Environmental regimes and traits drive the spatial mismatch between species richness and diversification rates of Chinese Clematis

Environmental regimes and traits drive the spatial mismatch between species richness and diversification rates of Chinese Clematis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 April 2026 V1 Latest version Share on Environmental regimes and traits drive the spatial mismatch between species richness and diversification rates of Chinese Clematis Authors : Xinru Zhang , Liguo Zhang , Mengmeng Wang , Wyckliffe Omondi Omollo , Xueqin Wang , Yu Meng , Mei Liang , Lei Xie , Jianfei Ye , Wei Wang 0000-0001-6901-6375 , and Miao Sun 0000-0001-5701-0478 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177508127.72900723/v1 167 views 98 downloads Contents Abstract Abstract 1 Introduction 2 Materials and Methods 3 Results 3.2 Diversification dynamics through time 3.3 Environment-dependent diversification 3.4 Trait-dependent diversification 3.5 Ancestral distribution areas reconstruction and colonization history 3.6 Traits evolution of in China 3.7 Species richness and diversification in relation to current environment 4 Discussion 5 Conclusions Reference Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Understanding the mechanisms underlying spatial mismatches between species richness and diversification rates remains a central challenge in macroecology. Here, we investigate how historical environmental regimes and intrinsic traits jointly shape biodiversity gradients in Chinese Clematis . We integrated a time-calibrated phylogeny of 169 species with extensive distribution records and functional morphological traits, and applied diversification analyses, ancestral reconstructions, and structural equation modelling (SEM) to disentangle the relative contributions of historical climate, contemporary environment, and trait variation. We reveal a pronounced geographic mismatch: species richness peaks in the humid southeast, whereas diversification rates are highest in the arid northwest. This pattern is associated with an early diversification burst (ca. 8.5–7.5 Ma) and subsequent northwestward expansion (ca. 5.5 Ma), linked to Late Miocene climatic dynamics. Adaptive traits promote diversification under harsh conditions, while environmental filtering and limited evolutionary time constrain lineage accumulation. Our results demonstrate that spatial biodiversity gradients emerge from the joint effects of environmental history and trait-mediated responses, leading to a decoupling of species richness and diversification. These findings highlight the importance of integrating macroevolutionary dynamics and ecological processes to understand the spatial structuring of biodiversity. Title: Environmental regimes and traits drive the spatial mismatch between species richness and diversification rates of Chinese Clematis Abstract Understanding the mechanisms underlying spatial mismatches between species richness and diversification rates remains a central challenge in macroecology. Here, we investigate how historical environmental regimes and intrinsic traits jointly shape biodiversity gradients in Chinese Clematis . We integrated a time-calibrated phylogeny of 169 species with extensive distribution records and functional morphological traits, and applied diversification analyses, ancestral reconstructions, and structural equation modelling (SEM) to disentangle the relative contributions of historical climate, contemporary environment, and trait variation. We reveal a pronounced geographic mismatch: species richness peaks in the humid southeast, whereas diversification rates are highest in the arid northwest. This pattern is associated with an early diversification burst (ca. 8.5–7.5 Ma) and subsequent northwestward expansion (ca. 5.5 Ma), linked to Late Miocene climatic dynamics. Adaptive traits promote diversification under harsh conditions, while environmental filtering and limited evolutionary time constrain lineage accumulation. Our results demonstrate that spatial biodiversity gradients emerge from the joint effects of environmental history and trait-mediated responses, leading to a decoupling of species richness and diversification. These findings highlight the importance of integrating macroevolutionary dynamics and ecological processes to understand the spatial structuring of biodiversity. Key words : biodiversity gradients, Chinese Clematis , diversification rates, ecological opportunity, environmental filtering, trait-environment interactions. 1 Introduction Geographic variation in species richness and its underlying drivers has long intrigued ecologists and evolutionary biologists. Although diversification rate is often considered a major predictor of species richness (Magallón & Gómez-Acevedo, 2018), numerous studies have demonstrated spatial mismatches between richness hotspots and areas of elevated diversification (Weir & Schluter, 2007; Schluter & Pennell, 2017; Sun et al., 2020; Tietje et al., 2022). Understanding such mismatches is fundamental for biodiversity conservation, as prioritization based solely on richness may overlook regions functioning as “evolutionary engines” (high diversification areas) or “evolutionary museums” (harboring ancient and evolutionarily stable lineages), both of which are critical for maintaining long-term evolutionary potential (Dagallier et al., 2020; Gopal et al., 2023). However, the mechanisms underlying such mismatch remain poorly understood. Most studies have addressed only partial components of these mechanisms, either emphasizing historical processes such as lineage accumulation and diversification dynamics (Stephens & Wiens, 2003; Li & Wiens, 2019; Rabosky, 2009), or focusing on the effects of contemporary environmental conditions on present-day richness patterns of organisms (Tietje et al., 2022). Explicit tests integrating deep-time diversification with present-day environmental filtering at broad spatial scales remain still rare. Accordingly, focusing on lineage-level analyses within specific and well-represented taxonomic groups allows these processes to be examined more explicitly. China provides an exceptional system for such analyses, given its steep climatic gradients, complex topography, and pronounced southeast–northwest asymmetries in biodiversity patterns (Lu et al., 2018; Lan et al., 2025; Sun et al., 2025a). Clematis (Ranunculaceae) is a species-rich genus comprising ca. 391 species worldwide, with China harboring nearly half of such diversity, including over 100 endemics (Ziman, 1981; Tamura, 1987, 1995; Wang & Li, 2005; Plants of the World Online, POWO; https://powo.science.kew.org/). Chinese Clematis occupies a wide range of habitats along a humid–arid gradient, including forests, bushes, and their