Long-term anthropogenic impacts result in widespread changes of plant species composition in Southern Atlantic Forest: evidences from systematic survey

Long-term anthropogenic impacts result in widespread changes of plant species composition in Southern Atlantic Forest: evidences from systematic survey | 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 Article Long-term anthropogenic impacts result in widespread changes of plant species composition in Southern Atlantic Forest: evidences from systematic survey Fernanda Ribeiro da Silva¹, André L. Giles¹, André L. de Gasper², and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7141134/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract It is well known that anthropogenic impacts reshape species composition and cause biodiversity loss, altering tree diversity patterns in natural forests. Here, we use a robust database encompassing three forest formations in the subtropical Atlantic Forest to determine how environmental conditions and anthropogenic impacts affect diversity patterns. We compare alpha and beta-diversity indexes using generalized dissimilarity models (GDM) within and between forest formations and two successional stages (intermediate and advanced). We found that beta-diversity across all forest formations and successional stages was mainly explained by species richness (53.3%), followed by environmental factors (26.9%), including precipitation seasonality and mean annual temperature, geographic distance (7.6%), and anthropogenic factors (7.5%). Beta-diversity within each forest formation was mainly explained by species richness and geographic distance. In the Araucaria forests, beta-diversity was also explained by grazing and precipitation seasonality, while in the Evergreen Forests by fire frequency. Differences in driver importance across forest formations might be related to ecological differences and distinct histories of anthropogenic impacts. By understanding the unique biodiversity patterns and human impacts across different forest types, our findings offer key insights for developing effective conservation strategies to ensure the long-term protection and resilience of these ecosystems. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences beta-diversity subtropical forest anthropic impacts Figures Figure 1 Figure 2 Figure 3 Introduction Anthropogenic impacts reshape species composition and biodiversity patterns in tropical forests [ 1 ], [ 2 ], [ 3 ]. Atlantic forest, one of the ‘hottest’ biodiversity hotspots [ 4 ] has suffered greater impacts since the European invasion in the 16th century [ 5 ]. Several disturbances are linked to anthropization, such as forest loss, species invasion, over-exploitation, landscape fragmentation, forest isolation and climate change [ 6 ] [ 7 ]. Currently, forest loss and land use change are the main impacts affecting Atlantic Forest [ 8 ] [ 9 ]. Such activities have reduced Atlantic Forest to a small amount of large fragments inserted in a less or more degraded landscape [ 6 ] [ 10 ], leading to poverty of biological communities, decreasing habitat quality and species diversity [ 11 ], and thus changing species composition[ 12 ]. Anthropogenic impacts can act as both a barrier as a bridge, either preventing or facilitating the colonization and establishment of plant species, affecting patterns of alpha and beta-diversity at multiple spatial scales (i.e. from local to regional assemblages) [ 13 ], [ 1 ]. In these processes, less sensitive species may be favored (winners) and proliferate, replacing more sensitive species (losers), thereby increasing similarity between plant communities (i.e., biotic homogenization with a decrease in beta-diversity) [ 14 ]. On the other hand, landscape fragmentation and forest loss cause isolation of populations by limiting seed dispersal process and/or by increasing heterogeneity among fragments, resulting in greater differences in assemblages between sites (i.e. biotic differentiation with a higher beta-diversity) [ 15 ],[ 1 ] . Noticeably, besides anthropogenic impacts, we must consider edaphic and environmental site characteristics which also drive compositional differentiation and are often treated as the main factors shaping plant communities when anthropogenic impacts are disregarded [ 16 ], [ 17 ], [ 18 ]. Notably, the Southern Brazilian Atlantic Forest is a unique type of rainforest with distinct climatic and edaphic conditions compared to those found in the northern and central Atlantic Forest [ 19 ]. In general, these forests are situated in transitional zones between tropical and temperate climates, typically characterized by year-round rainfall without a pronounced dry season. These climatic patterns support three major forest formations: Evergreen Forest, Seasonal Forest, and Araucaria Forest. The same historical process of anthropization has led these forests to small fragments in a degraded landscape [ 20 ]. Currently, less than 5.7% of the original forest cover is protected in formal Protected Areas (PAs) (with 1,6% PAs from the integral protection group and 4,1% from sustainable use group, see Brazilian conservation units panel, available in Microsoft Power BI ) and it is acknowledged that such protected areas do not cover substantial regions necessary for conservation of tree species[ 21 ]. Although subject to the same anthropization process as other Atlantic Forest regions, the distinct environmental drivers of these formations likely shape different biodiversity patterns, and anthropic impacts might affect in a distinct manner of plant species diversity. Although Lingner et al. [ 22 ] investigated the impacts of anthropogenic factors on forest attributes in Evergreen southern Atlantic Forest, efforts to understand these patterns have mainly focused on other regions [ 23 ], [ 24 ]. Here, we use a robust dataset to identify patterns of diversity and unveil how anthropic and environmental factors affect tree communities in the Southern Brazilian Atlantic forest. We aim to evaluate how environmental and anthropogenic factors affect the alpha- and beta- diversities of forest fragments across different successional stages and forest formations in the subtropical Atlantic Forest biome. To achieve this aim, we first characterized how the alpha- and beta-diversities varied across the three forest formations and successional stages in our dataset. Second, we tested the hypotheses that alpha and beta diversities will: (i) be more affected by climate than anthropogenic factors across forest formations than within forest formations, and that anthropogenic factors will gain importance within each forest formation; (ii) increase along with successional stage, being higher at advanced than at intermediate successional stages; (ii) decrease with anthropogenic impacts. Results Alpha diversity variation across forest formations Alpha-diversity, measured by the three hill series, varied significantly across forest formations. The Evergreen Forests presented the highest alpha-diversity compared to the Araucaria Forests and the Seasonal Forest, which did not differ between each other (Fig. 1 ; ANOVA, p < 0.001). Forests in advanced successional stages had higher alpha-diversity than intermediate successional stages only at the Evergreen Forests, but the alpha-diversity in the two successional stages did not differ at the Araucaria and Seasonal Forests (Fig. 1 ). The analyses across formations showed that species richness (i.e. hill0) increased with mean annual temperature, amount of forest in a 1000m² buffer, longitude, soil clay content, annual range of air temperature, and decreased with fire frequency, latitude, and the successional stage (in intermediate stage) (R²=0.48; p < 0.001) (Supplementary Table 1) Beta-diversity variation across forest formations Beta-diversity varied across forest formations. When calculated as the Sorensen index, the Evergreen and Araucaria forests had higher beta-diversity than the Seasonal forests. In addition, different from expected, beta-diversity was higher in intermediate successional stages than in advanced successional stages. The NMDS based on Bray-Curtis index shows that the floristic composition is significantly different among the three formations (ANOSIM - R = 0.124, p < 0.001). SUs of AF and EVF were more dispersed in the NMDS space, suggesting greater dissimilarity within the formation (i.e. beta-diversity) compared with SF SUs, which were closer together, indicating lower beta-diversity (AF = 0.76 ± 0.13; EVF = 0.76 ± 0.12, SF = 0.64 ± 0.12) (Fig. 2 ). The three groups show some overlap, suggesting similar floristic composition on some SUs classified as different forest formations, which is probably driven by the SUs located at ecotone zones between adjacent formations. (Supplementary Figure S1 ). Intermediate and advanced successional stages were grouped together by forest formation in the NMDS and therefore do not show a trend towards homogenization across forest formations related to successional stage. Moreover, when we analyse the dissimilarity between SUs within each forest formation separately, pairs of advanced successional forests are less dissimilar between each other than intermediate successional forests, for all three forest formations. This suggests that forest patches at later successional stages show lower dissimilarity than those at earlier stages. Considering only the forests at intermediate successional stage, SF had the lowest mean dissimilarity (0.64 ± 0.14) and EVF had slightly higher dissimilarity than AF (AF = 0.76 ± 0.15; EVF = 0.77 ± 0.15). Considering only the forests in advanced successional stages, SF showed lower dissimilarity (mean = 0.59 ± 0.14) compared to AF (0.73 ± 0.14) and EVF (0.74 ± 0.13), which showed similar values (Fig. 2 ). When we decomposed the beta-diversity within each forest formation, we found a greater contribution of species turnover (i.e. substitution of species) than nestedness (i.e. a subgroup of a species-rich group) components in all three forest formations. However, in SF nestedness was greater than in the other formations, demonstrating more shared species among communities in SF. Furthermore, unlike EVF and AF, for SF, beta-diversity in advanced forests was lower (Supplementary Fig S2). Environmental and anthropogenic impacts on beta-diversity Considering all forest types and both successional stages together, most of the variation was explained in order of importance by: species richness, precipitation seasonality, geographic proximity, mean annual temperature, fire frequency, cattle grazing, amount of surrounding forest, and soil clay content (Supplementary Table 1). The species dissimilarity within intermediate and advanced successional forests was explained by similar factors, with a similar relative contribution of anthropogenic factors (10.4% and 7.73% respectively) (Table 1 ). For intermediate successional forests, species dissimilarity was affected, in order of importance, by species richness, precipitation seasonality, geographic proximity, landscape fire frequency, euclidean distance between forest patches (i.e. isolation) in a 750m buffer, grazing, and amount of forest in a 1000m buffer around SUs. For advanced successional forests, species turnover was significantly affected by species richness, precipitation seasonality, geographic proximity, elevation, fire frequency, mean annual temperature, cattle grazing and the amount of surrounding forest (Supplementary Figure S3). Within EVF, when combining intermediate and advanced forest stages, beta-diversity was affected by species richness, landscape fire frequency and geographical proximity. At the advanced successional EVF, annual range of air temperature, elevation, temperature seasonality and geographical proximity significantly explained species turnover. For intermediate successional EVF, landscape fire frequency, species richness and geographic proximity significantly affected beta-diversity. Within AF, when combining intermediate and advanced successional stages, species dissimilarity was explained by species richness (i.e. hill0), precipitation seasonality, and geographical proximity. In advanced successional AF, species richness, geographical proximity, and grazing explained beta-diversity. In intermediate successional stage AF, only precipitation seasonality and species richness were important to explain beta-diversity (Table 1 ). Within SF, at combined intermediate and advanced successional stages, only species richness and geographic proximity explained dissimilarity. For advanced and intermediate successional SF, species dissimilarity was explained only by geographic distance (Table 1 ). Table 1 Relative importance in percentage of predictors explaining species dissimilarity based on GDM. Here we showed only predictors significantly affected by species dissimilarity. Int = intermediate successional stage and Adv = advanced successional stage. Predictor Global_All Global_Int Global_Adv EVF_All EVF_Ind EVF_Adv AF_All AF_Int AF_Adv SF_All SF_Ind SF_Adv Model explanation 59 46.3 59.2 41.7 42.6 52 28.9 39 35.3 28.1 29.3 39.1 Fire frequency 4.7 4.9 5.7 15.4 29.1 - - - - - - - Cattle grazing 1.4 3.2 1.2 - - - - - 1.3 - - - Forest 1000m2 - 2.3 - - - - - - - - - - Patch isolation 750m2 - 3.5 - - - - - - - - - - Precipitation seasonality 22.1 19.1 17.1 - - - 16.9 38.5 - - - - Mean annual temperature 4.7 - 1.6 - - - - - - - - - Annual range temperature - - - - - 20.7 - - - - - - Soil clay content 0.9 - - - - - - - - - - - Hill0 53.3 43.5 50.7 46.6 25.6 - 41.1 27.9 44.5 55.8 - - Geographic distance 7.6 8.2 10.3 15.4 3.7 3.7 12.9 - 18.3 24.8 17.4 30.1 Elevation - - - - - 11.7 - - - - - - Discussion We presented an overview of alpha and beta tree species diversity and their main drivers within three Atlantic Forest formations across the state of SC in Southern Brazil. Alpha-diversity is distinct among forest formations, with greater values in less anthropized forest formation (i.e. Evergreen Forest) and smaller values in Araucaria Forest and Seasonal Forest. Beyond bioclimatic factors, anthropization affects beta-diversity of tree species in the Southern Atlantic Forest. Contrary to our expectations, anthropization has resulted in advanced-succession remnants exhibiting greater species composition similarity than intermediate-succession