Exploring Phylogenetic Diversity and Floristic Shifts Along an Elevational Gradient in a Northeast Brazilian Alpine Ecosystem

Exploring Phylogenetic Diversity and Floristic Shifts Along an Elevational Gradient in a Northeast Brazilian Alpine Ecosystem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Phylogenetic Diversity and Floristic Shifts Along an Elevational Gradient in a Northeast Brazilian Alpine Ecosystem Amadeu dos Santos-Neto, Adauto de Souza Ribeiro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6032434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines plant diversity along an elevational gradient in the Serra de Itabaiana National Park, northeastern Brazil, focusing on species richness, phylogenetic diversity, and beta diversity. We identified 108 plant species across five elevation bands, with species richness declining linearly with increasing elevation. Precipitation and temperature positively influenced richness, but no significant relationship was observed for phylogenetic diversity metrics. Notably, phylogenetic diversity peaked at mid-elevations, exhibiting a hump-shaped pattern, while higher elevations hosted phylogenetically clustered communities, reflecting environmental filtering. Beta diversity analysis revealed turnover as the dominant driver of species and phylogenetic dissimilarity between elevation belts, emphasizing the role of species replacement over nestedness. These findings underscore the influence of climatic variables and environmental constraints on community composition, with distinct patterns of species adaptation across elevations. Contrary to expectations, phanerophytes dominated across the gradient, challenging the predicted prevalence of hemicryptophytes at higher elevations. Our study highlights the importance of integrating phylogenetic data to understand biodiversity dynamics in understudied regions like northeastern Brazil. Conservation efforts in Serra de Itabaiana should prioritize maintaining beta diversity to capture species turnover and ensure ecosystem resilience. These findings contribute valuable insights into the interplay of ecological and evolutionary processes shaping plant communities in low-elevation mountain systems. Botany Beta Diversity Environmental Filtering Species Turnover Phylogenetic Clustering Environmental Gradients. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The uneven distribution of biodiversity across the Earth's surface (Gaston 2000 ) and the mechanisms that promote this variation have been central questions in ecology (Hawkins 2001 ; Sanders and Rahbek 2012 ). In this context, environmental gradients, particularly latitude (De Frenne et al. 2013 ) and altitude (Rahbek 1995 ), are key models for studying organism distribution. The latitudinal gradient is well-known for increasing richness toward lower latitudes, while the elevational gradient reflects changes with elevation (Gillman et al. 2015 ). The elevational gradient of diversity is more complex and less predictable, not consistently mirroring the latitudinal gradient as previously assumed (Körner 2007 ; Payne et al. 2017 ). Elevation poses challenges for most organisms due to changes in the environmental conditions like temperature and precipitation (Alonso-Amelot 2008 ; Grabherr et al. 2010 ), requiring morpho-physiological adaptations (Tranquillini 1964 ). Despite these pressures, alpine ecosystems host remarkable diversity (Tenorio et al. 2023 ), with endemic species exceeding 90% in some regions (Tiwari et al. 2019 ; Carbutt 2019 ). Richness typically decreases with elevation (Griffiths et al. 2021 ), but this pattern can vary, showing either a monotonic decline or a hump-shaped curve, with higher richness at intermediate altitudes (Guo et al. 2013 ). Notably, species richness and phylogenetic diversity, which measures the evolutionary distance between species in a community, may mirror each other (Bañares-de-Dios et al. 2024a ) or diverge (Schroeder et al. 2024 ), reflecting complex ecological and evolutionary dynamics (Zhang et al. 2016 ). Several studies have focused on large mountain ranges (above 1000 meters), such as Ningxia Helan Mountain (He et al. 2023 ), the Himalaya (Sharma et al. 2019 ), and the Andes (Cuesta et al. 2017 ). However, smaller mountain ranges below 1000 meters remain understudied, often revealing complex ecological patterns crucial to understanding biodiversity (Silva et al. 2019c ; Ramos et al. 2020 ; Diniz et al. 2021a ). In Brazil, most studies on diversity patterns along elevational gradients focus on the southern and southeastern regions (Gastauer et al. 2020a ; Mariano et al. 2020a ; Bergamin et al. 2021 ). However, recent investigations into alpine communities in northeastern Brazil have shed light on their unique plant compositions (Lima et al. 2009 ; Leitman et al. 2014 ; Melo et al. 2016 ; Silva et al. 2019a ). Despite these efforts, the structural dynamics of these communities and the factors influencing them remain poorly understood (Silva et al. 2019c ; Ramos et al. 2020 ; Diniz et al. 2021a ; Pinto et al. 2023 ). The Serra de Itabaiana Mountain Range in northeastern Brazil is a low-elevation system, with its highest peak reaching only 670 meters above sea level (Dantas and Ribeiro 2010a ). This range supports a diverse plant community exceeding 800 species (Silva et al. 2019b ), with forested areas dominating the base and shrub-grassland vegetation characterizing the slopes (Dantas and Ribeiro 2010a ). Historically, this ecosystem has experienced significant disturbance from economic activities, some of which persist due to unresolved land expropriation issues and illegal practices, even after the area was granted legal protection (Figueirêdo and Souza). Notably, the range is home to endemic species, such as Aspilia itabaianensis J.U.Santos and Sinningia nordestina Chautems, Baracho & Siqueira-Filho, and threatened species, including Melocactus violaceus Pfeiff. and Griffinia espiritensis Ravenna (Silva et al. 2019b ). Given the ecological significance of the Serra de Itabaiana Mountain Range, this study aims to investigate altitudinal patterns in plant diversity, including species richness and phylogenetic diversity, as well as shifts in the phylogenetic structure of the plant community, beta diversity, beta phylogenetic diversity, and floristic composition along the elevation gradient. Additionally, it evaluates the influence of temperature and precipitation on these patterns. Based on the available literature, we predict that species richness will decrease with elevation (Rahbek 1995 ), while phylogenetic diversity will be higher at mid-elevation, likely due to more favorable environmental conditions at intermediate altitudes. Consistent with findings from similar studies, we also anticipate a phylogenetically clustered community at the highest elevations, driven by the harsher environmental conditions typical of these areas. Furthermore, we expect Hemicryptophytes to dominate higher elevations due to their morphological adaptations to colder and windier conditions. Finally, we predict that abiotic factors such as temperature and precipitation will play a significant role in shaping these patterns across the gradient. 2. Methods 2.1. Study Area Our study was conducted at Serra de Itabaiana National Park (SINP, Fig. 1 ), covering 7,966 hectares across the municipalities of Areia Branca, Itabaiana, Campo do Brito, Itaporanga D’Ajuda, and Laranjeiras (Dantas and Ribeiro 2010b ). It comprises three sub-units (Fig. 1 ): (1) Cumprida, (2) Cajueiro, and (3) Itabaiana mountain ranges, with three types of vegetation formations: forests, open natural areas, and open anthropized areas (Dantas and Ribeiro 2010c ). The climate in SINP is classified as 'As' according to the Köeppen-Geiger classification (Araújo et al. 2019 ) with elevations reaching up to 670 meters above sea level (Santos et al. 2021 ), making it the second-highest point in the state of Sergipe (Rocha et al. 2015 ). Unlike most federal protected areas in the state of Sergipe, the SINP is a rare example of a relatively well-documented biotic component (Silva et al. 2019a , 2022 ; Silva 2020 ; Vilela et al. 2021 ; Farias et al. 2024 ). However, However, despite its classification as a fully protected area, NPSI biodiversity remains threatened by frequent fires, agricultural activities in unexpropriated areas, sand and clay mining, vegetation removal, and hunting (Sobral et al. 2007 ). FIGURE 1 2.2. Sampling A pre-established trail between the point popularly known as "Poço das Moças" and the top of the SINP was used as a transect. Five elevational bands (EB) were established (between 250 and 650 m), each separated by 100 meters in elevation (Theurillat et al. 2007 ) and in each EB three 50 m² plots were delineated (Lembrechts et al. 2017 ). The plots are 20 meters apart from each other and 15 meters away from the trail margins; these distances could not be extended further due to the constraints imposed by the cliff range. The plots are 20 meters apart and 15 meters from trail margins, with distances limited by the cliff range. Samples were collected from areas of open grassy vegetation unaffected by human activity. Although SINP covers a large area, steep terrain above 250 meters restricts sampling. The park's dome includes a rocky wall, and other sections feature slopes with a medium to high risk. We characterized the floristic composition by considering all species present (even partially) within these quadrats over the course of a year, totaling 24 field excursions. In every field excursion, we visited all sites to ensure we included species with different phenologies. Each excursion typically lasted 6 to 8 hours, depending on weather conditions. The peak of summer posed challenges due to the intense heat, while the rainy season made the area less suitable for walking and increased the risk of falls. Specimens were identified with the aid of specialized literature (Dantas and Ribeiro 2010b ; Mendes et al. 2010 ; Silva et al. 2019a ) and classified according to their life form, following Raunkiaer’s classification (Smith 1913 ),which add up plant adaptation strategies, functional diversity shifts, and resilience across elevation gradient. The plants were then processed using standard methods for plant material (Alexiades 1996 ) and deposited in the internal collection of the Laboratory of Dendroecology and Plant Anatomy, part of the Department of Biology at the Federal University of Sergipe. 2.3. Environmental Data We obtained climate information for each site using data from the WorldClim platform ( https://www.worldclim.org/ ), utilizing the highest spatial resolution layers available (30 seconds or 1 km²) (Fick and Hijmans 2017 ). The available variables included minimum temperature (°C), maximum temperature (°C), mean temperature (°C), precipitation (mm), solar radiation (kJ m⁻² day⁻¹), wind speed (m s⁻¹), and water vapor pressure (kPa). For each variable, 12 high-resolution layers were downloaded, totaling 84 .tiff files. To extract the data, the files were converted into spatial objects using the raster() function, and the climatic data for the points of interest (coordinates of each inventoried plot) were extracted using the extract() function, both from the Raster package version 3.6–26 (Hijmans 2018 ). The resolution provided unique climate data for each elevation belt but not for individual plots. 2.4. Phylogenetic Tree Generation Based on the final plant list, we used the V.PhyloMaker2 package (version 0.1.0) with the GBOTB.extended.TPL.tre backbone under scenario "1" to generate a phylogenetic tree with 107 species (Jin and Qian 2022 ).This scenario conservatively places missing taxa at genus or family basal nodes, which reduces phylogenetic uncertainty while preserving hierarchical relationships. This conservative approach minimizes bias in phylogenetic placement, making it particularly suitable for detecting subtle phylogenetic patterns along narrow elevational gradients without overestimating relationships (Jin and Qian 2019 , 2022 ). For this, we included only taxa identified at the species or genus level to ensure accuracy and consistency (Bañares-de-Dios et al. 2024b ). Additionally, scenario "1" in V.PhyloMaker2 assumes that missing taxa belong to the same evolutionary lineage as the included taxa, making it a robust choice when handling incomplete taxonomic information. 