edges, and exhibits substantial morphological diversity (Tamura, 1995; Wang & Li, 2005; He et al., 2021). Despite this ecological and morphological diversity, the roles of intrinsic traits and environmental change in shaping Clematis diversification remain largely unexplored. Phylogenetic evidence indicates that Clematis differentiated in the late Miocene (He et al., 2021; Lyu et al., 2023), a period marked by declining atmospheric CO 2 , global cooling, aridification, intensified tectonism, and strengthened monsoonal dynamics (Herbert et al., 2016; Westerhold et al., 2020; Miao et al., 2022; Zhang et al., 2025). Such environmental changes have been implicated in promoting diversification across multiple plant lineages through interactions with key morphological innovations (Givnish et al., 2014; Soltis et al., 2019; Xia et al., 2021), yet their effects on Clematis diversification have not been explicitly tested. A densely sampled, time-calibrated phylogeny with spatial, evolutionary and ecological analyses is essential to disentangle richness–diversification relationships at regional scales (Olofsson et al., 2019; Mo et al., 2022; Maliet & Morlon, 2021). However, previous studies on Clematis phylogeny suffered sparse taxon sampling and conflicting topologies (Slomba et al., 2004; Miikeda et al., 2006; Xie et al., 2011; Lehtonen et al., 2016; Jiang et al., 2017). Recent advances in supertree grafting, such as the Taxonomic Addition for Complete Trees (TACT; Chang et al., 2020) and metachronogram (Ringelberg et al., 2023) approaches provide powerful tools to overcome chronic taxon-sampling biases and enable lineage-level macroevolutionary inference (Stadler, 2009, 2011; Cusimano et al. 2012; Thomas et al., 2013; Chang et al., 2020; Ringelberg et al., 2023). Additionally, with the advent of genomic sequencing, genome skimming, transcriptomics, and single nucleotide polymorphism (SNP) based datasets have substantially improved phylogenetic resolution of Clematis (e.g., Xiao et al., 2022, 2025; Lyu et al., 2023). The combination of these advancements allows the assembly of densely sampled ultrametric trees and robust estimation of diversification dynamics for Clematis . Here we leveraged the geographically heterogeneous genus Clematis in China as a model system to decode the drivers of spatial mismatch between species richness and diversification, thereby bridging the gap between historical macroevolution and contemporary biodiversity patterns. Specifically, we aim to (1) quantify the diversification dynamics across contrasting environmental gradients; (2) test the roles of paleo-environmental change and environmental tolerance traits in shaping its diversification dynamics; (3) assess whether present-day species richness and diversification rates are spatially coupled or decoupled; and (4) identify the potential evolutionary processes responsible for the observed mismatch. Our results reveal strong geographic heterogeneity in diversification rates, with repeated westward colonization events from southeastern China. Although episodic environmental change and key trait transitions elevated diversification in northwestern lineages, structural equation modeling shows that present-day species richness is mainly driven by environmental filtering rather than diversification rates alone. By integrating phylogenetics, trait evolution, and paleoclimatic modeling, this study provides a scalable perspective on how historical environmental legacies shape the resilience and distribution of temperate and subtropical flora, offering critical insights for conservation prioritization in an era of rapid global change. 2 Materials and Methods 2.1 Data assembly Species list. A list of total 169 Clematis species occurring in China was compiled and reconciled mainly based on the Species2000 database (Lin et al., 2025), POWO (2025), Global Biodiversity Information Facility (GBIF; https://www.gbif.org) and the literature (see details in Supporting information). Molecular datasets. Four molecular datasets were assembled: two nuclear SNP datasets, referred to as SNPs-2022 (Xiao et al., 2022) and SNPs-2023 (Lyu et al., 2023); a plastome coding sequence dataset (Plastome CDS dataset, hereafter); and a seven-locus dataset, including the nuclear ribosomal internal transcribed spacer (nrITS) and plastid atpB - rbcL , matK , psbA - trnQ , rbcL , rpoB - trnC , and rps16 (Seven-locus dataset, hereafter). Among these datasets, the Plastome CDS dataset was retrieved from the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/, accessed June 2, 2025), and the Seven-locus dataset was downloaded from NCBI using PyPHLAWD (Smith & Walker, 2018) and supplemented by the sequences from Jiang et al. (2017). The outgroup Anemoclema glaucifolium was assembled in all datasets, following previous studies (He et al., 2021; Xiao et al., 2022; Lyu et al., 2023). All datasets, except for the SNPs-2022 dataset, underwent additional cleaning and alignment (see Supporting information), and the associated details are provided in Supporting information. Morphological data. Morphological data for 169 Chinese Clematis species was compiled from Flora Reipublicae Popularis Sinicae (Wang, 1980), Flora of China (Wang & Bartholomew, 2001), the Chinese Virtual Herbarium (CVH; http://www.cvh.ac.cn/), the National Specimen Information Infrastructure (NSII; http://www.nsii.org.cn/), and other relevant taxonomic literature (Supporting information). A total of 36 traits were scored following the coding scheme of Lehtonen et al. (2016) with modifications (Supporting information). Phylogenetic signal of each trait was evaluated with RASP v4 (Yu et al., 2019) using both Blomberg’s K (Blomberg et al., 2003) and Pagel’s λ (Pagel, 1999). Only 34 traits that exhibit significant phylogenetic signal (P < 0.05; Supporting information) were retained for downstream analyses. Distribution data. To explore the spatial patterns of Clematis in China, we downloaded 20,262 georeferenced occurrence records from NSII, CVH, and GBIF (GBIF, 2023, 2024). Species names were standardized according to the complete Clematis species list mentioned above (Fig. 1; Supporting information), and records from the subspecific taxa were merged into the corresponding species entries. Coordinates quality was ensured by CoordinateCleaner v3.0.1 (Zizka et al., 2019), which filters out erroneous records (e.g., points in capitals, centroids, duplicated coordinates, zero coordinates, and marine locations). Finally, we retained 16,078 clean occurrence records for 169 Chinese Clematis species. Environment data. To fully investigate the environmental drivers of Clematis diversity in China, we assembled both current and paleo-environmental datasets. Current environmental variables comprised 28 bioclimatic