remnants across all forest formations. Our results indicate that conservation strategies for tree species in the Southern Atlantic Forest must manage specific anthropogenic interventions while accounting for the particularities of each forest formation. Evergreen forests exhibit the highest species richness and the greatest species turnover (i.e., beta diversity) among the forest formations. This pattern may be explained by the fact that this is the best conserved formation in the Southern Atlantic Forest [ 21 ], in addition to its broader environmental variation (ranging from coastal to montane areas) and less restrictive environmental conditions[ 25 ]. Indeed, we found that mean annual temperature and elevation affected beta-diversity. The frequency of landscape fires also affects beta-diversity in EVF, even though fire occurs more frequently in AF (Supplementary Fig. 6), suggesting that this forest type is more sensitive to this type of anthropogenic disturbance. In fact, landscape fire frequency might be an important driver for diversity patterns increasing the spread of generalist species less sensitive to fires, characterized by broad environmental niches and large population sizes. Additionally, most generalist species can withstand disturbances and environmental constraints typically found in secondary forests. Despite the fact that EVF forest remnants are better conserved compared with AF and SF, the land use history and fire frequency can promote the range expansion of generalist species in some remnants. On the other hand, EVF harbors a high number of rare species[ 26 ], a characteristic that may contribute to increasing beta-diversity. These facts, combined with broader environmental variation, may lead to greater compositional dissimilarity and consequently higher species turnover. Our results indicate that the average species richness in remnants of Araucaria Forest (AF) is lower than in Evergreen Forest (EVF) and comparable to Seasonal Forest (SF). The reduced diversity observed in AF, compared to EVF, can be attributed to the combined effects of colder temperatures and higher altitudes [ 19 ], which limit species occurrence along with a legacy of land use degradation [ 20 ]. Despite these constraints, AF exhibits beta-diversity similar to EVF, with low beta-nestedness, indicating significant species turnover compared to that observed in EVF. Two potential mechanisms may explain this pattern, both of which could be operating concurrently: (1) the broader geographic range of AF and its associated environmental heterogeneity [ 27 ] may drive high beta-diversity through species turnover across fragments; and (2) degradation may reduce local species richness, resulting in regional-scale species turnover (i.e., biotic differentiation) that corroborated with our model showing beta-diversity significantly affected by species richness. Additionally, geographic distance, precipitation seasonality, and cattle grazing alter species composition, highlighting the environmental influence and impact of cattle ranching on forest remnants in AF [ 28 ] (Supplementary Fig S6). We observed low beta-diversity and high tree species similarity among remnants of Seasonal Forest (SF), particularly when focusing on advanced successional stages. Compared to other forest formations, SF exhibited greater beta-nestedness of tree species, indicating that its plant communities tend to be subsets of neighboring communities. Seasonal Forest (SF) has been regarded as a more recently established vegetation type in Santa Catarina, developing after the grasslands and Araucaria Forests [ 29 ]. Additionally, Gasper et al. [ 30 ] proposed that SFs form a transitional zone with the Araucaria Forests (AF). These historical and ecological factors may contribute to its lower species diversity and higher number of shared species compared to other forest types. On the other hand, Seasonal Forests (SF) not only occupies a smaller area compared to Evergreen Forests (EVF) and Araucaria Forests (AF), but also experiences higher levels of fragmentation and historical land-use degradation, making it the most threatened forest type among these formations [ 31 ]. Despite these pressures, alpha-diversity in SF was similar to that in AF, suggesting potential local biodiversity loss in both ecoregions. The greater similarity among advanced successional forests, compared to intermediate ones, may reflect the intense historical exploitation of the Atlantic Forest. Even forests in advanced successional stages have been subjected to anthropogenic impacts, which have affected their ecological structure [ 32 ]. This pattern was further supported by the GDM analysis, in which we considered only advanced forests across all formations. In addition to environmental variables, anthropogenic factors such as cattle grazing, fire frequency in the surrounding landscape, and the amount of nearby forest cover were identified as significant drivers of community dissimilarity. However, it is important to acknowledge certain limitations that may help explain the absence of a clear pattern of homogenization. First, we lack definitive information on the age of the study sites. Second, the successional trajectories may have been interrupted by anthropogenic disturbances not captured by the variables included in our analysis. Third, potential inaccuracies in our classification of successional stages may have influenced the results. Such factors may have confounded the identification of overarching ecological trends. Anthropic factors influence beta diversity when all forest formations are considered together, but their effect is less pronounced within each formation individually. This suggests that the presence and nature of anthropogenic impacts vary among forest types, which may lead to different ecological responses. In Seasonal Forest (SF), no anthropogenic factors were found to significantly influence beta diversity. A plausible explanation is the greater historical degradation of this forest type, which may have already filtered tree species through repeated anthropogenic disturbances. Several indicators—such as low diversity indices, high similarity among SF remnants, and a greater proportion of shared species across plant communities—point to a simplification of biodiversity in SF. Indeed, previous studies have shown that most regenerating species in these areas belong to early successional stages (i.e., pioneer species)[ 33 ]. Taken together, these findings suggest that biotic homogenization may be underway in SF. However, further research is needed to confirm this hypothesis. Implications for conservation strategies and actions Araucaria Forest (AF) and Evergreen Forest (EVF) exhibit high species turnover (i.e., beta diversity) across spatial and environmental gradients. These forests are known to host a rich assemblage of rare and threatened species [ 21 ], which play a crucial role in maintaining ecosystem functions [ 34 ], [ 35 ]. Moreover, networks of small habitat patches support a greater number of species than large contiguous patches, even when considering only species of conservation concern [ 36 ]. Therefore, effective conservation strategies for forests characterized by high beta diversity should prioritize the establishment of networks of protected areas that capture this variation. Rather than focusing solely on large individual parks, conservation efforts should be emphasized creating multiple spatially distinct reserves to better preserve biodiversity and ecosystem functionality [ 37 ]. However, a recent study showed that larger fragments support more complex communities and sustain key ecological processes that smaller patches cannot maintain in fragmented landscapes [ 38 ]. While we advocate for the conservation of all classes of small forest patches within the Atlantic Forest region, our results highlight that within-site species richness (i.e., alpha diversity) varies along spatial gradients. This suggests that conservation efforts could be more effective if focused on sites with high species diversity, and especially on key sites of critical conservation value, rather than on species-poor areas [ 16 ]. In this context, studies on species distributions are essential for setting conservation priorities. Furthermore, understanding the ecology of rare and threatened species targeted by restoration initiatives can greatly enhance biodiversity recovery efforts. For restoration efforts to be effective, they must incorporate species diversity considerations, as many restored sites currently exhibit low diversity and abundance [ 39 ]. Incorporating a diverse range of tree species in restoration activities is crucial for promoting long-term ecological stability in these ecosystems. Identifying the main drivers of biodiversity loss can guide the development of more effective mitigation strategies. For example, cattle grazing alters species composition in the Araucaria Forest (AF), highlighting the need to engage rural landowners in forest conservation efforts. This stakeholder group is crucial, as most forested areas are privately owned by individual farmers [ 40 ]. Increasing awareness about the ecological and economic benefits of native forests—such as ecosystem services and sustainable resource use—can strengthen conservation initiatives. Replacing extensive cattle grazing with more management practices may offer a viable alternative. Management often allows for better land utilization without promoting deforestation, while also enhancing livestock productivity. To ensure long-term biodiversity conservation and ecosystem resilience, sustainable agricultural practices that balance ecological and economic interests should be promoted. To effectively monitor and manage tree assemblages in the Evergreen Forest (EVF) affected by landscape fires, fire prevention measures must be implemented. Controlling fire spread is essential to minimize its impact on species composition and maintain ecosystem stability. Strategies such as firebreaks, controlled burns, and community-based fire prevention programs can help mitigate damage and contribute to forest conservation. The Atlantic Forest represents the main refuge for Brazil’s rich floral diversity in the south of Brazil, yet it remains highly threatened by human activities. In the face of escalating challenges such as climate change, this study provides crucial insights into the patterns of tree diversity across the Southern Atlantic Forest. Our findings highlight the importance of considering biodiversity variation among different forest formations and the role of anthropogenic impacts in shaping these patterns. These results are essential for guiding the development of targeted and effective conservation strategies that promote the long-term resilience and survival of these ecosystems. Methods The Evergreen Forest (EVF) originally covered 29309 km² [ 41 ], accounting for almost 31% of the Santa Catarina state. According to Vibrans et al. [ 42 ], only 16821 km² of forest fragments remain, most of which in the intermediate stage of regeneration, representing 40% of the original forest cover [ 43 ]. This forest is located at the eastern part of the state, rising from the coastal plains at sea level along an altitudinal gradient at the mountain ranges up to approximately 1500 meters a.s.l. [ 41 ]. The Araucaria Forest (AF) originally covered an area of 42851 km² in Santa Catarina, which is 44.94% of the state's territory [ 41 ]. Currently, the AF covers 22% of its original area [ 43 ], depicting the significant alteration of its original coverage. This forest is located between regions from 500 to 1,500 meters a.s.l. at the altiplano (ou highlands) [ 44 ]. The Seasonal Semi-deciduous Forest (SF), is located in the western part of the state, mainly in the Uruguai river basin, in an altitudinal range between 150 and 800 m (exceptionally 900 m [ 41 ]). SFs are even more threatened than EVF and AF, severely affected by human occupation in Santa Catarina state since the second half of the 20th century. Currently, only around 16.3% of its original area remains, with 52% of the fragments being smaller than 50 hectares, indicating the extensive fragmentation of those forests. Data were compiled from the Floristic and Forest Inventory of Santa Catarina State (hereafter FlorestaSC) sampled between 2007 and 2010 (see fieldwork details in [ 45 ]). It includes the three forest formations present in the Santa Catarina State- Evergreen Forest (EVF), Araucaria Forest (AF) and Seasonal Forest (SF). The sampling design for the FlorestaSC is based on a 10 km × 10 km grid covering the entire state of Santa Catarina (SC), except for the SF, where a finer 5 km × 5 km grid was used. This forest type has the smallest coverage, and the 10 km × 10 km grid would have resulted in too few sample points [ 42 ]. The FlorestaSC’ database englobes 418 sampled units, where the number of sampling units for each forest formation is proportional to the total area of each formation [ 46 ]. We excluded samples in the early successional stage, as well as samples with an area lower than or equal to 0.35ha. Thus, in our study, we analyzed 331 sampling units, 60 in SF (35 in intermediate successional stage and 25 in advanced successional stage), 129 in EVF (50 in intermediate successional stage and 79 in advanced successional stage), and 142 in AF (63 in intermediate successional stage and 79 in advanced successional stage) (Fig. 3 ). We used the successional stages classification as defined by FlorestaSC botanists, including a quantitative and qualitative assessment, taking into account the anthropogenic impacts to which these forests are subjected and their structural attributes, as the number of plant strata, the presence of large trees, the presence of lianas and epiphytes and the diversity of plants. In this way, intermediate successional forests present species richness between 10 and 30 species, with climax species still lacking in significant numbers; multiple vegetation layers and where large, shade-tolerant species are absent; and advanced successional forests were marked by greater species richness (> 30 species), the presence of large-diameter trees and epiphytes, resembling the old-growth stage, although showing signs of human impact. All plots are located in landscapes partially or totally