2.5. Phylogenetic Structure and Signal We employed three metrics related to community phylogenetic structure: 1. standardized effect size of Phylogenetic Diversity (ses.PD). Due to the existence of several metrics of phylogenetic diversity, here, we define that the phylogenetic diversity index is the one proposed by Faith ( 1992 ). This index is the sum of all phylogenetic branch lengths connecting the species (Faith 1992 ; Qian and Deng 2023 ) and it is widely used as a biodiversity metric (Tucker et al. 2017 ). 2. standardized effect size of Mean Pairwise Distances (ses.MPD), which estimates the average phylogenetic relatedness among all pairs of species within a community and indicates deep relatedness in the tree. The third metric, standardized effect size of Mean Nearest Taxon Distances (ses.MNTD), estimates the average phylogenetic relatedness, that is an estimate of the average phylogenetic relatedness between each taxon in a community and its closest relative in another community (Webb 2000 ; Kellar et al. 2015 ). All three metrics included here are widely used in studies investigating changes in plant community structure along environmental gradients (Worthy et al. 2019 ; Nosrati et al. 2023 ). Tests were conducted in the R environment (version 4.3.1), using ses.PD(), ses.MPD(), and ses.MNTD() functions from the Picante package version 1.8.2 (Kembel et al. 2010 ), without abundance weighting (abundance.weighted = FALSE). 2.6. Data Analysis We investigated patterns of richness and Phylogenetic Structure (PS) along the gradient using Linear Mixed Models (LMMs) for richness and Generalized Linear Mixed Models (GLMMs) for the phylogenetic structure metrics. Richness, along with each phylogenetic structure metric, served as the response variable, with elevation as the main predictor, while variation within elevation bands was accounted for as a random effect (Bolker et al. 2009 ). We used the same approach (LMMs) to assess the influence of climatic variables on richness and PD. In this analysis, we tested various combinations of the climatic variables as predictors, elevation band as random effect, and selected the best-fitting model based on the Akaike Information Criterion (AIC). For models with richness, we used a Poisson distribution (Inouye et al. 2017 ), and for models with PS metrics, we used Gaussian distribution (Mienna et al. 2020 ). To explore the relationship between these phylogenetic indices and environmental factors, we used the lme4 package to fit linear mixed models (LMMs) with the lmer function. The models included Temperature and Precipitation as fixed effects, with random effects accounting for variation across elevation bands. The lmerTest package was used to automatically calculate p-values for the fixed effects, allowing us to assess the significance of environmental variables in shaping the phylogenetic structure of the community. We performed a Permutational Multivariate Analysis of Variance (PERMANOVA) with 9999 permutations (Anderson 2017 ), using Bray-Curtis’s dissimilarity index to identify the importance of climatic variables in floristic composition variation along the elevation gradient (Haug et al. 2019 ; Martínez-Camilo et al. 2022 ). For this, we created a dissimilarity matrix using the vegdist() function and conducted PERMANOVA using the adonis2() function. Non-metric Multidimensional Scaling (NMDS) with metaMDS() function was used to visualize changes in floristic composition along the elevation gradient and its relationship with predictor environmental variables. Due to the high correlation between variables, we only include mean annual temperature and annual precipitation. We performed both PERMANOVA and NMDS using the Vegan package version 2.6-4 (Oksanen et al. 2023 ). NMDS plots were generated using ggplot2 package version 3.5-0 (Wickham 2016 ) in R environment (R Development Core Team 2021). We checked the normality of the residuals for the response variables using the Shapiro-Wilk test to ensure the assumptions of the analysis were met and examined the residual distribution with Q-Q, Residuals vs Fitted, Scale-Location and Residual vs. Leverage (Nobis and Schweingruber 2013 ). Additionally, we assessed multicollinearity among the predictor variables by calculating the Variance Inflation Factor respecting a threshold of 4, with moderate linearity (Shrestha 2020 ). We used the VIF() function from the car package 3.1-2 (Fox et al. 2023 ). 3. Results 3.1. Community Summary We identified 108 species of flowering plants (Fig. 2 , Supplementary Material 1) along the studied gradient. These species belong to 85 genera, 46 families, and 23 orders. The richest order was Poales, with 15 species, followed by Myrtales (13 species), and Fabales, Gentianales, and Malpighiales (12 species each). Leguminosae was the most diverse family, comprising nine species, followed by Apocynaceae (six species), and Verbenaceae, Melastomataceae, Cyperaceae, and Rubiaceae (five species each). The richest genera were Mandevilla Lindl. and Cuphea P. Browne (four species each), followed by Lantana L., Polygala L., Chamaecrista (L.) Moench, and Myrcia DC. (three species each). FIGURE 2 Most species exhibit a specialist distribution pattern, being restricted to a single elevation belt. This is followed by species occurring in two or three elevation belts, and finally by those present in four or five belts, which represent the most generalist distribution (Fig. 3 a). Overall, most species are classified as phanerophytes (48.6%) and hemicryptophytes (34.7%), with fewer species categorized as chamaephytes (10.9%), geophytes (3.4%), and therophytes (2.5%). Phanerophytes consistently dominated across all elevation belts, followed by hemicryptophytes, chamaephytes, geophytes, and therophytes in that order (Fig. 3 b). FIGURE 3 3.2 Alpha Diversity The species richness across the elevation gradient shows a clear decline with increasing elevation (β = -0.002492, z = -7.985, p < 0.05, Fig. 4 a). At 250 m, the community has the highest richness with 69 species, which decreases progressively at higher elevations: 58 species at 350 m, 36 species at 450 m, 32 species at 550 m, and 27 species at 650 m. FIGURE 4 We found significant relationships between richness and the predictors, precipitation (Fig. 4 b) and temperature (Fig. 4 c). Richness increased significantly with higher precipitation (β = 0.010214, z = 2.074, p = 0.038, Fig. 4 b) and temperature (β = 0.232604, z = 3.068, p = 0.002, Fig. 4 b). The model included a random intercept for elevation belts, but the variance for the elevation grouping was estimated to be zero, suggesting that random variation at the elevation level did not contribute to explaining species richness. 3.3 Alpha Phylogenetic Diversity Our findings for ses.PD (observed PD: 2026.886, Z = -2.263, p < 0.05) and SES.MNTD (observed MNTD: 99.864, Z = -2.167, p < 0.05) suggest that the community at the top of the gradient (650 m) is phylogenetically clustered. None of the other elevation bands differed significantly from what would be expected by chance, mirroring the pattern observed for ses.MPD. None of our models with elevation and phylogenetic structure: ses.PD (β=−0.0057, t = − 2.43, p = 0.085, Fig. 5 a), ses.MPD (β=−0.0019, t = − 1.35, p = 0.199, Fig. 5 b) and ses.MNTD (β=−0.0056, t = − 2.52, p = 0.076, Fig. 5 a) were not significant with elevation. FIGURE 5 Our models did not show relationships between the climate predictors and the phylogenetic structure. For SES.PD, while temperature (β = 0.493, t = 1.228, p = 0.272, Fig. 5 a) and precipitation (β = 0.039, t = 1.285, p = 0.247, Fig. 5 d) had positive estimates, their effects were not statistically significant. Similarly, for SES.MNTD, temperature (β = 0.494, t = 1.231, p = 0.271, Fig. 5 b) and precipitation (β = 0.036, t = 1.208, p = 0.270, Fig. 5 e) showed non-significant relationships. Lastly, for SES.MPD, neither temperature (β = 0.186, t = 0.571, p = 0.576, Fig. 5 c) nor precipitation (β = 0.012, t = 0.471, p = 0.644, Fig. 5 f) significantly influenced the response variable. In all models, a random intercept for elevation bands was included. However, the variance for the elevation grouping was small, indicating minimal or no contribution of random variation at the elevation level to explaining the observed patterns in phylogenetic diversity. 3.4 Trends Between Families and Orders Several plant orders exhibit notable trends across the elevation gradient (Supplementary Material 2). Gentianales remains important throughout the gradient, with a peak at 350 m (13.56%) and a strong presence at 650 m (13.79%). Lamiales show a steady increase, reaching their peak at 650 m (17.24%). Fabales follows a similar trend, with its highest importance at 650 m (10.34%). In contrast, Myrtales, Rosales, and Malvales show a marked decrease at higher elevations, particularly at 650 m, where Myrtales drop to 3.45% and Rosales to total absence. Poales increase consistently across the gradient, peaking at 650 m (20.69%). Other orders such as Boraginales, Caryophyllales, and Arecales fluctuate in the present but do not show drastic changes. Solanales appear only at mid-elevations, and Brassicales and Zingiberales are absent at higher elevations. Families also exhibit distinct patterns across the elevation gradient (Supplementary Material 2). Fabaceae shows a significant increase in importance at higher elevations, particularly peaking at 650 m (10.34%). Similarly, Arecaceae, Orchidaceae, and Malpighiaceae also become more prominent at mid to high elevations, with Orchidaceae peaking at 650 m. In contrast, families like Gentianaceae and Bignoniaceae show peaks at mid-elevations (350 m to 450 m) but decrease at higher elevations, particularly at 650 m. Some families, including Dilleniaceae, Sapindaceae, and Myrtaceae, drop to zero at 650 m. Additionally, families such as Lythraceae, Orobancaceae, and Malvaceae disappear at higher elevations. 3.5 β-Diversity The analysis of beta diversity across elevation belts revealed that species dissimilarity (βsor) is primarily driven by species replacement (βsim = 0.3909) rather than species loss or gain (βsne = 0.1270). Turnover (βsim) values ranged from 0.21 to 0.58 (Fig. 6 a), indicating that differences in species composition between sites are largely due to species replacement. In contrast, nestedness (βsne) values were consistently lower, ranging from 0.04 to 0.19 (Fig. 6 a), suggesting that species loss or gain contributes less to community differences. Total beta diversity (βsor) ranged from 0.25 to 0.75 (Fig. 6 a), with the highest dissimilarity observed between the most distant elevation belts (e.g., 250 m and 650 m), reflecting significant species turnover. Conversely, closer elevation belts (e.g., 250 m and 350 m) exhibited lower βsor values, indicating more similar species composition and reduced turnover. FIGURE 6 These findings emphasize that species replacement dominates beta diversity patterns, particularly along larger elevation gradients, likely driven by environmental differences. This was supported by our PERMANOVA results, which showed that species composition varies significantly along the gradient, with temperature variation explaining 52.77% (F = 19.47, p < 0.01, (Fig. 7 ) of the variation in species composition across the gradient, while precipitation explains 17.70% (F = 5.426, p < 0.01, Fig. 4 ). FIGURE 7 3.6 Phylogenetic β-Diversity The analysis of phylogenetic beta diversity across elevation belts revealed that phylogenetic dissimilarity is primarily driven by phylogenetic turnover rather than nestedness. Turnover values ranged from 0.15 to 0.37 (Fig. 6 b), indicating that differences in phylogenetic composition between sites are largely due to replacement of evolutionary lineages. In contrast, nestedness values were consistently lower, ranging from 0.05 to 0.23 (Fig. 6 b), suggesting that lineage loss or gain contributes less to phylogenetic differences. Total phylogenetic beta diversity ranged from 0.20 to 0.55 (Fig. 6 b), with the highest dissimilarity observed between the most distant elevation belts (e.g., 250 m and 650 m), reflecting significant phylogenetic turnover. Conversely, closer elevation belts (e.g., 250 m and 350 m) exhibited lower total beta diversity values, indicating more similar phylogenetic composition and reduced turnover. 