indices (e.g., temperature, precipitation, solar radiation, vapor pressure, and aridity) from CHELSA database v2.1 (Karger et al., 2021), CRUTS database v4.09 (Harris et al., 2020) and Global Aridity Index and Potential Evapotranspiration database v3 (Zomer et al., 2022), as well as two topographic factors, including the relief degree of land surface (RDLS for short, hereafter) from You et al. (2018), and elevation from Hijmans et al. (2005). Environmental values for downstream model fitting analyses were extracted from these rasters based on species occurrence points described above and averaged within each grid cell to characterize the ecological niche of Clematis species. Paleo-environmental variables incorporated historical records of atmospheric CO 2 concentration (paleo-CO 2 ; Rae et al., 2021), East Asian monsoon rainfall (paleo-rainfall; Wan et al., 2025), global surface temperature (paleo-temperature; Boschman & Condamine, 2022), elevation (paleo-elevation; Miao et al., 2022) and tectonic event counts (paleo-CCTE; Miao et al., 2022). Detailed descriptions are provided in Supporting information. All continuous environment-derived predictors mentioned above were standardized for downstream analyses using z-score transformation (Shalabi et al., 2006) to ensure comparability of effect sizes. 2.2 Phylogeny construction We constructed a time-calibrated phylogeny comprising 169 Chinese Clematis species (Fig. 1) by integrating four molecular and one morphological datasets using both metachronogram (Ringelberg et al., 2023) and TACT (Chang et al., 2020) approaches under a hierarchical grafting framework (see details of tree dating and grafting in Supporting information). Briefly, the metachronogram procedure was employed to sequentially graft three dated molecular subtrees (the SNPs-2023 tree, the Plastome CDS tree, and the Seven-locus tree [Supporting information]), onto the SNPs-2022 backbone tree (Supporting information), thereby generating one comprehensive molecular tree representing all the molecular data available to date from public repository (hereafter “the molecular tree”; Supporting information). Subsequently, we used TACT to incorporate species lacking molecular data into the molecular tree generated above. In a standard TACT procedure, additional species were incorporated under the guidance of a taxonomic tree with their hierarchical classification information (Chang et al., 2020). Instead of using a conventional taxonomic tree, here we used a backbone-constraint maximum parsimony (MP) tree inferred by PAUP v4.0a169 (Swofford, 2003) based on 34 morphological characters with significant phylogenetic signal (Supporting information). Finally, we obtained a fully resolved and ultrametric supertree of 169 Chinese Clematis species based on the molecular and taxonomic data. To capture uncertainties, the TACT procedures above were repeated 100 times to yield 100 complete trees (hereafter “100 complete trees”, available on https://doi.org/10.5281/zenodo.18847044), providing a robust phylogenetic tree distribution for subsequent diversification analyses. 2.3 Diversification analysis To explore the diversification dynamics and investigate the potential drivers for diversity of Clematis in China, we used the 100 complete trees above and conducted a serial complementary diversification approaches. Diversification dynamics through time. Diversification dynamics of Chinese Clematis were analyzed using BAMM v2.5.0 (Rabosky, 2014). Inferred diversification dynamics were interpreted as reflecting relative evolutionary processes within Chinese Clematis lineages rather than absolute genus-wide rates. Accordingly, incomplete taxon sampling was accounted for by specifying a global sampling fraction of 0.43 (169/391; POWO, 2025; also see Xiao et al., 2022, 2025) in BAMM and other diversification analyses. Prior settings were defined using the ‘setBAMMpriors’ function in BAMMtools v2.1.12 (Rabosky et al., 2014). BAMM was run on 100 complete trees for 30 million generations or extended to 40 million generations when necessary to achieve convergence (effective sample sizes [ESS] > 200; assessed using CODA v0.19.4 [Plummer et al., 2006]). The rate shifts, rates-through-time (RTT) dynamics, and species-specific diversification rates (i.e., tip rates) were conducted in BAMMtools after removing the first 25% as burn-in. Posterior probabilities of rate-shift configurations, the mean RTT values, and the tip rates were summarized across 100 complete trees. As a complementary approach, ClaDS (Maliet et al., 2019) analyses were performed using PANDA in Julia with the same global sampling fraction and default parameters, and the algorithm automatically terminates upon convergence. Environment-dependent analyses. To examine whether environmental factors influenced diversification of Clematis in China, RPANDA v1.4 (Morlon et al., 2016) was deployed with total 37 diversification models (one null, six time-dependent, and 30 environment-dependent models; Supporting information). We considered five paleo-environment variables, including paleo-CO 2 (Rae et al., 2021), paleo-rainfall (Wan et al., 2025), paleo-temperature (Boschman & Condamine, 2022), paleo-elevation and paleo-CCTE (Miao et al., 2022), and each factor applied to six models upon speciation and extinction process (see Supporting information). The best model was selected by the highest Akaike weight (AW; Wagenmakers & Farrell, 2004) value under the corrected Akaike’s information criterion (AICc; Akaike, 1974). Additionally, we tested correlations between tree-wide diversification rates from BAMM/ClaDS and five paleo-environment variables across 500 time-slice intervals using linear and exponential regressions, respectively, following Sun et al. (2020). Trait-dependent analyses. To assess whether diversification of Chinese Clematis was associated with intrinsic traits, we applied a suite of trait-dependent diversification models. First, trait-independent heterogeneity in diversification was evaluated using the Missing State Speciation and Extinction model (MiSSE) in hisse v2.1.11 (Beaulieu & O’Meara, 2016), with model support assessed AW. We then analyzed 30 significant traits (Supporting information) using State Speciation and Extinction (SSE) frameworks. Binary traits were examined using Binary State Speciation and Extinction (BiSSE; Maddison et al., 2007), Hidden-State Speciation and Extinction (HiSSE), and Character-Independent Diversification models (CID2 and CID4; Beaulieu & O’Meara, 2016) in hisse. The best model was chosen by the highest AW, and diversification parameters (speciation, extinction, and net diversification rates) were estimated by model-averaging. In full HiSSE, hidden states (A and B) represent alternative diversification regimes