affected by human activities [ 42 ]. Data Analyses Alpha diversity estimation and variation across forest formations In order to estimate species alpha-diversity, we calculated the Hill's diversity index for each forest formation (SF, EVF, AF) considering all selected plots. Hill’s diversity index is recognized for providing the true diversity of plant communities [47]. When q=0, all relative abundances are raised to zero (equal weight for rare species, corresponding to average species richness or number of species within an SU); when q=1, abundances are as shown and comparable to the exponential of Shannon entropy index; while q=2, abundances are squared, giving greater weight to the most abundant species, corresponding to the inverse of Simpson's index (the probability that two randomly selected individuals belong to the same species). Beta-diversity estimation and variation across forest formations We defined beta-diversity as the variation in species composition among SUs. To capture different aspects of this variation, we used two complementary dissimilarity indices: the Sorensen index, which is based on species presence/absence and reflects species turnover and nestedness (see below), and the Bray–Curtis index, which incorporates species abundances and reflects changes in both species presence and their relative abundances. Using the Sorensen dissimilarity index (betasor), we partitioned the beta-diversity across SUs into: 1) nestedness (betanes), which is when assemblages represent a subgroup of species from a richest group; and 2) beta simpson (betasim), which is the turnover of species or substitution of species when there is a replacement of species between sites. We used the Betapart package [48] in the R, versão 4.3.2. In addition, to investigate how similar in species composition are the three forest formations and the two successional stages, we performed a NMDS ordination based on the Bray-curtis dissimilarity, which accounts for the relative abundances of individual species. To test floristic separation, the variation of NMDS scores between forest formations was tested using a non-parametric multivariate analysis (ANOSIM). Drivers of diversity: Environmental and anthropogenic factors We considered climate, soil, and anthropogenic predictors to understand their influence on beta- and alpha-diversity among the three forest formations. Previously, we tested the Pearson correlation between numerical predictors to eliminate strong potential covariates. 1) Environmental factors: Climate and soil Environmental factors were selected based on their ecological relevance to our inquiries. Climate data were extracted from the Chelsa database [49]. The climatic predictors selected were the historical mean of: mean annual air temperature - Bio1 (°C), temperature seasonality-Bio4 (°C), Bio5: mean daily maximum air temperature of the warmest month (°C), Bio7: annual range of air temperature (°C), Bio12: annual precipitation amount (kg m - ²), Bio13: precipitation amount of the wettest month (kg m - ²), Bio14: precipitation amount of the driest month (kg m - ²), Bio15: precipitation seasonality (kg m - ²). After eliminating highly correlated factors, we used four climatic factors in further analyses: mean annual air temperature, temperature seasonality, mean daily maximum air temperature of the warmest month, and precipitation seasonality. Edaphic variables were extracted from the SoilGrids database (soilgrids.org), at 250 m resolution and averaged over the first 30 cm of the soil depth. Soil conditions used in further analyses were: soil pH, Bulk density (g cm - ³ - soil dry mass over soil volume), volumetric carbon concentration in the soil (C proportion of sand (>0.05 mm), silt 0.002 mm - 0.05 mm) and clay (<0.002 mm). 2) Anthropogenic factors Anthropogenic impacts were described at two scales. At the landscape scale, we extracted landscape descriptors of forest cover and fragmentation when the field inventories took place, and the land-use history during 26 years before it. At the plot scale, we used the occurrence of different descriptors of anthropogenic disturbance that were visually identified during the field expeditions (data from FlorestaSC). Below we describe the methods for acquiring each metric. Landscape descriptors of forest cover and fragmentation - For each SU, we set three concentric circles (buffers) with different radii sizes: 750m, 1000m, and 1500m, considering the sampling unit as the central point of each landscape. For each buffer size, we calculated the a) amount of forest vegetation (expressed in hectares), which represents the forest cover and the quantity of possible species habitats in the landscape (McGarigal & Marks, 1995), and the b) patch isolation (expressed in meters) measured as the mean distance between the edges of forest patches, which is related to the reduction of habitat quality in the fragment. Forest cover was extracted from the land use and land cover product of the Mapbiomas 7 th collection for the year 2010, which was just before the forest inventories were done. The two landscape metrics were acquired using the “raster” and "landscapemetrics" packages, from the R software. 2.2) The land-use history around each SU was characterized by three metrics: land-use duration, number of land-use changes and fire frequency during the period between 1988 and 2011. For that, we applied a buffer of 1000m radius around each SU and extracted the following metrics: i) Previous land-use duration was characterized as the average of the total number of years each pixel was classified as non-forest. This metric represents how long the surroundings of the SU have beeg used for alternative land uses, as longer land use duration leads to stronger anthropogenic impacts on forest ecosystems [50] . ii) Number of land-use changes is the frequency that a land cover class changed to another along the time series. It was calculated within the 1.000m buffer as the average number of changes in the pixels within the buffer. iii) Fire frequency was estimated as the average number of years each pixel was detected as burned along the time series within a buffer of 5.000m. We did not consider the number of times a pixel was burned within a year, hence recording only one fire event per pixel per year, which represents the number of years a pixel has been burned over the time series. We used a buffer of 5.000m because the source data was the MODIS fire product that has a resolution of 500m. 2.3) Local scale anthropogenic disturbances were identified within each sampling unit by visual interpretation of the presence or absence (binary information) of ten types of disturbance as identified in the field by the FlorestaSC teams: roads crossing, yerba mate exploitation, fire scars, garbage dumping, presence of exotic species, grazing, hunting traces, vegetation suppression and selective logging. We explored this information in two ways: a) using an anthropogenic index calculated as the sum of the presence of all disturbances dividing by 10 to each SUs (Bastos et al. 2022), and b) we selected the three anthropogenic impacts that were most robust and relevant for this study to be used separately in the models: cattle grazing, clear-cutting and roads. Statistical analyses Evaluating the drivers of diversity To describe how the alpha diversity differs between forest formations and successional stages we performed ANOVA tests on the hill number series (hill0, hill1, hill2). To evaluate how species richness (hill0= is affected by environmental and anthropogenic factors, we performed linear regression analyses. Before this analysis we verified multicollinearity removing predictors with high correlation (VIF>10). To investigate the role of geographical distance, environmental and anthropogenic factors on species dissimilarity (beta-diversity) we used two Generalized Dissimilarity Model (GDM). First we built a GDM for all SUs across forest formations, in order to evaluate how the predictors explain the beta diversity across the entire Santa Catarina state. Then, we built a GDM for SUs within each forest formation and successional stages separately in order to understand which and how the predictors explain the beta diversity within each forest formation (EVF, AF and SF). The GDM explains pairwise dissimilarities between sampling units by adjusting a linear model with geographic distances and other drivers of interest employing a maximum likelihood and I-splines to model and forecast species turnover [51]. This model uses Pairwise dissimilarities calculated using the Bray-Curtis index, which accounts for species abundances through the Bray-Curtis index to test the influence of predictors on species turnover components [52]. The predictors measured on different scales were transformed during model fitting to ensure that the adjusted distances between pairs of locations for different predictors can be meaningfully compared and integrated. A model selection procedure is applied, where the most parsimonious and significant predictors are retained. The statistical significance of the chosen model and of the predictors was tested using 999 permutations (α=0.05). The statistical analyses and graphing were carried out using the 'gdm' package in the R software [52]. Declarations Author Contribution FRS and ACJ conceived the study. FRS and AG performed the analyses. FRS wrote the first draft of the manuscript. ALG, AV e MBC, made substantial contributions to the revision of the manuscript and approved the final version. Acknowledgement We acknowledge the Instituto Serrapilheira for providing financial support for this project. ALG acknowledges CNPq for the productivity (grant no.: 307861/2023-6). ACV was supported by CNPq for research grants (305199/2022-6; 408242/2021-3) Data Availability The datasets analysed during the current study are available from the corresponding author on reasonable request." References Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation? Trends Ecol. Evol. 31 , 67–80 (2016). Furtado, S. G. Menini Neto, L. What is the role of topographic heterogeneity and climate on the distribution and conservation of vascular epiphytes in the Brazilian Atlantic Forest? Biodivers. Conserv. 30 , 1415–1431 (2021). Rito, K. F. et al. Unraveling the drivers of plant taxonomic and phylogenetic beta-diversity in a human-modified tropical dry forest. Biodivers. Conserv. 30 , 1049–1065 (2021). Zachos, F. E. & Habel, J. C. (eds) Biodiversity hotspots: distribution and protection of conservation priority areas (Springer Science & Business Media, 2011). Carlucci, M. B., Marcilio-Silva, V. & Torezan, J. M. The southern Atlantic Forest: use, degradation, and perspectives for conservation. The Atlantic Forest: History, biodiversity, threats and opportunities of the mega-diverse Forest , 91–111 (2021). Tabarelli, M., Pinto, L. P., Silva, J. M., Hirota, M. & Bede, L. Challenges and opportunities for biodiversity conservation in the Brazilian Atlantic Forest. Conserv. Biol. 19 , 695–700 (2005). Morris, R. J. Anthropogenic impacts on tropical forest biodiversity: a network structure and ecosystem functioning perspective. Philos. Trans. R Soc. 365 (1558), 3709–3718 (2010). Faria, D., Delabie, J. H. C. & Dias, M. H. The hileia baiana: an assessment of natural and historical aspects of the land use and degradation of the central corridor of the brazilian atlantic forest. The Atlantic forest: history, biodiversity, threats and opportunities of the mega-diverse forest , 63–90 (2021). Silveira, J. G. D. et al. Land use, land cover change and sustainable intensification of agriculture and livestock in the Amazon and the Atlantic Forest in Brazil. Sustainability 14 , 2563 (2022). Rezende, C. L. et al. From hotspot to hopespot: an opportunity for the Brazilian Atlantic Forest. Perspect. Ecol. Conserv. 16 , 208–214 (2018). Faria, D. et al. The breakdown of ecosystem functionality driven by deforestation in a global biodiversity hotspot. Biol. Cons . 283 , 110126 (2023). de Andrade, E. R. et al. Unveiling the impacts of forest loss on taxonomic and phylogenetic beta-diversity of juvenile and adult tropical trees. Biodivers. Conserv. 33 , 807–823 (2024). Olden, J. D. & Poff, N. L. Toward a mechanistic understanding and prediction of biotic homogenization. Am. Nat. 162 , 442–460 (2003). Tabarelli, M., Peres, C. A. & Melo, F. P. The ‘few winners and many losers’ paradigm revisited: emerging prospects for tropical forest biodiversity. Biol. Cons . 155 , 136–140 (2012). Arroyo-Rodríguez, V. et al. Plant β‐diversity in fragmented rain forests: testing floristic homogenization and differentiation hypotheses. J. Ecol. 101 , 1449–1458 (2013). Tuomisto, H., Ruokolainen, K. & Yli-Halla, M. Dispersal, environment, and floristic variation of western Amazonian forests. Science 299 , 241–244 (2003). Rito, K. F., Arroyo-Rodríguez, V., Queiroz, R. T., Leal, I. R. & Tabarelli, M. Precipitation mediates the effect of human disturbance on the Brazilian Caatinga vegetation. J. Ecol. 105 , 828–838 (2017). Furtado, S. G. Menini Neto, L. What is the role of topographic heterogeneity and climate on the distribution and conservation of vascular epiphytes in the Brazilian Atlantic Forest? Biodivers. Conserv. 30 , 1415–1431 (2021). Oliveira-Filho, A. T., Budke, J. C., Jarenkow, J. A., Eisenlohr, P. V. & Neves, D. R. Delving into the variations in tree species composition and richness across South American subtropical Atlantic and Pampean forests. J. Plant. Ecol. 8 , 242–260 (2015). Vibrans, A. C. et al. Structure of mixed ombrophyllous forests with Araucaria angustifolia (Araucariaceae) under external stress in Southern Brazil. Rev. Biol. Trop. 59 , 1371–1387 (2011). Grittz, G. S., Machado, G. M. O., Vibrans, A. C. & Gasper, A. L. Commonness as a reliable surrogacy strategy for the conservation planning of rare tree species in the subtropical Atlantic Forest. Biodivers Conserv 1–13 (2024). Lingner, D. V., Rodrigues, A. V., Oliveira, L. Z., De Gasper, A. L. & Vibrans, A. C. Modelling changes in forest attributes driven by human activities at different spatial scales in the subtropical Atlantic Forest. Biodivers. Conserv. 29 , 1283–1299 (2020). Lôbo, D., Leão, T., Melo, F. P., Santos, A. M. & Tabarelli, M. Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. Divers. Distrib. 