4. Discussion Our findings represent 55% of the richness previously identified for the Areias Brancas community (Dantas and Ribeiro 2010b ) and 13% of the species recorded for the NPSI community, encompassing Atlantic Forest and Caatinga species (Silva et al. 2019a ). While no prior study has floristically characterized undisturbed open fields along this gradient, we documented numerous herbaceous and shrub species from regional inventories, excluding steep slopes dominated by grasses due to safety concerns. Among the 10 most stable families along the gradient, eight (Fabaceae, Asteraceae, Apocynaceae, Rubiaceae, Cyperaceae, Melastomataceae, Poaceae, and Orchidaceae) are also among the 10 most species-rich in the Caatinga biome, with only Malpighiaceae and Verbenaceae absent (Zappi et al. 2015 ). This highlights the importance of adaptations like drought tolerance and water storage, often present in these families, which enable many Caatinga species to thrive in arid environments (Accioly et al. 2024 ). Verbenaceae, though not diverse in the Caatinga, thrives in high-elevation environments, with species like Lantana L. and Stachytarpheta Vahl found in Sergipe's mountains (Machado et al. 2012 ). Lantana species adapts to arid habitats through deep roots and low stomatal density to minimize water loss (Lu-Irving et al. 2021 ), while Stachytarpheta survives in rocky, high-altitude vegetation by reducing transpiration with waxy leaf coatings (Barreto et al. 2016 ). Similarly, Fabaceae, Rubiaceae, and Apocynaceae adapt to harsh conditions with strategies like nitrogen fixation, UV protection, thick cuticles, and succulent leaves that conserve water (Delprete and Jardim 2012 ; Fishbein et al. 2018 ; Morim et al. 2024 ). These adaptations enable them to thrive in nutrient-poor, arid environments. Similarly, several key genera in this study play crucial roles in Brazil's higher elevation ecosystems, likely due to their ecological traits (Halbritter et al. 2018a ). For instance, some Polygala L. species have trichome-covered leaves that reflect sunlight and reduce transpiration (de Almeida Campos Cordeiro and Neri 2019 ; Martinez et al. 2022). Similarly, Mandevilla Lindl. (Aragón et al. 2023 ), Lantana L. (Aragón et al. 2023 ), Cuphea P.Browne (Graham 1988 ), Chamaecrista (L.) Moench (Coutinho et al. 2013 ),, and Myrcia DC. (Costa et al. 2020 ) exhibit traits like waxy coatings, small or hairy leaves, and deep root systems, which enhance survival in arid, high-altitude environments. The plant community consists mainly of Phanerophytes and Hemicryptophytes, dominated by low shrubs and small trees (~ 3 m tall) like Kielmeyera rugosa Choisy and Jacaranda obovata Cham. The tree component of these areas has been previously described as a sparse tree layer (Dantas and Ribeiro 2010c ), which disappears at higher elevations (Jankowski et al. 2013 ). The dominance of Phanerophytes, such as Acritopappus confertus (Gardner) R.M.King & H.Rob. and Stigmaphyllon paralias A. Juss., and hemicryptophytes, including Sauvagesia erecta L. and Epistephium lucidum Cogn., in elevated environments follows a well-recognized pattern (Silveira et al. 2016 ; Neri et al. 2017 ; Rawat et al. 2021a ). In contrast to the findings of most studies in this field, which often report hemicryptophytes becoming hyper-dominant at higher elevations due to their adaptability to harsher conditions (Di Biase et al. 2021a ; Rawat et al. 2021b ), while phanerophytes tend to become insignificant or disappear entirely (Rawat et al. 2021b ), what was not found by this study (Silva Mota et al. 2018a ; de Almeida Campos Cordeiro and Neri 2019 ; Di Biase et al. 2021b ). This pattern could be explained by many reasons. Certain phanerophytes may be adapted to higher elevation conditions due to soil and nutrient availability, the presence of sheltered areas (e.g., valleys, rocky outcrops), or historical and biogeographic factors. According to Neri et al. ( 2017 ), soil depth can be a crucial factor influencing the prevalence of phanerophytes in higher regions, as these species require deeper soils for their longer roots and seed bank formation. However, this dominance could also reflect environmental changes at the surveyed sites, suggesting an expansion of shrub communities (woody plant encroachment) within grass communities (Palaj and Kollár 2021 ). Additionally, this condition may be linked to intense grazing, deforestation, frequent forest fires, and anthropogenic pressures such as firewood and forage collection (Rawat et al. 2021a ). In general terms, the decline in chamaephytes (e.g., Lantana fucata Lindl. and Melocactus violaceus N.P.Taylor). It diverges from the common pattern for this group, which typically becomes more prominent in high-altitude environments (Matteodo et al. 2013 ). Chamaephytes are adapted to arid environments (Araújo et al. 2011 ) and usually can protect their buds by keeping them at ground level (Caiafa and Silva 2005 ). They also can regulate water release and absorption during hot summers (Fadl et al. 2022 ), but without drastic environmental changes, small phanerophytes may replace them (Irl et al. 2020 ). We observed a consistent monotonic decline in richness with elevation, the second most common pattern, after the hump-shaped pattern (Dani et al. 2023 ), though remains the predominant for smaller gradients (< 1000 m) (Rahbek 2005 ). This pattern has been observed in gradients in the Amazon Forest of Brazil (Campos et al. 2022 ) and Guyana (Gastauer et al. 2020b ), as well as in the Brazilian Atlantic Forest (Rezende et al. 2015 ) and Grasslands (Silva Mota et al. 2018b ). Although prevalent in Neotropical ecosystems (G. and Givnish 1998 ; Sánchez-González and López-Mata 2005 ; Toledo-Garibaldi and Williams‐Linera 2014 ; He et al. 2024), it was not observed in the in the Caatinga, where richness increased with elevation, mostly associated to the water availability (Silva et al. 2014 ). This decline along the gradient likely reflects strong environmental filters that limit species distributions (Paciencia 2008 ; Körner et al. 2011 ). Environmental variables, particularly temperature, vary significantly with elevation, typically decreasing by 0.3 to 0.6°C per 100 meters of elevation gain (Grytnes and McCain 2013 ). Such temperature reductions directly affect plant physiology, growth, and reproduction, shaping community composition and diversity (Halbritter et al. 2018b ). Plant richness often decreases with elevation due to harsher abiotic conditions, shorter growing seasons, and reduced atmospheric pressure, which limit germination and reproduction (Blume-Werry et al. 2016 ). Nutrient-poor, acidic soils and lower primary productivity at higher altitudes further restrict diversity (Fyllas et al. 2017 ; Müller et al. 2017 ). Additionally, high elevations face stronger winds, increased UV radiation (Blumthaler et al. 1997), and reduced pollination (Adedoja et al. 2018) and seed dispersal (Anadon-Rosell et al. 2020), limiting plant reproductive success. Interestingly, while species richness declines with elevation, phylogenetic diversity peaks at mid-elevation, following a hump-shaped pattern (Rowe and Lidgard 2009 ). This pattern has been widely observed in mountain regions worldwide, including the Hengduan Mountains in southwestern China (Li et al. 2014 ), Shiretoko Peninsula in Japan (Kitagawa et al. 2018 ), the Western Himalayas in India, and Changbaishan in China (Manish and Pandit 2018 ). The hump-shaped pattern has also been observed when measuring species richness (Tuomisto et al. 2014 ; Kamimura et al. 2017 ; Li et al. 2022 ) and is typically associated with the moderate environmental conditions found at intermediate elevations (Zhu et al. 2009 ). However, the mechanisms underlying this relationship remain only partially understood (Acharya et al. 2011 ). Both ses.PD and ses.MNTD indicate a clustered community at higher elevations. Clustered communities at higher elevations are common (Qian et al. 2014 ), and found in Brazilian Grasslands (de Mattos et al. 2019 ; Gastauer et al. 2020b ) and Tropical Forest (Mariano et al. 2020b ). These ecosystems often selects specialized traits, and since phylogenetically related species tend to share these traits, they typically form clustered communities (Burns and Strauss 2011 ). The divergent results for ses.MNTD (terminal) and ses.MPD (basal) (Mazel et al. 2016 ) suggest clustering occurs at finer phylogenetic scales without impacting overall community phylogenetic diversity. This highlights the importance of local environmental factors and habitat heterogeneity. While the community shows no significant phylogenetic structure, distinct microenvironments, like mountaintops, may drive phylogenetic clustering. Neither ses.PD, ses.MPD, nor ses.MNTD are influenced by elevation, temperature, or precipitation, consistent with findings for tropical montane Atlantic Cloud Forest (Mariano et al. 2024 )d ramo communities (Gastauer et al. 2020) in Brazil. However, this contrasts with results from Upland Forest in Northeastern Brazil (Pinto et al. 2023 ) and Tropical Forests in China (Qian et al. 2014 ; Zhu et al. 2019 ; Xue et al. 2024 ), where these indices are variable-dependent. Such differences likely reflect regional, methodological, and evolutionary factors. Beyond climatic variables, soil texture variability, not studied by us, often better explains phylogenetic diversity and structure (Campos et al. 2022 ; Xue et al. 2024 ). Floristic composition shifts markedly along elevation gradients, a pattern extensively documented across diverse biomes, including Grasslands (Urbanetz et al. 2012 ), Tropical (Robertson et al. 2010; Valente et al. 2011 ) and Dry Forest (Diniz et al. 2021b ). Additionally, this pattern has been described in key mountainous regions of Brazil, including the Mantiqueira mountain range (Meireles et al. 2008 ), Mar mountain range (Sanchez et al. 2013 ), and Cipó mountain range (Silva Mota et al. 2018a ). where unique floristic assemblages emerge along altitudinal gradients, reflecting the complex interplay of ecological and evolutionary processes. As mentioned previously in our discussion, the temperature plays an important role here. It significantly influences species distribution (Grace 1987 ; Amissah et al. 2014 ) by affecting key processes like photosynthesis (Öquist 1983 ), respiration (Criddle et al. 1997 ), and transpiration (Smith and Geller 1979 ). Plants thrive within specific temperature ranges, and along gradients, only those with suitable physiological adaptations can survive and reproduce. Warmer temperatures accelerate growth and development, while cooler temperatures slow these processes, leading to variations in species composition as plants adapt to differing thermal conditions. The plant community along this gradient is primarily shaped by species replacement (even when phylogenetic relatedness is included), with higher turnover observed between distant elevation belts (Guclu et al. 2024 ). This turnover is influenced by ecological and evolutionary processes, where species that are adapted to different environmental conditions replace one another as you move up or down the gradient and this was found across many elevation gradients ( Silva Mota et al. 2018c; Wani et al. 2022 ; Thorne et al. 2022 ; Cordeiro et al. 2023 ; Guclu et al. 2024 ). Nonetheless, the turnover of species was greater than the turnover of phylogenetic lineages across various elevations, highlighting that diversification predominantly took place at the species level rather than at higher taxonomic levels like genus or family (de Andrade Kamimura et al. 2022 ). Additionally, these findings show that the community at the highest elevations is not simply a subset of the species found across the gradient, but rather hosts many unique species, underscoring the distinct ecological conditions at these upper elevations (Baselga 2010 ). In conclusion, this study provides the first detailed analysis of plant communities along an elevation gradient in the Serra de Itabaiana National Park, Sergipe, and is among the few in Northeast Brazil that incorporates phylogenetic data. Our results revealed a decline in species richness with increasing elevation, which met our prediction that species richness would decrease with elevation. Phanerophytes remained stable and dominant across the gradient, even at higher elevations, which did not meet our prediction that Hemicryptophytes would dominate higher elevations due to their morphological adaptations. Phylogenetic diversity followed a hump-shaped pattern, peaking at mid-elevations, which confirmed our prediction that phylogenetic diversity would be higher at mid-elevation due to more favorable environmental conditions. At the highest elevations, we observed a phylogenetically clustered community, consistent with our prediction that environmental conditions at these elevations would favor phylogenetic clustering. In terms of beta diversity, we observed significant turnover in species composition across elevations, highlighting the role of environmental filtering in shaping plant community structure, which was consistent with our expectation that abiotic factors, such as temperature and precipitation, would play a role in shaping these patterns. 