inferred by the HiSSE model rather than additional traits (Supporting information). Multi-state traits were examined using Multi-State Speciation and Extinction (MuSSE) models in diversitree v0.9.20 (FitzJohn, 2012). Four distinct models were fitted and evaluated using AW and ANOVA significance tests, following Zhang et al. (2021; also see Supporting information). For supported traits, state-specific net diversification rates were estimated using Bayesian MCMC with an exponential prior for 10,000 generations. Continuous traits were analyzed using Quantitative State Speciation and Extinction (QuaSSE; FitzJohn, 2010) in diversitree and complemented by ES-sim tests (Harvey & Rabosky, 2017) to assess correlations between traits and tip rates (estimated from BAMM and ClaDS) using 10,000 permutations. All continuous traits were represented by the median value (Supporting information) and standardized using z-score transformation prior to analysis. Detailed model specifications are provided in Supporting information. 2.4 Spatial analysis To explore the spatial pattern of Clematis in China, we aggregated occurrence records to a 100 km × 100 km grid. Maps of China were adapted from the standard maps released by the National Administration of Surveying, Mapping and Geoinformation of China (http://www.sbsm.gov.cn; review drawing number: GS(2016)1576). The species richness and mean tip rates (estimated by BAMM and ClaDS) were calculated based on the number of unique species in each grid after removing duplicates. To provide a biogeographic framework for subsequent analysis, we adopted the Heihe-Tengchong Line (Hu Line; Hu, 1935) to delineate China into southeastern and northwestern regions based on the conventional geographical division (Wang et al., 1995; Ding et al., 2021). Grid cells located southeast of the Hu Line were assigned to the southeastern region, those northwest to the northwestern region, and cells on the line were treated as a transition zone (Supporting information). 2.5 Ancestral states reconstruction We reconstructed ancestral states of distributional areas and key traits to further explore how historical biogeography and trait evolution contributed to current richness patterns and ecological adaptation in Chinese Clematis . To balance phylogenetic reliability and taxonomic completeness, analyses were conducted using two complementary phylogenetic trees: (i) the molecular tree, and (ii) 100 complete trees. Here we primarily reported results from the molecular tree, with results from 100 complete trees used for validation (see Supporting information). Ancestral ranges were inferred using BioGeoBEARS v1.1.3 (Matzke, 2014) under three models (DEC, DIVALIKE, BAYAREALIKE) with a founder-event speciation parameter (+j) test for each model (Matzke, 2014), and the best-fitting model was chosen based on the highest AW (Supporting information). Species distributions were coded into southeast China (“a”), northwest China (“b”), and outside China (“c”) (Supporting information). Colonization events and corresponding timing were summarized for internal nodes with supported transitions, and events occurred outside China were excluded. Ancestral trait reconstruction was performed using phytools v2.4.4 (Revell, 2024). For traits with significant SSE model supported (Supporting information), three discrete evolution models (ER, ARD, SYM; see Supporting information for model descriptions) were fitted and selected by the highest AW. Stochastic character mapping was applied with 1,000 simulations per phylogeny under the best model. Additionally, we further examined trait states at the most recent common ancestor (MRCA) of the earliest diverging lineage that colonized northwestern China to assess whether morphological shifts may have facilitated this initial colonization (Supporting information). 2.6 Structural equation modeling We used grid-based spatial metrics, including species richness, mean tip rates estimated from BAMM, and environmental variables, to test whether/which present environmental factor(s) contribute to the diversification and/or richness of Clematis in China using structural equation modeling (SEM; Anderson & Gerbing, 1988). Environmental variables were summarized as the mean and standard deviation of all occurrence points within each cell, and subsequently standardized using z-score transformation prior to analysis. Candidate predictors were identified using Spearman’s correlations and generalized boosted regression models (GBMs) following Tietje et al. (2022). Model structure was guided by theoretical expectations and variable importance (Supporting information). Variables with strong multicollinearity (Variance Inflation Factor [VIF; O’brien, 2007]) ≥ 10) were excluded. SEM was fitted using lavaan v0.6-19 (Rosseel, 2012). Model fit was assessed using χ²/df, CFI, SRMR, and RMSEA, and only significant paths (P < 0.05) were retained in the final models. To assess regional heterogeneity across the Hu Line, unconstrained and constrained models were compared using the scaled χ² difference test (Satorra & Bentler, 2001). Significant declines in fit (P < 0.05) indicated structural divergence, justifying separate SEM for each region. Spatial autocorrelation was accounted for by correcting effective sample size with Moran’s I of model residuals using the ape package v5.8.1 (Paradis & Schliep, 2018). Model robustness was evaluated by repeating all analyses using using mean tip rates estimated from ClaDS. See more details in Supporting information. 3 Results 3.1 Phylogeny of in China In this study, we assembled a comprehensive and robust time-calibrated phylogeny for 169 Clematis species in China by integrating molecular and morphological trait datasets (Figs. 1, S1–S2). The resulting complete tree represents four subgenera (subgen. Cheiropsis , Subgen . Clematis , Subgen. Viorna , Subgen. Atragene ) and 10 sections (sect. Cheiropsis , Sect. Clematis , Sect. Meclatis, Sect. Fruticella , Sect. Naraveliopsis , Sect. Viticella , Sect. Tubulosae , Sect. Viorna , Sect. Archiclematis , Sect. Atragene ) recognized by Wang & Li (2005). To account for topological uncertainty, 100 TACT replicates (available on https://doi.org/10.5281/zenodo.18847044) were performed to reflect both taxonomic and phylogenetic robustness, providing a reliable framework for downstream analyses. The crown age of Chinese Clematis was estimated approximately 9.38 Ma (Fig. 1). Fig. 1 . A representative complete tree of 169 Clematis species in China. The outer circular layer depicts the distribution region across Hu Line for each species (see Supporting information). Two black dots are secondary calibration points used for divergence time estimation. Branches informed by the molecular data are marked by black triangles. Chinese