17 , 287–296 (2011). Rito, K. F. et al. Unraveling the drivers of plant taxonomic and phylogenetic β-diversity in a human-modified tropical dry forest. Biodivers. Conserv. 30 , 1049–1065 (2021). Lingner, D. V. et al. Floresta Ombrófila Densa de Santa Catarina-Brasil: agrupamento e ordenação baseados em amostragem sistemática. Ciênc Florest . 25 , 933–946 (2015). Vibrans, A. C., Sevegnani, L., Gasper, A. L. & Lingner, D. V. (eds) Espécies arbóreas raras da flora de Santa Catarina Vol. 7 (UFSC/FAPESC, 2013). Sevegnani, L., Uhlmann, A., Gasper, A. L., Meyer, L. & Vibrans, A. C. Climate Affects the Structure of Mixed Rain Forest in Southern Sector of Atlantic Domain in Brazil. Acta Oecol. 77 , 109–117 (2016). Sampaio, M. B. & Guarino, E. S. G. Efeitos do pastoreio de bovinos na estrutura populacional de plantas em fragmentos de Floresta Ombrófila Mista. Rev. Árvore . 31 , 1035–1046 (2007). Pennington, R., Prado, D. E. & Pendry, C. A. Neotropical seasonally dry forests and Quaternary vegetation changes. J. Biogeogr. 27 , 261–273 (2000). Gasper, A. L. D., Uhlmann, A., Vibrans, A. C. & Sevegnani, L. Variação da estrutura da floresta estacional decidual no estado de Santa Catarina e sua relação com a altitude e o clima. Ciênc Florest . 25 , 77–89 (2015). Gasper, A. L. et al. Inventário florístico florestal de Santa Catarina: espécies da Floresta Estacional Decidual. Rodriguésia 64 , 427–443 (2013). Vibrans, A. C. et al. Insights from a large-scale inventory in the Southernern Brazilian Atlantic Forest. Sci. Agric. 77 , e20180036 (2019). Schorn, L. A. et al. Estrutura do componente arbóreo/arbustivo da Floresta Estacional Decidual em Santa Catarina. Em Volume II - Floresta Estacional Decidual , 1o ed, 139–59. Blumenau: Edifurb, (2012). Lyons, K. G., Brigham, C. A., Traut, B. H. & Schwartz, M. W. Rare species and ecosystem functioning. Cons biol. 19 , 1019–1024 (2005). Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477 , 199–202 (2011). Riva, F. & Fahrig, L. The disproportionately high value of small patches for biodiversity conservation. Conserv. Lett. 15 , e12881 (2022). Fahrig, L. Why do several small patches hold more species than few large patches? Glob Ecol. Biogeogr. 29 , 615–628 (2020). Gonçalves-Souza, T. et al. Species turnover does not rescue biodiversity in fragmented landscapes. Nature , 1–5 (2025). Almeida, C., Reid, J. L., de Lima, R. A. F., Pinto, L. F. G. & Viani, R. A. G. High-diversity Atlantic Forest restoration plantings fail to represent local floras. Perspect. Ecol. Conserv. 23 , 6–11 (2024). Reis, M. S. et al. Domesticated landscapes in Araucaria Forests, Southern Brazil: a multispecies local conservation-by-use system. Front. Ecol. Evol. 6 , 11 (2018). Klein, R. M. Mapa fitogeográfico do estado de Santa Catarina p. 24 (Herbário Barbosa Rodrigues, 1978). Vibrans, A. C., Sevegnani, L., Lingner, D. V., de Gasper, A. L. & Sabbagh, S. Inventário florístico florestal de Santa Catarina (IFFSC): aspectos metodológicos e operacionais. Pesq Florest Bras. 30 , 291–291 (2010). Vibrans, A. C., McRoberts, R. E., Moser, P. & Nicoletti, A. L. Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina. Remote Sens. Environ. 130 , 87–95 (2013). Backes, E. et al. Distribuição geográfica atual da Floresta com Araucária: condicionamento climático. In C. R. Fonseca (Eds.). Floresta com Araucária: Ecologia, Conservação e Desenvolvimento Sustentável , 45–68 (2009). Vibrans, A. C. et al. Unprecedented large-area turnover estimates for the subtropical Brazilian Atlantic Forest based on systematically-gathered data. Ecol. Manage. 505 , 119902 (2022). Santos, A. S., Saraiva, D. D., Mueller, S. C. & Overbeck, G. E. Interactive effects of environmental filtering predict beta-diversity patterns in a subtropical forest metacommunity. Plant. Ecol. Evol. Syst. 17 , 96–106 (2015). Jost, L. Partitioning diversity into independent alpha and beta components. Ecology 88 , 2427–2439 (2007). Baselga, A. Partitioning the turnover and nestedness components of beta-diversity. Glob Ecol. Biogeogr. 19 , 134–143 (2010). Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data . 4 , 170122. https://doi.org/10.1038/sdata.2017.122 (2017). Jakovac, C. C. et al. The role of land‐use history in driving successional pathways and its implications for the restoration of tropical forests. Biol. Rev. 96 , 1114–1134 (2021). Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13 , 252–264 (2007). Mokany, K., Ware, C., Woolley, S. N., Ferrier, S. & Fitzpatrick, M. C. A working guide to harnessing generalized dissimilarity modelling for biodiversity analysis and conservation assessment. Glob. Ecol. Biogeogr. 31 , 802–821 (2022). Additional Declarations No competing interests reported. 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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-7141134","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502655908,"identity":"794df0d2-5cbe-47e3-9288-7ff8acc558ce","order_by":0,"name":"Fernanda Ribeiro da Silva¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBAC9gYGhsMMDDYJYF5CARFaeA6AtaQlMLCBtBgQqYUZqAuihYEoLey9Dw8X/Dmfxy/fnfjhgQGDPL/YAQJaeI4bHJ7ZdrtYso13swTQYYYzZyfg12IvkcZwmLfhduKGY7wbQFoSDG4T0MIj/4zhMM+fcyAtm38Qp0WCDaiF7QBIyzYibeEBOmxmW3LizLbcbRYJBhKE/cLDfoz5c8Efu8R+5rObb/6osJHnlyagBR1IkKZ8FIyCUTAKRgF2AABE0EJsjjjuTAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Santa Catarina (UFSC)","correspondingAuthor":true,"prefix":"","firstName":"Fernanda","middleName":"Ribeiro da","lastName":"Silva¹","suffix":""},{"id":502655909,"identity":"d2c774a0-3e53-48e1-953b-a06e744266c4","order_by":1,"name":"André L. Giles¹","email":"","orcid":"","institution":"University of Santa Catarina (UFSC)","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"L.","lastName":"Giles¹","suffix":""},{"id":502655910,"identity":"51730e40-b846-41c7-9ef4-b3cd2e356632","order_by":2,"name":"André L. de Gasper²","email":"","orcid":"","institution":"Universidade Regional de Blumenau","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"L.","lastName":"de Gasper²","suffix":""},{"id":502655911,"identity":"34b4339c-2c2d-4443-a321-d5c742ad657a","order_by":3,"name":"Alexander Vibrans²","email":"","orcid":"","institution":"Universidade Regional de Blumenau","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Vibrans²","suffix":""},{"id":502655912,"identity":"ce98eb4d-6611-4cb6-8115-fe6c8cc0b0ff","order_by":4,"name":"Marcio Baldissera Cure","email":"","orcid":"","institution":"UD Escolar Quilombola Vidal Martins","correspondingAuthor":false,"prefix":"","firstName":"Marcio","middleName":"Baldissera","lastName":"Cure","suffix":""},{"id":502655913,"identity":"44a9e09d-906f-4e68-b37a-e005663cfd24","order_by":5,"name":"Catarina C. Jakovac¹","email":"","orcid":"","institution":"University of Santa Catarina (UFSC)","correspondingAuthor":false,"prefix":"","firstName":"Catarina","middleName":"C.","lastName":"Jakovac¹","suffix":""}],"badges":[],"createdAt":"2025-07-16 14:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7141134/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7141134/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24131-3","type":"published","date":"2025-11-17T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89579664,"identity":"d416c687-1da5-4b36-87a0-95a5e37244c5","added_by":"auto","created_at":"2025-08-21 13:49:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44595,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in alpha-diversity across forest formations. hill0 (species richness),hill1 (exponential of Shannon entropy index), and hill2 (inverse of Simpson's index) for intermediate (orange color) and advanced successional stages (green color) in the three forest formations (EVF-Evergreen Forest, AF-Araucaria Forest, SF-Seasonal Forest).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7141134/v1/543ad32329f83bb5801fad11.jpg"},{"id":89579666,"identity":"b64c85ac-ff53-42cf-814c-ccccec398705","added_by":"auto","created_at":"2025-08-21 13:49:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47811,"visible":true,"origin":"","legend":"\u003cp\u003eOrdination analysis (NMDS) of the three forest formations, based on species relative abundance. Green represents Evergreen Forest (EVF), blue represents Seasonal Forest (SF), and yellow represents Araucaria Forest (AF). Circles represent advanced successional forests and triangles represent intermediate successional forests.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7141134/v1/a634eae26ec13d624549bc09.jpg"},{"id":89580614,"identity":"8ec39ea9-65bd-4f6e-a64c-28e3b2f97ad4","added_by":"auto","created_at":"2025-08-21 13:57:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49925,"visible":true,"origin":"","legend":"\u003cp\u003eSample plots are spread along the Santa Catarina state (indicated in black in the Brazilian map). Forest types and successional stages are indicated according to the legend. AF: Araucária Forest; SF: Seasonal Forest; and EVF: Evergreen Forest.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7141134/v1/ed97730bcebd2bbcae291376.jpg"},{"id":96649988,"identity":"b5dc84da-8bcc-47c7-9554-f07fb345375a","added_by":"auto","created_at":"2025-11-24 16:03:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1257357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7141134/v1/f5613810-3aa4-4366-a50e-fd7d08fe8fcc.pdf"},{"id":89579669,"identity":"d738dec8-7857-4d63-af74-e4ebe5b53bc8","added_by":"auto","created_at":"2025-08-21 13:49:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1989774,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarmaterialpaper.docx","url":"https://assets-eu.researchsquare.com/files/rs-7141134/v1/911b82e785d64ad4eb30d22d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term anthropogenic impacts result in widespread changes of plant species composition in Southern Atlantic Forest: evidences from systematic survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnthropogenic impacts reshape species composition and biodiversity patterns in tropical forests [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Atlantic forest, one of the \u0026lsquo;hottest\u0026rsquo; biodiversity hotspots [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] has suffered greater impacts since the European invasion in the 16th century [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several disturbances are linked to anthropization, such as forest loss, species invasion, over-exploitation, landscape fragmentation, forest isolation and climate change [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Currently, forest loss and land use change are the main impacts affecting Atlantic Forest [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Such activities have reduced Atlantic Forest to a small amount of large fragments inserted in a less or more degraded landscape [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], leading to poverty of biological communities, decreasing habitat quality and species diversity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and thus changing species composition[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnthropogenic impacts can act as both a barrier as a bridge, either preventing or facilitating the colonization and establishment of plant species, affecting patterns of alpha and beta-diversity at multiple spatial scales (i.e. from local to regional assemblages) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In these processes, less sensitive species may be favored (winners) and proliferate, replacing more sensitive species (losers), thereby increasing similarity between plant communities (i.e., biotic homogenization with a decrease in beta-diversity) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. On the other hand, landscape fragmentation and forest loss cause isolation of populations by limiting seed dispersal process and/or by increasing heterogeneity among fragments, resulting in greater differences in assemblages between sites (i.e. biotic differentiation with a higher beta-diversity) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e],[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] .\u003c/p\u003e\u003cp\u003eNoticeably, besides anthropogenic impacts, we must consider edaphic and environmental site characteristics which also drive compositional differentiation and are often treated as the main factors shaping plant communities when anthropogenic impacts are disregarded [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNotably, the Southern Brazilian Atlantic Forest is a unique type of rainforest with distinct climatic and edaphic conditions compared to those found in the northern and central Atlantic Forest [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In general, these forests are situated in transitional zones between tropical and temperate climates, typically characterized by year-round rainfall without a pronounced dry season. These climatic patterns support three major forest formations: Evergreen Forest, Seasonal Forest, and Araucaria Forest. The same historical process of anthropization has led these forests to small fragments in a degraded landscape [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Currently, less than 5.7% of the original forest cover is protected in formal Protected Areas (PAs) (with 1,6% PAs from the integral protection group and 4,1% from sustainable use group, see Brazilian conservation units panel, available in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMicrosoft Power BI\u003c/span\u003e) and it is acknowledged that such protected areas do not cover substantial regions necessary for conservation of tree species[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although subject to the same anthropization process as other Atlantic Forest regions, the distinct environmental drivers of these formations likely shape different biodiversity patterns, and anthropic impacts might affect in a distinct manner of plant species diversity. Although Lingner et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] investigated the impacts of anthropogenic factors on forest attributes in Evergreen southern Atlantic Forest, efforts to understand these patterns have mainly focused on other regions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Here, we use a robust dataset to identify patterns of diversity and unveil how anthropic and environmental factors affect tree communities in the Southern Brazilian Atlantic forest.