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Flora 238:32–42. https://doi.org/10.1016/j.flora.2017.03.010 Silva Mota G, Luz GR, Mota NM, et al (2018b) Changes in species composition, vegetation structure, and life forms along an altitudinal gradient of rupestrian grasslands in south-eastern Brazil. Flora 238:32–42. https://doi.org/10.1016/j.flora.2017.03.010 Silveira FAO, Negreiros D, Barbosa NPU, et al (2016) Ecology and evolution of plant diversity in the endangered campo rupestre: a neglected conservation priority. Plant Soil 403:129–152. https://doi.org/10.1007/s11104-015-2637-8 Smith WG (1913) Raunkiaer’s “Life-Forms” and Statistical Methods. Journal of Ecology 1:16–26. https://doi.org/10.2307/2255456 Smith WK, Geller GN (1979) Plant transpiration at high elevations: Theory, field measurements, and comparisons with desert plants. Oecologia 41:109–122. https://doi.org/10.1007/BF00344841 Sobral IS, Santana RK de O, Gomes LJ, et al (2007) AVALIAÇÃO DOS IMPACTOS AMBIENTAIS NO PARQUE NACIONAL SERRA DE ITABAIANA - SE. Caminhos de Geografia 8:102–110. https://doi.org/10.14393/RCG82415713 Tenorio EA, Montoya P, Norden N, et al (2023) Mountains exhibit a stronger latitudinal diversity gradient than lowland regions. Journal of Biogeography 50:1026–1036. https://doi.org/10.1111/jbi.14597 Theurillat J-P, Iocchi M, Cutini M, De Marco G (2007) Vascular plant richness along an elevation gradient at Monte Velino (Central Apennines, Italy). Biogeographia 28:. https://doi.org/10.21426/B628110003 Thorne JH, Choe H, Dorji L, et al (2022) Species richness and turnover patterns for tropical and temperate plants on the elevation gradient of the eastern Himalayan Mountains. Front Ecol Evol 10:. https://doi.org/10.3389/fevo.2022.942759 Tiwari A, Uprety Y, Rana SK (2019) Plant endemism in the Nepal Himalayas and phytogeographical implications. Plant Diversity 41:174–182. https://doi.org/10.1016/j.pld.2019.04.004 Toledo‐Garibaldi M, Williams‐Linera G (2014) Tree diversity patterns in successive vegetation types along an elevation gradient in the Mountains of Eastern Mexico. Ecological Research 29:1097–1104. https://doi.org/10.1007/s11284-014-1196-4 Tranquillini W (1964) The Physiology of Plants at High Altitudes. Annual Review of Plant Biology 15:345–362. https://doi.org/10.1146/annurev.pp.15.060164.002021 Tucker CM, Cadotte MW, Carvalho SB, et al (2017) A guide to phylogenetic metrics for conservation, community ecology and macroecology. Biological Reviews 92:698–715. https://doi.org/10.1111/brv.12252 Tuomisto H, Zuquim G, Cárdenas G (2014) Species richness and diversity along edaphic and climatic gradients in Amazonia. Ecography 37:1034–1046. https://doi.org/10.1111/ecog.00770 Urbanetz C, Lehn CR, Salis SM, et al (2012) COMPOSIÇÃO E DISTRIBUIÇÃO DE ESPÉCIES ARBÓREAS EM GRADIENTE ALTITUDINAL, MORRARIA DO URUCUM, BRASIL. Oecologia Australis 16:859–877 Valente ASM, Garcia PO, Salimena FRG, Oliveira Filho AT de (2011) Composição, estrutura e similaridade florística da Floresta Atlântica, na Serra Negra, Rio Preto - MG. Composition, structure and floristic similarity of Atlantic Forest, Serra Negra, Rio Preto - MG. https://doi.org/10.1590/2175-7860201162209 Vilela DS, Farias ABS, Santos JC (2021) Heteragrion lencionii (Odonata: Heteragrionidae) sp. nov. from Serra de Itabaiana National Park, Northeastern Brazil. Zootaxa 4966:476482. https://doi.org/10.11646/zootaxa.4966.4.6 Wani ZA, Khan S, Bhat JA, et al (2022) Pattern of β-Diversity and Plant Species Richness along Vertical Gradient in Northwest Himalaya, India. Biology 11:1064. https://doi.org/10.3390/biology11071064 Webb CO (2000) Exploring the Phylogenetic Structure of Ecological Communities: An Example for Rain Forest Trees. The American Naturalist 156:145–155. https://doi.org/10.1086/303378 Wickham H (2016) ggplot2. Springer International Publishing, Cham Worthy SJ, Jiménez Paz RA, Pérez ÁJ, et al (2019) Distribution and Community Assembly of Trees Along an Andean Elevational Gradient. Plants (Basel) 8:326. https://doi.org/10.3390/plants8090326 Xue G, Zeng J, Huang J, et al (2024) Effects of Soil Properties and Altitude on Phylogenetic and Species Diversity of Forest Plant Communities in Southern Subtropical China. Sustainability 16:11020. https://doi.org/10.3390/su162411020 Zappi DC, Filardi FLR, Leitman P, et al (2015) Growing knowledge: an overview of Seed Plant diversity in Brazil. Rodriguésia 66:1085–1113. https://doi.org/10.1590/2175-7860201566411 Zhang W, Huang D, Wang R, et al (2016) Altitudinal Patterns of Species Diversity and Phylogenetic Diversity across Temperate Mountain Forests of Northern China. PLOS ONE 11:e0159995. https://doi.org/10.1371/journal.pone.0159995 Zhu Y, Jiang Y, Liu Q, et al (2009) Elevational trends of biodiversity and plant traits do not converge—a test in the Helan Range, NW China. Plant Ecol 205:273–283. https://doi.org/10.1007/s11258-009-9616-1 Zhu Z-X, Nizamani MM, Sahu SK, et al (2019) Tree abundance, richness, and phylogenetic diversity along an elevation gradient in the tropical forest of Diaoluo Mountain in Hainan, China. Acta Oecologica 101:103481. https://doi.org/10.1016/j.actao.2019.103481 Supplementary Information Supplementary Information 1 and Supplementary Information 2 files are not available with this version. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6032434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415971396,"identity":"c501d74b-1590-469c-b6c1-8f1fe1ffc6b7","order_by":0,"name":"Amadeu dos Santos-Neto","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3079-8888","institution":"South Dakota State University","correspondingAuthor":true,"prefix":"","firstName":"Amadeu","middleName":"dos","lastName":"Santos-Neto","suffix":""},{"id":415971397,"identity":"bd551f19-af10-4781-8feb-3a5c8b792481","order_by":1,"name":"Adauto de Souza Ribeiro","email":"","orcid":"https://orcid.org/0000-0001-7734-3974","institution":"Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Adauto","middleName":"de Souza","lastName":"Ribeiro","suffix":""}],"badges":[],"createdAt":"2025-02-14 17:19:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6032434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6032434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76720351,"identity":"17e1bc2f-65c5-4d86-aa5c-45967b3601fb","added_by":"auto","created_at":"2025-02-20 04:30:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":457426,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the Serra de Itabaiana National Park, located in the state of Sergipe, Northeastern Brazil. Different colors indicate the location of transects in different elevation belts\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/201ee03e3612d7d9bf142fae.png"},{"id":76720355,"identity":"30d85c5a-2c29-4178-969e-04638d02721e","added_by":"auto","created_at":"2025-02-20 04:30:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":856700,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies found in this study in the grassy vegetation of the Serra de Itabaiana National Park. a - Lantana camara L., b - Sauvagesia erecta L c - Humiria balsamifera (Aubl.) A.St.-Hil. d - Chamaecrista cytisoides (DC. ex Collad.) H.S.Irwin \u0026amp; Barneby, e - Byrsonima sericea DC., f - Mandevilla scabra (Hoffmanns. ex Roem. \u0026amp; Schult.) K.Schum., g - Lantana gracilis T.Silva, h - Cuphea flavaSpreng., i - Melocactus violaceus Pfeiff., j - Vellozia dasypusSeub., k - Cuphea pulchraMoric. l - Acritopappus confertus (Gardner) R.M.King \u0026amp; H.Rob\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/b1b92ac50cff900b578beaff.png"},{"id":76721078,"identity":"a80f7786-8abf-4692-b122-446628e10047","added_by":"auto","created_at":"2025-02-20 04:38:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46685,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of species (%) occurring in one or more elevation belts (a) and proportion of species in each life form category (b)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/7df882150792753236b70c50.png"},{"id":76720354,"identity":"e21776b0-2c3a-4e16-89bf-953a4d4ec258","added_by":"auto","created_at":"2025-02-20 04:30:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51154,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between species richness and (a) elevation, (b) precipitation, and (c) temperature, based on mixed linear regression models. Shaded areas represent 95% confidence intervals around the fitted regression lines. Species richness decreases with elevation (green), while it increases with precipitation (blue) and temperature (red). Points represent observed values\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/79b5dd02e24af5008ead1292.png"},{"id":76720368,"identity":"543383dc-84b3-4678-b995-7c08938bbf85","added_by":"auto","created_at":"2025-02-20 04:30:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105268,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between phylogenetic diversity metrics and environmental variables based on mixed linear regression models. Panels show standardized effect sizes (ses) of phylogenetic diversity (ses.PD, ses.MPD, ses.MNTD) in relation to (a-c) elevation, and (g-i) precipitation. Shaded areas represent 95% confidence intervals, and points indicate observed values. Negative trends are evident for ses.PD (blue) and ses.MPD (red) with elevation, while ses.MNTD (green) shows weak positive or neutral trends across variables.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/2617bfa312840a511a3cb08e.png"},{"id":76720359,"identity":"a8fe0ab7-8712-4d8a-983e-1207c19b2e8c","added_by":"auto","created_at":"2025-02-20 04:30:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94205,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise comparisons of beta diversity components across elevation bands. Panels illustrate (A) Turnover, (B) Phylogenetic Turnover, (C) Nestedness, (D) Phylogenetic Nestedness, (E) Total Beta Diversity, and (F) Phylogenetic Total Beta Diversity. Blue shades indicate lower values, and red/orange shades indicate higher values for the respective diversity components.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/e56f9431986d281f0cc80675.png"},{"id":76720366,"identity":"374fed04-8a86-48b9-8019-cf41efca15bd","added_by":"auto","created_at":"2025-02-20 04:30:45","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":77297,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS ordination of plant community composition across elevation gradients. Points represent sites, colored by elevation bands (light blue: 200 m; dark blue: 600 m). Species are labeled with abbreviations. Environmental variables (precipitation and temperature) are shown as vectors, indicating their influence on community composition. Stress =\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/01490b69ac5358d71ccd511c.png"},{"id":76722991,"identity":"6b2e0cde-6327-4f0c-90a0-f7ca9203e8d5","added_by":"auto","created_at":"2025-02-20 04:54:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2536494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6032434/v1/d319b49b-c206-4a70-a08f-a4e43b583637.