Exploring plant α- and β-diversity patterns along tropical altitudinal gradients in the Southern Andes of Ecuador: a potential pathway to biodiversity conservation

Exploring plant α- and β-diversity patterns along tropical altitudinal gradients in the Southern Andes of Ecuador: a potential pathway to biodiversity conservation | 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 plant α- and β-diversity patterns along tropical altitudinal gradients in the Southern Andes of Ecuador: a potential pathway to biodiversity conservation Nubia Guzmán, Rodolfo Gentili, Mayra Jiménez, Raffaella Ansaloni, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8295166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The Tropical Andes provide ideal opportunities to study diversity patterns along altitudinal gradients. However, they remain poorly characterized, particularly in terms of detailed, ground-level data. The α- and β-diversity trends of vascular plants were studied along an altitudinal gradient from 1000 to 4200 m in the Jubones and the Cajas National Park areas, located in southern Ecuador using stratified sampling transects to generate floristic inventories with 146 plots surveyed in total. This gradient included dry tropical forests restricted to inter-Andean valleys, montane forests, páramo and Polylepis forests. A total of 891 species belonging to 391 genera and 118 families were recorded. Our findings for all plant species revealed a clear hump-shaped richness pattern, with peaks between 2800–3500 m with a high proportion of endemic species (13.6%). β-diversity was highest at lower elevations, then decreased with altitude. Marked variations in the diversity patterns were found with in-depth observations of the different growth forms of trees, shrubs, and herbs. Climatic factors such as temperature and precipitation were key determinants of species composition among the studied regions. Understanding diversity distribution in these regions is critical for predicting future plant refugia and developing effective conservation strategies in the face of climate change. Andes Biodiversity refugia The Cajas National Park The Jubones Valley Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Across mountain regions, the interactions among climatic factors (temperature, precipitation, atmospheric pressure, etc.) strongly influence the distribution and abundance of plant species and therefore determine the overall productivity of an altitudinal gradient and its biodiversity levels (Gentry, 1988; Rahbek et al., 2019). Particularly, biodiversity data collected along altitudinal gradients will also reflect the combined effects of regional peculiarities (meso- and microclimate, geology, geomorphology, etc.) with the general altitudinal phenomena (Gentry, 1988; Körner, 2007; Gentili et al., 2013). Consequently, the pattern in which plant species richness changes with altitude can exhibit various shapes. Specifically, a hump-shaped curve with maximum richness at mid-altitudes was observed on a mountain in Norway (Grytnes et al., 2006), in grassland pastures of the European Alpes (Fontana et al., 2020), in dryland ecosystems in China (He et al., 2023), or in the tropical and subtropical forests in northwest South America (Malizia et al., 2020). Other patterns include a linear decline, as seen in high-altitude alpine regions (Sekar et al., 2023), the high tropical Andes (Cuesta et al., 2017), or in temperate and tropical montane forests (Gentry, 1988; Homeier et al., 2010); and even a U-shaped pattern as identified for herbs in some temperate mountain regions (Zhang et al., 2016). Overall, it has been demonstrated that understanding the distribution of species diversity along altitudinal gradients is crucial for conservation, as patterns are often driven by environmental constraints and/or anthropogenic pressures. In addition, the possible presence of endemic species in such systems can highlights their evolutionary importance and conservation value (Di Musciano et al., 2024). The Tropical Andes are among the regions with the highest plant diversity in the world (Pérez-Escobar et al., 2022), with approximately 45,000 plant species (Myers et al., 2000), ̴ 15% of the global total, accounting for only 1% of the world's surface area (Pennington et al., 2010). This hyper-diversity and the unusual presence of endemic species are related to these mountain systems' complex topography and biogeographic history (Richter et al., 2009; Pennington et al., 2010). The timing and rate of Andean uplift have been highly uneven across its range, influencing regional climate, hydrological conditions, nutrient cycling, landscape development, and thus potential plant evolution mechanisms at a continental scale (Pérez-Escobar et al., 2022). For these reasons, the Andes mountains of South America are considered privileged sites for understanding ecosystem and ecological function along with variations in biodiversity, particularly by investigating altitudinal gradients (Malhi et al., 2010; Rahbek et al., 2019). Several authors have studied altitudinal biodiversity gradients of the Tropical Andes. It is well known that these mountains are characterized by high floristic β-diversity, which is partly related to the high diversity of the ecological communities in these environments and the presence of plant species with narrow geographic ranges and high habitat specialization (Gentry, 1988; Quintana et al., 2017a; Cuesta et al., 2017; Rahbek et al., 2019). To date, most studies have highlighted peaks in diversity at intermediate altitudes along the Andean mountains. For example, Kessler (2001) described a hump-shaped curve for some plant families in the Bolivian Andes, with peaks at mid-elevations or constant values from the lowlands to mid-elevations, followed by a marked decline at high elevations. Similar patterns have been observed in other studies across the Andes, although differences in the elevation of the maximum peak richness have been recorded (Kessler, 2000; Krömer et al., 2005; Salazar et al., 2013). In contrast, studies of trends in specific growth forms along altitudinal gradients have revealed discordant patterns. For example, Gentry (1988) reported a linear decrease in woody plant richness with altitude in Andean forests, from the lowland tropics (1500 m) to near tree line (3100 m). Similarly, Cuesta et al. (2017) demonstrated a clear decline in species richness with increasing elevation (from 3200 to 5500 m) in highland Andean ecosystems, including Puna and Páramo species. In contrast, Cueva et al. (2019) reported a linear increase in the number of tree species in the Andean dry forests of Ecuador with elevations from 1700 to 2500 m. It is unclear whether a general relationship exists between species richness and elevation under tropical montane conditions, given their variability in terms of climatic, topographic, and altitudinal gradients (Kessler, 2000). Published studies have captured only a small portion of the biodiversity patterns present in the tropical Andes. Specifically, studies in southern Ecuador have been limited to a few altitudinal ranges or only woody plants, particularly trees (Gentry, 1988; Cabrera et al., 2019; Cueva et al., 2019; Jadán et al., 2021), even though most vascular plants in tropical areas generally belong to non-tree growth forms (Jørgensen and León-Yánez, 1999). Overall, no comprehensive studies on altitudinal gradients by growth forms have been performed in this region. That is why understanding patterns of α- and β-diversity vegetation in scarcely explored mountain regions with diverse regions and environmental conditions, can be critical for predicting future shifts in plant distributions during a period of environmental change that is threatening biodiversity worldwide (Parolo and Rossi, 2008; Malhi et al., 2010; Cuesta et al., 2017; Tovar et al., 2022). In the present study, the diversity patterns of vascular plants were investigated along an altitudinal gradient encompassing two important regions of the southern Ecuadorian Andes as a baseline for future studies on biodiversity trends. According to the IUCN (2024), such areas should be of special conservation interest, as nearly 76% of the recorded endemic species are classified under some threat category. Based on fieldwork, the α and β-diversity patterns of trees, shrubs and herbaceous species, and total vascular plants were analyzed across elevations from 1000 to 4200 m a.s.l. − from 1000 to 3000 m in the Jubones Valley and from 3300 to 4200 m in the Cajas National Park (CNP). These two areas encompass several Andean ecosystems, including dry tropical forests restricted to inter-Andean valleys, montane forests, Polylepis forest, and high-elevation grasslands-páramo. Specifically, this study attempts to answer the following questions: 1) How does vascular plant species richness (α-diversity) change along the altitudinal gradient within the target areas? 2) Are the β-diversity different between the altitudinal levels from low to high elevations? 3) How α- and β-diversity patterns along altitudinal gradients change by growth forms (trees, shrubs, and herbs)? 4) What are the main climatic constraints associated with these variations? Materials and methods Study area The study was carried out in the Andean mountains of southern Ecuador in the province of Azuay and the northern part of Loja province (Fig. 1 ). Floristic data were collected from 146 sampling points distributed in the important conservation areas of the Cajas National Park (CNP 2°50’ S/79°13’ W), and the Jubones Valley (3°20’ S/79°18’ W). The Jubones Valley is located in a special area of the Andes within the transitional zone between the Northern Andes and the Central Andes, known as the “Amotape-Huancabamba” depression (Weigend, 2002 ). The sampling points in the Jubones Valley were located in dry tropical forests restricted to inter-Andean valleys; one of the least studied ecosystems in Ecuador (Quintana et al., 2017a ). At the lowest elevations, seasonal climate variations encourage the growth of deciduous species and cacti such as Espostoa frutescens and Armatocereus godingianus . Here, there is a scarcity of perennial grasses while shrubs dominate, and only a few tree species grow such as Vachellia macracantha, Tara spinosa , and Tecoma stans . From 2100 to about 2400 m a.s.l., there is a transitional vegetation zone adapted to increasingly humid conditions. Above 2400 m, the montane forest starts to appear, similar to that described in the CNP (Fig. 2 a). In the CNP, the sampling points was conducted in montane forests dominated by trees that can reach 15 m in height, such as Hedyosmum cumbalense , Weinmannia fagaroides , Myrcianthes rhopaloides , and Ocotea infrafoveolata. Genera such as Miconia , Viburnum , and Piper characterized the subcanopy (Minga et al., 2021 ). Páramo hosts plant species adapted to extreme climatic conditions and are dominated by tussocks such as Calamagrostis intermedia or species such as Plantago spp. that can form rigid cushions, along with shrubs belonging to the genera Gynoxys , Diplostephium , and Baccharis . The genus Polylepis , endemic to the Andes, is the only tree that forms forested patches above 3400 m a.s.l. (Minga et al., 2016 ; Romoleroux et al., 2019 ; Ansaloni et al., 2022 ). These forest patches were also sampled in the study reported here (Fig. 2 b). As described, the vegetation of the Jubones region changes progressively with increasing altitude, transitioning into montane forest and converging with the vegetation of the CNP on the western side. Therefore, we treated the sampling points from both regions as part of a continuous altitudinal gradient, in order to captured the greatest possible variety of vegetation types and plant species for studying altitudinal patterns in our study area. In the Jubones Valley, the soils are formed from rhyolitic rocks, which are rich in siliceous sediments and have a pH of 6–7 (Hall and Calle, 1982 ). The climate ranges from arid to humid. Below 2000 m a.s.l., it is highly seasonal, with annual precipitations of around 1100 mm and marked dry seasons lasting between 5 and 6 months. During this dry season, the precipitation can remain below 100 mm, resulting in a desert-like climate. The annual temperature in this area varies from 15°C to 28°C (Hasan and Wyseure, 2018 ). The CNP consists of volcanic-origin soils with high organic matter content ( ̴ 20%), acidic pH, high porosity, and high water retention capacity. The annual precipitation ranges from 800 to 1300 mm. The temperatures within this area show strong diurnal variation with average daytime temperatures between 6°C and 18°C around 3000 m a.s.l. and between 3°C and 6°C at about 4000 m a.s.l. Temperatures below 0°C occasionally occur above 4100 m a.s.l. throughout the year (Ansaloni et al., 2022 ). Floristic sampling Field work was conducted from January - June 2016 in CNP and from January - September 2017–2018 in the Jubones Valley. The floristic inventory was carried out using a stratified transect to record the different plant growth forms. A 50 x 10 m transect (500 m 2 ) was used to inventory all tree species with dbh ≥ 2.5 cm. To record shrub (woody plants with multiple erect or prostrate perennial stems ≥ 50 cm tall) and herbaceous species, the transect was divided into 10 subplots uniformly distributed along its length. Each subplot consisted of 5 x 2 m for shrubs, and 1 x 1 m for herbaceous species (see Fig. S1 ). This resulted in a total transect-sampled area of 500 m 2 for trees, 100 m² for shrubs, and 10 m² for herbs. For shrubs, the percent cover (%) of each species was visually estimated in each subplots, while for herbs, we used the Braun-Blanquet scale (Braun–Blanquet, 1979 ). Epiphyte growth forms were not sampled in this study because the sampling design was unsuitable to capture these. The transects were located along an altitudinal gradient from 1000 to 3000 m a.s.l. in the Jubones Valley (32 transects) and from 3300 to 4200 m a.s.l. in the CNP (114 transects), in places impacted as little as possible by human activities. Elevations below 1000 m were excluded, considering that the lowest limit of the Jubones Valley is ~ 700 m a.s.l., and these areas are highly degraded due to human disturbance (i.e., logging). Elevations of 3100 m and 3200 m were also not included, as the few remaining natural fragments of vegetation were difficult to access. To supplement the biodiversity dataset for the highest elevations of CNP (above 3700 m), additional unpublished data from another project related to the vegetation analysis of the region was used. The vegetation sampling was conducted in quadrats of 3 x 3 m to 5 x 5 m to record herb and shrub species. These quadrats represent 42% of the total sampling points in the CNP. Species were identified and incorporated into the collections of the Herbarium of the University of Azuay (HA), Cuenca, Ecuador. The classification system adopted for the circumscription of families and genera was the one proposed by The Angiosperm Phylogeny Group (APG, 2016). Endemic species were classified based on the information from the Red Book of Endemic Plants of Ecuador (Valencia et al., 2000 ) and Tropicos ( 2024 ). Environmental data To identify the main bioclimatic factors shaping the altitudinal trends in the study area, the following bioclimatic data was extracted: temperature (°C), precipitation (mm), and solar radiation (kj m -2 day -1 ) (see Fig. S1 ), from the WorldClim version 2.1 dataset (Fick and Hijmans, 2017 ) at a spatial resolution of 30 seconds ( ̴ 1 km 2 ). To obtain slope and aspect data, a digital elevation model (DEM) was built to generate a slope and aspect maps in ArcGIS 10.6.1. Data analysis The floristic assessment was first carried out by classifying the transects into 100-meter altitudinal bands, from 1000 m up to their respective upper elevation limits. This is a common way to make a synthesis of altitudinal biodiversity data (Gentili et al., 2013 ; Sekar et al., 2023 ). This classification resulted in a total of thirty-one altitudinal bands ranging from 1000 to 4200 m a.s.l., each containing a different number of transects. The 3100 m and 3200 m bands were not included for the reasons outlined above. Analysis of α- and β-diversity patterns by growth form: trees, shrubs, and herbs Patterns of α- and β-diversity were analyzed separately for each growth form − trees, shrubs, and herbs − using abundance matrices based on the number of individuals for trees and percent cover for shrubs and herbs. Because different ecological processes can drive biodiversity patterns at different spatial scales, and overlooking one scale may lead to incomplete or biased interpretations (Chase and Knight, 2013 ; Tello et al., 2015 ), we analyzed the α- and β-diversity patterns of each growth form at two spatial scales. At small scale, we calculated diversity among individual transects (n = 146) within each altitudinal band to capture species richness and heterogeneity within each band. At large scale, we pooled the species composition of all transects within each altitudinal band (n = 31) to calculate diversity between bands. Since differences in sampling effort (i.e., varying numbers of transects per altitudinal band) could influence the observed diversity patterns, we applied the individual-based rarefaction method of Gotelli and Colwell ( 2001 ) at both spatial scales. Rarefied species richness was calculated by resampling (1000 times) the abundance matrices to a fixed number of individuals (for trees) or a fixed percent cover (for shrubs and herbs). The specific values for individual or cover varied depending on the spatial scale (see Online Resource 2). As another metric of alpha diversity, we calculated the exponential Shannon index using the formula: \(\:\text{exp}\left(H{\prime\:}\right)=exp\:\left(-\sum\:_{i=1}^{S}{p}_{i}\:\text{l}\text{n}{p}_{i}\right)\) , where \(\:S\) is the total number of species in the community, and \(\:{p}_{i}\) is the proportion of individuals of the species i (Jost, 2007 ). Beta diversity was calculated using the Bray-Curtis dissimilarity index with the “betapart” package in R (Baselga and Orme, 2012 ), by computing pairwise dissimilarities between individual transects (small scale) and altitudinal bands (large scale). The resulting mean dissimilarity values were then used to fit the GAM models. We compared the observed and expected β-diversities under a null model, following Tello et al. ( 2015 ). This null model maintains the number of species and their abundance distribution while randomizing the spatial location of species occurrences − among transect within each altitudinal band at the small scale, and among altitudinal bands at the large scale. We ran 999 randomizations on the rarefied matrices. The mean β-diversity values from the null distributions were compared with the observed values at each spatial scale. The difference was then divided by the standard deviation of the null distribution to calculate standardized effect sizes (SES), representing the degree to which β-diversity deviates from the null expectation. To reveal the patterns of α- and β-diversity along the altitudinal gradient, we plotted fitted models of rarefied species