\u003c/p\u003e\u003cp\u003eWe aim to evaluate how environmental and anthropogenic factors affect the alpha- and beta- diversities of forest fragments across different successional stages and forest formations in the subtropical Atlantic Forest biome. To achieve this aim, we first characterized how the alpha- and beta-diversities varied across the three forest formations and successional stages in our dataset. Second, we tested the hypotheses that alpha and beta diversities will: (i) be more affected by climate than anthropogenic factors across forest formations than within forest formations, and that anthropogenic factors will gain importance within each forest formation; (ii) increase along with successional stage, being higher at advanced than at intermediate successional stages; (ii) decrease with anthropogenic impacts.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAlpha diversity variation across forest formations\u003c/h2\u003e\u003cp\u003eAlpha-diversity, measured by the three hill series, varied significantly across forest formations. The Evergreen Forests presented the highest alpha-diversity compared to the Araucaria Forests and the Seasonal Forest, which did not differ between each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Forests in advanced successional stages had higher alpha-diversity than intermediate successional stages only at the Evergreen Forests, but the alpha-diversity in the two successional stages did not differ at the Araucaria and Seasonal Forests (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analyses across formations showed that species richness (i.e. hill0) increased with mean annual temperature, amount of forest in a 1000m\u0026sup2; buffer, longitude, soil clay content, annual range of air temperature, and decreased with fire frequency, latitude, and the successional stage (in intermediate stage) (R\u0026sup2;=0.48; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table\u0026nbsp;1)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBeta-diversity variation across forest formations\u003c/h3\u003e\n\u003cp\u003eBeta-diversity varied across forest formations. When calculated as the Sorensen index, the Evergreen and Araucaria forests had higher beta-diversity than the Seasonal forests. In addition, different from expected, beta-diversity was higher in intermediate successional stages than in advanced successional stages. The NMDS based on Bray-Curtis index shows that the floristic composition is significantly different among the three formations (ANOSIM - R\u0026thinsp;=\u0026thinsp;0.124, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SUs of AF and EVF were more dispersed in the NMDS space, suggesting greater dissimilarity within the formation (i.e. beta-diversity) compared with SF SUs, which were closer together, indicating lower beta-diversity (AF\u0026thinsp;=\u0026thinsp;0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13; EVF\u0026thinsp;=\u0026thinsp;0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12, SF\u0026thinsp;=\u0026thinsp;0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The three groups show some overlap, suggesting similar floristic composition on some SUs classified as different forest formations, which is probably driven by the SUs located at ecotone zones between adjacent formations. (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIntermediate and advanced successional stages were grouped together by forest formation in the NMDS and therefore do not show a trend towards homogenization across forest formations related to successional stage. Moreover, when we analyse the dissimilarity between SUs within each forest formation separately, pairs of advanced successional forests are less dissimilar between each other than intermediate successional forests, for all three forest formations. This suggests that forest patches at later successional stages show lower dissimilarity than those at earlier stages.\u003c/p\u003e\u003cp\u003eConsidering only the forests at intermediate successional stage, SF had the lowest mean dissimilarity (0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14) and EVF had slightly higher dissimilarity than AF (AF\u0026thinsp;=\u0026thinsp;0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15; EVF\u0026thinsp;=\u0026thinsp;0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15). Considering only the forests in advanced successional stages, SF showed lower dissimilarity (mean\u0026thinsp;=\u0026thinsp;0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14) compared to AF (0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14) and EVF (0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13), which showed similar values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen we decomposed the beta-diversity within each forest formation, we found a greater contribution of species turnover (i.e. substitution of species) than nestedness (i.e. a subgroup of a species-rich group) components in all three forest formations. However, in SF nestedness was greater than in the other formations, demonstrating more shared species among communities in SF. Furthermore, unlike EVF and AF, for SF, beta-diversity in advanced forests was lower (Supplementary Fig S2).\u003c/p\u003e\n\u003ch3\u003eEnvironmental and anthropogenic impacts on beta-diversity\u003c/h3\u003e\n\u003cp\u003eConsidering all forest types and both successional stages together, most of the variation was explained in order of importance by: species richness, precipitation seasonality, geographic proximity, mean annual temperature, fire frequency, cattle grazing, amount of surrounding forest, and soil clay content (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eThe species dissimilarity within intermediate and advanced successional forests was explained by similar factors, with a similar relative contribution of anthropogenic factors (10.4% and 7.73% respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For intermediate successional forests, species dissimilarity was affected, in order of importance, by species richness, precipitation seasonality, geographic proximity, landscape fire frequency, euclidean distance between forest patches (i.e. isolation) in a 750m buffer, grazing, and amount of forest in a 1000m buffer around SUs.\u003c/p\u003e\u003cp\u003eFor advanced successional forests, species turnover was significantly affected by species richness, precipitation seasonality, geographic proximity, elevation, fire frequency, mean annual temperature, cattle grazing and the amount of surrounding forest (Supplementary Figure S3).\u003c/p\u003e\u003cp\u003eWithin EVF, when combining intermediate and advanced forest stages, beta-diversity was affected by species richness, landscape fire frequency and geographical proximity. At the advanced successional EVF, annual range of air temperature, elevation, temperature seasonality and geographical proximity significantly explained species turnover. For intermediate successional EVF, landscape fire frequency, species richness and geographic proximity significantly affected beta-diversity.\u003c/p\u003e\u003cp\u003eWithin AF, when combining intermediate and advanced successional stages, species dissimilarity was explained by species richness (i.e. hill0), precipitation seasonality, and geographical proximity. In advanced successional AF, species richness, geographical proximity, and grazing explained beta-diversity. In intermediate successional stage AF, only precipitation seasonality and species richness were important to explain beta-diversity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWithin SF, at combined intermediate and advanced successional stages, only species richness and geographic proximity explained dissimilarity. For advanced and intermediate successional SF, species dissimilarity was explained only by geographic distance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative importance in percentage of predictors explaining species dissimilarity based on GDM. Here we showed only predictors significantly affected by species dissimilarity. Int\u0026thinsp;=\u0026thinsp;intermediate successional stage and Adv\u0026thinsp;=\u0026thinsp;advanced successional stage.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal_All\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlobal_Int\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGlobal_Adv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEVF_All\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEVF_Ind\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEVF_Adv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAF_All\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eAF_Int\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAF_Adv\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eSF_All\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eSF_Ind\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSF_Adv\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel explanation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e28.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e39.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFire frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle grazing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest 1000m2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatch isolation 750m2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation seasonality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean annual temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual range temperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil clay content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHill0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e44.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e55.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeographic distance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e24.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e30.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe presented an overview of alpha and beta tree species diversity and their main drivers within three Atlantic Forest formations across the state of SC in Southern Brazil. Alpha-diversity is distinct among forest formations, with greater values in less anthropized forest formation (i.e. Evergreen Forest) and smaller values in Araucaria Forest and Seasonal Forest. Beyond bioclimatic factors, anthropization affects beta-diversity of tree species in the Southern Atlantic Forest. Contrary to our expectations, anthropization has resulted in advanced-succession remnants exhibiting greater species composition similarity than intermediate-succession remnants across all forest formations. Our results indicate that conservation strategies for tree species in the Southern Atlantic Forest must manage specific anthropogenic interventions while accounting for the particularities of each forest formation.\u003c/p\u003e\u003cp\u003eEvergreen forests exhibit the highest species richness and the greatest species turnover (i.e., beta diversity) among the forest formations. This pattern may be explained by the fact that this is the best conserved formation in the Southern Atlantic Forest [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], in addition to its broader environmental variation (ranging from coastal to montane areas) and less restrictive environmental conditions[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Indeed, we found that mean annual temperature and elevation affected beta-diversity. The frequency of landscape fires also affects beta-diversity in EVF, even though fire occurs more frequently in AF (Supplementary Fig.\u0026nbsp;6), suggesting that this forest type is more sensitive to this type of anthropogenic disturbance. In fact, landscape fire frequency might be an important driver for diversity patterns increasing the spread of generalist species less sensitive to fires, characterized by broad environmental niches and large population sizes. Additionally, most generalist species can withstand disturbances and environmental constraints typically found in secondary forests. Despite the fact that EVF forest remnants are better conserved compared with AF and SF, the land use history and fire frequency can promote the range expansion of generalist species in some remnants. On the other hand, EVF harbors a high number of rare species[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], a characteristic that may contribute to increasing beta-diversity. These facts, combined with broader environmental variation, may lead to greater compositional dissimilarity and consequently higher species turnover.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOur results indicate that the average species richness in remnants of Araucaria Forest (AF) is lower than in Evergreen Forest (EVF) and comparable to Seasonal Forest (SF).\u003c/b\u003e The reduced diversity observed in AF, compared to EVF, can be attributed to the combined effects of colder temperatures and higher altitudes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which limit species occurrence along with a legacy of land use degradation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Despite these constraints, AF exhibits beta-diversity similar to EVF, with low beta-nestedness, indicating significant species turnover compared to that observed in EVF. Two potential mechanisms may explain this pattern, both of which could be operating concurrently: (1) the broader geographic range of AF and its associated environmental heterogeneity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] may drive high beta-diversity through species turnover across fragments; and (2) degradation may reduce local species richness, resulting in regional-scale species turnover (i.e., biotic differentiation) that corroborated with our model showing beta-diversity significantly affected by species richness. Additionally, geographic distance, precipitation seasonality, and cattle grazing alter species composition, highlighting the environmental influence and impact of cattle ranching on forest remnants in AF [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (Supplementary Fig S6).