endemic Clematis species are highlighted by yellow circles at the corresponding tips. 3.2 Diversification dynamics through time For the temporal dynamics of diversification in Chinese Clematis , rate-through-time (RTT) plot revealed that curves of net diversification and speciation rates increase in the late Miocene ( ca. 8.5–7.5 Ma), followed by a gradual decline towards the present (Fig. 2a); while extinction rates remained relatively stable; and no significant diversification rate shift was detected (Supporting information). Notably, we detected a clear spatial mismatch along the Hu Line between species richness and diversification rates in Chinese Clematis (Fig. 2c–h; see Supporting information) . After mapping species richness (Fig. 2c), and tip rates from BAMM (Fig. 2d) as well as ClaDS (Fig. 2e) onto equal-area grids, we found that southeastern China harbors more than twice species richness of the northwest (Fig. 2f), whereas diversification rates are significantly higher in the northwest (Fig. 2g–h). Although northwestern and southeastern lineages show broadly similar temporal diversification trajectories, the southeastern lineages had higher rates between ca. 9.38–5 Ma, after which diversification became higher in the northwest (Fig. 1b). Consistently, the ClaDS analysis supported similar overall temporal dynamic pattern in Chinese Clematis (Supporting information). Fig. 2. Spatio-temporal diversification pattern of Clematis in China. a, BAMM rate curves (mean of each complete tree) of speciation (blue), net diversification (pink), and extinction (gray) with shaded ribbons representing 95% confidence intervals; b, BAMM net diversification rate-through-time plot for northwestern (orange) and southeastern (dark blue) Chinese Clematis lineages colored by regions (shaded ribbons represent 95% confidence intervals; see Supporting information for details of region division); c, Spatial pattern of species richness (defined as the number of unique species present in each grid cell; red = high and blue = low); d–e, spatial pattern of tip rates estimated by BAMM ( d ) and ClaDS ( e ), reported as the mean of each grid cell (red = high and blue = low); f–h, boxplot of species richness ( f ) and mean tip rates estimated by BAMM ( g ) and ClaDS ( h ) along the Hu Line (northwest: orange, and southeast: blue). All results were based on 100 complete trees. Differences in species richness and tip rates across the Hu Line were assessed using Wilcoxon rank-sum tests. Significance levels: ****P < 0.0001. 3.3 Environment-dependent diversification Model comparison revealed clear environmentally driven effects on diversification dynamics of Clematis in China (Figs. 3, Supporting information). Among all models, diversification dynamics of Chinese Clematis was best explained by an exponential increase in speciation rate correlated with rising paleo-CO 2 levels (α = 0.144 ± 0.031; AW = 0.275 ± 0.015; Fig. 3a; Supporting information), better than time-dependent models (α = -0.071 ± 0.001; AW = 0.034 ± 0.002; Supporting information). Other environmental variables also were identified as potential predictors for influencing diversification of Chinese Clematis , but received weak support: paleo-rainfall (α = 0.307 ± 0.003, AW = 0.029 ± 0.002, Fig. 3b), paleo-CCTE (α = 0.192 ± 0.002, AW = 0.028 ± 0.002, Fig. 3c; Supporting information), and paleo-temperature (β = -0.133 ± 0.008; AW = 0.022 ± 0.001; Fig. 3d; Supporting information). Moreover, across 100 complete trees, the associations between tree-wide BAMM and ClaDS diversification rates and global paleo-environmental data are also consistent with patterns observed in model-based RPANDA analyse (Supporting information). Fig. 3. RPANDA environment-dependent diversification models of Clematis in China. Diversification rates under the environment-dependent optimal model for paleo-CO 2 ( a ), paleo-rainfall ( b ), paleo-CCTE ( c ), paleo-temperature ( d ). Models are sorted by decreasing AW. Curves are based on the mean parameter estimate for each model. α (β) indicates the rate of change in speciation (extinction) rates with environment and sign +/- stands for positive/negative correlation; AW means AICc weight. 3.4 Trait-dependent diversification The MiSSE model strongly supported that three hidden states are potentially associated with the diversification of Chinese Clematis across 85% of trees (AW = 0.466 ± 0.007; Supporting information). Among 14 binary traits, achene surface indumentum was the only character best explained by the full BiSSE model (AW = 0.409 ± 0.014) and full HiSSE model (AW = 0.355 ± 0.012), whereas other traits (achene margin, growth habit, inflorescence position, leaf teeth, leaf texture, life form, pedicel exposure, phyllotaxy of seedling, pollen aperture type, sepal exposure, sepal texture, stamen indumentum, and staminodes) were best supported by CID-4 or CID-2 models in the majority of trees (Supporting information). Model estimates consistently showed significantly higher diversification rates in lineages with pubescent achenes than in those with glabrous achenes (Fig. 4a). In analyses where the full HiSSE model received the strongest support (Supporting information), this difference was maintained and amplified under an inferred hidden diversification regime (Fig. 4a). For eight multistate traits, model support varied (Supporting information). The free λ model was best supported for leaf type (AW = 0.774 ± 0.035), sepal indumentum (AW = 0.884 ± 0.018) and sepal number (AW = 0.373 ± 0.035), indicating there is diversification heterogeneity among different states of these traits. In contrast, inflorescence architecture, sepal color, sepal expansion, sepal spreading, and stamen connectives were better explained by the null model (Supporting information). For traits with supported diversification-rate heterogeneity, posterior estimates revealed clear among-state differences: species with pinnate leaves exhibited the highest diversification rates; species pubescent on the inner sepal surface showed the highest rates among indumentum states; and species with four or four-to-six sepals had higher rates than those with five-to-eight sepals (Fig. 4b–d). For eight continuous traits, QuaSSE models with directional trait evolution (non-zero drift in the diffusion process) consistently outperformed drift-free models (simple linear/sigmoidal models) (Supporting information). However, ES-sim results showed no significant correlations between diversification rates and any continuous traits across the two diversification-rate estimators (BAMM and ClaDS; all P > 0.05; Supporting information). In conclusion, none of the examined continuous traits show detectable associations with lineage diversification. Fig. 4. Comparisons of net diversification rates among different states of four significant traits in Chinese Clematis . a, Achene surface indumentum; b , Leaf type; c , Sepal indumentum; d , Sepal number. Diversification rates were estimated under the best-fitting BiSSE/HiSSE ( a ) and MuSSE ( b–d ) models. For a , the four categories represent combinations of the observed trait (glabrous/pubescent) and hidden states (A/B); BiSSE (56 trees supports) estimates are categorized under state A, while HiSSE (24 trees supports) involves both A and B. Sample sizes (after outlier removal) and median diversification rates for each state are shown above the bars. Statistical differences among states were assessed using Wilcoxon rank-sum tests, with P values adjusted via the Benjamini–Hochberg (BH) procedure. Significance levels: *P < 0.05, **P < 0.01, ***P < 0.001. 