richness, exponential Shannon diversity, and Bray-Curtis dissimilarity indices as functions of elevation. We applied generalized additive models (GAMs) in a similar way as in previous studies (Grytnes et al., 2006 ; Quintana et al., 2017a ; He et al., 2023 ). GAMs allow the determination of the shape of response curves and are not constrained by an underlying assumption of linearity (Guisan et al., 2002 ). To fit the model, the “mgcv” package in R (Wood, 2011 ) was used by employing quasi-Poisson or Gaussian distribution (link = “log”) for the implementation of regression splines as smoothers. All models were fitted separately for each diversity metric, growth form, and spatial scale. Analysis of α- and β-diversity patterns for total vascular plant species To represent total vascular plant α- and β-diversity, we considered all growth forms. Due to differences in abundance metrics and sampled area resulting from the sampling design, we used a presence/absence matrix for this analysis and conducted it only at the small spatial scale. Species richness was estimated using rarefaction based on the minimum sampled transect area (10m 2 ). We then calculated β-diversity using the Sørensen dissimilarity index, as the data were based on presence/absence. The resulting values were used to fit the GAM models, as described above. Endemism of species The number of endemic species, classified based on Valencia et al. ( 2000 ) and Tropicos ( 2024 ) as described in the floristic sampling section, was plotted in a heatmap to represent the richness of endemic species across altitudinal bands along the gradient from 1000 to 4200 m a.s.l. The number of species was shown by growth form (trees, shrubs and herbs) as well as for total vascular plant species. Influence of environmental drivers on species composition To test the influence of environmental drivers on changes in species composition across transects, we performed a canonical correspondence analysis (CCA) for total species as well as for each growth form separately. The subset of environmental variables used was selected based on a combination of Pearson’s correlation coefficient to reduce the degree of multicollinearity. The least correlated environmental variables were chosen with a correlation index below 0.75, such as mean temperature (°C), annual precipitation (mm), mean solar radiation (kJ m -2 day -1 ), slope (degrees), and aspect (degrees) designated as relative east aspect (sine of aspect) and relative north aspect (cosine of aspect). The Monte Carlo permutation test was performed to assess the significance of ordination axes. The CCA analysis was carried out using CANOCO version 4.5 (Lepš and Šmilauer, 2003 ). Results A total of 891 vascular plant taxa—comprising 123 tree species, 239 shrub species, and 529 herb species—belonging to 391 genera and 118 families were recorded across 146 sampling points along an altitudinal gradient from 1000 to 4200 m a.s.l. in the Jubones Valley (1000–3000 m a.s.l.) and the CNP (3300–4200 m a.s.l.) (Fig. 3 a; Table S1 ). The Jubones Valley accounted for a total of 380 species, belonging to 234 genera and 91 families. In terms of growth forms, herbs contributed to the highest richness (48%), followed by shrubs (31%) and trees (21%) (Fig. 3 a-b). The families with the highest number of species were Asteraceae, Poaceae, Fabaceae, Solanaceae, and Malvaceae, which together accounted for more than 30% of the total sampled species richness. The CNP encompassed a total of 569 species, belonging to 238 genera and 86 families. Herbaceous species represented 65% of the total richness, followed by shrubs (24%) and trees (11%) (Fig. 3 a-b). The families with the highest number of species were Asteraceae, Poaceae, Cyperaceae, Rosaceae, and Melastomataceae, comprising 41% of the total richness. α- and β-diversity patterns by growth form: trees, shrubs, and herbs The GAM models calculated for the different growth forms revealed distinct patterns. For trees, a hump-shaped pattern was observed, with the highest richness occurring around 2700–3000 m. In contrast, the lowest richness was found at both ends of the altitudinal gradient, between 1100–1400 m and 3800–4200 m. At the larger scale, the overall pattern remained the same, although the peak shifted slightly, expanding to a broader range between 2700–3500 m. The exp-Shannon index followed the same patterns as previously described (Fig. 4 ; Table S2 ). Shrub species exhibited a bimodal pattern, which was more evident at the larger scale. At the small scale, shrubs showed two diversity peaks, one occurring around 1700–2000 m and another between 3000–3500 m a.s.l. However, at the larger scale, this pattern became more constrained, showing the highest richness at higher elevations, between 3500–3700 m. Pattern that was consistent with the exp-Shannon diversity index (Fig. 4 ; Table S2 ). Herbaceous species richness exhibited a tendency for a unimodal pattern when analyzed by rarefied richness, with the highest peak occurring between 2900–3400 m at the small scale. This overall trend was consistent with the exp-Shannon index. Nevertheless, at the larger scale, rarefied richness was notably higher between 3500–3900 m, in the case of the exp-Shannon index, a bimodal pattern emerged, with diversity peaks around 1600–1900 m and 3500–3700 m (Fig. 4 ; Table S2 ). The β-diversity analysis, based on Bray-Curtis dissimilarity index, conducted between each pair of transects (small scale) and between each pair of altitudinal bands (large scale), revealed distinct patterns for trees, shrubs and herbs. However, each growth forms exhibited similar β-diversity trends across both spatial scales, with overall higher β-diversity observed at the small scale. The β-diversity pattern for trees at both spatial scales showed an inverted hump-shaped curve, with the lowest values occurring at mid-elevations (2300–2900 m). In contrast, the highest β-diversity was observed at lower, followed by slightly lower—but still elevated—values at higher elevations. Shrubs exhibited the highest β-diversity peak between 2500 m and 2900 m, which was more pronounced at the large scale, particularly around 2700–3000 m. After this peak, a noticeable decline was observed, followed by an increase again above 3800 m. Herbs β-diversity showed the highest values at lower elevations (1000 m), remaining relatively constant up to mid-elevations. This was followed by a marked decline above 2800 m at the small scale and above 3000 m at the large scale, with a slight increase again above 3900 m for both scales (Fig. 5 ; Table S3). The null model analysis revealed that the observed β-diversity was consistently higher than expected under the null model across both spatial scales and for all growth forms (see Table S5). Standardized effect sizes (SES) were greater at small scale, SES = 23.14 for trees, SES = 37.87 for shrubs, and SES = 41.62 for herbs, compared to the large scale, where SES = 46.36 for trees, SES = 52.90 for shrubs, and SES = 52.85 for herbs. These results suggest that species tend to be more aggregated within local transects but also within altitudinal bands than would be expected by chance. α- and β-diversity patterns for total vascular plant species The GAM models calculated for all recorded vascular plant species at small scale revealed a hump-shaped pattern, with the highest richness peak occurring between 2800 m and 3500 m. In contrast, the lowest richness was found at both extremes of the altitudinal gradient, particularly at 1100–1400 m (Jubones Valley) and at 3900–4200 m (CNP) (Fig. 6 ; Table S4). In terms of β-diversity, a decreasing trend along the altitudinal gradient was observed. The highest β-diversity values occurred at lower elevations, at 1000 m, slightly decreasing toward mid-elevations, and declined sharply above 3000 m. However, an increase in β-diversity was observed again above 3800 m (Fig. 6 ). Endemism of species by growth form and total vascular plant species Remarkably, 13.6% (121 spp.) of the registered species are endemic to Ecuador, including 50 herb, 48 shrub, and 23 tree species; only 1% are introduced (12 spp.). The highest endemism was found in the CNP region with a total of 87 species, while 42 endemic species were recorded in the Jubones Valley. The distribution of these endemic species by growth form exhibited a distinct separation along the altitudinal bands. Endemic herbs were at their maximum richness above 3400 m, with little or no presence at lower elevations. On the other hand, endemic shrub species were recorded at both extremes of the altitudinal gradient, ranging from 1000–2000 m and 3300–4000 m. Tree endemism was prevalent at mid to high elevations between 2100 and 3600 m (Fig. 7 ). Influence of environmental drivers on species composition The first two axes of CCA ordination analysis conducted for all vascular plant species, with eigenvalues of 0.925 and 0.615, explained the largest amount of variance in the data, which is strongly associated with environmental variables, accounting for 62% of the variance (Fig. 8 ; Table 1 ). As expected, temperature and precipitation were the main environmental drivers. Particularly, in the Jubones Valley, species composition was strongly correlated with increasing values of temperature and solar radiation, especially the transects at lower elevations. Whereas species occurrence in the CNP is associated with increasing precipitation values, noticeable at mid-elevations, with slope (°) exerting a comparatively minor influence. The Monte Carlo permutation test indicated significant statistical differences for both the ordination axes and tested variables (p = 0.001). Temperature, precipitation, solar radiation, and slope, were significant in explaining the variable effect on sampling distribution (all with p < 0.001). However, temperature (LambdaA = 0.87) and precipitation (LambdaA = 0.35) exhibited the highest levels of variance, with a clear correlation with the 1st canonical axis. Separate CCA analyses for trees, shrubs, and herbs revealed a consistent ordination patterns with those observed for total species, with temperature and precipitation remaining the most influential environmental variables (see Fig. S2 ; Table S6). Table 1 Results of the Canonical Correspondence Analysis (CCA) carried out for total vascular plants along an altitudinal gradient from 1000 to 4200 m, in Jubones Valley (1000 to 3000 m) and CNP (3300 to 4200 m) in the southern Ecuadorian Andes, (n = 146 transects). The analysis is explained by the full set of environmental variables: temperature, precipitation, solar radiation, slope, and aspect Axes 1 2 3 4 Total inertia Eigenvalues 0.92 0.61 0.35 0.27 21.36 Species-environmental correlations: 0.99 0.91 0.84 0.83 Cumulative % variance of species data 4.3 7.2 8.9 10.2 Cumulative % variance of species-environment relation 37.3 62.2 76.7 87.6 Sum of all eigenvalues 21.36 Sum of all canonical eigenvalues 2.47 Variable LambdaA P F Temperature 0.87 0.000 11.83 Precipitation 0.35 0.000 4.97 Solar radiation 0.27 0.000 3.76 Slope 0.22 0.000 3.21 Eastness 0.07 0.169 1.13 Northnes 0.07 0.650 0.94 Discussion In this study, a total of 891 vascular plant taxa were recorded across a wide altitudinal gradient ranging from 1000 to 4200 m a.s.l. in scarcely studied areas of the tropical Andes of southern Ecuador, covering the Jubones (1000–3000 m) and CNP (3300–4200 m) regions. Species richness patterns showed marked variation among different growth forms. Total species richness showed clear altitudinal pattern, with peaks between 2800–3500 m. A high proportion of endemic species (13.6%) was recorded, with the highest number of endemic species at 3500–3800 m in CNP. Patterns of β-diversity also differed among growth form. The patterns remained consistent when compared across spatial scales, although with slight variations. Among the environmental factors considered, temperature and precipitation had a marked effect on diversity patterns (i.e., species/samples distribution) along the altitudinal gradients and strongly influenced species distribution. α- diversity patterns Regarding total species richness, we identified a unimodal relationship between species richness and altitude characterized by a hump-shaped pattern. This pattern is in line with other studies in the tropical Andes (e.g., Kessler, 2001 ; Krömer et al., 2005 ; Salazar et al., 2013 ; Malizia et al., 2020 ), which have also documented mid-elevation peaks in species richness. However, the richness peak greatly varies depending on the elevation range under consideration, the taxonomic group, the growth form, and the area sampled, among other factors. For instance, in Andean forests, Malizia et al. ( 2020 ) found the highest tree species richness at 1000 m. In an in-depth study of specific taxonomic groups, Kessler ( 2001 ) found a richness peak at around 1100 m for the Acanthaceae family, while families such as Melastomataceae and Araceae peaked between 1000 and 1500 m in the Bolivian Andes. Krömer et al. ( 2005 ) showed that epiphytes reached maximum diversity between 1300 and 2000 m in the Yungas forest of Bolivia. In contrast, the study reported here found the richness peak for fully sampled vascular plants at higher elevations, around 2800–3500 m. This discrepancy can be ascribed to the spatial complexity of the Andean mountain system, where both topography and climate play crucial roles in shaping biodiversity patterns. Additionally, regional factors such as nutrient availability, slope variation, geological disturbances, environmental history, and other unclear factors may also influence local species richness as stressed by previous authors (Richter et al., 2009 ; Rahbek et al., 2019 ). It is important to emphasize that in the study reported here, various vegetation zones were observed to coexist within the same altitudinal range. Specifically, montane forest, treeline, páramo, and Polylepis forest were all recorded around 3500 m a.s.l. The coexistence and overlapping of different vegetation zones at the same elevation likely contributes to the observed peak in species diversity. On the other hand, the transects at both extremes of our altitudinal gradient showed a significantly reduced number of species. This trend likely reflects the extreme climatic conditions at these elevations and the limited adaptability of species to survive in such environments. In the Jubones Valley, the long seasonal dry periods with higher temperatures contrast with the colder conditions, including occasional frost, in the Cajas National Park (CNP). Accumulation curves indicated that additional sampling effort is needed above 4000 m. However, since these altitudes represent the upper limit of vegetation distribution extending up to 4200 m in the southern mountains of Ecuador (Minga et al., 2016 ; Ansaloni et al., 2022 ), a substantial increase in species richness is not likely. Instead, greater species rarity would most likely be expected as shown by Sklenář et al. ( 2021 ). In the Jubones Valley, increasing the sampling effort would likely result in a higher species count. However, compared to Quintana et al. ( 2017a ), who reported 313 species in dry tropical forests restricted to the inter-Andean valleys across Ecuador, around 65% of such diversity was collected in the current study with 203 species characteristics of this vegetation type. One of the most striking results of this study was the variation in diversity patterns observed across different growth forms. Regarding α-diversity, this reveals not only shifts in the peaks of maximum richness for trees and herbs, but also notable changes in shrub patterns. This reflects, in part, the complex interactions between species' physiological adaptations to the different environment types across the altitudinal range. In detail, the maximum peak of trees species richness (2700–3500 m) corresponds to the high montane evergreen forest. Montane forests are well known for their high species diversity largely due to the unique microclimatic conditions of these areas, which include significant fluctuations in day/night temperatures, high relative humidity, and frequent rainfall including drizzle and mist (Bush and Silman, 2004 ; Körner, 2007 ). Additionally, steep slopes, which commonly dominate the topography of these habitats, play a crucial role in the creation and maintenance of high diversity and heterogeneity (Kessler, 2000 ; Richter et al., 2009 ), since they form a mosaic of microhabitats with niche specialization. Moreover, natural phenomena, such as landslides, which are common in steep slopes, further contribute to biodiversity by creating habitat islands and different successional stages of plant communities (Kessler, 2000 ; Körner, 2007 ). For shrub species, the highest richness peaks were found between 1700–2000 m in the Jubones Valley and 3500–3700 m in CNP; both of which are transition zones. Shrubs often exhibit adaptations that allow them to thrive in these intermediate conditions between distinct ecosystems. Indeed, for certain Andean shrub genera, these transitional areas represent centers of diversity, such as Ribes or Nassa genera (Mutke et al., 2014 ). Their resilience to biotic and abiotic fluctuations such as water, light, and nutrient availability, enables them to colonize transitional zones and at the same time, modifies certain climatic factors (i.e., night air temperature) (D’Odorico et al., 2010 ). In the dry tropical forests restricted to inter-Andean valleys, shrubs may play a crucial role in maintaining biodiversity by supporting the creation and persistence of biological corridors that connect grasslands, shrublands, and forests, thereby providing a stable environment for more sensitive growth forms (Mutke et al., 2014 ). β-diversity patterns Total β-diversity showed high values of Bray-Curtis dissimilarity, ranging from 0.67 to 0.98. These results showed a clear pattern for all vascular plant species with the highest values at lower elevations, followed by an abrupt decrease above 3000 m. A similar pattern was observed for herbaceous species. However, contrasting trends were found among other growth forms, tree species exhibited an inverted hump-shaped pattern, whereas shrub species followed a unimodal pattern. The Andes are known for their high β-diversity indices, largely due to their highly heterogeneous habitats across slopes (Homeier et al., 2010 ; Quintana et al., 2017a ; Cuesta et al., 2017 ; Rahbek et al., 2019 ). Very high turnover is reported to be a characteristic of dry tropical forests at relatively small spatial scales, which may be caused by dispersal limitation among the geographic groups and in situ speciation within them (Pennington et al., 2000 ; Dryflor, 2016). The higher restriction of species at lower elevations found in this study area could be attributed to their response to the climatic characteristics of the sampling area (i.e., dry forest ecosystems). Evolutionary and historical factors, as well as dispersal barriers, may be the main determinants of species replacement as suggested by Nanda et al. ( 2021 ). The Jubones Valley region is located within a geological boundary formed by the transition from the Northern Andes to the Central Andes, which is geographically expressed by a