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eExploring Phylogenetic Diversity and Floristic Shifts Along an Elevational Gradient in a Northeast Brazilian Alpine Ecosystem\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe uneven distribution of biodiversity across the Earth's surface (Gaston \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and the mechanisms that promote this variation have been central questions in ecology (Hawkins \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sanders and Rahbek \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this context, environmental gradients, particularly latitude (De Frenne et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and altitude (Rahbek \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), are key models for studying organism distribution. The latitudinal gradient is well-known for increasing richness toward lower latitudes, while the elevational gradient reflects changes with elevation (Gillman et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe elevational gradient of diversity is more complex and less predictable, not consistently mirroring the latitudinal gradient as previously assumed (K\u0026ouml;rner \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Payne et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Elevation poses challenges for most organisms due to changes in the environmental conditions like temperature and precipitation (Alonso-Amelot \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Grabherr et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), requiring morpho-physiological adaptations (Tranquillini \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e1964\u003c/span\u003e). Despite these pressures, alpine ecosystems host remarkable diversity (Tenorio et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with endemic species exceeding 90% in some regions (Tiwari et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Carbutt \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRichness typically decreases with elevation (Griffiths et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but this pattern can vary, showing either a monotonic decline or a hump-shaped curve, with higher richness at intermediate altitudes (Guo et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Notably, species richness and phylogenetic diversity, which measures the evolutionary distance between species in a community, may mirror each other (Ba\u0026ntilde;ares-de-Dios et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) or diverge (Schroeder et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), reflecting complex ecological and evolutionary dynamics (Zhang et al. \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have focused on large mountain ranges (above 1000 meters), such as Ningxia Helan Mountain (He et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the Himalaya (Sharma et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the Andes (Cuesta et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, smaller mountain ranges below 1000 meters remain understudied, often revealing complex ecological patterns crucial to understanding biodiversity (Silva et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2019c\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Diniz et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Brazil, most studies on diversity patterns along elevational gradients focus on the southern and southeastern regions (Gastauer et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Mariano et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Bergamin et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, recent investigations into alpine communities in northeastern Brazil have shed light on their unique plant compositions (Lima et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Leitman et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Melo et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). Despite these efforts, the structural dynamics of these communities and the factors influencing them remain poorly understood (Silva et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2019c\u003c/span\u003e; Ramos et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Diniz et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Pinto et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Serra de Itabaiana Mountain Range in northeastern Brazil is a low-elevation system, with its highest peak reaching only 670 meters above sea level (Dantas and Ribeiro \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). This range supports a diverse plant community exceeding 800 species (Silva et al. \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e), with forested areas dominating the base and shrub-grassland vegetation characterizing the slopes (Dantas and Ribeiro \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). Historically, this ecosystem has experienced significant disturbance from economic activities, some of which persist due to unresolved land expropriation issues and illegal practices, even after the area was granted legal protection (Figueir\u0026ecirc;do and Souza). Notably, the range is home to endemic species, such as \u003cem\u003eAspilia itabaianensis\u003c/em\u003e J.U.Santos and \u003cem\u003eSinningia nordestina\u003c/em\u003e Chautems, Baracho \u0026amp; Siqueira-Filho, and threatened species, including \u003cem\u003eMelocactus violaceus\u003c/em\u003e Pfeiff. and \u003cem\u003eGriffinia espiritensis\u003c/em\u003e Ravenna (Silva et al. \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the ecological significance of the Serra de Itabaiana Mountain Range, this study aims to investigate altitudinal patterns in plant diversity, including species richness and phylogenetic diversity, as well as shifts in the phylogenetic structure of the plant community, beta diversity, beta phylogenetic diversity, and floristic composition along the elevation gradient. Additionally, it evaluates the influence of temperature and precipitation on these patterns. Based on the available literature, we predict that species richness will decrease with elevation (Rahbek \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), while phylogenetic diversity will be higher at mid-elevation, likely due to more favorable environmental conditions at intermediate altitudes. Consistent with findings from similar studies, we also anticipate a phylogenetically clustered community at the highest elevations, driven by the harsher environmental conditions typical of these areas. Furthermore, we expect Hemicryptophytes to dominate higher elevations due to their morphological adaptations to colder and windier conditions. Finally, we predict that abiotic factors such as temperature and precipitation will play a significant role in shaping these patterns across the gradient.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eOur study was conducted at Serra de Itabaiana National Park (SINP, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), covering 7,966 hectares across the municipalities of Areia Branca, Itabaiana, Campo do Brito, Itaporanga D\u0026rsquo;Ajuda, and Laranjeiras (Dantas and Ribeiro \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e). It comprises three sub-units (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (1) Cumprida, (2) Cajueiro, and (3) Itabaiana mountain ranges, with three types of vegetation formations: forests, open natural areas, and open anthropized areas (Dantas and Ribeiro \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010c\u003c/span\u003e). The climate in SINP is classified as 'As' according to the K\u0026ouml;eppen-Geiger classification (Ara\u0026uacute;jo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with elevations reaching up to 670 meters above sea level (Santos et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), making it the second-highest point in the state of Sergipe (Rocha et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Unlike most federal protected areas in the state of Sergipe, the SINP is a rare example of a relatively well-documented biotic component (Silva et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Silva \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vilela et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Farias et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, However, despite its classification as a fully protected area, NPSI biodiversity remains threatened by frequent fires, agricultural activities in unexpropriated areas, sand and clay mining, vegetation removal, and hunting (Sobral et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling\u003c/h2\u003e \u003cp\u003eA pre-established trail between the point popularly known as \"Po\u0026ccedil;o das Mo\u0026ccedil;as\" and the top of the SINP was used as a transect. Five elevational bands (EB) were established (between 250 and 650 m), each separated by 100 meters in elevation (Theurillat et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and in each EB three 50 m\u0026sup2; plots were delineated (Lembrechts et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The plots are 20 meters apart from each other and 15 meters away from the trail margins; these distances could not be extended further due to the constraints imposed by the cliff range. The plots are 20 meters apart and 15 meters from trail margins, with distances limited by the cliff range. Samples were collected from areas of open grassy vegetation unaffected by human activity. Although SINP covers a large area, steep terrain above 250 meters restricts sampling. The park's dome includes a rocky wall, and other sections feature slopes with a medium to high risk.\u003c/p\u003e \u003cp\u003eWe characterized the floristic composition by considering all species present (even partially) within these quadrats over the course of a year, totaling 24 field excursions. In every field excursion, we visited all sites to ensure we included species with different phenologies. Each excursion typically lasted 6 to 8 hours, depending on weather conditions. The peak of summer posed challenges due to the intense heat, while the rainy season made the area less suitable for walking and increased the risk of falls. Specimens were identified with the aid of specialized literature (Dantas and Ribeiro \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e; Mendes et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e) and classified according to their life form, following Raunkiaer\u0026rsquo;s classification (Smith \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e1913\u003c/span\u003e),which add up plant adaptation strategies, functional diversity shifts, and resilience across elevation gradient. The plants were then processed using standard methods for plant material (Alexiades \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) and deposited in the internal collection of the Laboratory of Dendroecology and Plant Anatomy, part of the Department of Biology at the Federal University of Sergipe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Environmental Data\u003c/h2\u003e \u003cp\u003eWe obtained climate information for each site using data from the WorldClim platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), utilizing the highest spatial resolution layers available (30 seconds or 1 km\u0026sup2;) (Fick and Hijmans \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The available variables included minimum temperature (\u0026deg;C), maximum temperature (\u0026deg;C), mean temperature (\u0026deg;C), precipitation (mm), solar radiation (kJ m⁻\u0026sup2; day⁻\u0026sup1;), wind speed (m s⁻\u0026sup1;), and water vapor pressure (kPa). For each variable, 12 high-resolution layers were downloaded, totaling 84 .tiff files. To extract the data, the files were converted into spatial objects using the raster() function, and the climatic data for the points of interest (coordinates of each inventoried plot) were extracted using the extract() function, both from the Raster package version 3.6\u0026ndash;26 (Hijmans \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The resolution provided unique climate data for each elevation belt but not for individual plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Phylogenetic Tree Generation\u003c/h2\u003e \u003cp\u003eBased on the final plant list, we used the V.PhyloMaker2 package (version 0.1.0) with the \u003cb\u003eGBOTB.extended.TPL.tre\u003c/b\u003e backbone under scenario \"1\" to generate a phylogenetic tree with 107 species (Jin and Qian \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).This scenario conservatively places missing taxa at genus or family basal nodes, which reduces phylogenetic uncertainty while preserving hierarchical relationships. This conservative approach minimizes bias in phylogenetic placement, making it particularly suitable for detecting subtle phylogenetic patterns along narrow elevational gradients without overestimating relationships (Jin and Qian \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For this, we included only taxa identified at the species or genus level to ensure accuracy and consistency (Ba\u0026ntilde;ares-de-Dios et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Additionally, scenario \"1\" in V.PhyloMaker2 assumes that missing taxa belong to the same evolutionary lineage as the included taxa, making it a robust choice when handling incomplete taxonomic information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Phylogenetic Structure and Signal\u003c/h2\u003e \u003cp\u003eWe employed three metrics related to community phylogenetic structure: 1. standardized effect size of Phylogenetic Diversity (ses.PD). Due to the existence of several metrics of phylogenetic diversity, here, we define that the phylogenetic diversity index is the one proposed by Faith (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). This index is the sum of all phylogenetic branch lengths connecting the species (Faith \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Qian and Deng \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and it is widely used as a biodiversity metric (Tucker et al. \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). 