3.5 Ancestral distribution areas reconstruction and colonization history BioGeoBEARS analyses of the molecular tree strongly support the initial lineage differentiation of Chinese Clematis occurred in the southeastern China, with a possible extension outside China (state “ac”; probability = 0.991; Fig. 5a). Model selection strongly supported BAYAREALIKE as the best-fitting model for the molecular tree (AW = 0.680; Supporting information), with all alternative models receiving extremely low Akaike weights (Supporting information). This model also inferred a westward colonization into northwestern China since ~5.5 Ma, though the precise source (southeast vs. outside China) remained uncertain (Fig. 5a). Analyses of the 100 complete trees confirmed these major findings. Across these trees, the ancestral range was again placed in southeastern China alone (state “a”; probability = 0.714 ± 0.007; Supporting information). Model selection across 100 replicates most frequently supported DEC (69%), followed by DEC+J (24%) and BAYAREALIKE+J (7%) (Supporting information). Despite variation among models, the inferred dispersal pattern, repeated colonization from southeastern China toward the northwest, remained robust. The timing of the initial dispersal was highly consistent, with 100 complete trees dating the event to 5.686 ± 0.009 Ma, closely matching the estimate from the molecular tree. Colonization dynamics were also mainly concordant between the two tree sets. The molecular tree recovered five colonization events from the southeastern ancestral area into northwestern China, two of which were also supported by 100 complete trees (Figs. 5a, S8). When combining results across the 100 complete trees, three major pulses of westward colonization were detected at approximately 5.5 Ma, 3 Ma, and 0.5 Ma (Fig. 5a), indicating episodic dispersal toward the northwest through time. 3.6 Traits evolution of in China Ancestral state reconstructions indicate distinct evolutionary histories among four key morphological traits in Chinese Clematis : achene surface indumentum, sepal number, sepal indumentum, and leaf type. The molecular tree indicated glabrous achenes (possibility = 0.590; Fig. 5b), four sepals (1.000; Supporting information), pubescent sepals on the back (0.269; Supporting information), and ternate compound leaves (0.375; Supporting information) are the most probable ancestral states with higher probabilities. Our reconstruction analysis also reveals that the MRCA of the earliest Chinese Clematis lineages likely possessed pubescent achenes (0.830; Fig. 5b), four sepals (1.000; Supporting information), glabrous sepals on both surfaces (0.452; Supporting information), and ternate compound leaves (0.370; Supporting information), traits that may have facilitated their initial northwestern colonization. Regarding transition patterns, achene surface indumentum mostly from glabrous to pubescent (0.34 vs. 0.03; Fig. 5c), consistent with from state with lower diversification rates to higher (Fig. 4a), while sepal number, sepal indumentum, and leaf type did not show such consistency (Fig. 4b–d; Supporting information). Ancestral reconstructions based on 100 complete trees generally corroborated the patterns inferred from the molecular tree above. The complete tree sets all supported four sepals (0.758 ± 0.037), glabrous achenes (0.539 ± 0.004), and ternate compound leaves (0.468 ± 0.022) as the most probable ancestral states for Chinese Clematis , although it favored inner-surface sepal pubescence (0.404 ± 0.007) rather than pubescence on the back (Supporting information). Similarly, the complete trees also supported four sepals (0.982 ± 0.003) and pubescent achenes (0.860 ± 0.002), but inner-surface sepal pubescence (0.806 ± 0.011) and multicompound leaves (0.359 ± 0.010; Supporting information) for the MRCA of the northwestern clade. Both the molecular and complete tree sets consistently supported glabrous achenes as the ancestral state in Chinese Clematis and then a subsequent transition to pubescence occurred prior to the colonization of northwestern China (Fig. 5b; Supporting information). Likewise, the four-sepal state was recovered as ancestral and retained in the northwestern lineages (Supporting information). Fig. 5. Ancestral states reconstruction for distribution range and achene surface indumentum of Clematis in China. a, Ancestral distribution range reconstruction and colonization history of Clematis in China based on the molecular tree using BioGeoBEARS. Pie charts at each node represent the marginal probabilities of ancestral ranges; tip labels indicate current species distributions; density plot shows colonization events from southeast China (state “a”) to northwest China (state “b”) across the 100 complete trees; b , Ancestral state reconstruction for achene surface indumentum trait inferred from 1000 stochastic character mapping (SIMMAP) using phytools; c , Estimated transition rates between glabrous achenes and pubescent achenes states. Red stars indicate possible colonization (a) or morphological shifts (b) events. The root node of the phylogeny and the ancestral node corresponding to the earliest inferred colonization of northwestern China are highlighted by blue and yellow dashed circles, respectively. 