\u003c/p\u003e\u003cp\u003e\u003cb\u003eWe observed low beta-diversity and high tree species similarity among remnants of Seasonal Forest (SF), particularly when focusing on advanced successional stages.\u003c/b\u003e Compared to other forest formations, SF exhibited greater beta-nestedness of tree species, indicating that its plant communities tend to be subsets of neighboring communities. Seasonal Forest (SF) has been regarded as a more recently established vegetation type in Santa Catarina, developing after the grasslands and Araucaria Forests [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, Gasper et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] proposed that SFs form a transitional zone with the Araucaria Forests (AF). These historical and ecological factors may contribute to its lower species diversity and higher number of shared species compared to other forest types. On the other hand, Seasonal Forests (SF) not only occupies a smaller area compared to Evergreen Forests (EVF) and Araucaria Forests (AF), but also experiences higher levels of fragmentation and historical land-use degradation, making it the most threatened forest type among these formations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Despite these pressures, alpha-diversity in SF was similar to that in AF, suggesting potential local biodiversity loss in both ecoregions.\u003c/p\u003e\u003cp\u003eThe greater similarity among advanced successional forests, compared to intermediate ones, may reflect the intense historical exploitation of the Atlantic Forest. Even forests in advanced successional stages have been subjected to anthropogenic impacts, which have affected their ecological structure [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This pattern was further supported by the GDM analysis, in which we considered only advanced forests across all formations. In addition to environmental variables, anthropogenic factors such as cattle grazing, fire frequency in the surrounding landscape, and the amount of nearby forest cover were identified as significant drivers of community dissimilarity. However, it is important to acknowledge certain limitations that may help explain the absence of a clear pattern of homogenization. First, we lack definitive information on the age of the study sites. Second, the successional trajectories may have been interrupted by anthropogenic disturbances not captured by the variables included in our analysis. Third, potential inaccuracies in our classification of successional stages may have influenced the results. Such factors may have confounded the identification of overarching ecological trends.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnthropic factors influence beta diversity when all forest formations are considered together, but their effect is less pronounced within each formation individually.\u003c/b\u003e This suggests that the presence and nature of anthropogenic impacts vary among forest types, which may lead to different ecological responses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn Seasonal Forest (SF), no anthropogenic factors were found to significantly influence beta diversity.\u003c/b\u003e A plausible explanation is the greater historical degradation of this forest type, which may have already filtered tree species through repeated anthropogenic disturbances. Several indicators\u0026mdash;such as low diversity indices, high similarity among SF remnants, and a greater proportion of shared species across plant communities\u0026mdash;point to a simplification of biodiversity in SF. Indeed, previous studies have shown that most regenerating species in these areas belong to early successional stages (i.e., pioneer species)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Taken together, these findings suggest that biotic homogenization may be underway in SF. However, further research is needed to confirm this hypothesis.\u003c/p\u003e\n\u003ch3\u003eImplications for conservation strategies and actions\u003c/h3\u003e\n\u003cp\u003eAraucaria Forest (AF) and Evergreen Forest (EVF) exhibit high species turnover (i.e., beta diversity) across spatial and environmental gradients. These forests are known to host a rich assemblage of rare and threatened species [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which play a crucial role in maintaining ecosystem functions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Moreover, networks of small habitat patches support a greater number of species than large contiguous patches, even when considering only species of conservation concern [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, effective conservation strategies for forests characterized by high beta diversity should prioritize the establishment of networks of protected areas that capture this variation. Rather than focusing solely on large individual parks, conservation efforts should be emphasized creating multiple spatially distinct reserves to better preserve biodiversity and ecosystem functionality [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, a recent study showed that larger fragments support more complex communities and sustain key ecological processes that smaller patches cannot maintain in fragmented landscapes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile we advocate for the conservation of all classes of small forest patches within the Atlantic Forest region, our results highlight that within-site species richness (i.e., alpha diversity) varies along spatial gradients. This suggests that conservation efforts could be more effective if focused on sites with high species diversity, and especially on key sites of critical conservation value, rather than on species-poor areas [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this context, studies on species distributions are essential for setting conservation priorities. Furthermore, understanding the ecology of rare and threatened species targeted by restoration initiatives can greatly enhance biodiversity recovery efforts. For restoration efforts to be effective, they must incorporate species diversity considerations, as many restored sites currently exhibit low diversity and abundance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Incorporating a diverse range of tree species in restoration activities is crucial for promoting long-term ecological stability in these ecosystems. Identifying the main drivers of biodiversity loss can guide the development of more effective mitigation strategies. For example, cattle grazing alters species composition in the Araucaria Forest (AF), highlighting the need to engage rural landowners in forest conservation efforts. This stakeholder group is crucial, as most forested areas are privately owned by individual farmers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Increasing awareness about the ecological and economic benefits of native forests—such as ecosystem services and sustainable resource use—can strengthen conservation initiatives. Replacing extensive cattle grazing with more management practices may offer a viable alternative. Management often allows for better land utilization without promoting deforestation, while also enhancing livestock productivity. To ensure long-term biodiversity conservation and ecosystem resilience, sustainable agricultural practices that balance ecological and economic interests should be promoted.\u003c/p\u003e\u003cp\u003eTo effectively monitor and manage tree assemblages in the Evergreen Forest (EVF) affected by landscape fires, fire prevention measures must be implemented. Controlling fire spread is essential to minimize its impact on species composition and maintain ecosystem stability. Strategies such as firebreaks, controlled burns, and community-based fire prevention programs can help mitigate damage and contribute to forest conservation.\u003c/p\u003e\u003cp\u003eThe Atlantic Forest represents the main refuge for Brazil’s rich floral diversity in the south of Brazil, yet it remains highly threatened by human activities. In the face of escalating challenges such as climate change, this study provides crucial insights into the patterns of tree diversity across the Southern Atlantic Forest. Our findings highlight the importance of considering biodiversity variation among different forest formations and the role of anthropogenic impacts in shaping these patterns. These results are essential for guiding the development of targeted and effective conservation strategies that promote the long-term resilience and survival of these ecosystems.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003eThe Evergreen Forest (EVF) originally covered 29309 km² [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], accounting for almost 31% of the Santa Catarina state. According to Vibrans et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], only 16821 km² of forest fragments remain, most of which in the intermediate stage of regeneration, representing 40% of the original forest cover [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This forest is located at the eastern part of the state, rising from the coastal plains at sea level along an altitudinal gradient at the mountain ranges up to approximately 1500 meters a.s.l. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The Araucaria Forest (AF) originally covered an area of 42851 km² in Santa Catarina, which is 44.94% of the state's territory [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Currently, the AF covers 22% of its original area [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], depicting the significant alteration of its original coverage. This forest is located between regions from 500 to 1,500 meters a.s.l. at the altiplano (ou highlands) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The Seasonal Semi-deciduous Forest (SF), is located in the western part of the state, mainly in the Uruguai river basin, in an altitudinal range between 150 and 800 m (exceptionally 900 m [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]). SFs are even more threatened than EVF and AF, severely affected by human occupation in Santa Catarina state since the second half of the 20th century. Currently, only around 16.3% of its original area remains, with 52% of the fragments being smaller than 50 hectares, indicating the extensive fragmentation of those forests.\u003c/p\u003e\u003cp\u003eData were compiled from the Floristic and Forest Inventory of Santa Catarina State (hereafter FlorestaSC) sampled between 2007 and 2010 (see fieldwork details in [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]). It includes the three forest formations present in the Santa Catarina State- Evergreen Forest (EVF), Araucaria Forest (AF) and Seasonal Forest (SF). The sampling design for the FlorestaSC is based on a 10 km × 10 km grid covering the entire state of Santa Catarina (SC), except for the SF, where a finer 5 km × 5 km grid was used. This forest type has the smallest coverage, and the 10 km × 10 km grid would have resulted in too few sample points [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The FlorestaSC’ database englobes 418 sampled units, where the number of sampling units for each forest formation is proportional to the total area of each formation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. We excluded samples in the early successional stage, as well as samples with an area lower than or equal to 0.35ha. Thus, in our study, we analyzed 331 sampling units, 60 in SF (35 in intermediate successional stage and 25 in advanced successional stage), 129 in EVF (50 in intermediate successional stage and 79 in advanced successional stage), and 142 in AF (63 in intermediate successional stage and 79 in advanced successional stage) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe used the successional stages classification as defined by FlorestaSC botanists, including a quantitative and qualitative assessment, taking into account the anthropogenic impacts to which these forests are subjected and their structural attributes, as the number of plant strata, the presence of large trees, the presence of lianas and epiphytes and the diversity of plants. In this way, intermediate successional forests present species richness between 10 and 30 species, with climax species still lacking in significant numbers; multiple vegetation layers and where large, shade-tolerant species are absent; and advanced successional forests were marked by greater species richness (\u0026gt; 30 species), the presence of large-diameter trees and epiphytes, resembling the old-growth stage, although showing signs of human impact. All plots are located in landscapes partially or totally affected by human activities [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData Analyses\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eAlpha diversity estimation and variation across forest formations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to estimate species alpha-diversity, we calculated the Hill's diversity index for each forest formation (SF, EVF, AF) considering all selected plots. Hill’s diversity index is recognized for providing the true diversity of plant communities [47]. When q=0, all relative abundances are raised to zero (equal weight for rare species, corresponding to average species richness or number of species within an SU); when q=1, abundances are as shown and comparable to the exponential of Shannon entropy index; while q=2, abundances are squared, giving greater weight to the most abundant species, corresponding to the inverse of Simpson's index (the probability that two randomly selected individuals belong to the same species).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeta-diversity estimation and variation across forest formations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe defined beta-diversity as the variation in species composition among SUs. To capture different aspects of this variation, we used two complementary dissimilarity indices: the Sorensen index, which is based on species presence/absence and reflects species turnover and nestedness (see below), and the Bray–Curtis index, which incorporates species abundances and reflects changes in both species presence and their relative abundances.