi.org/10.1016/j.biocon.2007.09.018\u003c/li\u003e\n\u003cli\u003eSantos G, Santos B, Nascimento HEM, Tabarelli M (2012) Contrasting Demographic Structure of Short- and Long-lived Pioneer Tree Species on Amazonian Forest Edges. Biotropica 0:1\u0026ndash;8\u003c/li\u003e\n\u003cli\u003eSheil, D.; Jennings, S.; Savill P (2000) Long-term permanent plot observations of vegetation dynamics in Budongo, a Ugandan rain forest. J Trop Ecol 16:765\u0026ndash;800\u003c/li\u003e\n\u003cli\u003eSheil, D. \u0026amp; May R (1996) Mortality and recruitment rat evaluations in heterogeneous tropical forests. J Ecol 84:91\u0026ndash;100\u003c/li\u003e\n\u003cli\u003eSterck F, Van Gelder HA, Poorter L (2006) Mechanical branch constraints contribute to life-history variation across tree species in a Bolivian forest. J Ecol 94:1192\u0026ndash;1200. https://doi.org/10.1111/j.1365-2745.2006.01162.x\u003c/li\u003e\n\u003cli\u003eSuding KN, Lavorel S, Chapin FS, et al (2008) Scaling environmental change through the community-level: A trait-based response-and-effect framework for plants. Glob Chang Biol 14:1125\u0026ndash;1140. https://doi.org/10.1111/j.1365-2486.2008.01557.x\u003c/li\u003e\n\u003cli\u003eTabarelli M, Cardoso, Maria Silva DA, Gascon C (2004) Forest fragmentation, synergisms and the impoverishment of neotropical forests. Biodi 13:1419\u0026ndash;1425\u003c/li\u003e\n\u003cli\u003eTabarelli M, Lopes A V., Peres CA (2008a) Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40:657\u0026ndash;661. https://doi.org/10.1111/j.1744-7429.2008.00454.x\u003c/li\u003e\n\u003cli\u003eTabarelli M, Lopes A V, Peres CA (2008b) Edge-effects Drive Tropical Forest Fragments Towards an Early-Successional System. 40:657\u0026ndash;661\u003c/li\u003e\n\u003cli\u003eTabarelli M, Peres C a (2002) Abiotic and vertebrate seed dispersal in the Brazilian Atlantic forest: implications for forest regeneration. Biol Conserv 106:165\u0026ndash;176. https://doi.org/10.1016/S0006-3207(01)00243-9\u003c/li\u003e\n\u003cli\u003eTabarelli M, Peres CA, Melo FPL (2012) The \u0026ldquo;few winners and many losers\u0026rdquo; paradigm revisited: Emerging prospects for tropical forest biodiversity. Biol Conserv 155:136\u0026ndash;140. https://doi.org/10.1016/j.biocon.2012.06.020\u003c/li\u003e\n\u003cli\u003eTurner IM, Corlett RT (1996) The conservation value of small, isolated fragments of lowland tropical rain forest. Trends Ecol Evol 11:330\u0026ndash;333. https://doi.org/10.1016/0169-5347(96)10046-X\u003c/li\u003e\n\u003cli\u003eVald\u0026eacute;s A, Lenoir J, Frenne P De, et al (2019) High ecosystem service delivery potential of small woodlands in agricultural landscapes. J Appl Ecol 1\u0026ndash;13. https://doi.org/10.1111/1365-2664.13537\u003c/li\u003e\n\u003cli\u003eVan Breugel M, Bongers F, Mart\u0026iacute;nez-Ramos M (2007) Species dynamics during early secondary forest succession: Recruitment, mortality and species turnover. Biotropica 39:610\u0026ndash;619. https://doi.org/10.1111/j.1744-7429.2007.00316.x\u003c/li\u003e\n\u003cli\u003eVan Der Sande M, Arets EJMM, Pe\u0026ntilde;a-Claros M, et al (2016) Old- growth Neotropical forests are shifting in species and trait composition. Ecol Monogr 86:228\u0026ndash;243. https://doi.org/10.1890/15-1815.1\u003c/li\u003e\n\u003cli\u003eVan Gelder HA, Poorter L, Sterck FJ (2006) Wood mechanics , allometry , and life-history variation in a tropical rain forest tree community. New Phytol 171:367\u0026ndash;378\u003c/li\u003e\n\u003cli\u003eVilela, E. A. \u0026amp; Ramalho MAP (1979) An\u0026aacute;lise das temperaturas e precipita\u0026ccedil;\u0026otilde;es pluviom\u0026eacute;tricas de Lavras, Minas Gerais. Ci\u0026ecirc;ncia e Pr\u0026aacute;tica 3:71-79.