depression known as “Amotape-Huancabamba” (Weigend, 2002 ). This depression, which is considered a possible center of endemism (Weigend, 2002 ; Richter et al., 2009 ; Quintana et al., 2017b ), can function as a biogeographic barrier for the north-south dispersal of Andean taxa. In addition, this depression may also provide a dispersal route between the lowland plant taxa of the Pacific and the humid valleys on the eastern side of the Andes (Weigend, 2002 ; Quintana et al., 2017a , b ). The increase of β-diversity in all growth forms, at higher elevations above 3700 m in the CNP could reflect different mechanisms of community assembly. These mechanisms can be different from those at lower elevations. Higher elevations host specific habitats and environmental conditions that support unique species. In Ecuadorian páramos, up to 12 phytosociological communities can be distinguished, many of which are composed of exclusive species (Hofstede et al., 2003 ; Ansaloni et al., 2022 ). In our sampling areas above 3500 m, four communities were identified inside the páramo: a) tussock grassland, b) carpets cushion páramo, c) scrublands, and d) super-páramo scrublands dominated by Andicolea , as described by Minga et al. ( 2016 ) and Ansaloni et al. ( 2022 ). It should be noted that number of endemic species in these high-elevation areas were high where not only species exclusive to the Andean region of Ecuador were recorded, but also species that are exclusively endemic to the Cajas National Park. These species included Carex azuayae , Draba steyermarkii , Gentianella hirculus , Gentianella longibarbata , Halenia serpyllifolia , Lysipomia vitreola , Xenophyllum roseum , Andicolea azuayensis (Valencia et al., 2000 ; Ansaloni et al., 2022 ). The β-diveristy pattern of tree species, which showed a decrease with elevation, was similar to that observed by Báez et al. ( 2015 ) in the North-Central Andean forests. This change in pattern and the decrease in tree β-diversity could reflect the dominance of a few widespread tree species in comparison to other growth forms (Quintana et al., 2017a ). For instance, the lower altitudes (1000 to 1400 m) of the sampling area in this study were dominated by a single tree species, Vachellia macracantha . In species-poor communities, the presence or absence of a few dominant taxa, especially trees, will determine changes in the light, nutrient and soil conditions of the ecosystem, which can, in turn, lead to shifts in the entire plant community (Kessler, 2000 ). Environmental drivers Climate factors are fundamental drivers of floristic variation and shape biodiversity patterns. The effects of these factors along an altitudinal gradient differ substantially from those observed in lowlands with higher elevations, undergoing pronounced shifts mainly in temperature, precipitation, and solar radiation (Bush and Silman, 2004 ; Körner, 2007 ). In the study reported here, temperature and precipitation were highlighted as the most important climatic factors influencing floristic patterns. The two sampling areas exhibited marked differences linked to climatic factors. The higher elevation areas in the Cajas National Park (3300 to 4200 m a.s.l.) are predominantly influenced by humid eastern currents coming from the inter-Andean and Amazonian valleys. Here, high precipitation was an important driver favouring increased biodiversity levels. Higher productivity and species richness were recorded by previous studies performed in Andean Mountain forests (Gentry, 1988 ; Toledo et al., 2011 ; Godoy-Bürki et al., 2017 ). In contrast, the Jubones Valley area (1000 to 3000 m a.s.l.) is influenced by dry western currents from the Pacific coast (Pérez-Escobar et al., 2022 ), resulting in higher temperatures, which can exert strong control on species richness leading to a drop in α-diversity, especially at lower elevations. The significant role of temperature in regulating floristic variation in lowland regions, with a well-defined dry season of variable length, can shape the physiognomy of vegetation types (Toledo et al., 2011 ). This could explain why temperature particularly affects herb and tree species diversity decreasing richness but conserving high levels of species turnover (β-diversity). However, it contributed more to the shrub component of the vascular flora in this study; indeed, in this area, shrubs reached their peak and dominated the landscape, along with some species adapted to survive under certain drought and temperature conditions (Quintana et al., 2017a ), particularly from the Fabaceae, Asteraceae, and Malvaceae families. These families are known to contain species that are specialists in dry forest habitats (Dryflor, 2016). In lowlands, species turnover remained high for shrubs. The abrupt change in β-diversity along the altitudinal gradient from about 2900 to 3300 m, reflected the dramatic changes in the composition of Andean ecosystems. After the local substitution of species at low altitudes, there is a marked change in species composition. Therefore, above 3300 m, the lower altitudinal bands do not share any species with altitudinal bands at higher elevations. This suggests that unique environmental conditions (e.g., precipitations and temperatures) could restrict the colonization and membership of low-altitude species towards higher elevations. In addition, these climatic constraints could potentially maintain a clear separation of community assembly dynamics between the Jubones Valley and the Cajas National Park. In mountain regions, topography also plays a critical role by modifying factors such as solar radiation, slope, and aspect that foster unique ecological niches (Kessler, 2000 ; Körner, 2007 ). In this study, slope and solar radiation were especially correlated with the species sampled along the altitudinal gradient. Overall, the combination of climate and topography generates important environmental drivers that can shape the biodiversity patterns of vascular plants at the micro-scale, as observed in sampling areas of this study in the Tropical Andean mountains of southern Ecuador. Conclusions and implications for conservation and future research The findings of this study have important implications for the conservation of biodiversity in the southern Ecuadorian Andes, especially for the identification and conservation of refugium areas for local biodiversity hotspots across southern Andean slopes, under the current period of environmental change (Gentili et al., 2015 ). The studied regions need additional sampling to capture the high biodiversity levels suggested by our analyses, especially in the upper ranges of the páramo and in 3100–3200 m a.s.l. altitudinal bands, which was not included in this study. The vulnerability of high-altitude ecosystems to species loss, as shown by the increased β-diversity at higher elevations, suggests that these areas may be particularly sensitive to climate change (Cuesta et al., 2020 ). On the other hand, low to mid-elevation ecosystems, where species richness and turnover are highest, should be prioritized for conservation and new sampling efforts, since they harbour a unique assemblage of species, many of which are endemic. Indeed, these areas include one of the few remaining relicts of dry tropical forest, which is among the most threatened Andean forest types in the world (Quintana et al., 2007a), and represent the first flora inventory in the Jubones Valley that covers all the vegetation zones of this important area. Declarations Competing interests The authors have no competing interests to declare. Funding No external funding was received for conducting this study. Author Contribution N.G. wrote the main manuscript text and prepared figures.; N.G. and R.G. performed statistical analyses; R.A., M.J., D.M. e N.G. performed sampling activities; N.G. and R.G. contributed to the manuscript conceptualization; S.C and D.M provided resources; R.A. and D.M. supervised the project; all authors reviewed and edited the final draft of the manuscript. Acknowledgement We thank the Vice-Rectorate of Research to the University of Azuay for funding the project. We are also grateful to the Ministry of the Environment of Ecuador for the research authorization. We thank Adolfo Verdugo and all the researchers and students who were involved in the collection of the field data. References APG IV. (2016). An Update of the Angiosperm Phylogeny Group Classification for the Orders and Families of Flowering Plants. Botanical Journal of the Linnean Society 181:1–20. https://doi.org/10.1111/boj.12385 Ansaloni, R., Izco, J., Amigo, J. & Minga, D. (2022). Analysis of the páramo vascular flora in the Cajas National Park (Central Andes, Ecuador). Mediterranean Botany 43. https://doi.org/10.5209/mbot.76491 Báez, S., Malizia, A., Carilla, J. et al. (2015). Large-Scale Patterns of Turnover and Basal Area Change in Andean Forests. PLoS ONE 10(5). https://doi.org/10.1371/journal.pone.0126594 Baselga, A. & Orme, D. (2012). Betapart: An R package for the study of beta diversity. Methods in Ecology and Evolution 3:808–812. https://doi.org/10.1111/j.2041-210X.2012.00224.x Braun–Blanquet, J. (1979). Fitosociología. Bases para el estudio de las comunidades vegetales. Blume Ediciones, Madrid. Bush, M.B. & Silman, M.R. (2004). Observations on Late Pleistocene cooling and precipitation in the lowland Neotropics. Journal of Quaternary Science 19:677–684. https://doi.org/10.1002/jqs.883 Cabrera, O., Benítez, Á., Cumbicus, N. et al. (2019). Geomorphology and altitude effects on the diversity and structure of the vanishing montane forest of southern Ecuador. Diversity 11. https://doi.org/10.3390/D11030032 Chase, J.M. & Knight, T.M. (2013). Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecology letters, 16:17–26. https://doi.org/10.1111/ele.12112 Cuesta, F., Muriel, P., Llambí, L.D. et al. (2017). Latitudinal and altitudinal patterns of plant community diversity on mountain summits across the tropical Andes. Ecography 40:1381–1394. https://doi.org/10.1111/ecog.02567 Cuesta, F., Tovar, C., Llambí, L.D. et al. (2020). Thermal niche traits of high alpine plant species and communities across the tropical Andes and their vulnerability to global warming. Journal of Biogeography 47:408–420. https://doi.org/10.1111/jbi.13759 Cueva, E., Lozano, D. & Yaguana, C. (2019). Effect of the altitudinal gradient in the floristic composition, structure and arboreal biomass of the Andean dry forest, Loja, Ecuador. Bosque 40:365–378. https://doi.org/10.4067/S0717-92002019000300365 Di Musciano, M., Calvia, G., Ruggero, A., Farris, E., Ricci, L., Frattaroli, A.R. & Bagella, S. 2024. Elevational patterns of plant species richness and phylogenetic diversity in a Mediterranean island. Perspectives in Plant Ecology, Evolution and Systematics 65: 125815, https://doi.org/10.1016/j.ppees.2024.125815 D’Odorico, P., Fuentes, J.D., Pockman, W.T. et al. (2010). Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere 1:1–11. https://doi.org/10.1890/ES10-00073.1 DRYFLOR et al. (2016). Plant diversity patterns in neotropical dry forests and their conservation implications. Science 353:1383–1387. https://doi.org/10.1126/science.aaf5080 Fick, S. & Hijmans, R. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37:4302–4315. https://doi.org//10.1002/joc.5086 Fontana, V., Guariento, E., Hilpold, A. et al. (2020). Species richness and beta diversity patterns of multiple taxa along an elevational gradient in pastured grasslands in the European Alps. Scientific Reports 10:12516. https://doi.org/10.1038/s41598-020-69569-9 Gentili, R., Armiraglio, S., Sgorbati, S. & Baroni, C. (2013). Geomorphological disturbance affects ecological driving forces and plant turnover along an altitudinal stress gradient on alpine slopes. Plant Ecology 214:571–586. https://doi.org/10.1007/s11258-013-0190-1 Gentili, R., Badola, H.K. & Birks, H.J.B. (2015). Alpine biodiversity and refugia in a changing climate. Biodiversity 16:193–195. http://dx.doi.org/10.1080/14888386.2015.1117023 Gentry, A. (1988). Changes in Plant Community Diversity and Floristic Composition on Environmental and Geographical Gradients. Annals of the Missouri Botanical Garden 75:1–34. https://doi.org/10.2307/2399464 Godoy-Bürki, A.C., Biganzoli, F., Sajama, J.M. et al. (2017). Tropical high Andean drylands: species diversity and its environmental determinants in the Central Andes. Biodiversity and Conservation 26:1257–1273. https://doi.org/10.1007/s10531-017-1311-2 Gotelli, N.J. & Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 4(4):379–391. https://doi.org/10.1046/j.1461-0248.2001.00230.x Grytnes, J.A., Heegaard, E. & Ihlen, P.G. (2006). Species richness of vascular plants, bryophytes, and lichens along an altitudinal gradient in western Norway. Acta Oecologica 29:241–246. https://doi.org/10.1016/j.actao.2005.10.007 Guisan, A., Edwards, T.C. & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157:89–100. https://doi.org/https://doi.org/10.1016/S0304-3800(02)00204-1 Hall, M.L. & Calle, J. (1982). Geochronological Control for the Main Tectonic-Magmatic Events of Ecuador. Earth-Science Reviews 18:215–239 Hasan, M.M. & Wyseure, G. (2018). Impact of climate change on hydropower generation in Rio Jubones Basin, Ecuador. Water Science and Engineering 11:157–166. https://doi.org/10.1016/j.wse.2018.07.002 He, X., Arif, M., Zheng, J. et al. (2023). Plant diversity patterns along an elevation gradient: the relative impact of environmental and spatial variation on plant diversity and assembly in arid and semi-arid regions. Frontiers in Environmental Science 11:1021157. https://doi.org/10.3389/fenvs.2023.1021157 Hofstede, R., Segarra, P. & Mena, P. (2003). Los páramos del Mundo. Proyecto Atlas Mundial de los Páramos. Global Peatland Initioative/NC-IUCN/EcoCIencia. Quito. Homeier, J., Breckle, S.W., Günter, S. et al. (2010). Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest. Biotropica 42:140–148. https://doi.org/10.1111/j.1744-7429.2009.00547.x IUCN. (2024). The IUCN Red List of Threatened Species. Version 2024-1. https://www.iucnredlist.org Accessed 5 June 2024. Jadán, O., Donoso, D.A., Cedillo, H. et al. (2021). Floristic groups, and changes in diversity and structure of trees, in tropical montane forests in the southern Andes of Ecuador. Diversity 13:400. https://doi.org/10.3390/d13090400 Jørgensen, P., León-Yánez, S., editors. (1999). Catalogue of the vascular plants of Ecuador. Missouri Botanical Garden Press, St. Louis, Missouri, U.S.A. Jost, L. (2007). Partitioning diversity into independent alpha and beta components. Ecology, 88(10):2427–2439. Kessler, M. (2000). Altitudinal zonation of Andean cryptogam communities. Journal of Biogeography 27:275–282. https://doi.org/10.1046/j.1365-2699.2000.00399.x Kessler, M. (2001). Patterns of diversity and range size of selected plant groups along an elevational transect in the Bolivian Andes. Biodiversity and Conservation 10:1897–1921. https://doi.org/10.1023/A:1013130902993 Körner, C. (2007). The use of “altitude” in ecological research. Trends in Ecology & Evolution 22:569–574. https://doi.org/10.1016/j.tree.2007.09.006 Krömer, T., Kessler, M., Gradstein, S.R. & Acebey, A. (2005). Diversity patterns of vascular epiphytes along an elevational gradient in the Andes. Journal of Biogeography 32:1799–1809. https://doi.org/10.1111/j.1365-2699.2005.01318.x Lepš, J. & Šmilauer, P. (2003). In: Multivariate Analysis of Ecological Data Using CANOCO. Cambridge University Press, Cambridge. Malhi, Y., Silman, M., Salinas, N. et al. (2010). Introduction: Elevation gradients in the tropics: laboratories for ecosystem ecology and global change research. Global Change Biology 16:3171–3175. https://doi.org/10.1111/j.1365-2486.2010.02323.x Malizia, A., Blundo, C., Carilla, J. et al. (2020). Elevation and latitude drives structure and tree species composition in Andean forests: Results from a large-scale plot network. PLoS ONE 15(4). https://doi.org/10.1371/journal.pone.0231553 Minga, D., Ansaloni, R., Verdugo, A. & Ulloa-Ulloa, C. 1era. Ed. (2016). Flora del páramo del Cajas, Ecuador. Imprenta Digital Universidad del Azuay, Cuenca. https://doi.org/10.33324/ceuazuay.42 Minga, D., Guzmán, N., Jiménez, M. & Verdugo, A. (2021). Plantas nativas de los ecosistemas del Azuay, un acercamiento conceptual al Jardín Botánico de Cuenca-Ecuador. Imprenta Digital Universidad del Azuay, Cuenca. https://doi.org/10.33324/ceuazuay.204 Mutke, J., Jacobs, R., Meyer, K. et al. (2014). Diversity patterns of selected Andean plant groups correspond to topography and habitat dynamics, not orogeny. Frontiers in Genetics 5. https://doi.org/10.3389/fgene.2014.00351 Myers, N., Mittermeier, R.A., Mittermeier, C.G. et al. (2000). Biodiversity hotspots for conservation priorities. Nature 403:853–858. https://doi.org/10.1038/35002501 Nanda, S.A., Haq, M., Singh, S.P. et al. (2021). Species richness and β-diversity patterns of macrolichens along elevation gradients across the Himalayan Arc. Scientific Reports 11:20155. https://doi.org/10.1038/s41598-021-99675-1 Parolo, G. & Rossi, G. (2008). Upward migration of vascular plants following a climate warming trend in the Alps. Basic and Applied Ecology 9:100–107. https://doi.org/10.1016/j.baae.2007.01.005 Pennington, R.T., Prado, D.E. & Pendry, C.A. (2000). Neotropical seasonally dry forests and Quaternary vegetation changes. Journal of Biogeography 27:261–273. https://doi.org/10.1046/j.1365-2699.2000.00397.x Pennington, R.T., Lavin, M., Särkinen, T. et al. (2010). Contrasting plant diversification histories within the Andean biodiversity hotspot. Proc. Natl. Acad. Sci. U.S.A. 107:13783–13787. https://doi.org/10.1073/pnas.1001317107 Pérez-Escobar, O.A., Zizka, A., Bermúdez, M.A. et al. (2022). The Andes through time: evolution and distribution of Andean floras. Trends in Plant Science 27:364–378. https://doi.org/10.1016/j.tplants.2021.09.010 Quintana, C., Girardello, M., Barfod, A.S. & Balslev, H. (2017a). Diversity patterns, environmental drivers and changes in vegetation composition in dry inter-Andean valleys. Journal of Plant Ecology 10:461–475. https://doi.org/10.1093/jpe/rtw036 Quintana, C., Pennington, R.T., Ulloa, C. & Balslev, H. (2017b). Biogeographic Barriers in the Andes: Is the Amotape-Huancabamba Zone a Dispersal Barrier for Dry Forest Plants? Annals of the Missouri Botanical Garden 102:542–550. https://doi.org/10.3417/D-17-00003A Rahbek, C., Borregaard, M.K., Colwell, R.K. et al. (2019). Humboldt’s enigma: What causes global patterns of mountain biodiversity? Science 365:1108–1113. https://doi.org/10.1126/science.aax0149 Richter, M., Diertl, K.H., Emck, P. et al. (2009). Reasons for an outstanding plant diversity in the tropical Andes of Southern Ecuador. Landscape Online 12:1–35. https://doi.org/10.3097/LO.200912 Romoleroux, K., Cárate-Tandalla, D., Erler, R., Navarrete, H. (2019). Plantas vasculares de los bosques de Polylepis en los páramos de Oyacachi. Version 2019.0. https://bioweb.bio/floraweb/polylepis/ . Accessed 28 September 2024. Salazar, L., Homeier, J., Kessler, M. et al. (2013). Diversity patterns of ferns along elevational gradients in Andean tropical forests. Plant Ecology & Diversity 8:13–24. https://doi.org/10.1080/17550874.2013.843036 Särkinen, T., Iganci, J.R.V., Linares-Palomino, R. et al. (2011). Forgotten forests - issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study. BMC Ecology 11:27. https://doi.org/10.1186/1472-6785-11-27 Sekar, K., Thapliyal, N., Pandey, A. et al. (2023). Plant species diversity and density patterns along altitude gradient covering high-altitude alpine regions of west Himalaya, India. Geology, Ecology, and Landscapes 1–15. https://doi.org/10.1080/24749508.2022.2163606 Sklenář, P., Romoleroux, K., Muriel, P. et al. (2021). Distribution changes in páramo plants from the equatorial high Andes in response to increasing temperature and humidity variation since 1880. Alpine Botany 131:201–212. https://doi.org/10.1007/s00035-021-00270-x Tello, J.S., Myers, J.A., Macía, M.J., Fuentes, A.F., Cayola, L., Arellano, G., Loza, M., Torrez, V., Cornejo, M., Miranda, T. & Jorgensen, P. (2015). Elevational Gradients in β-Diversity Reflect Variation in the Strength of Local Community Assembly Mechanisms across Spatial Scales. PLos ONE 10(3):e0121458. https://doi.org/10.1371/journal.pone.0121458 Toledo, M., Poorter, L., Peña-Claros., M. et al. (2011). Patterns and Determinants of Floristic Variation across Lowland Forests of Bolivia. Biotropica 43:405–413. https://doi.org/10.1111/j.1744-7429.2010.00711.x Tovar, C., Carril, A.F., Gutiérrez, A.G. et al. (2022). Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities. Journal of Biogeography 49:1420–1442. https://doi.org/10.1111/jbi.14389 Tropicos. Missouri Botanical Garden. http://www.tropicos.org Accessed June 2024. Valencia, R., Pitman, N., León-Yánez, S., Jørgensen, P. (eds.). (2000). Libro rojo de las plantas endémicas del Ecuador. Publicaciones del Herbario QCA. Pontificia Universidad Católica del Ecuador, Quito. Weigend, M. (2002). Observations on the Biogeography of the Amotape-Huancabamba Zone in Northern Peru. Botanical Review 68:38–54. https://doi.org/10.1663/0006-8101(2002)068[ 0038:OOTBOT]2.0.CO;2 Wood, S. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73:3–36. 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 Additional Declarations No competing interests reported. Supplementary Files ESM1.pdf ESM2.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Jan, 2026 Reviews received at journal 30 Dec, 2025 Reviews received at journal 19 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 08 Dec, 2025 Submission checks completed at journal 08 Dec, 2025 First submitted to journal 06 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8295166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559930019,"identity":"50895af1-bcd0-4240-acc6-159239ba3b1e","order_by":0,"name":"Nubia Guzmán","email":"","orcid":"","institution":"University of Azuay","correspondingAuthor":false,"prefix":"","firstName":"Nubia","middleName":"","lastName":"Guzmán","suffix":""},{"id":559930020,"identity":"f815c7b6-68f4-42e1-a5cd-b866624e0214","order_by":1,"name":"Rodolfo Gentili","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACA4YHNlBmQoGEHAMDYwNDYgMDD14tCWlgBmNDgoGEMVwLHj1IWoBsoGIIE6c15u3NGx8kJNgwyLv3Hn/wwMAifcPt5tYND3cwyNjj0CJz5lixQQLQGsMz5xJBDsvdcOdg243EM7gdJiGRYyaR+OMwg+GMHEOIlhuJQC1tBLQkJAC1zH8D1pJuQLQWeQkesJYEwlp4IH7hMeDJMZwB1GI4E6zljAQPzwEcWtiBIfYhwUZOvv2MwccfFXXyfDfSn938ucPGnr0BhzVQwGOAZqYEfvUgIE/AzFEwCkbBKBjBAAA9tlqCRWNY/wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":true,"prefix":"","firstName":"Rodolfo","middleName":"","lastName":"Gentili","suffix":""},{"id":559930021,"identity":"711c1309-7036-46af-ba05-d7457174d834","order_by":2,"name":"Mayra Jiménez","email":"","orcid":"","institution":"University of Azuay","correspondingAuthor":false,"prefix":"","firstName":"Mayra","middleName":"","lastName":"Jiménez","suffix":""},{"id":559930022,"identity":"308b3dd7-4151-4a3b-b48e-c62af3f6c900","order_by":3,"name":"Raffaella Ansaloni","email":"","orcid":"","institution":"University of Azuay","correspondingAuthor":false,"prefix":"","firstName":"Raffaella","middleName":"","lastName":"Ansaloni","suffix":""},{"id":559930024,"identity":"2c24c177-eb11-4522-b8a7-527b748bb2c8","order_by":4,"name":"Sandra Citterio","email":"","orcid":"","institution":"University of Milano-Bicocca","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Citterio","suffix":""},{"id":559930026,"identity":"893d2ebc-77dc-4927-87f7-462597c5e551","order_by":5,"name":"Danilo Minga","email":"","orcid":"","institution":"University of Azuay","correspondingAuthor":false,"prefix":"","firstName":"Danilo","middleName":"","lastName":"Minga","suffix":""}],"badges":[],"createdAt":"2025-12-06 14:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8295166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8295166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98219881,"identity":"bf73e63c-d1f8-4968-96ed-a3ed572c90e4","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4281818,"visible":true,"origin":"","legend":"","description":"","filename":"Guzmnetal2025ExploringplantanddiversitypatternsalongtropicalaltitudinalgradientsEcuador.docx","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/a3a3e17269fb5d47bed0c1ee.docx"},{"id":98433600,"identity":"9fdeef65-18ae-4f08-92d9-1c67656058d5","added_by":"auto","created_at":"2025-12-17 16:50:56","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7559,"visible":true,"origin":"","legend":"","description":"","filename":"66179a378e7346519648af0728552dd0.json","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/3c929e0d28f27f9c8201a113.json"},{"id":98219903,"identity":"a1f915d3-8458-4268-92fa-0cb22c70fb58","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":273736,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/f4becb991bb914a99ad8d86f.pdf"},{"id":98219893,"identity":"30f80f47-cc42-4cb6-af42-fbff9e5704bf","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142771,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/1e6477f0fddcfc8214917017.pdf"},{"id":98432934,"identity":"0783fb8e-9f3a-4f28-a15d-48f6f4d72775","added_by":"auto","created_at":"2025-12-17 16:50:07","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161820,"visible":true,"origin":"","legend":"","description":"","filename":"66179a378e7346519648af0728552dd01enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/a4af6f3f818b468fc4dd44b6.xml"},{"id":98219888,"identity":"2a433fc6-7db1-4efc-bf01-8eee45102e41","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"jpeg","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1052158,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/4c73d63ba0ecd5fde4b99e92.jpeg"},{"id":98431960,"identity":"2b3108ec-135d-4bc7-a58d-b00946b45fc6","added_by":"auto","created_at":"2025-12-17 16:48:42","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2185798,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/41f12920b5f007a813d79206.png"},{"id":98432664,"identity":"9f9ed3b1-5f1e-443c-84ef-694b0aac827a","added_by":"auto","created_at":"2025-12-17 16:49:46","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112696,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/407ca80d026c57a7f9c2688c.jpeg"},{"id":98219902,"identity":"d278d5da-7f6a-484a-a798-cb365f5a62da","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":581378,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/e5ef5ad90c355266ca7ded5f.jpeg"},{"id":98219895,"identity":"bd0b94a0-2dfe-4074-a64c-72fdec14fd80","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":104052,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/a397d579c4919cf5422e9477.png"},{"id":98219908,"identity":"f9befe54-8719-4931-9a69-0c3f2fffbc71","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52416,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/5afe3dfe42e2a27ea4af3809.png"},{"id":98219904,"identity":"b3447536-a62a-4546-8904-d15bc904a7e8","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"jpeg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":253977,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/0826bf54a394a1c0514aecc6.jpeg"},{"id":98219890,"identity":"b07dbc1d-0d3f-40ed-b388-3227ef4b5671","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":43029,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/4158a7feb1d26f4aa97b20f0.png"},{"id":98219898,"identity":"e7b791e9-b4ab-4504-a6ec-b6b3c562cd95","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":294372,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/3fe2b9d32176c56068ecee28.png"},{"id":98432235,"identity":"9c7e78d0-102b-4c29-a2e6-1f64e7a4104b","added_by":"auto","created_at":"2025-12-17 16:49:15","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":285878,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/74cf1cf552e2489057b7d6d5.png"},{"id":98433811,"identity":"b9cbc966-d687-41a7-9a1e-4614c2b34ca5","added_by":"auto","created_at":"2025-12-17 16:51:08","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16330,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/2ca9f4e34cb126d64c8f25ad.png"},{"id":98433785,"identity":"1bf643c4-b873-4193-bc8e-bad65e1b364a","added_by":"auto","created_at":"2025-12-17 16:51:07","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151239,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/94cc72ae1135fb8506bd4c41.png"},{"id":98432662,"identity":"6a4282ae-7f2f-4e2b-9b2f-984013f3181a","added_by":"auto","created_at":"2025-12-17 16:49:46","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32442,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/5ca29db3d869114acdf8a27e.png"},{"id":98219907,"identity":"b876206e-a5df-4c53-96e1-7d461d10ea53","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":14414,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/b4376aa4060b2426ffb3c797.png"},{"id":98433762,"identity":"a5ea95ec-6990-4d41-8fb8-593bab044918","added_by":"auto","created_at":"2025-12-17 16:51:06","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39781,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/b1da29dc716bc05cc87d6250.png"},{"id":98219905,"identity":"2532b32a-8c75-44a8-9b89-cf5fe0c60f9e","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9546,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/b9d48ac97df921018d7da7a8.png"},{"id":98219909,"identity":"85b967b8-9bf4-4879-880b-6a959f7976b5","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":161805,"visible":true,"origin":"","legend":"","description":"","filename":"66179a378e7346519648af0728552dd01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/968de0fa45603f2d2e5e6e70.xml"},{"id":98219911,"identity":"3bfcfc41-d87e-4c66-a5d3-ef1b8d770fad","added_by":"auto","created_at":"2025-12-15 11:12:43","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172368,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/1f90a977c2f1cb14b165caaa.html"},{"id":98219879,"identity":"72e82fab-261a-4d51-ae7d-e9678158d04f","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2598201,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area showing the 146 sampling points (orange dots) along the altitudinal gradient from 1000 to 4200 m, in the Jubones Valley (1000 to 3000 m) and CNP (3300 to 4200 m) in the southern Ecuadorian Andes. Below, an elevation profile of all sampling points is shown\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/ac6a08a2eb355b158eff7ee1.png"},{"id":98431946,"identity":"8b6723d6-f7c4-468f-9b38-05ade099988e","added_by":"auto","created_at":"2025-12-17 16:48:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":813475,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation types sampled along the altitudinal gradient from 1000 to 4200 m in the southern Ecuadorian Andes, \u003cstrong\u003ea)\u003c/strong\u003e Jubones Valley (1000 to 3000 m), with dry tropical forest restricted to inter-Andean valleys, \u003cstrong\u003eb)\u003c/strong\u003e CNP (3300 to 4200 m), with páramo, \u003cem\u003ePolylepis \u003c/em\u003eand montane forests. Below, a schematic diagram illustrates the vegetation types\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/1799c7d380b507f9eb32b9d7.png"},{"id":98432273,"identity":"89d7f1b4-8a1f-43e2-ac85-1676e6cc8dce","added_by":"auto","created_at":"2025-12-17 16:49:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212015,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of vascular plant species by taxonomic level and growth form, \u003cstrong\u003ea)\u003c/strong\u003e number of families, genera, and species, and \u003cstrong\u003eb)\u003c/strong\u003e total number of species by growth form (trees, shrubs and herbs) recorded in the Jubones Valley (1000 to 3000 m) and CNP (3300 to 4200 m) in the southern Ecuadorian Andes\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/93011a914507b77066778fc2.png"},{"id":98433733,"identity":"6916b2a5-761b-4384-ade5-a37157ca7dcb","added_by":"auto","created_at":"2025-12-17 16:51:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2141178,"visible":true,"origin":"","legend":"\u003cp\u003eAltitudinal patterns of α-diversity for trees, shrubs, and herbs at both small and large scales, along an altitudinal gradient from 1000 to 4200 m in the southern Ecuadorian Andes (Jubones Valley from 1000 to 3000 m; CNP from 3300 to 4200 m). The graphs depict the smoothed relationships between rarefied species richness and the exp-Shannon diversity index with elevation, obtained by fitting GAM models\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/7196601b6c7992aa253c3a14.png"},{"id":98432635,"identity":"8de73805-32f0-4f8f-b3ba-1abf9695de0b","added_by":"auto","created_at":"2025-12-17 16:49:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":267304,"visible":true,"origin":"","legend":"\u003cp\u003eAltitudinal patterns of β-diversity for trees, shrubs, and herbs at both small and large scales, along an altitudinal gradient from 1000 to 4200 m in the southern Ecuadorian Andes (Jubones Valley from 1000 to 3000 m; CNP from 3300 to 4200 m). The graphs depict the smoothed relationships between the Bray-Curtis dissimilarity index with elevation, obtained by fitting GAM models\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/55863d24686465d97e4c61d9.png"},{"id":98219885,"identity":"c527658f-7d35-4aac-9b98-fdec961be7cc","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":52416,"visible":true,"origin":"","legend":"\u003cp\u003eAltitudinal patterns of α- and β-diversity for total vascular species at small scale, along an altitudinal gradient from 1000 to 4200 m in the southern Ecuadorian Andes (Jubones Valley from 1000 to 3000 m; CNP from 3300 to 4200 m). The graphs depict the smoothed relationships between species richness and β- Sørensen dissimilarity index with elevation, obtained by fitting GAM models\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/bf1737e87883188440c163e2.png"},{"id":98433799,"identity":"cdbe3dff-975c-4fb0-86d5-194cfcb54e33","added_by":"auto","created_at":"2025-12-17 16:51:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":952145,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap showing the richness of endemic species for total vascular plants, as well as for trees, shrubs and herbs, along an altitudinal gradient from 1000 to 4200 m in the southern Ecuadorian Andes (Jubones Valley from 1000 to 3000 m; CNP from 3300 to 4200 m)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/7433f92299c5084b7c8fcac9.png"},{"id":98431991,"identity":"4aa3f420-a1e9-4e51-85c3-8cc9339fad25","added_by":"auto","created_at":"2025-12-17 16:48:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":116179,"visible":true,"origin":"","legend":"\u003cp\u003eCanonical Correspondence Analysis (CCA) of total vascular plants along an altitudinal gradient from 1000 to 4200 m in the southern Ecuadorian Andes. Environmental variables are represented by arrows, while sample scores are indicated by diamonds (Jubones Valley from 1000 to 3000 m) and circles (CNP from 3300 to 4200 m)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/e3e90415017ded0ed35c89c9.png"},{"id":98444979,"identity":"1fe727d9-6629-4f59-8894-638589ebf534","added_by":"auto","created_at":"2025-12-17 17:18:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8094305,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/d817df03-849a-4dc2-805b-396887e29332.pdf"},{"id":98219884,"identity":"0018fb4e-b965-471d-9ed4-86a2dad4f05b","added_by":"auto","created_at":"2025-12-15 11:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":273736,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/60f9264eedabaad5ae504620.pdf"},{"id":98433221,"identity":"ad9b4998-4892-4d63-b6b7-5d653e6094de","added_by":"auto","created_at":"2025-12-17 16:50:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142771,"visible":true,"origin":"","legend":"","description":"","filename":"ESM2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8295166/v1/fd81cf0c859d2a7a744ecc5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring plant α- and β-diversity patterns along tropical altitudinal gradients in the Southern Andes of Ecuador: a potential pathway to biodiversity conservation ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcross mountain regions, the interactions among climatic factors (temperature, precipitation, atmospheric pressure, etc.) strongly influence the distribution and abundance of plant species and therefore determine the overall productivity of an altitudinal gradient and its biodiversity levels (Gentry, 1988; Rahbek et al., 2019). Particularly, biodiversity data collected along altitudinal gradients will also reflect the combined effects of regional peculiarities (meso- and microclimate, geology, geomorphology, etc.) with the general altitudinal phenomena (Gentry, 1988; Körner, 2007; Gentili et al., 2013). Consequently, the pattern in which plant species richness changes with altitude can exhibit various shapes. Specifically, a hump-shaped curve with maximum richness at mid-altitudes was observed on a mountain in Norway (Grytnes et al., 2006), in grassland pastures of the European Alpes\u0026nbsp;(Fontana et al., 2020), in dryland ecosystems in China (He et al., 2023), or in the tropical and subtropical forests in northwest South America (Malizia et al., 2020). Other patterns include a linear decline, as seen in high-altitude alpine regions (Sekar et al., 2023), the high tropical Andes (Cuesta et al., 2017), or in temperate and tropical montane forests\u0026nbsp;(Gentry, 1988; Homeier et al., 2010); and even a U-shaped pattern as identified for herbs in some temperate mountain regions\u0026nbsp;(Zhang et al., 2016). Overall, it has been demonstrated that understanding the distribution of species diversity along altitudinal gradients is crucial for conservation, as patterns are often driven by environmental constraints and/or anthropogenic pressures. In addition, the possible presence of endemic species in such systems can highlights their evolutionary importance and conservation value (Di Musciano et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe Tropical Andes are among the regions with the highest plant diversity in the world (Pérez-Escobar et al., 2022), with approximately 45,000 plant species (Myers et al., 2000), ̴ 15% of the global total, accounting for only 1% of the world's surface area (Pennington et al., 2010). This hyper-diversity and the unusual presence of endemic species are related to these mountain systems' complex topography and biogeographic history (Richter et al., 2009; Pennington et al., 2010). The timing and rate of Andean uplift have been highly uneven across its range, influencing regional climate, hydrological conditions, nutrient cycling, landscape development, and thus potential plant evolution mechanisms at a continental scale (Pérez-Escobar et al., 2022). For these reasons, the Andes mountains of South America are considered privileged sites for understanding ecosystem and ecological function along with variations in biodiversity, particularly by investigating altitudinal gradients (Malhi et al., 2010; Rahbek et al., 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral authors have studied altitudinal biodiversity gradients of the Tropical Andes. It is well known that these mountains are characterized by high floristic β-diversity, which is partly related to the high diversity of the ecological communities in these environments and the presence of plant species with narrow geographic ranges and high habitat specialization (Gentry, 1988; Quintana et al., 2017a; Cuesta et al., 2017; Rahbek et al., 2019). To date, most studies have highlighted peaks in diversity at intermediate altitudes along the Andean mountains. For example, Kessler (2001) described a hump-shaped curve for some plant families in the Bolivian Andes, with peaks at mid-elevations or constant values from the lowlands to mid-elevations, followed by a marked decline at high elevations. Similar patterns have been observed in other studies across the Andes, although differences in the elevation of the maximum peak richness have been recorded (Kessler, 2000; Krömer et al., 2005; Salazar et al., 2013). In contrast, studies of trends in specific growth forms along altitudinal gradients have revealed discordant patterns. For example, Gentry (1988) reported a linear decrease in woody plant richness with altitude in Andean forests, from the lowland tropics (1500 m) to near tree line (3100 m). Similarly, Cuesta et al. (2017) demonstrated a clear decline in species richness with increasing elevation (from 3200 to 5500 m) in highland Andean ecosystems, including Puna and Páramo species. In contrast, Cueva et al. (2019) reported a linear increase in the number of tree species in the Andean dry forests of Ecuador with elevations from 1700 to 2500 m.