2. standardized effect size of Mean Pairwise Distances (ses.MPD), which estimates the average phylogenetic relatedness among all pairs of species within a community and indicates deep relatedness in the tree. The third metric, standardized effect size of Mean Nearest Taxon Distances (ses.MNTD), estimates the average phylogenetic relatedness, that is an estimate of the average phylogenetic relatedness between each taxon in a community and its closest relative in another community (Webb \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kellar et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). All three metrics included here are widely used in studies investigating changes in plant community structure along environmental gradients (Worthy et al. \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nosrati et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Tests were conducted in the R environment (version 4.3.1), using ses.PD(), ses.MPD(), and ses.MNTD() functions from the Picante package version 1.8.2 (Kembel et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), without abundance weighting (abundance.weighted\u0026thinsp;=\u0026thinsp;FALSE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data Analysis\u003c/h2\u003e \u003cp\u003eWe investigated patterns of richness and Phylogenetic Structure (PS) along the gradient using Linear Mixed Models (LMMs) for richness and Generalized Linear Mixed Models (GLMMs) for the phylogenetic structure metrics. Richness, along with each phylogenetic structure metric, served as the response variable, with elevation as the main predictor, while variation within elevation bands was accounted for as a random effect (Bolker et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). We used the same approach (LMMs) to assess the influence of climatic variables on richness and PD. In this analysis, we tested various combinations of the climatic variables as predictors, elevation band as random effect, and selected the best-fitting model based on the Akaike Information Criterion (AIC). For models with richness, we used a Poisson distribution (Inouye et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and for models with PS metrics, we used Gaussian distribution (Mienna et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo explore the relationship between these phylogenetic indices and environmental factors, we used the lme4 package to fit linear mixed models (LMMs) with the lmer function. The models included Temperature and Precipitation as fixed effects, with random effects accounting for variation across elevation bands. The lmerTest package was used to automatically calculate p-values for the fixed effects, allowing us to assess the significance of environmental variables in shaping the phylogenetic structure of the community.\u003c/p\u003e \u003cp\u003eWe performed a Permutational Multivariate Analysis of Variance (PERMANOVA) with 9999 permutations (Anderson \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), using Bray-Curtis\u0026rsquo;s dissimilarity index to identify the importance of climatic variables in floristic composition variation along the elevation gradient (Haug et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mart\u0026iacute;nez-Camilo et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For this, we created a dissimilarity matrix using the vegdist() function and conducted PERMANOVA using the adonis2() function. Non-metric Multidimensional Scaling (NMDS) with metaMDS() function was used to visualize changes in floristic composition along the elevation gradient and its relationship with predictor environmental variables. Due to the high correlation between variables, we only include mean annual temperature and annual precipitation. We performed both PERMANOVA and NMDS using the Vegan package version 2.6-4 (Oksanen et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NMDS plots were generated using ggplot2 package version 3.5-0 (Wickham \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in R environment (R Development Core Team 2021).\u003c/p\u003e \u003cp\u003eWe checked the normality of the residuals for the response variables using the Shapiro-Wilk test to ensure the assumptions of the analysis were met and examined the residual distribution with Q-Q, Residuals vs Fitted, Scale-Location and Residual vs. Leverage (Nobis and Schweingruber \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, we assessed multicollinearity among the predictor variables by calculating the Variance Inflation Factor respecting a threshold of 4, with moderate linearity (Shrestha \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We used the VIF() function from the car package 3.1-2 (Fox et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Community Summary\u003c/h2\u003e \u003cp\u003eWe identified 108 species of flowering plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Material 1) along the studied gradient. These species belong to 85 genera, 46 families, and 23 orders. The richest order was Poales, with 15 species, followed by Myrtales (13 species), and Fabales, Gentianales, and Malpighiales (12 species each). Leguminosae was the most diverse family, comprising nine species, followed by Apocynaceae (six species), and Verbenaceae, Melastomataceae, Cyperaceae, and Rubiaceae (five species each). The richest genera were \u003cem\u003eMandevilla\u003c/em\u003e Lindl. and \u003cem\u003eCuphea\u003c/em\u003e P. Browne (four species each), followed by \u003cem\u003eLantana\u003c/em\u003e L., \u003cem\u003ePolygala\u003c/em\u003e L., \u003cem\u003eChamaecrista\u003c/em\u003e (L.) Moench, and \u003cem\u003eMyrcia\u003c/em\u003e DC. (three species each).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMost species exhibit a specialist distribution pattern, being restricted to a single elevation belt. This is followed by species occurring in two or three elevation belts, and finally by those present in four or five belts, which represent the most generalist distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Overall, most species are classified as phanerophytes (48.6%) and hemicryptophytes (34.7%), with fewer species categorized as chamaephytes (10.9%), geophytes (3.4%), and therophytes (2.5%). Phanerophytes consistently dominated across all elevation belts, followed by hemicryptophytes, chamaephytes, geophytes, and therophytes in that order (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Alpha Diversity\u003c/h2\u003e \u003cp\u003eThe species richness across the elevation gradient shows a clear decline with increasing elevation (β = -0.002492, z = -7.985, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). At 250 m, the community has the highest richness with 69 species, which decreases progressively at higher elevations: 58 species at 350 m, 36 species at 450 m, 32 species at 550 m, and 27 species at 650 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWe found significant relationships between richness and the predictors, precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Richness increased significantly with higher precipitation (β\u0026thinsp;=\u0026thinsp;0.010214, z\u0026thinsp;=\u0026thinsp;2.074, p\u0026thinsp;=\u0026thinsp;0.038, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and temperature (β\u0026thinsp;=\u0026thinsp;0.232604, z\u0026thinsp;=\u0026thinsp;3.068, p\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The model included a random intercept for elevation belts, but the variance for the elevation grouping was estimated to be zero, suggesting that random variation at the elevation level did not contribute to explaining species richness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Alpha Phylogenetic Diversity\u003c/h2\u003e \u003cp\u003eOur findings for ses.PD (observed PD: 2026.886, Z = -2.263, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and SES.MNTD (observed MNTD: 99.864, Z = -2.167, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggest that the community at the top of the gradient (650 m) is phylogenetically clustered. None of the other elevation bands differed significantly from what would be expected by chance, mirroring the pattern observed for ses.MPD. None of our models with elevation and phylogenetic structure: ses.PD (β=\u0026minus;0.0057, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.43, p\u0026thinsp;=\u0026thinsp;0.085, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), ses.MPD (β=\u0026minus;0.0019, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.35, p\u0026thinsp;=\u0026thinsp;0.199, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and ses.MNTD (β=\u0026minus;0.0056, t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.52, p\u0026thinsp;=\u0026thinsp;0.076, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) were not significant with elevation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003eOur models did not show relationships between the climate predictors and the phylogenetic structure. For SES.PD, while temperature (β\u0026thinsp;=\u0026thinsp;0.493, t\u0026thinsp;=\u0026thinsp;1.228, p\u0026thinsp;=\u0026thinsp;0.272, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and precipitation (β\u0026thinsp;=\u0026thinsp;0.039, t\u0026thinsp;=\u0026thinsp;1.285, p\u0026thinsp;=\u0026thinsp;0.247, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed) had positive estimates, their effects were not statistically significant. Similarly, for SES.MNTD, temperature (β\u0026thinsp;=\u0026thinsp;0.494, t\u0026thinsp;=\u0026thinsp;1.231, p\u0026thinsp;=\u0026thinsp;0.271, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) and precipitation (β\u0026thinsp;=\u0026thinsp;0.036, t\u0026thinsp;=\u0026thinsp;1.208, p\u0026thinsp;=\u0026thinsp;0.270, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) showed non-significant relationships. Lastly, for SES.MPD, neither temperature (β\u0026thinsp;=\u0026thinsp;0.186, t\u0026thinsp;=\u0026thinsp;0.571, p\u0026thinsp;=\u0026thinsp;0.576, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) nor precipitation (β\u0026thinsp;=\u0026thinsp;0.012, t\u0026thinsp;=\u0026thinsp;0.471, p\u0026thinsp;=\u0026thinsp;0.644, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef) significantly influenced the response variable. In all models, a random intercept for elevation bands was included. However, the variance for the elevation grouping was small, indicating minimal or no contribution of random variation at the elevation level to explaining the observed patterns in phylogenetic diversity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Trends Between Families and Orders\u003c/h2\u003e \u003cp\u003eSeveral plant orders exhibit notable trends across the elevation gradient (Supplementary Material 2). Gentianales remains important throughout the gradient, with a peak at 350 m (13.56%) and a strong presence at 650 m (13.79%). Lamiales show a steady increase, reaching their peak at 650 m (17.24%). Fabales follows a similar trend, with its highest importance at 650 m (10.34%). In contrast, Myrtales, Rosales, and Malvales show a marked decrease at higher elevations, particularly at 650 m, where Myrtales drop to 3.45% and Rosales to total absence. Poales increase consistently across the gradient, peaking at 650 m (20.69%). Other orders such as Boraginales, Caryophyllales, and Arecales fluctuate in the present but do not show drastic changes. Solanales appear only at mid-elevations, and Brassicales and Zingiberales are absent at higher elevations.