\u003c/li\u003e\n\u003cli\u003eVill\u0026eacute;ger S, Mason NWH, Mouillot D (2008) New multidimensional functional diversity indices for a multifaced framework in functional ecology Dynamique de la diversit\u0026eacute; fonctionnelle des communaut\u0026eacute;s de poissons ( Lagune de Terminos , Mexique ). Ecology. https://doi.org/10.1890/07-1206.1\u003c/li\u003e\n\u003cli\u003eWang C, He J, Zhao TH, et al (2019) The smaller the leaf is, the faster the leaf water loses in a temperate forest. Front Plant Sci 10:1\u0026ndash;12. https://doi.org/10.3389/fpls.2019.00058\u003c/li\u003e\n\u003cli\u003eWickham H, Chang W (2016) Package \u0026lsquo;ggplot2\u0026rsquo;\u003c/li\u003e\n\u003cli\u003eWright IJ, Dong N, Maire V, et al (2017) Global climatic drivers of leaf size. Science (80- ) 357:\u003c/li\u003e\n\u003cli\u003eWright IJ, Reich PB, Westoby M, et al (2004) The worldwide leaf economics spectrum. Nature 428:821\u0026ndash;827. https://doi.org/10.1038/nature02403\u003c/li\u003e\n\u003cli\u003eZambrano J, Garzon-Lopez CX, Yeager L, et al (2020) Investigating the direct and indirect effects of forest fragmentation on plant functional diversity. PLoS One in press:1\u0026ndash;16. https://doi.org/10.1371/journal.pone.0235210\u003c/li\u003e\n\u003cli\u003eZanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, Miller RB, Swenson NG, Wiemann MC CJ (2009) Data from: Towards a worldwide wood economics spectrum. In: Dryad Digit. Repos. https://doi.org/10.5061/dryad.234/1. Accessed 11 Jun 2017\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"forest fragmentation, community dynamics, aboveground biomass, functional traits, disturbance","lastPublishedDoi":"10.21203/rs.3.rs-47498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-47498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Community composition on fragmented forest remnants has been shown to diverge from their natural successional path, revealing an alternative state which has been known as \u003cem\u003eretrogressive succession\u003c/em\u003e. Here we show the long-term monitoring of a small forest fragment concerning community structure, species richness, functional diversity and composition throughout 17 years to offer a means to generalize community performance under the impacts of forest fragmentation. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The adult tree community showed shifts on its functional composition: reduction in species’ maximum height and percentage of simple leaves, indicating an investment in resource-acquisitive and disturbance adapted traits. However, we also found a gradual increase in wood density throughout the years for the adult community. For the overall community, functional composition analysis indicated a gradual reduction in the percentage of simple leaves and a significant increase in aboveground biomass. The decrease in Hmax and simple leaves, especially for the adult tree community, are mostly related do microenvironmental conditions caused by edge effects, as desiccation and tree canopy damage.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our results show that natural regeneration is being negatively affected, except for aboveground biomass. Although these findings could have resulted from a transient dynamic, they constitute a warning to future conservation policies around the ecological integrity of small forest fragments.\u003c/p\u003e","manuscriptTitle":"Long-Term Impact of Forest Fragmentation on Tree Functional Diversity, Trait Composition and Aboveground Biomass.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-08-04 15:22:00","doi":"10.21203/rs.3.rs-47498/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f55444f0-56c0-41c8-ad93-4aad7b65c5ae","owner":[],"postedDate":"August 4th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":239449,"name":"Forestry"},{"id":239450,"name":"Behavioral Ecology"}],"tags":[],"updatedAt":"2020-08-22T16:36:59+00:00","versionOfRecord":[],"versionCreatedAt":"2020-08-04 15:22:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-47498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-47498","identity":"rs-47498","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}