\u003c/p\u003e\n\u003cp\u003eIt is unclear whether a general relationship exists between species richness and elevation under tropical montane conditions, given their variability in terms of climatic, topographic, and altitudinal gradients (Kessler, 2000). Published studies have captured only a small portion of the biodiversity patterns present in the tropical Andes. Specifically, studies in southern Ecuador have been limited to a few altitudinal ranges or only woody plants, particularly trees (Gentry, 1988; Cabrera et al., 2019; Cueva et al., 2019; Jadán et al., 2021), even though most vascular plants in tropical areas generally belong to non-tree growth forms (Jørgensen and León-Yánez, 1999). Overall, no comprehensive studies on altitudinal gradients by growth forms have been performed in this region. That is why understanding patterns of α- and β-diversity vegetation in scarcely explored mountain regions with diverse regions and environmental conditions, can be critical for predicting future shifts in plant distributions during a period of environmental change that is threatening biodiversity worldwide (Parolo and Rossi, 2008; Malhi et al., 2010; Cuesta et al., 2017; Tovar et al., 2022).\u003c/p\u003e\n\u003cp\u003eIn the present study, the diversity patterns of vascular plants were investigated along an altitudinal gradient encompassing two important regions of the southern Ecuadorian Andes as a baseline for future studies on biodiversity trends. According to the IUCN (2024), such areas should be of special conservation interest, as nearly 76% of the recorded endemic species are classified under some threat category. Based on fieldwork, the α and β-diversity patterns of trees, shrubs and herbaceous species, and total vascular plants were analyzed across elevations from 1000 to 4200 m a.s.l. − from 1000 to 3000 m in the Jubones Valley and from 3300 to 4200 m in the Cajas National Park (CNP). These two areas encompass several Andean ecosystems, including dry tropical forests restricted to inter-Andean valleys, montane forests, \u003cem\u003ePolylepis\u003c/em\u003e forest, and high-elevation grasslands-páramo. Specifically, this study attempts to answer the following questions: 1) How does vascular plant species richness (α-diversity) change along the altitudinal gradient within the target areas? 2) Are the β-diversity different between the altitudinal levels from low to high elevations? 3) How α- and β-diversity patterns along altitudinal gradients change by growth forms (trees, shrubs, and herbs)? 4) What are the main climatic constraints associated with these variations?\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe study was carried out in the Andean mountains of southern Ecuador in the province of Azuay and the northern part of Loja province (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Floristic data were collected from 146 sampling points distributed in the important conservation areas of the Cajas National Park (CNP 2\u0026deg;50\u0026rsquo; S/79\u0026deg;13\u0026rsquo; W), and the Jubones Valley (3\u0026deg;20\u0026rsquo; S/79\u0026deg;18\u0026rsquo; W).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Jubones Valley is located in a special area of the Andes within the transitional zone between the Northern Andes and the Central Andes, known as the \u0026ldquo;Amotape-Huancabamba\u0026rdquo; depression (Weigend, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The sampling points in the Jubones Valley were located in dry tropical forests restricted to inter-Andean valleys; one of the least studied ecosystems in Ecuador (Quintana et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). At the lowest elevations, seasonal climate variations encourage the growth of deciduous species and cacti such as \u003cem\u003eEspostoa frutescens\u003c/em\u003e and \u003cem\u003eArmatocereus godingianus\u003c/em\u003e. Here, there is a scarcity of perennial grasses while shrubs dominate, and only a few tree species grow such as \u003cem\u003eVachellia macracantha, Tara spinosa\u003c/em\u003e, and \u003cem\u003eTecoma stans\u003c/em\u003e. From 2100 to about 2400 m a.s.l., there is a transitional vegetation zone adapted to increasingly humid conditions. Above 2400 m, the montane forest starts to appear, similar to that described in the CNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). In the CNP, the sampling points was conducted in montane forests dominated by trees that can reach 15 m in height, such as \u003cem\u003eHedyosmum cumbalense\u003c/em\u003e, \u003cem\u003eWeinmannia fagaroides\u003c/em\u003e, \u003cem\u003eMyrcianthes rhopaloides\u003c/em\u003e, and \u003cem\u003eOcotea infrafoveolata.\u003c/em\u003e Genera such as \u003cem\u003eMiconia\u003c/em\u003e, \u003cem\u003eViburnum\u003c/em\u003e, and \u003cem\u003ePiper\u003c/em\u003e characterized the subcanopy (Minga et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). P\u0026aacute;ramo hosts plant species adapted to extreme climatic conditions and are dominated by tussocks such as \u003cem\u003eCalamagrostis intermedia\u003c/em\u003e or species such as \u003cem\u003ePlantago\u003c/em\u003e spp. that can form rigid cushions, along with shrubs belonging to the genera \u003cem\u003eGynoxys\u003c/em\u003e, \u003cem\u003eDiplostephium\u003c/em\u003e, and \u003cem\u003eBaccharis\u003c/em\u003e. The genus \u003cem\u003ePolylepis\u003c/em\u003e, endemic to the Andes, is the only tree that forms forested patches above 3400 m a.s.l. (Minga et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Romoleroux et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ansaloni et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These forest patches were also sampled in the study reported here (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs described, the vegetation of the Jubones region changes progressively with increasing altitude, transitioning into montane forest and converging with the vegetation of the CNP on the western side. Therefore, we treated the sampling points from both regions as part of a continuous altitudinal gradient, in order to captured the greatest possible variety of vegetation types and plant species for studying altitudinal patterns in our study area.\u003c/p\u003e\u003cp\u003eIn the Jubones Valley, the soils are formed from rhyolitic rocks, which are rich in siliceous sediments and have a pH of 6\u0026ndash;7 (Hall and Calle, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). The climate ranges from arid to humid. Below 2000 m a.s.l., it is highly seasonal, with annual precipitations of around 1100 mm and marked dry seasons lasting between 5 and 6 months. During this dry season, the precipitation can remain below 100 mm, resulting in a desert-like climate. The annual temperature in this area varies from 15\u0026deg;C to 28\u0026deg;C (Hasan and Wyseure, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The CNP consists of volcanic-origin soils with high organic matter content ( ̴ 20%), acidic pH, high porosity, and high water retention capacity. The annual precipitation ranges from 800 to 1300 mm. The temperatures within this area show strong diurnal variation with average daytime temperatures between 6\u0026deg;C and 18\u0026deg;C around 3000 m a.s.l. and between 3\u0026deg;C and 6\u0026deg;C at about 4000 m a.s.l. Temperatures below 0\u0026deg;C occasionally occur above 4100 m a.s.l. throughout the year (Ansaloni et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFloristic sampling\u003c/h2\u003e\u003cp\u003eField work was conducted from January - June 2016 in CNP and from January - September 2017\u0026ndash;2018 in the Jubones Valley. The floristic inventory was carried out using a stratified transect to record the different plant growth forms. A 50 x 10 m transect (500 m\u003csup\u003e2\u003c/sup\u003e) was used to inventory all tree species with dbh\u0026thinsp;\u0026ge;\u0026thinsp;2.5 cm. To record shrub (woody plants with multiple erect or prostrate perennial stems\u0026thinsp;\u0026ge;\u0026thinsp;50 cm tall) and herbaceous species, the transect was divided into 10 subplots uniformly distributed along its length. Each subplot consisted of 5 x 2 m for shrubs, and 1 x 1 m for herbaceous species (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This resulted in a total transect-sampled area of 500 m\u003csup\u003e2\u003c/sup\u003e for trees, 100 m\u0026sup2; for shrubs, and 10 m\u0026sup2; for herbs. For shrubs, the percent cover (%) of each species was visually estimated in each subplots, while for herbs, we used the Braun-Blanquet scale (Braun\u0026ndash;Blanquet, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). Epiphyte growth forms were not sampled in this study because the sampling design was unsuitable to capture these.\u003c/p\u003e\u003cp\u003eThe transects were located along an altitudinal gradient from 1000 to 3000 m a.s.l. in the Jubones Valley (32 transects) and from 3300 to 4200 m a.s.l. in the CNP (114 transects), in places impacted as little as possible by human activities. Elevations below 1000 m were excluded, considering that the lowest limit of the Jubones Valley is ~\u0026thinsp;700 m a.s.l., and these areas are highly degraded due to human disturbance (i.e., logging). Elevations of 3100 m and 3200 m were also not included, as the few remaining natural fragments of vegetation were difficult to access. To supplement the biodiversity dataset for the highest elevations of CNP (above 3700 m), additional unpublished data from another project related to the vegetation analysis of the region was used. The vegetation sampling was conducted in quadrats of 3 x 3 m to 5 x 5 m to record herb and shrub species. These quadrats represent 42% of the total sampling points in the CNP.\u003c/p\u003e\u003cp\u003eSpecies were identified and incorporated into the collections of the Herbarium of the University of Azuay (HA), Cuenca, Ecuador. The classification system adopted for the circumscription of families and genera was the one proposed by The Angiosperm Phylogeny Group (APG, 2016). Endemic species were classified based on the information from the Red Book of Endemic Plants of Ecuador (Valencia et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Tropicos (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnvironmental data\u003c/h3\u003e\n\u003cp\u003eTo identify the main bioclimatic factors shaping the altitudinal trends in the study area, the following bioclimatic data was extracted: temperature (\u0026deg;C), precipitation (mm), and solar radiation (kj m\u003csup\u003e-2\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e) (see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), from the WorldClim version 2.1 dataset (Fick and Hijmans, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) at a spatial resolution of 30 seconds ( ̴ 1 km\u003csup\u003e2\u003c/sup\u003e). To obtain slope and aspect data, a digital elevation model (DEM) was built to generate a slope and aspect maps in ArcGIS 10.6.1.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe floristic assessment was first carried out by classifying the transects into 100-meter altitudinal bands, from 1000 m up to their respective upper elevation limits. This is a common way to make a synthesis of altitudinal biodiversity data (Gentili et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sekar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This classification resulted in a total of thirty-one altitudinal bands ranging from 1000 to 4200 m a.s.l., each containing a different number of transects. The 3100 m and 3200 m bands were not included for the reasons outlined above.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalysis of α- and β-diversity patterns by growth form: trees, shrubs, and herbs\u003c/h3\u003e\n\u003cp\u003ePatterns of α- and β-diversity were analyzed separately for each growth form\u0026thinsp;\u0026minus;\u0026thinsp;trees, shrubs, and herbs\u0026thinsp;\u0026minus;\u0026thinsp;using abundance matrices based on the number of individuals for trees and percent cover for shrubs and herbs.\u003c/p\u003e\u003cp\u003eBecause different ecological processes can drive biodiversity patterns at different spatial scales, and overlooking one scale may lead to incomplete or biased interpretations (Chase and Knight, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tello et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we analyzed the α- and β-diversity patterns of each growth form at two spatial scales. At small scale, we calculated diversity among individual transects (n\u0026thinsp;=\u0026thinsp;146) within each altitudinal band to capture species richness and heterogeneity within each band. At large scale, we pooled the species composition of all transects within each altitudinal band (n\u0026thinsp;=\u0026thinsp;31) to calculate diversity between bands.\u003c/p\u003e\u003cp\u003eSince differences in sampling effort (i.e., varying numbers of transects per altitudinal band) could influence the observed diversity patterns, we applied the individual-based rarefaction method of Gotelli and Colwell (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) at both spatial scales. Rarefied species richness was calculated by resampling (1000 times) the abundance matrices to a fixed number of individuals (for trees) or a fixed percent cover (for shrubs and herbs). The specific values for individual or cover varied depending on the spatial scale (see Online Resource 2). As another metric of alpha diversity, we calculated the exponential Shannon index using the formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{exp}\\left(H{\\prime\\:}\\right)=exp\\:\\left(-\\sum\\:_{i=1}^{S}{p}_{i}\\:\\text{l}\\text{n}{p}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e is the total number of species in the community, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the proportion of individuals of the species \u003cem\u003ei\u003c/em\u003e (Jost, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeta diversity was calculated using the Bray-Curtis dissimilarity index with the \u0026ldquo;betapart\u0026rdquo; package in R (Baselga and Orme, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), by computing pairwise dissimilarities between individual transects (small scale) and altitudinal bands (large scale). The resulting mean dissimilarity values were then used to fit the GAM models. We compared the observed and expected β-diversities under a null model, following Tello et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This null model maintains the number of species and their abundance distribution while randomizing the spatial location of species occurrences\u0026thinsp;\u0026minus;\u0026thinsp;among transect within each altitudinal band at the small scale, and among altitudinal bands at the large scale. We ran 999 randomizations on the rarefied matrices. The mean β-diversity values from the null distributions were compared with the observed values at each spatial scale. The difference was then divided by the standard deviation of the null distribution to calculate standardized effect sizes (SES), representing the degree to which β-diversity deviates from the null expectation.\u003c/p\u003e\u003cp\u003eTo reveal the patterns of α- and β-diversity along the altitudinal gradient, we plotted fitted models of rarefied species richness, exponential Shannon diversity, and Bray-Curtis dissimilarity indices as functions of elevation. We applied generalized additive models (GAMs) in a similar way as in previous studies (Grytnes et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Quintana et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; He et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). GAMs allow the determination of the shape of response curves and are not constrained by an underlying assumption of linearity (Guisan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). To fit the model, the \u0026ldquo;mgcv\u0026rdquo; package in R (Wood, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used by employing quasi-Poisson or Gaussian distribution (link = \u0026ldquo;log\u0026rdquo;) for the implementation of regression splines as smoothers. All models were fitted separately for each diversity metric, growth form, and spatial scale.\u003c/p\u003e\n\u003ch3\u003eAnalysis of α- and β-diversity patterns for total vascular plant species\u003c/h3\u003e\n\u003cp\u003eTo represent total vascular plant α- and β-diversity, we considered all growth forms. Due to differences in abundance metrics and sampled area resulting from the sampling design, we used a presence/absence matrix for this analysis and conducted it only at the small spatial scale. Species richness was estimated using rarefaction based on the minimum sampled transect area (10m\u003csup\u003e2\u003c/sup\u003e). We then calculated β-diversity using the S\u0026oslash;rensen dissimilarity index, as the data were based on presence/absence. The resulting values were used to fit the GAM models, as described above.