\u003c/p\u003e \u003cp\u003eFamilies also exhibit distinct patterns across the elevation gradient (Supplementary Material 2). Fabaceae shows a significant increase in importance at higher elevations, particularly peaking at 650 m (10.34%). Similarly, Arecaceae, Orchidaceae, and Malpighiaceae also become more prominent at mid to high elevations, with Orchidaceae peaking at 650 m. In contrast, families like Gentianaceae and Bignoniaceae show peaks at mid-elevations (350 m to 450 m) but decrease at higher elevations, particularly at 650 m. Some families, including Dilleniaceae, Sapindaceae, and Myrtaceae, drop to zero at 650 m. Additionally, families such as Lythraceae, Orobancaceae, and Malvaceae disappear at higher elevations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 β-Diversity\u003c/h2\u003e \u003cp\u003eThe analysis of beta diversity across elevation belts revealed that species dissimilarity (βsor) is primarily driven by species replacement (βsim\u0026thinsp;=\u0026thinsp;0.3909) rather than species loss or gain (βsne\u0026thinsp;=\u0026thinsp;0.1270). Turnover (βsim) values ranged from 0.21 to 0.58 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), indicating that differences in species composition between sites are largely due to species replacement. In contrast, nestedness (βsne) values were consistently lower, ranging from 0.04 to 0.19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), suggesting that species loss or gain contributes less to community differences. Total beta diversity (βsor) ranged from 0.25 to 0.75 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), with the highest dissimilarity observed between the most distant elevation belts (e.g., 250 m and 650 m), reflecting significant species turnover. Conversely, closer elevation belts (e.g., 250 m and 350 m) exhibited lower βsor values, indicating more similar species composition and reduced turnover.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThese findings emphasize that species replacement dominates beta diversity patterns, particularly along larger elevation gradients, likely driven by environmental differences. This was supported by our PERMANOVA results, which showed that species composition varies significantly along the gradient, with temperature variation explaining 52.77% (F\u0026thinsp;=\u0026thinsp;19.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) of the variation in species composition across the gradient, while precipitation explains 17.70% (F\u0026thinsp;=\u0026thinsp;5.426, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Phylogenetic β-Diversity\u003c/h2\u003e \u003cp\u003eThe analysis of phylogenetic beta diversity across elevation belts revealed that phylogenetic dissimilarity is primarily driven by phylogenetic turnover rather than nestedness. Turnover values ranged from 0.15 to 0.37 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), indicating that differences in phylogenetic composition between sites are largely due to replacement of evolutionary lineages. In contrast, nestedness values were consistently lower, ranging from 0.05 to 0.23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), suggesting that lineage loss or gain contributes less to phylogenetic differences.\u003c/p\u003e \u003cp\u003eTotal phylogenetic beta diversity ranged from 0.20 to 0.55 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), with the highest dissimilarity observed between the most distant elevation belts (e.g., 250 m and 650 m), reflecting significant phylogenetic turnover. Conversely, closer elevation belts (e.g., 250 m and 350 m) exhibited lower total beta diversity values, indicating more similar phylogenetic composition and reduced turnover.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur findings represent 55% of the richness previously identified for the \u003cem\u003eAreias Brancas\u003c/em\u003e community (Dantas and Ribeiro \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e) and 13% of the species recorded for the NPSI community, encompassing Atlantic Forest and Caatinga species (Silva et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). While no prior study has floristically characterized undisturbed open fields along this gradient, we documented numerous herbaceous and shrub species from regional inventories, excluding steep slopes dominated by grasses due to safety concerns.\u003c/p\u003e \u003cp\u003eAmong the 10 most stable families along the gradient, eight (Fabaceae, Asteraceae, Apocynaceae, Rubiaceae, Cyperaceae, Melastomataceae, Poaceae, and Orchidaceae) are also among the 10 most species-rich in the Caatinga biome, with only Malpighiaceae and Verbenaceae absent (Zappi et al. \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This highlights the importance of adaptations like drought tolerance and water storage, often present in these families, which enable many Caatinga species to thrive in arid environments (Accioly et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVerbenaceae, though not diverse in the Caatinga, thrives in high-elevation environments, with species like \u003cem\u003eLantana\u003c/em\u003e L. and \u003cem\u003eStachytarpheta\u003c/em\u003e Vahl found in Sergipe's mountains (Machado et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eLantana\u003c/em\u003e species adapts to arid habitats through deep roots and low stomatal density to minimize water loss (Lu-Irving et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while \u003cem\u003eStachytarpheta\u003c/em\u003e survives in rocky, high-altitude vegetation by reducing transpiration with waxy leaf coatings (Barreto et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, Fabaceae, Rubiaceae, and Apocynaceae adapt to harsh conditions with strategies like nitrogen fixation, UV protection, thick cuticles, and succulent leaves that conserve water (Delprete and Jardim \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fishbein et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Morim et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These adaptations enable them to thrive in nutrient-poor, arid environments.\u003c/p\u003e \u003cp\u003eSimilarly, several key genera in this study play crucial roles in Brazil's higher elevation ecosystems, likely due to their ecological traits (Halbritter et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). For instance, some \u003cem\u003ePolygala\u003c/em\u003e L. species have trichome-covered leaves that reflect sunlight and reduce transpiration (de Almeida Campos Cordeiro and Neri \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Martinez et al. 2022). Similarly, \u003cem\u003eMandevilla\u003c/em\u003e Lindl. (Arag\u0026oacute;n et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eLantana\u003c/em\u003e L. (Arag\u0026oacute;n et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eCuphea\u003c/em\u003e P.Browne (Graham \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), \u003cem\u003eChamaecrista\u003c/em\u003e (L.) Moench (Coutinho et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e),, and \u003cem\u003eMyrcia\u003c/em\u003e DC. (Costa et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) exhibit traits like waxy coatings, small or hairy leaves, and deep root systems, which enhance survival in arid, high-altitude environments.\u003c/p\u003e \u003cp\u003eThe plant community consists mainly of Phanerophytes and Hemicryptophytes, dominated by low shrubs and small trees (~\u0026thinsp;3 m tall) like \u003cem\u003eKielmeyera rugosa\u003c/em\u003e Choisy and \u003cem\u003eJacaranda obovata\u003c/em\u003e Cham. The tree component of these areas has been previously described as a sparse tree layer (Dantas and Ribeiro \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010c\u003c/span\u003e), which disappears at higher elevations (Jankowski et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The dominance of Phanerophytes, such as \u003cem\u003eAcritopappus confertus\u003c/em\u003e (Gardner) R.M.King \u0026amp; H.Rob. and \u003cem\u003eStigmaphyllon paralias\u003c/em\u003e A. Juss., and hemicryptophytes, including \u003cem\u003eSauvagesia erecta\u003c/em\u003e L. and \u003cem\u003eEpistephium lucidum\u003c/em\u003e Cogn., in elevated environments follows a well-recognized pattern (Silveira et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Neri et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rawat et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to the findings of most studies in this field, which often report hemicryptophytes becoming hyper-dominant at higher elevations due to their adaptability to harsher conditions (Di Biase et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Rawat et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), while phanerophytes tend to become insignificant or disappear entirely (Rawat et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), what was not found by this study (Silva Mota et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; de Almeida Campos Cordeiro and Neri \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Di Biase et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). This pattern could be explained by many reasons. Certain phanerophytes may be adapted to higher elevation conditions due to soil and nutrient availability, the presence of sheltered areas (e.g., valleys, rocky outcrops), or historical and biogeographic factors. According to Neri et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), soil depth can be a crucial factor influencing the prevalence of phanerophytes in higher regions, as these species require deeper soils for their longer roots and seed bank formation. However, this dominance could also reflect environmental changes at the surveyed sites, suggesting an expansion of shrub communities (woody plant encroachment) within grass communities (Palaj and Koll\u0026aacute;r \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, this condition may be linked to intense grazing, deforestation, frequent forest fires, and anthropogenic pressures such as firewood and forage collection (Rawat et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general terms, the decline in chamaephytes (e.g., \u003cem\u003eLantana fucata\u003c/em\u003e Lindl. and \u003cem\u003eMelocactus violaceus\u003c/em\u003e N.P.Taylor). It diverges from the common pattern for this group, which typically becomes more prominent in high-altitude environments (Matteodo et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Chamaephytes are adapted to arid environments (Ara\u0026uacute;jo et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and usually can protect their buds by keeping them at ground level (Caiafa and Silva \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). They also can regulate water release and absorption during hot summers (Fadl et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but without drastic environmental changes, small phanerophytes may replace them (Irl et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe observed a consistent monotonic decline in richness with elevation, the second most common pattern, after the hump-shaped pattern (Dani et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), though remains the predominant for smaller gradients (\u0026lt;\u0026thinsp;1000 m) (Rahbek \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This pattern has been observed in gradients in the Amazon Forest of Brazil (Campos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Guyana (Gastauer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), as well as in the Brazilian Atlantic Forest (Rezende et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Grasslands (Silva Mota et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Although prevalent in Neotropical ecosystems (G. and Givnish \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; S\u0026aacute;nchez-Gonz\u0026aacute;lez and L\u0026oacute;pez-Mata \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Toledo-Garibaldi and Williams‐Linera \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; He et al. 2024), it was not observed in the in the Caatinga, where richness increased with elevation, mostly associated to the water availability (Silva et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis decline along the gradient likely reflects strong environmental filters that limit species distributions (Paciencia \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; K\u0026ouml;rner et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Environmental variables, particularly temperature, vary significantly with elevation, typically decreasing by 0.3 to 0.6\u0026deg;C per 100 meters of elevation gain (Grytnes and McCain \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Such temperature reductions directly affect plant physiology, growth, and reproduction, shaping community composition and diversity (Halbritter et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). Plant richness often decreases with elevation due to harsher abiotic conditions, shorter growing seasons, and reduced atmospheric pressure, which limit germination and reproduction (Blume-Werry et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nutrient-poor, acidic soils and lower primary productivity at higher altitudes further restrict diversity (Fyllas et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; M\u0026uuml;ller et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, high elevations face stronger winds, increased UV radiation (Blumthaler et al. 1997), and reduced pollination (Adedoja et al. 2018) and seed dispersal (Anadon-Rosell et al. 2020), limiting plant reproductive success.