3.7 Species richness and diversification in relation to current environment Our SEM revealed significant effects of environmental factors on species richness and diversification of Clematis in China (Fig. 6; Supporting information). A scaled χ² difference test revealed significant structural divergence across the Hu Line (P < 0.001), justifying separate SEMs for the northwestern and southeastern regions. All models showed excellent (χ²/df 0.1, CFI > 0.99, RMSEA < 0.01, SRMR < 0.01; Supporting information), and spatial autocorrelation was accounted for in final model specifications (Supporting information). Species richness was positively associated with diversification rates across national, northwestern, and southeastern scales (all P < 0.001; Supporting information). Regarding environmental influences, patterns differed among different scales. At the national scale, species richness was significantly positively correlated with mean temperature of the wettest quarter (β = 0.367, P < 0.001), while significantly negatively correlated with vapor pressure (β = -0.341, P < 0.001). Diversification rates were positively related to elevation (β = 0.844, P < 0.001) and maximum temperature of the warmest month (β = 0.642, P < 0.001), but negatively to vapor pressure (β = -0.529, P < 0.001). In the northwest, species richness was significantly promoted by mean annual temperature (β = 0.638, P < 0.001) and the number of wet days (β = 0.414, P < 0.001), but significantly suppressed by potential evapotranspiration (PET; β = -0.392, P = 0.007). Diversification rates were mainly enhanced by elevation (β = 0.782, P < 0.001) and mean annual temperature (β = 0.658, P < 0.001), but reduced by precipitation seasonality (β = -0.334, P < 0.001). In the southeast, species richness was significantly enhanced by cloud cover (β = 0.489, P < 0.001), but inhibited by vapor pressure (β = –0.373, P < 0.001). Diversification rates were promoted by elevation (β = 0.540, P < 0.001), the number of wet days (β = 0.443, P < 0.001), while mean precipitation (β = -0.478, P < 0.001) had negative impacts. Other factors seem involved as well, albeit with weaker overall influence (see details in Fig. 6; Supporting information). SEM analysis using ClaDS tip rates yielded a similarly robust model fit and results were highly consistent across all tested levels (Supporting information). Fig. 6. The structural equation model depicts drivers for species richness and diversification in Clematis across China. a , National level; b , Northwest region; c, Southeast region. The diversification rate is the averaged tip rates derived from BAMM; the width of arrows is proportional to relative effect size; blue arrows represent positive effects, and red arrows represent negative effects; significance levels: *P < 0.05, **P < 0.01, ***P < 0.001. 4 Discussion Our analyses revealed a pronounced spatial mismatch between species richness and diversification rates of Chinese Clematis : species richness is significantly higher in the humid southeast and lower in the arid northwest, whereas diversification rates show the opposite pattern (Fig. 2c–h). Similar mismatches have been widely documented across plants and animals (Weir & Schluter, 2007; Schluter & Pennell, 2017; Sun et al., 2020; Tietje et al., 2022). To clarify how this mismatch emerged, we traced the evolutionary and diversification history of Chinese Clematis , and examined how contemporary environmental conditions shape present-day spatial patterns of species richness and diversification rates. Understanding the mechanisms behind such mismatch patterns is a critical first step toward effective conservation planning. 4.1 Evolutionary history and diversification dynamics of in China Our temporal analysis of ancestral states and diversification dynamics reveals a markedly asynchronous evolutionary history between the southeast and the northwest. Chinese Clematis likely became differentiated in the southeast and adjacent regions at during ca. 8.5–7.5 Ma, followed by a long-term decline (Figs. 2a, S4a). By this time, southeastern China likely already harbored substantial lineage diversity, suggesting that the region had begun to function as a refugial area dominated by long-term lineage persistence. In contrast, Chinese Clematis colonized the northwest around 5.5 Ma (Figs. 5a, S8), after which diversification rates in the northwest exceeded those in the southeast (Figs. 2b, S4b). This temporal decoupling between lineage accumulation and subsequent diversification across regions provides a coherent explanation for the present-day spatial mismatch between species richness and diversification rates. Our results align with the time-for-speciation hypothesis, which proposes that richness primarily reflects the duration since regional colonization (Stephens & Wiens, 2003; McPeek & Brown, 2007; Wiens, 2011; Li & Wiens, 2019). 4.1.1 Diversification dynamics driven by environment and traits The early-burst diversification detected in Chinese Clematis is consistent with the global pattern predicted by Xiao et al. (2025). Similar early-burst dynamics have also been documented in other angiosperm groups (e.g., Kong et al., 2021; Liu et al., 2024; Zuntini et al., 2024), and they are typically interpreted as rapid exploitation of newly available ecological opportunities (Simpson, 1953; Gavrilets & Losos, 2009; Li et al., 2025). Such opportunities can be triggered by extrinsic factors, such as the availability of unoccupied niches or the release of ecological constraints, as well as intrinsic factors, where key innovations enable lineages to access novel adaptive zones (Gavrilets & Losos, 2009; Miller et al., 2023; Li et al., 2025). Our results are best interpreted within the framework of the ecological opportunity hypothesis and support the complementary mechanisms. Specifically, Our environmental- and trait-dependent diversification results indicated that Chinese Clematis diversification was shaped by a synergy between environmentally mediated opportunities and lineage-specific traits. This interplay promoted rapid radiation during late Miocene expansion, followed by constrained diversification as environmental conditions deteriorated. The diversification of Chinese Clematis was closely synchronized with major paleo-environmental reorganizations in East Asia during the late Miocene (Wang et al., 2012; Kong et al., 2017; Ye et al., 2019; Cao et al., 2025). We propose that the intensification of the East Asian monsoon and synchronous tectonic uplift provided the fundamental scaffold for this diversification by increasing topographic complexity and habitat fragmentation (Figs. 3b–c; Supporting information). These geological processes likely generated profound environmental heterogeneity, creating a vast array of novel ecological niches (Ding et al., 2020; Li et al., 2021; Song et al., 2024). Within this context, the atmospheric CO 2 and temperature may also play a role in diversification dynamics, with the former potentially exerting a pivotal influence (Fig. 3a, b; Supporting information). Specifically, elevated CO 2 levels may have expanded ecological opportunity by augmenting photosynthetic efficiency and water-use capacity (Brodribb et al., 2009; Leakey et al., 2019; Flexas & Carriquí, 2020), while warmer Miocene temperatures likely accelerated metabolic and mutation rates (Brown et al., 2004; Price et al., 2010; Clarke, 2025). Such physio-evolutionary advantages would be