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEndemism of species\u003c/h2\u003e\u003cp\u003eThe number of endemic species, classified based on Valencia et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Tropicos (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as described in the floristic sampling section, was plotted in a heatmap to represent the richness of endemic species across altitudinal bands along the gradient from 1000 to 4200 m a.s.l. The number of species was shown by growth form (trees, shrubs and herbs) as well as for total vascular plant species.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInfluence of environmental drivers on species composition\u003c/h3\u003e\n\u003cp\u003eTo test the influence of environmental drivers on changes in species composition across transects, we performed a canonical correspondence analysis (CCA) for total species as well as for each growth form separately. The subset of environmental variables used was selected based on a combination of Pearson\u0026rsquo;s correlation coefficient to reduce the degree of multicollinearity. The least correlated environmental variables were chosen with a correlation index below 0.75, such as mean temperature (\u0026deg;C), annual precipitation (mm), mean solar radiation (kJ m\u003csup\u003e-2\u003c/sup\u003e day\u003csup\u003e-1\u003c/sup\u003e), slope (degrees), and aspect (degrees) designated as relative east aspect (sine of aspect) and relative north aspect (cosine of aspect). The Monte Carlo permutation test was performed to assess the significance of ordination axes. The CCA analysis was carried out using CANOCO version 4.5 (Lepš and Šmilauer, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 891 vascular plant taxa\u0026mdash;comprising 123 tree species, 239 shrub species, and 529 herb species\u0026mdash;belonging to 391 genera and 118 families were recorded across 146 sampling points along an altitudinal gradient from 1000 to 4200 m a.s.l. in the Jubones Valley (1000\u0026ndash;3000 m a.s.l.) and the CNP (3300\u0026ndash;4200 m a.s.l.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The Jubones Valley accounted for a total of 380 species, belonging to 234 genera and 91 families. In terms of growth forms, herbs contributed to the highest richness (48%), followed by shrubs (31%) and trees (21%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). The families with the highest number of species were Asteraceae, Poaceae, Fabaceae, Solanaceae, and Malvaceae, which together accounted for more than 30% of the total sampled species richness. The CNP encompassed a total of 569 species, belonging to 238 genera and 86 families. Herbaceous species represented 65% of the total richness, followed by shrubs (24%) and trees (11%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). The families with the highest number of species were Asteraceae, Poaceae, Cyperaceae, Rosaceae, and Melastomataceae, comprising 41% of the total richness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eα- and β-diversity patterns by growth form: trees, shrubs, and herbs\u003c/h2\u003e\u003cp\u003eThe GAM models calculated for the different growth forms revealed distinct patterns. For trees, a hump-shaped pattern was observed, with the highest richness occurring around 2700\u0026ndash;3000 m. In contrast, the lowest richness was found at both ends of the altitudinal gradient, between 1100\u0026ndash;1400 m and 3800\u0026ndash;4200 m. At the larger scale, the overall pattern remained the same, although the peak shifted slightly, expanding to a broader range between 2700\u0026ndash;3500 m. The exp-Shannon index followed the same patterns as previously described (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eShrub species exhibited a bimodal pattern, which was more evident at the larger scale. At the small scale, shrubs showed two diversity peaks, one occurring around 1700\u0026ndash;2000 m and another between 3000\u0026ndash;3500 m a.s.l. However, at the larger scale, this pattern became more constrained, showing the highest richness at higher elevations, between 3500\u0026ndash;3700 m. Pattern that was consistent with the exp-Shannon diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHerbaceous species richness exhibited a tendency for a unimodal pattern when analyzed by rarefied richness, with the highest peak occurring between 2900\u0026ndash;3400 m at the small scale. This overall trend was consistent with the exp-Shannon index. Nevertheless, at the larger scale, rarefied richness was notably higher between 3500\u0026ndash;3900 m, in the case of the exp-Shannon index, a bimodal pattern emerged, with diversity peaks around 1600\u0026ndash;1900 m and 3500\u0026ndash;3700 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe β-diversity analysis, based on Bray-Curtis dissimilarity index, conducted between each pair of transects (small scale) and between each pair of altitudinal bands (large scale), revealed distinct patterns for trees, shrubs and herbs. However, each growth forms exhibited similar β-diversity trends across both spatial scales, with overall higher β-diversity observed at the small scale.\u003c/p\u003e\u003cp\u003eThe β-diversity pattern for trees at both spatial scales showed an inverted hump-shaped curve, with the lowest values occurring at mid-elevations (2300\u0026ndash;2900 m). In contrast, the highest β-diversity was observed at lower, followed by slightly lower\u0026mdash;but still elevated\u0026mdash;values at higher elevations. Shrubs exhibited the highest β-diversity peak between 2500 m and 2900 m, which was more pronounced at the large scale, particularly around 2700\u0026ndash;3000 m. After this peak, a noticeable decline was observed, followed by an increase again above 3800 m. Herbs β-diversity showed the highest values at lower elevations (1000 m), remaining relatively constant up to mid-elevations. This was followed by a marked decline above 2800 m at the small scale and above 3000 m at the large scale, with a slight increase again above 3900 m for both scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe null model analysis revealed that the observed β-diversity was consistently higher than expected under the null model across both spatial scales and for all growth forms (see Table S5). Standardized effect sizes (SES) were greater at small scale, SES\u0026thinsp;=\u0026thinsp;23.14 for trees, SES\u0026thinsp;=\u0026thinsp;37.87 for shrubs, and SES\u0026thinsp;=\u0026thinsp;41.62 for herbs, compared to the large scale, where SES\u0026thinsp;=\u0026thinsp;46.36 for trees, SES\u0026thinsp;=\u0026thinsp;52.90 for shrubs, and SES\u0026thinsp;=\u0026thinsp;52.85 for herbs. These results suggest that species tend to be more aggregated within local transects but also within altitudinal bands than would be expected by chance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eα- and β-diversity patterns for total vascular plant species\u003c/h2\u003e\u003cp\u003eThe GAM models calculated for all recorded vascular plant species at small scale revealed a hump-shaped pattern, with the highest richness peak occurring between 2800 m and 3500 m. In contrast, the lowest richness was found at both extremes of the altitudinal gradient, particularly at 1100\u0026ndash;1400 m (Jubones Valley) and at 3900\u0026ndash;4200 m (CNP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; Table S4). In terms of β-diversity, a decreasing trend along the altitudinal gradient was observed. The highest β-diversity values occurred at lower elevations, at 1000 m, slightly decreasing toward mid-elevations, and declined sharply above 3000 m. However, an increase in β-diversity was observed again above 3800 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEndemism of species by growth form and total vascular plant species\u003c/h2\u003e\u003cp\u003eRemarkably, 13.6% (121 spp.) of the registered species are endemic to Ecuador, including 50 herb, 48 shrub, and 23 tree species; only 1% are introduced (12 spp.). The highest endemism was found in the CNP region with a total of 87 species, while 42 endemic species were recorded in the Jubones Valley. The distribution of these endemic species by growth form exhibited a distinct separation along the altitudinal bands. Endemic herbs were at their maximum richness above 3400 m, with little or no presence at lower elevations. On the other hand, endemic shrub species were recorded at both extremes of the altitudinal gradient, ranging from 1000\u0026ndash;2000 m and 3300\u0026ndash;4000 m. Tree endemism was prevalent at mid to high elevations between 2100 and 3600 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInfluence of environmental drivers on species composition\u003c/h2\u003e\u003cp\u003eThe first two axes of CCA ordination analysis conducted for all vascular plant species, with eigenvalues of 0.925 and 0.615, explained the largest amount of variance in the data, which is strongly associated with environmental variables, accounting for 62% of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As expected, temperature and precipitation were the main environmental drivers. Particularly, in the Jubones Valley, species composition was strongly correlated with increasing values of temperature and solar radiation, especially the transects at lower elevations. Whereas species occurrence in the CNP is associated with increasing precipitation values, noticeable at mid-elevations, with slope (\u0026deg;) exerting a comparatively minor influence. The Monte Carlo permutation test indicated significant statistical differences for both the ordination axes and tested variables (p\u0026thinsp;=\u0026thinsp;0.001). Temperature, precipitation, solar radiation, and slope, were significant in explaining the variable effect on sampling distribution (all with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, temperature (LambdaA\u0026thinsp;=\u0026thinsp;0.87) and precipitation (LambdaA\u0026thinsp;=\u0026thinsp;0.35) exhibited the highest levels of variance, with a clear correlation with the 1st canonical axis. Separate CCA analyses for trees, shrubs, and herbs revealed a consistent ordination patterns with those observed for total species, with temperature and precipitation remaining the most influential environmental variables (see Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e; Table S6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the Canonical Correspondence Analysis (CCA) carried out for total vascular plants along an altitudinal gradient from 1000 to 4200 m, in Jubones Valley (1000 to 3000 m) and CNP (3300 to 4200 m) in the southern Ecuadorian Andes, (n\u0026thinsp;=\u0026thinsp;146 transects). The analysis is explained by the full set of environmental variables: temperature, precipitation, solar radiation, slope, and aspect\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAxes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal inertia\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies-environmental correlations:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative % variance of species data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCumulative % variance of species-environment relation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e87.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSum of all eigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSum of all canonical eigenvalues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLambdaA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrecipitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSolar radiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEastness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthnes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, a total of 891 vascular plant taxa were recorded across a wide altitudinal gradient ranging from 1000 to 4200 m a.s.l. in scarcely studied areas of the tropical Andes of southern Ecuador, covering the Jubones (1000\u0026ndash;3000 m) and CNP (3300\u0026ndash;4200 m) regions. Species richness patterns showed marked variation among different growth forms. Total species richness showed clear altitudinal pattern, with peaks between 2800\u0026ndash;3500 m. A high proportion of endemic species (13.6%) was recorded, with the highest number of endemic species at 3500\u0026ndash;3800 m in CNP. Patterns of β-diversity also differed among growth form. The patterns remained consistent when compared across spatial scales, although with slight variations. Among the environmental factors considered, temperature and precipitation had a marked effect on diversity patterns (i.e., species/samples distribution) along the altitudinal gradients and strongly influenced species distribution.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eα- diversity patterns\u003c/h2\u003e\u003cp\u003eRegarding total species richness, we identified a unimodal relationship between species richness and altitude characterized by a hump-shaped pattern. This pattern is in line with other studies in the tropical Andes (e.g., Kessler, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kr\u0026ouml;mer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Salazar et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Malizia et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which have also documented mid-elevation peaks in species richness. However, the richness peak greatly varies depending on the elevation range under consideration, the taxonomic group, the growth form, and the area sampled, among other factors. For instance, in Andean forests, Malizia et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found the highest tree species richness at 1000 m. In an in-depth study of specific taxonomic groups, Kessler (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) found a richness peak at around 1100 m for the Acanthaceae family, while families such as Melastomataceae and Araceae peaked between 1000 and 1500 m in the Bolivian Andes. Kr\u0026ouml;mer et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) showed that epiphytes reached maximum diversity between 1300 and 2000 m in the Yungas forest of Bolivia. In contrast, the study reported here found the richness peak for fully sampled vascular plants at higher elevations, around 2800\u0026ndash;3500 m.\u003c/p\u003e\u003cp\u003eThis discrepancy can be ascribed to the spatial complexity of the Andean mountain system, where both topography and climate play crucial roles in shaping biodiversity patterns. Additionally, regional factors such as nutrient availability, slope variation, geological disturbances, environmental history, and other unclear factors may also influence local species richness as stressed by previous authors (Richter et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rahbek et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is important to emphasize that in the study reported here, various vegetation zones were observed to coexist within the same altitudinal range. Specifically, montane forest, treeline, p\u0026aacute;ramo, and \u003cem\u003ePolylepis\u003c/em\u003e forest were all recorded around 3500 m a.s.l. The coexistence and overlapping of different vegetation zones at the same elevation likely contributes to the observed peak in species diversity.\u003c/p\u003e\u003cp\u003eOn the other hand, the transects at both extremes of our altitudinal gradient showed a significantly reduced number of species. This trend likely reflects the extreme climatic conditions at these elevations and the limited adaptability of species to survive in such environments. In the Jubones Valley, the long seasonal dry periods with higher temperatures contrast with the colder conditions, including occasional frost, in the Cajas National Park (CNP). Accumulation curves indicated that additional sampling effort is needed above 4000 m. However, since these altitudes represent the upper limit of vegetation distribution extending up to 4200 m in the southern mountains of Ecuador (Minga et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ansaloni et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a substantial increase in species richness is not likely. Instead, greater species rarity would most likely be expected as shown by Sklen\u0026aacute;ř et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Jubones Valley, increasing the sampling effort would likely result in a higher species count. However, compared to Quintana et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), who reported 313 species in dry tropical forests restricted to the inter-Andean valleys across Ecuador, around 65% of such diversity was collected in the current study with 203 species characteristics of this vegetation type.