\u003c/p\u003e \u003cp\u003eInterestingly, while species richness declines with elevation, phylogenetic diversity peaks at mid-elevation, following a hump-shaped pattern (Rowe and Lidgard \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This pattern has been widely observed in mountain regions worldwide, including the Hengduan Mountains in southwestern China (Li et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Shiretoko Peninsula in Japan (Kitagawa et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the Western Himalayas in India, and Changbaishan in China (Manish and Pandit \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The hump-shaped pattern has also been observed when measuring species richness (Tuomisto et al. \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kamimura et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and is typically associated with the moderate environmental conditions found at intermediate elevations (Zhu et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, the mechanisms underlying this relationship remain only partially understood (Acharya et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth ses.PD and ses.MNTD indicate a clustered community at higher elevations. Clustered communities at higher elevations are common (Qian et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and found in Brazilian Grasslands (de Mattos et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gastauer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e) and Tropical Forest (Mariano et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). These ecosystems often selects specialized traits, and since phylogenetically related species tend to share these traits, they typically form clustered communities (Burns and Strauss \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe divergent results for ses.MNTD (terminal) and ses.MPD (basal) (Mazel et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggest clustering occurs at finer phylogenetic scales without impacting overall community phylogenetic diversity. This highlights the importance of local environmental factors and habitat heterogeneity. While the community shows no significant phylogenetic structure, distinct microenvironments, like mountaintops, may drive phylogenetic clustering.\u003c/p\u003e \u003cp\u003eNeither ses.PD, ses.MPD, nor ses.MNTD are influenced by elevation, temperature, or precipitation, consistent with findings for tropical montane Atlantic Cloud Forest (Mariano et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)d ramo communities (Gastauer et al. 2020) in Brazil. However, this contrasts with results from Upland Forest in Northeastern Brazil (Pinto et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Tropical Forests in China (Qian et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where these indices are variable-dependent. Such differences likely reflect regional, methodological, and evolutionary factors. Beyond climatic variables, soil texture variability, not studied by us, often better explains phylogenetic diversity and structure (Campos et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFloristic composition shifts markedly along elevation gradients, a pattern extensively documented across diverse biomes, including Grasslands (Urbanetz et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), Tropical (Robertson et al. 2010; Valente et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Dry Forest (Diniz et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Additionally, this pattern has been described in key mountainous regions of Brazil, including the Mantiqueira mountain range (Meireles et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), Mar mountain range (Sanchez et al. \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and Cip\u0026oacute; mountain range (Silva Mota et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). where unique floristic assemblages emerge along altitudinal gradients, reflecting the complex interplay of ecological and evolutionary processes.\u003c/p\u003e \u003cp\u003eAs mentioned previously in our discussion, the temperature plays an important role here. It significantly influences species distribution (Grace \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Amissah et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) by affecting key processes like photosynthesis (\u0026Ouml;quist \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), respiration (Criddle et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and transpiration (Smith and Geller \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Plants thrive within specific temperature ranges, and along gradients, only those with suitable physiological adaptations can survive and reproduce. Warmer temperatures accelerate growth and development, while cooler temperatures slow these processes, leading to variations in species composition as plants adapt to differing thermal conditions.\u003c/p\u003e \u003cp\u003eThe plant community along this gradient is primarily shaped by species replacement (even when phylogenetic relatedness is included), with higher turnover observed between distant elevation belts (Guclu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This turnover is influenced by ecological and evolutionary processes, where species that are adapted to different environmental conditions replace one another as you move up or down the gradient and this was found across many elevation gradients ( Silva Mota et al. 2018c; Wani et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thorne et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cordeiro et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guclu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nonetheless, the turnover of species was greater than the turnover of phylogenetic lineages across various elevations, highlighting that diversification predominantly took place at the species level rather than at higher taxonomic levels like genus or family (de Andrade Kamimura et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, these findings show that the community at the highest elevations is not simply a subset of the species found across the gradient, but rather hosts many unique species, underscoring the distinct ecological conditions at these upper elevations (Baselga \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides the first detailed analysis of plant communities along an elevation gradient in the Serra de Itabaiana National Park, Sergipe, and is among the few in Northeast Brazil that incorporates phylogenetic data. Our results revealed a decline in species richness with increasing elevation, which met our prediction that species richness would decrease with elevation. Phanerophytes remained stable and dominant across the gradient, even at higher elevations, which did not meet our prediction that Hemicryptophytes would dominate higher elevations due to their morphological adaptations. Phylogenetic diversity followed a hump-shaped pattern, peaking at mid-elevations, which confirmed our prediction that phylogenetic diversity would be higher at mid-elevation due to more favorable environmental conditions. At the highest elevations, we observed a phylogenetically clustered community, consistent with our prediction that environmental conditions at these elevations would favor phylogenetic clustering. In terms of beta diversity, we observed significant turnover in species composition across elevations, highlighting the role of environmental filtering in shaping plant community structure, which was consistent with our expectation that abiotic factors, such as temperature and precipitation, would play a role in shaping these patterns. These findings emphasize the complex interplay between climate, evolutionary traits, and ecological processes in determining plant biodiversity. Given that our results showed how plant communities change across the elevation gradient, beta diversity should be incorporated into future conservation efforts to better capture the dynamics of species turnover and improve conservation planning in this ecologically significant region.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAccioly A do N, Farias R de P, Arruda ECP de (2024) Plants in the caatinga possess multiple adaptative leaf morphoanatomical traits concurrently, a pattern revealed from a systematic review. Journal of Arid Environments 222:105162. https://doi.org/10.1016/j.jaridenv.2024.105162\u003c/li\u003e\n\u003cli\u003eAcharya BK, Chettri B, Vijayan L (2011) Distribution pattern of trees along an elevation gradient of Eastern Himalaya, India. 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Acta Oecologica 101:103481. https://doi.org/10.1016/j.actao.2019.103481\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003eSupplementary Information 1 and Supplementary Information 2 files are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Beta Diversity, Environmental Filtering, Species Turnover, Phylogenetic Clustering, Environmental Gradients.","lastPublishedDoi":"10.21203/rs.3.rs-6032434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6032434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines plant diversity along an elevational gradient in the Serra de Itabaiana National Park, northeastern Brazil, focusing on species richness, phylogenetic diversity, and beta diversity. We identified 108 plant species across five elevation bands, with species richness declining linearly with increasing elevation. Precipitation and temperature positively influenced richness, but no significant relationship was observed for phylogenetic diversity metrics. Notably, phylogenetic diversity peaked at mid-elevations, exhibiting a hump-shaped pattern, while higher elevations hosted phylogenetically clustered communities, reflecting environmental filtering. Beta diversity analysis revealed turnover as the dominant driver of species and phylogenetic dissimilarity between elevation belts, emphasizing the role of species replacement over nestedness. These findings underscore the influence of climatic variables and environmental constraints on community composition, with distinct patterns of species adaptation across elevations. Contrary to expectations, phanerophytes dominated across the gradient, challenging the predicted prevalence of hemicryptophytes at higher elevations. Our study highlights the importance of integrating phylogenetic data to understand biodiversity dynamics in understudied regions like northeastern Brazil. Conservation efforts in Serra de Itabaiana should prioritize maintaining beta diversity to capture species turnover and ensure ecosystem resilience. These findings contribute valuable insights into the interplay of ecological and evolutionary processes shaping plant communities in low-elevation mountain systems.\u003c/p\u003e","manuscriptTitle":"Exploring Phylogenetic Diversity and Floristic Shifts Along an Elevational Gradient in a Northeast Brazilian Alpine Ecosystem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 04:30:40","doi":"10.21203/rs.3.rs-6032434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2a744b7-8dd4-4e09-939f-5850cf3e2ba1","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44553297,"name":"Botany"}],"tags":[],"updatedAt":"2025-02-20T04:30:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 04:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6032434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6032434","identity":"rs-6032434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}