\u003c/p\u003e\n\u003cp\u003eUsing the Sorensen dissimilarity index (betasor), we partitioned the beta-diversity across SUs into: 1) nestedness (betanes), which is when assemblages represent a subgroup of species from a richest group; and 2) beta simpson (betasim), which is the turnover of species or substitution of species when there is a replacement of species between sites. We used the \u003cem\u003eBetapart\u003c/em\u003e package [48] in the R, versão 4.3.2.\u003c/p\u003e\n\u003cp\u003eIn addition, to investigate how similar in species composition are the three forest formations and the two successional stages, we performed a NMDS ordination based on the Bray-curtis dissimilarity, which accounts for the relative abundances of individual species. To test floristic separation, the variation of NMDS scores between forest formations was tested using a non-parametric multivariate analysis (ANOSIM).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrivers of diversity: Environmental and anthropogenic factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe considered climate, soil, and anthropogenic predictors to understand their influence on beta- and alpha-diversity among the three forest formations. Previously, we tested the Pearson correlation between numerical predictors to eliminate strong potential covariates.\u003c/p\u003e\n\u003cp\u003e1) Environmental factors: Climate and soil\u003c/p\u003e\n\u003cp\u003eEnvironmental factors were selected based on their ecological relevance to our inquiries. Climate data were extracted from the Chelsa database [49]. The climatic predictors selected were the historical mean of: mean annual air temperature - Bio1 (°C), temperature seasonality-Bio4 (°C), Bio5: mean daily maximum air temperature of the warmest month (°C), Bio7: annual range of air temperature (°C), Bio12: annual precipitation amount (kg m\u003csup\u003e-\u003c/sup\u003e²), Bio13: precipitation amount of the wettest month (kg m\u003csup\u003e-\u003c/sup\u003e²), Bio14: precipitation amount of the driest month (kg m\u003csup\u003e-\u003c/sup\u003e²), Bio15: precipitation seasonality (kg m\u003csup\u003e-\u003c/sup\u003e²). After eliminating highly correlated factors, we used four climatic factors in further analyses: mean annual air temperature, temperature seasonality, mean daily maximum air temperature of the warmest month, and precipitation seasonality.\u003c/p\u003e\n\u003cp\u003eEdaphic variables were extracted from the SoilGrids database (soilgrids.org), at 250 m resolution and averaged over the first 30 cm of the soil depth. Soil conditions used in further analyses were: soil pH, Bulk density (g cm \u003csup\u003e-\u003c/sup\u003e³ - soil dry mass over soil volume), volumetric carbon concentration in the soil (C proportion of sand (\u0026gt;0.05 mm), silt 0.002 mm - 0.05 mm) and clay (\u0026lt;0.002 mm).\u003c/p\u003e\n\u003cp\u003e2) Anthropogenic factors\u003c/p\u003e\n\u003cp\u003eAnthropogenic impacts were described at two scales. At the landscape scale, we extracted landscape descriptors of forest cover and fragmentation when the field inventories took place, and the land-use history during 26 years before it.\u003c/p\u003e\n\u003cp\u003eAt the plot scale, we used the occurrence of different descriptors of anthropogenic disturbance that were visually identified during the field expeditions (data from FlorestaSC). Below we describe the methods for acquiring each metric.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLandscape descriptors of forest cover and fragmentation\u003c/strong\u003e - For each SU, we set three concentric circles (buffers) with different radii sizes: 750m, 1000m, and 1500m, considering the sampling unit as the central point of each landscape. For each buffer size, we calculated the a) amount of forest vegetation (expressed in hectares), which represents the forest cover and the quantity of possible species habitats in the landscape (McGarigal \u0026amp; Marks, 1995), and the b) patch isolation (expressed in meters) measured as the mean distance between the edges of forest patches, which is related to the reduction of habitat quality in the fragment. Forest cover was extracted from the land use and land cover product of the Mapbiomas 7\u003csup\u003eth\u003c/sup\u003e collection for the year 2010, which was just before the forest inventories were done. The two landscape metrics were acquired using the “raster” and \"landscapemetrics\" packages, from the R software.\u003c/p\u003e\n\u003cp\u003e2.2) The land-use history around each SU was characterized by three metrics: land-use duration, number of land-use changes and fire frequency during the period between 1988 and 2011. For that, we applied a buffer of 1000m radius around each SU and extracted the following metrics: i)\u0026nbsp;Previous land-use duration was characterized as the average of the total number of years each pixel was classified as non-forest. This metric represents how long the surroundings of the SU have beeg used for alternative land uses, as longer land use duration leads to stronger anthropogenic impacts on forest ecosystems [50] . ii) Number of land-use changes is the frequency that a land cover class changed to another along the time series. It was calculated within the 1.000m buffer as the average number of changes in the pixels within the buffer. iii) Fire frequency was estimated as the average number of years each pixel was detected as burned along the time series within a buffer of 5.000m. We did not consider the number of times a pixel was burned within a year, hence recording only one fire event per pixel per year, which represents the number of years a pixel has been burned over the time series. We used a buffer of 5.000m because the source data was the MODIS fire product that has a resolution of 500m.\u003c/p\u003e\n\u003cp\u003e2.3) Local scale anthropogenic disturbances were identified within each sampling unit by visual interpretation of the presence or absence (binary information) of ten types of disturbance as identified in the field by the FlorestaSC teams: roads crossing, yerba mate exploitation, fire scars, garbage dumping, presence of exotic species, grazing, hunting traces, vegetation suppression and selective logging. We explored this information in two ways: a) using an anthropogenic index calculated as the sum of the presence of all disturbances dividing by 10 to each SUs (Bastos et al. 2022), and b) we selected the three anthropogenic impacts that were most robust and relevant for this study to be used separately in the models: cattle grazing, clear-cutting and roads.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluating the drivers of diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo describe how the alpha diversity differs between forest formations and successional stages we performed ANOVA tests on the hill number series (hill0, hill1, hill2). To evaluate how species richness (hill0= is affected by environmental and anthropogenic factors, we performed linear regression analyses. Before this analysis we verified multicollinearity removing predictors with high correlation (VIF\u0026gt;10).\u003c/p\u003e\n\u003cp\u003eTo investigate the role of geographical distance, environmental and anthropogenic\u0026nbsp;factors on species dissimilarity (beta-diversity) we used two Generalized\u0026nbsp;Dissimilarity Model (GDM). First we built a GDM for all SUs across forest formations, in order to evaluate how the predictors explain the beta diversity across the entire Santa Catarina state. Then, we built a GDM for SUs within each forest formation\u0026nbsp;and successional stages separately in order to understand which and how the predictors explain the beta diversity within each forest formation (EVF, AF and SF).\u003c/p\u003e\n\u003cp\u003eThe GDM explains pairwise dissimilarities between sampling units by adjusting a linear model with geographic distances and other drivers of interest employing a maximum likelihood and I-splines to model and forecast species turnover [51]. This model uses Pairwise dissimilarities calculated using the Bray-Curtis index, which accounts for species abundances through the Bray-Curtis index to test the influence of predictors on species turnover components [52]. The predictors measured on different scales were transformed during model fitting to ensure that the adjusted distances between pairs of locations for different predictors can be meaningfully compared and integrated. A model selection procedure is applied, where the most parsimonious and significant predictors are retained. The statistical significance of the chosen model and of the predictors was tested using 999 permutations (α=0.05). The statistical analyses and graphing were carried out using the 'gdm' package in the R software [52].\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eFRS and ACJ conceived the study. FRS and AG performed the analyses. FRS wrote the first draft of the manuscript. ALG, AV e MBC, made substantial contributions to the revision of the manuscript and approved the final version.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe acknowledge the Instituto Serrapilheira for providing financial support for this project. ALG acknowledges CNPq for the productivity (grant no.: 307861/2023-6). ACV was supported by CNPq for research grants (305199/2022-6; 408242/2021-3)\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on reasonable request.\u0026quot;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSocolar, J. B., Gilroy, J. J., Kunin, W. E. \u0026amp; Edwards, D. P. How should beta-diversity inform biodiversity conservation? \u003cem\u003eTrends Ecol. Evol.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 67\u0026ndash;80 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurtado, S. G. Menini Neto, L. What is the role of topographic heterogeneity and climate on the distribution and conservation of vascular epiphytes in the Brazilian Atlantic Forest? \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 1415\u0026ndash;1431 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRito, K. F. et al. Unraveling the drivers of plant taxonomic and phylogenetic beta-diversity in a human-modified tropical dry forest. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 1049\u0026ndash;1065 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZachos, F. E. \u0026amp; Habel, J. C. (eds) \u003cem\u003eBiodiversity hotspots: distribution and protection of conservation priority areas\u003c/em\u003e (Springer Science \u0026amp; Business Media, 2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarlucci, M. B., Marcilio-Silva, V. \u0026amp; Torezan, J. M. The southern Atlantic Forest: use, degradation, and perspectives for conservation. \u003cem\u003eThe Atlantic Forest: History, biodiversity, threats and opportunities of the mega-diverse Forest\u003c/em\u003e, 91\u0026ndash;111 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabarelli, M., Pinto, L. P., Silva, J. M., Hirota, M. \u0026amp; Bede, L. Challenges and opportunities for biodiversity conservation in the Brazilian Atlantic Forest. \u003cem\u003eConserv. Biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 695\u0026ndash;700 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorris, R. J. Anthropogenic impacts on tropical forest biodiversity: a network structure and ecosystem functioning perspective. \u003cem\u003ePhilos. Trans. R Soc.\u003c/em\u003e \u003cb\u003e365\u003c/b\u003e (1558), 3709\u0026ndash;3718 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaria, D., Delabie, J. H. C. \u0026amp; Dias, M. H. The hileia baiana: an assessment of natural and historical aspects of the land use and degradation of the central corridor of the brazilian atlantic forest. \u003cem\u003eThe Atlantic forest: history, biodiversity, threats and opportunities of the mega-diverse forest\u003c/em\u003e, 63\u0026ndash;90 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilveira, J. G. D. et al. Land use, land cover change and sustainable intensification of agriculture and livestock in the Amazon and the Atlantic Forest in Brazil. \u003cem\u003eSustainability\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 2563 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRezende, C. L. et al. From hotspot to hopespot: an opportunity for the Brazilian Atlantic Forest. \u003cem\u003ePerspect. Ecol. Conserv.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 208\u0026ndash;214 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaria, D. et al. The breakdown of ecosystem functionality driven by deforestation in a global biodiversity hotspot. \u003cem\u003eBiol. Cons\u003c/em\u003e. \u003cb\u003e283\u003c/b\u003e, 110126 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Andrade, E. R. et al. Unveiling the impacts of forest loss on taxonomic and phylogenetic beta-diversity of juvenile and adult tropical trees. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 807\u0026ndash;823 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlden, J. D. \u0026amp; Poff, N. L. Toward a mechanistic understanding and prediction of biotic homogenization. \u003cem\u003eAm. Nat.\u003c/em\u003e \u003cb\u003e162\u003c/b\u003e, 442\u0026ndash;460 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTabarelli, M., Peres, C. A. \u0026amp; Melo, F. P. The \u0026lsquo;few winners and many losers\u0026rsquo; paradigm revisited: emerging prospects for tropical forest biodiversity. \u003cem\u003eBiol. Cons\u003c/em\u003e. \u003cb\u003e155\u003c/b\u003e, 136\u0026ndash;140 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArroyo-Rodr\u0026iacute;guez, V. et al. Plant β‐diversity in fragmented rain forests: testing floristic homogenization and differentiation hypotheses. \u003cem\u003eJ. Ecol.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e, 1449\u0026ndash;1458 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTuomisto, H., Ruokolainen, K. \u0026amp; Yli-Halla, M. Dispersal, environment, and floristic variation of western Amazonian forests. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e299\u003c/b\u003e, 241\u0026ndash;244 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRito, K. F., Arroyo-Rodr\u0026iacute;guez, V., Queiroz, R. T., Leal, I. R. \u0026amp; Tabarelli, M. Precipitation mediates the effect of human disturbance on the Brazilian Caatinga vegetation. \u003cem\u003eJ. Ecol.