\u003c/p\u003e\u003cp\u003eOne of the most striking results of this study was the variation in diversity patterns observed across different growth forms. Regarding α-diversity, this reveals not only shifts in the peaks of maximum richness for trees and herbs, but also notable changes in shrub patterns. This reflects, in part, the complex interactions between species' physiological adaptations to the different environment types across the altitudinal range. In detail, the maximum peak of trees species richness (2700\u0026ndash;3500 m) corresponds to the high montane evergreen forest. Montane forests are well known for their high species diversity largely due to the unique microclimatic conditions of these areas, which include significant fluctuations in day/night temperatures, high relative humidity, and frequent rainfall including drizzle and mist (Bush and Silman, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; K\u0026ouml;rner, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, steep slopes, which commonly dominate the topography of these habitats, play a crucial role in the creation and maintenance of high diversity and heterogeneity (Kessler, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Richter et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), since they form a mosaic of microhabitats with niche specialization. Moreover, natural phenomena, such as landslides, which are common in steep slopes, further contribute to biodiversity by creating habitat islands and different successional stages of plant communities (Kessler, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; K\u0026ouml;rner, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor shrub species, the highest richness peaks were found between 1700\u0026ndash;2000 m in the Jubones Valley and 3500\u0026ndash;3700 m in CNP; both of which are transition zones. Shrubs often exhibit adaptations that allow them to thrive in these intermediate conditions between distinct ecosystems. Indeed, for certain Andean shrub genera, these transitional areas represent centers of diversity, such as \u003cem\u003eRibes\u003c/em\u003e or \u003cem\u003eNassa\u003c/em\u003e genera (Mutke et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Their resilience to biotic and abiotic fluctuations such as water, light, and nutrient availability, enables them to colonize transitional zones and at the same time, modifies certain climatic factors (i.e., night air temperature) (D\u0026rsquo;Odorico et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the dry tropical forests restricted to inter-Andean valleys, shrubs may play a crucial role in maintaining biodiversity by supporting the creation and persistence of biological corridors that connect grasslands, shrublands, and forests, thereby providing a stable environment for more sensitive growth forms (Mutke et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003eβ-diversity patterns\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eTotal β-diversity showed high values of Bray-Curtis dissimilarity, ranging from 0.67 to 0.98. These results showed a clear pattern for all vascular plant species with the highest values at lower elevations, followed by an abrupt decrease above 3000 m. A similar pattern was observed for herbaceous species. However, contrasting trends were found among other growth forms, tree species exhibited an inverted hump-shaped pattern, whereas shrub species followed a unimodal pattern. The Andes are known for their high β-diversity indices, largely due to their highly heterogeneous habitats across slopes (Homeier et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Quintana et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Cuesta et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rahbek et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Very high turnover is reported to be a characteristic of dry tropical forests at relatively small spatial scales, which may be caused by dispersal limitation among the geographic groups and \u003cem\u003ein situ\u003c/em\u003e speciation within them (Pennington et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Dryflor, 2016). The higher restriction of species at lower elevations found in this study area could be attributed to their response to the climatic characteristics of the sampling area (i.e., dry forest ecosystems). Evolutionary and historical factors, as well as dispersal barriers, may be the main determinants of species replacement as suggested by Nanda et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Jubones Valley region is located within a geological boundary formed by the transition from the Northern Andes to the Central Andes, which is geographically expressed by a depression known as \u0026ldquo;Amotape-Huancabamba\u0026rdquo; (Weigend, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This depression, which is considered a possible center of endemism (Weigend, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Richter et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Quintana et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e), can function as a biogeographic barrier for the north-south dispersal of Andean taxa. In addition, this depression may also provide a dispersal route between the lowland plant taxa of the Pacific and the humid valleys on the eastern side of the Andes (Weigend, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Quintana et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe increase of β-diversity in all growth forms, at higher elevations above 3700 m in the CNP could reflect different mechanisms of community assembly. These mechanisms can be different from those at lower elevations. Higher elevations host specific habitats and environmental conditions that support unique species. In Ecuadorian p\u0026aacute;ramos, up to 12 phytosociological communities can be distinguished, many of which are composed of exclusive species (Hofstede et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Ansaloni et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In our sampling areas above 3500 m, four communities were identified inside the p\u0026aacute;ramo: a) tussock grassland, b) carpets cushion p\u0026aacute;ramo, c) scrublands, and d) super-p\u0026aacute;ramo scrublands dominated by \u003cem\u003eAndicolea\u003c/em\u003e, as described by Minga et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Ansaloni et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It should be noted that number of endemic species in these high-elevation areas were high where not only species exclusive to the Andean region of Ecuador were recorded, but also species that are exclusively endemic to the Cajas National Park. These species included \u003cem\u003eCarex azuayae\u003c/em\u003e, \u003cem\u003eDraba steyermarkii\u003c/em\u003e, \u003cem\u003eGentianella hirculus\u003c/em\u003e, \u003cem\u003eGentianella longibarbata\u003c/em\u003e, \u003cem\u003eHalenia serpyllifolia\u003c/em\u003e, \u003cem\u003eLysipomia vitreola\u003c/em\u003e, \u003cem\u003eXenophyllum roseum\u003c/em\u003e, \u003cem\u003eAndicolea azuayensis\u003c/em\u003e (Valencia et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ansaloni et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe β-diveristy pattern of tree species, which showed a decrease with elevation, was similar to that observed by B\u0026aacute;ez et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in the North-Central Andean forests. This change in pattern and the decrease in tree β-diversity could reflect the dominance of a few widespread tree species in comparison to other growth forms (Quintana et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). For instance, the lower altitudes (1000 to 1400 m) of the sampling area in this study were dominated by a single tree species, \u003cem\u003eVachellia macracantha\u003c/em\u003e. In species-poor communities, the presence or absence of a few dominant taxa, especially trees, will determine changes in the light, nutrient and soil conditions of the ecosystem, which can, in turn, lead to shifts in the entire plant community (Kessler, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental drivers\u003c/h2\u003e\u003cp\u003eClimate factors are fundamental drivers of floristic variation and shape biodiversity patterns. The effects of these factors along an altitudinal gradient differ substantially from those observed in lowlands with higher elevations, undergoing pronounced shifts mainly in temperature, precipitation, and solar radiation (Bush and Silman, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; K\u0026ouml;rner, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In the study reported here, temperature and precipitation were highlighted as the most important climatic factors influencing floristic patterns.\u003c/p\u003e\u003cp\u003eThe two sampling areas exhibited marked differences linked to climatic factors. The higher elevation areas in the Cajas National Park (3300 to 4200 m a.s.l.) are predominantly influenced by humid eastern currents coming from the inter-Andean and Amazonian valleys. Here, high precipitation was an important driver favouring increased biodiversity levels. Higher productivity and species richness were recorded by previous studies performed in Andean Mountain forests (Gentry, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Toledo et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Godoy-B\u0026uuml;rki et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, the Jubones Valley area (1000 to 3000 m a.s.l.) is influenced by dry western currents from the Pacific coast (P\u0026eacute;rez-Escobar et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), resulting in higher temperatures, which can exert strong control on species richness leading to a drop in α-diversity, especially at lower elevations. The significant role of temperature in regulating floristic variation in lowland regions, with a well-defined dry season of variable length, can shape the physiognomy of vegetation types (Toledo et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This could explain why temperature particularly affects herb and tree species diversity decreasing richness but conserving high levels of species turnover (β-diversity). However, it contributed more to the shrub component of the vascular flora in this study; indeed, in this area, shrubs reached their peak and dominated the landscape, along with some species adapted to survive under certain drought and temperature conditions (Quintana et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), particularly from the Fabaceae, Asteraceae, and Malvaceae families. These families are known to contain species that are specialists in dry forest habitats (Dryflor, 2016). In lowlands, species turnover remained high for shrubs.\u003c/p\u003e\u003cp\u003eThe abrupt change in β-diversity along the altitudinal gradient from about 2900 to 3300 m, reflected the dramatic changes in the composition of Andean ecosystems. After the local substitution of species at low altitudes, there is a marked change in species composition. Therefore, above 3300 m, the lower altitudinal bands do not share any species with altitudinal bands at higher elevations. This suggests that unique environmental conditions (e.g., precipitations and temperatures) could restrict the colonization and membership of low-altitude species towards higher elevations. In addition, these climatic constraints could potentially maintain a clear separation of community assembly dynamics between the Jubones Valley and the Cajas National Park.\u003c/p\u003e\u003cp\u003eIn mountain regions, topography also plays a critical role by modifying factors such as solar radiation, slope, and aspect that foster unique ecological niches (Kessler, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; K\u0026ouml;rner, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In this study, slope and solar radiation were especially correlated with the species sampled along the altitudinal gradient. Overall, the combination of climate and topography generates important environmental drivers that can shape the biodiversity patterns of vascular plants at the micro-scale, as observed in sampling areas of this study in the Tropical Andean mountains of southern Ecuador.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions and implications for conservation and future research","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003cp\u003eThe findings of this study have important implications for the conservation of biodiversity in the southern Ecuadorian Andes, especially for the identification and conservation of refugium areas for local biodiversity hotspots across southern Andean slopes, under the current period of environmental change (Gentili et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The studied regions need additional sampling to capture the high biodiversity levels suggested by our analyses, especially in the upper ranges of the p\u0026aacute;ramo and in 3100\u0026ndash;3200 m a.s.l. altitudinal bands, which was not included in this study. The vulnerability of high-altitude ecosystems to species loss, as shown by the increased β-diversity at higher elevations, suggests that these areas may be particularly sensitive to climate change (Cuesta et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). On the other hand, low to mid-elevation ecosystems, where species richness and turnover are highest, should be prioritized for conservation and new sampling efforts, since they harbour a unique assemblage of species, many of which are endemic. Indeed, these areas include one of the few remaining relicts of dry tropical forest, which is among the most threatened Andean forest types in the world (Quintana et al., 2007a), and represent the first flora inventory in the Jubones Valley that covers all the vegetation zones of this important area.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo external funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.G. wrote the main manuscript text and prepared figures.; N.G. and R.G. performed statistical analyses; R.A., M.J., D.M. e N.G. performed sampling activities; N.G. and R.G. contributed to the manuscript conceptualization; S.C and D.M provided resources; R.A. and D.M. supervised the project; all authors reviewed and edited the final draft of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Vice-Rectorate of Research to the University of Azuay for funding the project. We are also grateful to the Ministry of the Environment of Ecuador for the research authorization. We thank Adolfo Verdugo and all the researchers and students who were involved in the collection of the field data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAPG IV. (2016). An Update of the Angiosperm Phylogeny Group Classification for the Orders and Families of Flowering Plants. Botanical Journal of the Linnean Society 181:1\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/boj.12385\u003c/span\u003e\u003cspan address=\"10.1111/boj.12385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnsaloni, R., Izco, J., Amigo, J. \u0026amp; Minga, D. (2022). Analysis of the p\u0026aacute;ramo vascular flora in the Cajas National Park (Central Andes, Ecuador). Mediterranean Botany 43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5209/mbot.76491\u003c/span\u003e\u003cspan address=\"10.5209/mbot.76491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026aacute;ez, S., Malizia, A., Carilla, J. et al. (2015). Large-Scale Patterns of Turnover and Basal Area Change in Andean Forests. PLoS ONE 10(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0126594\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0126594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaselga, A. \u0026amp; Orme, D. (2012). Betapart: An R package for the study of beta diversity. Methods in Ecology and Evolution 3:808\u0026ndash;812. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.2041-210X.2012.00224.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210X.2012.00224.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBraun\u0026ndash;Blanquet, J. (1979). Fitosociolog\u0026iacute;a. Bases para el estudio de las comunidades vegetales. Blume Ediciones, Madrid.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBush, M.B. \u0026amp; Silman, M.R. (2004). Observations on Late Pleistocene cooling and precipitation in the lowland Neotropics. Journal of Quaternary Science 19:677\u0026ndash;684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jqs.883\u003c/span\u003e\u003cspan address=\"10.1002/jqs.883\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCabrera, O., Ben\u0026iacute;tez, \u0026Aacute;., Cumbicus, N. et al. (2019). Geomorphology and altitude effects on the diversity and structure of the vanishing montane forest of southern Ecuador. Diversity 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/D11030032\u003c/span\u003e\u003cspan address=\"10.3390/D11030032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChase, J.M. \u0026amp; Knight, T.M. (2013). Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecology letters, 16:17\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12112\u003c/span\u003e\u003cspan address=\"10.1111/ele.12112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuesta, F., Muriel, P., Llamb\u0026iacute;, L.D. et al. (2017). Latitudinal and altitudinal patterns of plant community diversity on mountain summits across the tropical Andes. Ecography 40:1381\u0026ndash;1394. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ecog.02567\u003c/span\u003e\u003cspan address=\"10.1111/ecog.02567\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuesta, F., Tovar, C., Llamb\u0026iacute;, L.D. et al. (2020). Thermal niche traits of high alpine plant species and communities across the tropical Andes and their vulnerability to global warming. Journal of Biogeography 47:408\u0026ndash;420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jbi.13759\u003c/span\u003e\u003cspan address=\"10.1111/jbi.13759\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCueva, E., Lozano, D. \u0026amp; Yaguana, C. (2019). Effect of the altitudinal gradient in the floristic composition, structure and arboreal biomass of the Andean dry forest, Loja, Ecuador. Bosque 40:365\u0026ndash;378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4067/S0717-92002019000300365\u003c/span\u003e\u003cspan address=\"10.4067/S0717-92002019000300365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDi Musciano, M., Calvia, G., Ruggero, A., Farris, E., Ricci, L., Frattaroli, A.R. \u0026amp; Bagella, S. 2024. Elevational patterns of plant species richness and phylogenetic diversity in a Mediterranean island. Perspectives in Plant Ecology, Evolution and Systematics 65: 125815, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ppees.2024.125815\u003c/span\u003e\u003cspan address=\"10.1016/j.ppees.2024.125815\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eD\u0026rsquo;Odorico, P., Fuentes, J.D., Pockman, W.T. et al. (2010). Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere 1:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/ES10-00073.1\u003c/span\u003e\u003cspan address=\"10.1890/ES10-00073.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDRYFLOR et al. (2016). Plant diversity patterns in neotropical dry forests and their conservation implications. Science 353:1383\u0026ndash;1387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aaf5080\u003c/span\u003e\u003cspan address=\"10.1126/science.aaf5080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFick, S. \u0026amp; Hijmans, R. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37:4302\u0026ndash;4315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org//10.1002/joc.5086\u003c/span\u003e\u003cspan address=\"/10.1002/joc.5086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFontana, V., Guariento, E., Hilpold, A. et al. (2020). Species richness and beta diversity patterns of multiple taxa along an elevational gradient in pastured grasslands in the European Alps. Scientific Reports 10:12516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-69569-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-69569-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGentili, R., Armiraglio, S., Sgorbati, S. \u0026amp; Baroni, C. (2013). Geomorphological disturbance affects ecological driving forces and plant turnover along an altitudinal stress gradient on alpine slopes. Plant Ecology 214:571\u0026ndash;586. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11258-013-0190-1\u003c/span\u003e\u003cspan address=\"10.1007/s11258-013-0190-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGentili, R., Badola, H.K. \u0026amp; Birks, H.J.B. (2015). Alpine biodiversity and refugia in a changing climate. Biodiversity 16:193\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1080/14888386.2015.1117023\u003c/span\u003e\u003cspan address=\"10.1080/14888386.2015.1117023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGentry, A. (1988). Changes in Plant Community Diversity and Floristic Composition on Environmental and Geographical Gradients. Annals of the Missouri Botanical Garden 75:1\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2399464\u003c/span\u003e\u003cspan address=\"10.2307/2399464\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodoy-B\u0026uuml;rki, A.C., Biganzoli, F., Sajama, J.M. et al. (2017). Tropical high Andean drylands: species diversity and its environmental determinants in the Central Andes. Biodiversity and Conservation 26:1257\u0026ndash;1273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10531-017-1311-2\u003c/span\u003e\u003cspan address=\"10.1007/s10531-017-1311-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGotelli, N.J. \u0026amp; Colwell, R.K. (2001). Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 4(4):379\u0026ndash;391. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1461-0248.2001.00230.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1461-0248.2001.00230.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrytnes, J.A., Heegaard, E. \u0026amp; Ihlen, P.G. (2006). Species richness of vascular plants, bryophytes, and lichens along an altitudinal gradient in western Norway. Acta Oecologica 29:241\u0026ndash;246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.actao.2005.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.actao.2005.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuisan, A., Edwards, T.C. \u0026amp; Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157:89\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/S0304-3800(02)00204-1\u003c/span\u003e\u003cspan address=\"10.1016/S0304-3800(02)00204-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHall, M.L. \u0026amp; Calle, J. (1982). Geochronological Control for the Main Tectonic-Magmatic Events of Ecuador. Earth-Science Reviews 18:215\u0026ndash;239\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHasan, M.M. \u0026amp; Wyseure, G. (2018). Impact of climate change on hydropower generation in Rio Jubones Basin, Ecuador. Water Science and Engineering 11:157\u0026ndash;166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wse.2018.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.wse.2018.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe, X., Arif, M., Zheng, J. et al. (2023). Plant diversity patterns along an elevation gradient: the relative impact of environmental and spatial variation on plant diversity and assembly in arid and semi-arid regions. Frontiers in Environmental Science 11:1021157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fenvs.2023.1021157\u003c/span\u003e\u003cspan address=\"10.3389/fenvs.2023.1021157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHofstede, R., Segarra, P. \u0026amp; Mena, P. (2003). Los p\u0026aacute;ramos del Mundo. Proyecto Atlas Mundial de los P\u0026aacute;ramos. Global Peatland Initioative/NC-IUCN/EcoCIencia. Quito.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHomeier, J., Breckle, S.W., G\u0026uuml;nter, S. et al. (2010). Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest. Biotropica 42:140\u0026ndash;148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7429.2009.00547.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7429.2009.00547.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIUCN. (2024). The IUCN Red List of Threatened Species. Version 2024-1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 5 June 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJad\u0026aacute;n, O., Donoso, D.A., Cedillo, H. et al. (2021). Floristic groups, and changes in diversity and structure of trees, in tropical montane forests in the southern Andes of Ecuador. Diversity 13:400. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/d13090400\u003c/span\u003e\u003cspan address=\"10.3390/d13090400\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJ\u0026oslash;rgensen, P., Le\u0026oacute;n-Y\u0026aacute;nez, S., editors. (1999). Catalogue of the vascular plants of Ecuador. Missouri Botanical Garden Press, St. Louis, Missouri, U.S.A.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJost, L. (2007). Partitioning diversity into independent alpha and beta components. Ecology, 88(10):2427\u0026ndash;2439.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKessler, M. (2000). Altitudinal zonation of Andean cryptogam communities. Journal of Biogeography 27:275\u0026ndash;282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-2699.2000.00399.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2699.2000.00399.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKessler, M. (2001). Patterns of diversity and range size of selected plant groups along an elevational transect in the Bolivian Andes. Biodiversity and Conservation 10:1897\u0026ndash;1921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1013130902993\u003c/span\u003e\u003cspan address=\"10.1023/A:1013130902993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eK\u0026ouml;rner, C. (2007). The use of \u0026ldquo;altitude\u0026rdquo; in ecological research. Trends in Ecology \u0026amp; Evolution 22:569\u0026ndash;574. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2007.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2007.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKr\u0026ouml;mer, T., Kessler, M., Gradstein, S.R. \u0026amp; Acebey, A. (2005). Diversity patterns of vascular epiphytes along an elevational gradient in the Andes. Journal of Biogeography 32:1799\u0026ndash;1809. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2699.2005.01318.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2699.2005.01318.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLepš, J. \u0026amp; Šmilauer, P. (2003). In: Multivariate Analysis of Ecological Data Using CANOCO. Cambridge University Press, Cambridge.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalhi, Y., Silman, M., Salinas, N. et al. (2010). Introduction: Elevation gradients in the tropics: laboratories for ecosystem ecology and global change research. Global Change Biology 16:3171\u0026ndash;3175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2486.2010.02323.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2486.2010.02323.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalizia, A., Blundo, C., Carilla, J. et al. (2020). Elevation and latitude drives structure and tree species composition in Andean forests: Results from a large-scale plot network. PLoS ONE 15(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0231553\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0231553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinga, D., Ansaloni, R., Verdugo, A. \u0026amp; Ulloa-Ulloa, C. 1era. Ed. (2016). Flora del p\u0026aacute;ramo del Cajas, Ecuador. Imprenta Digital Universidad del Azuay, Cuenca. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33324/ceuazuay.42\u003c/span\u003e\u003cspan address=\"10.33324/ceuazuay.42\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMinga, D., Guzm\u0026aacute;n, N., Jim\u0026eacute;nez, M. \u0026amp; Verdugo, A. (2021). Plantas nativas de los ecosistemas del Azuay, un acercamiento conceptual al Jard\u0026iacute;n Bot\u0026aacute;nico de Cuenca-Ecuador. Imprenta Digital Universidad del Azuay, Cuenca. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.33324/ceuazuay.204\u003c/span\u003e\u003cspan address=\"10.33324/ceuazuay.204\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMutke, J., Jacobs, R., Meyer, K. et al. (2014). Diversity patterns of selected Andean plant groups correspond to topography and habitat dynamics, not orogeny. Frontiers in Genetics 5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2014.00351\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2014.00351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMyers, N., Mittermeier, R.A., Mittermeier, C.G. et al. (2000). Biodiversity hotspots for conservation priorities. Nature 403:853\u0026ndash;858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/35002501\u003c/span\u003e\u003cspan address=\"10.1038/35002501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNanda, S.A., Haq, M., Singh, S.P. et al. (2021). Species richness and β-diversity patterns of macrolichens along elevation gradients across the Himalayan Arc. Scientific Reports 11:20155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-99675-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-99675-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParolo, G. \u0026amp; Rossi, G. (2008). Upward migration of vascular plants following a climate warming trend in the Alps. Basic and Applied Ecology 9:100\u0026ndash;107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.baae.2007.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.baae.2007.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePennington, R.T., Prado, D.E. \u0026amp; Pendry, C.A. (2000). Neotropical seasonally dry forests and Quaternary vegetation changes. Journal of Biogeography 27:261\u0026ndash;273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-2699.2000.00397.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2699.2000.00397.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePennington, R.T., Lavin, M., S\u0026auml;rkinen, T. et al. (2010). Contrasting plant diversification histories within the Andean biodiversity hotspot. Proc. Natl. Acad. Sci. U.S.A. 107:13783\u0026ndash;13787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1001317107\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1001317107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Escobar, O.A., Zizka, A., Berm\u0026uacute;dez, M.A. et al. (2022). The Andes through time: evolution and distribution of Andean floras. Trends in Plant Science 27:364\u0026ndash;378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tplants.2021.09.010\u003c/span\u003e\u003cspan address=\"10.1016/j.tplants.2021.09.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuintana, C., Girardello, M., Barfod, A.S. \u0026amp; Balslev, H. (2017a). Diversity patterns, environmental drivers and changes in vegetation composition in dry inter-Andean valleys. Journal of Plant Ecology 10:461\u0026ndash;475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jpe/rtw036\u003c/span\u003e\u003cspan address=\"10.1093/jpe/rtw036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuintana, C., Pennington, R.T., Ulloa, C. \u0026amp; Balslev, H. (2017b). Biogeographic Barriers in the Andes: Is the Amotape-Huancabamba Zone a Dispersal Barrier for Dry Forest Plants? Annals of the Missouri Botanical Garden 102:542\u0026ndash;550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3417/D-17-00003A\u003c/span\u003e\u003cspan address=\"10.3417/D-17-00003A\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahbek, C., Borregaard, M.K., Colwell, R.K. et al. (2019). Humboldt\u0026rsquo;s enigma: What causes global patterns of mountain biodiversity? Science 365:1108\u0026ndash;1113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aax0149\u003c/span\u003e\u003cspan address=\"10.1126/science.aax0149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichter, M., Diertl, K.H., Emck, P. et al. (2009). Reasons for an outstanding plant diversity in the tropical Andes of Southern Ecuador. Landscape Online 12:1\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3097/LO.200912\u003c/span\u003e\u003cspan address=\"10.3097/LO.200912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRomoleroux, K., C\u0026aacute;rate-Tandalla, D., Erler, R., Navarrete, H. (2019). Plantas vasculares de los bosques de \u003cem\u003ePolylepis\u003c/em\u003e en los p\u0026aacute;ramos de Oyacachi. Version 2019.0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioweb.bio/floraweb/polylepis/\u003c/span\u003e\u003cspan address=\"https://bioweb.bio/floraweb/polylepis/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 28 September 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalazar, L., Homeier, J., Kessler, M. et al. (2013). Diversity patterns of ferns along elevational gradients in Andean tropical forests. Plant Ecology \u0026amp; Diversity 8:13\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/17550874.2013.843036\u003c/span\u003e\u003cspan address=\"10.1080/17550874.2013.843036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026auml;rkinen, T., Iganci, J.R.V., Linares-Palomino, R. et al. (2011). Forgotten forests - issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study. BMC Ecology 11:27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1472-6785-11-27\u003c/span\u003e\u003cspan address=\"10.1186/1472-6785-11-27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSekar, K., Thapliyal, N., Pandey, A. et al. (2023). Plant species diversity and density patterns along altitude gradient covering high-altitude alpine regions of west Himalaya, India. Geology, Ecology, and Landscapes 1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/24749508.2022.2163606\u003c/span\u003e\u003cspan address=\"10.1080/24749508.2022.2163606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSklen\u0026aacute;ř, P., Romoleroux, K., Muriel, P. et al. (2021). Distribution changes in p\u0026aacute;ramo plants from the equatorial high Andes in response to increasing temperature and humidity variation since 1880. Alpine Botany 131:201\u0026ndash;212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00035-021-00270-x\u003c/span\u003e\u003cspan address=\"10.1007/s00035-021-00270-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTello, J.S., Myers, J.A., Mac\u0026iacute;a, M.J., Fuentes, A.F., Cayola, L., Arellano, G., Loza, M., Torrez, V., Cornejo, M., Miranda, T. \u0026amp; Jorgensen, P. (2015). Elevational Gradients in β-Diversity Reflect Variation in the Strength of Local Community Assembly Mechanisms across Spatial Scales. PLos ONE 10(3):e0121458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0121458\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0121458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToledo, M., Poorter, L., Pe\u0026ntilde;a-Claros., M. et al. (2011). Patterns and Determinants of Floristic Variation across Lowland Forests of Bolivia. Biotropica 43:405\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-7429.2010.00711.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-7429.2010.00711.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTovar, C., Carril, A.F., Guti\u0026eacute;rrez, A.G. et al. (2022). Understanding climate change impacts on biome and plant distributions in the Andes: Challenges and opportunities. Journal of Biogeography 49:1420\u0026ndash;1442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jbi.14389\u003c/span\u003e\u003cspan address=\"10.1111/jbi.14389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTropicos. Missouri Botanical Garden. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tropicos.org\u003c/span\u003e\u003cspan address=\"http://www.tropicos.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed June 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValencia, R., Pitman, N., Le\u0026oacute;n-Y\u0026aacute;nez, S., J\u0026oslash;rgensen, P. (eds.). (2000). Libro rojo de las plantas end\u0026eacute;micas del Ecuador. Publicaciones del Herbario QCA. Pontificia Universidad Cat\u0026oacute;lica del Ecuador, Quito.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeigend, M. (2002). Observations on the Biogeography of the Amotape-Huancabamba Zone in Northern Peru. Botanical Review 68:38\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1663/0006-8101(2002)068[\u003c/span\u003e\u003cspan address=\"10.1663/0006-8101(2002)068[\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e0038:OOTBOT]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood, S. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73:3\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang, 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 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0159995\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0159995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-biosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Biosystems](https://link.springer.com/journal/44473)","snPcode":"44473","submissionUrl":"https://submission.springernature.com/new-submission/44473/3?","title":"Plant Biosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Andes, Biodiversity refugia, The Cajas National Park, The Jubones Valley","lastPublishedDoi":"10.21203/rs.3.rs-8295166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8295166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Tropical Andes provide ideal opportunities to study diversity patterns along altitudinal gradients. However, they remain poorly characterized, particularly in terms of detailed, ground-level data. The α- and β-diversity trends of vascular plants were studied along an altitudinal gradient from 1000 to 4200 m in the Jubones and the Cajas National Park areas, located in southern Ecuador using stratified sampling transects to generate floristic inventories with 146 plots surveyed in total. This gradient included dry tropical forests restricted to inter-Andean valleys, montane forests, p\u0026aacute;ramo and \u003cem\u003ePolylepis\u003c/em\u003e forests. A total of 891 species belonging to 391 genera and 118 families were recorded. Our findings for all plant species revealed a clear hump-shaped richness pattern, with peaks between 2800\u0026ndash;3500 m with a high proportion of endemic species (13.6%). β-diversity was highest at lower elevations, then decreased with altitude. Marked variations in the diversity patterns were found with in-depth observations of the different growth forms of trees, shrubs, and herbs. Climatic factors such as temperature and precipitation were key determinants of species composition among the studied regions. Understanding diversity distribution in these regions is critical for predicting future plant refugia and developing effective conservation strategies in the face of climate change.\u003c/p\u003e","manuscriptTitle":"Exploring plant α- and β-diversity patterns along tropical altitudinal gradients in the Southern Andes of Ecuador: a potential pathway to biodiversity conservation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 11:12:37","doi":"10.21203/rs.3.rs-8295166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-26T14:00:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T04:16:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T19:53:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179399389646907390978841885664184660829","date":"2025-12-15T13:20:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138310587250252893763403974940536449305","date":"2025-12-11T20:02:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266015971230259386288255894442235629919","date":"2025-12-11T16:11:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T17:50:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T07:42:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T07:41:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant Biosystems","date":"2025-12-06T13:53:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-biosystems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Plant Biosystems](https://link.springer.com/journal/44473)","snPcode":"44473","submissionUrl":"https://submission.springernature.com/new-submission/44473/3?","title":"Plant Biosystems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"61c47001-e418-40ff-85ce-48793e32300b","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T21:08:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 11:12:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8295166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8295166","identity":"rs-8295166","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}