\u003c/em\u003e \u003cb\u003e105\u003c/b\u003e, 828\u0026ndash;838 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFurtado, S. G. Menini Neto, L. What is the role of topographic heterogeneity and climate on the distribution and conservation of vascular epiphytes in the Brazilian Atlantic Forest? \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 1415\u0026ndash;1431 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOliveira-Filho, A. T., Budke, J. C., Jarenkow, J. A., Eisenlohr, P. V. \u0026amp; Neves, D. R. Delving into the variations in tree species composition and richness across South American subtropical Atlantic and Pampean forests. \u003cem\u003eJ. Plant. Ecol.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 242\u0026ndash;260 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVibrans, A. C. et al. Structure of mixed ombrophyllous forests with \u003cem\u003eAraucaria angustifolia\u003c/em\u003e (Araucariaceae) under external stress in Southern Brazil. \u003cem\u003eRev. Biol. Trop.\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e, 1371\u0026ndash;1387 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrittz, G. S., Machado, G. M. O., Vibrans, A. C. \u0026amp; Gasper, A. L. Commonness as a reliable surrogacy strategy for the conservation planning of rare tree species in the subtropical Atlantic Forest. \u003cem\u003eBiodivers Conserv\u003c/em\u003e 1\u0026ndash;13 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLingner, D. V., Rodrigues, A. V., Oliveira, L. Z., De Gasper, A. L. \u0026amp; Vibrans, A. C. Modelling changes in forest attributes driven by human activities at different spatial scales in the subtropical Atlantic Forest. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 1283\u0026ndash;1299 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026ocirc;bo, D., Le\u0026atilde;o, T., Melo, F. P., Santos, A. M. \u0026amp; Tabarelli, M. Forest fragmentation drives Atlantic forest of northeastern Brazil to biotic homogenization. \u003cem\u003eDivers. Distrib.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 287\u0026ndash;296 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRito, K. F. et al. Unraveling the drivers of plant taxonomic and phylogenetic β-diversity in a human-modified tropical dry forest. \u003cem\u003eBiodivers. Conserv.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 1049\u0026ndash;1065 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLingner, D. V. et al. Floresta Ombr\u0026oacute;fila Densa de Santa Catarina-Brasil: agrupamento e ordena\u0026ccedil;\u0026atilde;o baseados em amostragem sistem\u0026aacute;tica. \u003cem\u003eCi\u0026ecirc;nc Florest\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 933\u0026ndash;946 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVibrans, A. C., Sevegnani, L., Gasper, A. L. \u0026amp; Lingner, D. V. (eds) \u003cem\u003eEsp\u0026eacute;cies arb\u0026oacute;reas raras da flora de Santa Catarina\u003c/em\u003eVol. 7 (UFSC/FAPESC, 2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSevegnani, L., Uhlmann, A., Gasper, A. L., Meyer, L. \u0026amp; Vibrans, A. C. Climate Affects the Structure of Mixed Rain Forest in Southern Sector of Atlantic Domain in Brazil. \u003cem\u003eActa Oecol.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 109\u0026ndash;117 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSampaio, M. B. \u0026amp; Guarino, E. S. G. Efeitos do pastoreio de bovinos na estrutura populacional de plantas em fragmentos de Floresta Ombr\u0026oacute;fila Mista. \u003cem\u003eRev. \u0026Aacute;rvore\u003c/em\u003e. \u003cb\u003e31\u003c/b\u003e, 1035\u0026ndash;1046 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePennington, R., Prado, D. E. \u0026amp; Pendry, C. A. Neotropical seasonally dry forests and Quaternary vegetation changes. \u003cem\u003eJ. Biogeogr.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 261\u0026ndash;273 (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasper, A. L. D., Uhlmann, A., Vibrans, A. C. \u0026amp; Sevegnani, L. Varia\u0026ccedil;\u0026atilde;o da estrutura da floresta estacional decidual no estado de Santa Catarina e sua rela\u0026ccedil;\u0026atilde;o com a altitude e o clima. \u003cem\u003eCi\u0026ecirc;nc Florest\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 77\u0026ndash;89 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasper, A. L. et al. Invent\u0026aacute;rio flor\u0026iacute;stico florestal de Santa Catarina: esp\u0026eacute;cies da Floresta Estacional Decidual. \u003cem\u003eRodrigu\u0026eacute;sia\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 427\u0026ndash;443 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVibrans, A. C. et al. Insights from a large-scale inventory in the Southernern Brazilian Atlantic Forest. \u003cem\u003eSci. Agric.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, e20180036 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchorn, L. A. et al. Estrutura do componente arb\u0026oacute;reo/arbustivo da Floresta Estacional Decidual em Santa Catarina. Em \u003cem\u003eVolume II - Floresta Estacional Decidual\u003c/em\u003e, 1o ed, 139\u0026ndash;59. Blumenau: Edifurb, (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLyons, K. G., Brigham, C. A., Traut, B. H. \u0026amp; Schwartz, M. W. Rare species and ecosystem functioning. \u003cem\u003eCons biol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 1019\u0026ndash;1024 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsbell, F. et al. High plant diversity is needed to maintain ecosystem services. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e477\u003c/b\u003e, 199\u0026ndash;202 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRiva, F. \u0026amp; Fahrig, L. The disproportionately high value of small patches for biodiversity conservation. \u003cem\u003eConserv. Lett.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, e12881 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFahrig, L. Why do several small patches hold more species than few large patches? \u003cem\u003eGlob Ecol. Biogeogr.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 615\u0026ndash;628 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGon\u0026ccedil;alves-Souza, T. et al. Species turnover does not rescue biodiversity in fragmented landscapes. \u003cem\u003eNature\u003c/em\u003e, 1\u0026ndash;5 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmeida, C., Reid, J. L., de Lima, R. A. F., Pinto, L. F. G. \u0026amp; Viani, R. A. G. High-diversity Atlantic Forest restoration plantings fail to represent local floras. \u003cem\u003ePerspect. Ecol. Conserv.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 6\u0026ndash;11 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReis, M. S. et al. Domesticated landscapes in Araucaria Forests, Southern Brazil: a multispecies local conservation-by-use system. \u003cem\u003eFront. Ecol. Evol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 11 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlein, R. M. \u003cem\u003eMapa fitogeogr\u0026aacute;fico do estado de Santa Catarina\u003c/em\u003ep. 24 (Herb\u0026aacute;rio Barbosa Rodrigues, 1978).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVibrans, A. C., Sevegnani, L., Lingner, D. V., de Gasper, A. L. \u0026amp; Sabbagh, S. Invent\u0026aacute;rio flor\u0026iacute;stico florestal de Santa Catarina (IFFSC): aspectos metodol\u0026oacute;gicos e operacionais. \u003cem\u003ePesq Florest Bras.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 291\u0026ndash;291 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVibrans, A. C., McRoberts, R. E., Moser, P. \u0026amp; Nicoletti, A. L. Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina. \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e \u003cb\u003e130\u003c/b\u003e, 87\u0026ndash;95 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBackes, E. et al. Distribui\u0026ccedil;\u0026atilde;o geogr\u0026aacute;fica atual da Floresta com Arauc\u0026aacute;ria: condicionamento clim\u0026aacute;tico. In C. R. Fonseca (Eds.). \u003cem\u003eFloresta com Arauc\u0026aacute;ria: Ecologia, Conserva\u0026ccedil;\u0026atilde;o e Desenvolvimento Sustent\u0026aacute;vel\u003c/em\u003e, 45\u0026ndash;68 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVibrans, A. C. et al. Unprecedented large-area turnover estimates for the subtropical Brazilian Atlantic Forest based on systematically-gathered data. \u003cem\u003eEcol. Manage.\u003c/em\u003e \u003cb\u003e505\u003c/b\u003e, 119902 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos, A. S., Saraiva, D. D., Mueller, S. C. \u0026amp; Overbeck, G. E. Interactive effects of environmental filtering predict beta-diversity patterns in a subtropical forest metacommunity. \u003cem\u003ePlant. Ecol. Evol. Syst.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 96\u0026ndash;106 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJost, L. Partitioning diversity into independent alpha and beta components. \u003cem\u003eEcology\u003c/em\u003e \u003cb\u003e88\u003c/b\u003e, 2427\u0026ndash;2439 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaselga, A. Partitioning the turnover and nestedness components of beta-diversity. \u003cem\u003eGlob Ecol. Biogeogr.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 134\u0026ndash;143 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. \u003cem\u003eSci. Data\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, 170122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sdata.2017.122\u003c/span\u003e\u003cspan address=\"10.1038/sdata.2017.122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJakovac, C. C. et al. The role of land‐use history in driving successional pathways and its implications for the restoration of tropical forests. \u003cem\u003eBiol. Rev.\u003c/em\u003e \u003cb\u003e96\u003c/b\u003e, 1114\u0026ndash;1134 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerrier, S., Manion, G., Elith, J. \u0026amp; Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. \u003cem\u003eDivers. Distrib.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 252\u0026ndash;264 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMokany, K., Ware, C., Woolley, S. N., Ferrier, S. \u0026amp; Fitzpatrick, M. C. A working guide to harnessing generalized dissimilarity modelling for biodiversity analysis and conservation assessment. \u003cem\u003eGlob. Ecol. Biogeogr.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 802\u0026ndash;821 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"beta-diversity, subtropical forest, anthropic impacts","lastPublishedDoi":"10.21203/rs.3.rs-7141134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7141134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIt is well known that anthropogenic impacts reshape species composition and cause biodiversity loss, altering tree diversity patterns in natural forests. Here, we use a robust database encompassing three forest formations in the subtropical Atlantic Forest to determine how environmental conditions and anthropogenic impacts affect diversity patterns. We compare alpha and beta-diversity indexes using generalized dissimilarity models (GDM) within and between forest formations and two successional stages (intermediate and advanced). We found that beta-diversity across all forest formations and successional stages was mainly explained by species richness (53.3%), followed by environmental factors (26.9%), including precipitation seasonality and mean annual temperature, geographic distance (7.6%), and anthropogenic factors (7.5%). Beta-diversity within each forest formation was mainly explained by species richness and geographic distance. In the Araucaria forests, beta-diversity was also explained by grazing and precipitation seasonality, while in the Evergreen Forests by fire frequency. Differences in driver importance across forest formations might be related to ecological differences and distinct histories of anthropogenic impacts. By understanding the unique biodiversity patterns and human impacts across different forest types, our findings offer key insights for developing effective conservation strategies to ensure the long-term protection and resilience of these ecosystems.\u003c/p\u003e","manuscriptTitle":"Long-term anthropogenic impacts result in widespread changes of plant species composition in Southern Atlantic Forest: evidences from systematic survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 13:49:22","doi":"10.21203/rs.3.rs-7141134/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T08:33:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T19:43:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T19:06:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186919804582864673077911401454407068855","date":"2025-08-16T19:34:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238970920471060279781383817259551694414","date":"2025-08-16T12:32:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141221294108641154907545874751802349694","date":"2025-08-15T13:35:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153170411314516209165620337178102857558","date":"2025-08-14T11:27:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T02:58:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T02:56:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T15:32:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T16:37:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-07T16:34:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"360ffd85-14c7-4233-8f2b-b0f0f5a15281","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53395607,"name":"Biological sciences/Ecology"},{"id":53395608,"name":"Earth and environmental sciences/Ecology"},{"id":53395609,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-11-24T15:59:47+00:00","versionOfRecord":{"articleIdentity":"rs-7141134","link":"https://doi.org/10.1038/s41598-025-24131-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-17 15:57:02","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-08-21 13:49:22","video":"","vorDoi":"10.1038/s41598-025-24131-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24131-3","workflowStages":[]},"version":"v1","identity":"rs-7141134","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7141134","identity":"rs-7141134","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}