particularly adaptive in the structurally complex landscapes and steep climatic gradients of East Asia, where resource availability and hydraulic stress vary sharply. Conversely, the gradual decline in diversification rates (Fig. 2a) corresponds with progressive environmental deterioration, most notably the reduction in atmospheric CO 2 , which triggered global cooling and continental aridification (Zhang et al., 2025). This contraction of ecological space likely constrained further radiation in Chinese Clematis , reflecting a macro-evolutionary pattern observed across multiple plant and animal lineages (Condamine et al., 2015; Kergoat et al., 2018; Lewitus et al., 2018; Condamine et al., 2019; Kong et al., 2021). Trait evolution in Chinese Clematis appears to respond to changing environmental conditions, facilitating the exploitation of emerging ecological opportunities. Traits like pubescent achenes, four sepals, pinnate leaves, and pubescent on the inner sepal surface are also linked to higher diversification (Fig. 4), suggesting that functional trait variation may have modulated lineage diversification during periods of environmental expansion in the late Miocene. In particular, variation in achene surface structure and floral traits, may have influenced establishment success, and ecological tolerance under increasingly open and seasonally dry conditions, which is frequently associated with adaptive radiation (Drummond et al., 2012; Lagomarsino et al., 2016; Fernández‐Mazuecos et al., 2019; Miller et al., 2023; Peng et al., 2025). 4.1.2 Expansion towards the northwest Our biogeographic analyses show that Clematis expanded northwestward at ~5.5 Ma from southeastern China (Figs. 5a, S8). This timing coincides with the late Miocene–Pliocene transition (~5.3 Ma), when increased precipitation in the southeastern monsoon region contrasted sharply with intensified evaporation and reduced rainfall in the northwest (Farnsworth et al., 2019; Ao et al., 2021). The resulting decline of low-elevation vegetation in northwest likely generated ecological opportunities for Clematis expansion. Our results suggest that specific trait transitions mediated how Chinese Clematis lineages capitalized on these burgeoning opportunities. Ancestral state reconstructions reveal that pubescent achenes, characterized by dense and short surface hairs, originated during the early diversification phase (~7–8 Ma; Fig. 5b; Supporting information). This transition likely accelerated diversification rates (Fig. 4a; Supporting information). Functionally, this dense pubescence likely served as a physio-evolutionary adaptation to the increasingly arid and thermally unstable environments of the late Miocene. Such trichome layers may create a stable micro-boundary layer, effectively reducing non-stomatal water loss and buffering the embryo against rapid temperature fluctuations or intense UV radiation common in open, high-elevation habitats. By enhancing seed resilience and recruitment success under moisture-stressed conditions, these pubescent achenes likely underpinned the successful northwestward colonization of Chinese Clematis at ~5.5 Ma (Fig. 4a–g). These findings support the interpretation of pubescent achenes as a key innovation (Miller et al., 2023), enabling the genus to traverse steep environmental gradients (Drummond et al., 2012; Lagomarsino et al., 2016; Fernández-Mazuecos et al., 2019; Peng et al., 2025). Furthermore, other synergistic factors, such as the emergence of shrubby lineages with improved drought tolerance (He et al., 2021) and ancient hybridization events providing adaptive genetic variation (He et al., 2025), likely reinforced the ability of Clematis to expand northwestward. Northwest lineages consistently maintained higher diversification rates than southeastern lineages after colonization (Fig. 2b), mirroring the contemporary spatial patterns (Fig. 2d–e, g–f). The northwest, which is tectonically active and environmentally heterogeneous, likely continued to provide ecological opportunities that promoted episodic speciation (Qiang et al., 2001; Zheng et al., 2004; Li et al., 2021; Miao et al., 2022). 4.2 Contemporary ecological constraints on species richness and diversification Our results suggest that paleo-environmental change shaped the evolutionary potential of Chinese Clematis by generating ecological opportunity, whereas contemporary ecological conditions primarily constrain the realization of this potential into modern patterns of species richness and diversification (Fig. 6; Supporting information). Our SEM results indicate that diversification promotes species richness overall, and reveal a shift from richness-limited dynamics in the northwest to diversification-limited dynamics in the southeast (Fig. 6). This shift is shaped by the interactions among topography, temperature, water and climatic stability. Across China, Clematis species richness is promoted by temperature, consistent with evidence that warming increases plant species richness across northern China (Sun et al., 2025b), and diversification rates are elevated by elevation, but constrained by atmospheric vapor pressure (Fig. 6; Supporting information). These effects vary regionally due to contrasting climatic regimes (Kreft & Jetz, 2007; Harris et al., 2020; Karger et al., 2021). In the northwest, high elevation facilitates episodic speciation, yet ecological harshness, particularly high potential evapotranspiration, restricts long-term species persistence (Fig. 6b). Conversely, the warm and climatically stable southeast supports lineage persistence and long-term accumulation (Fig. 6c), yielding high species richness (Lu et al., 2018; Sun et al., 2025a). Yet, these same stable and humid conditions appear to inhibit diversification (Fig. 6c). This pattern may be associated with niche saturation or reduced diversification driven by persistent cloud cover and abundant precipitation, consistent with global observation (Tietje et al., 2022). 5 Conclusions By integrating fully sampled and time-calibrated phylogeny with macroecological modeling, this study mechanistically explains the spatial mismatch between species richness and diversification in a hyper-diverse lineage. Our workflow offers a scalable solution for assembling dense phylogeny in species-rich but under-sampled clades across tree of life. Such frameworks is essential for biodiversity conservation and predicting lineage responses to climate change. Our results indicate that ecological processes and evolutionary adaptation together shaped the biodiversity patterns of Chinese Clematis . This lineage radiated during the late Miocene paleo-environmental reorganizations of East Asia, followed by a pronounced southeast–northwest divergence across the Hu Line from the Pliocene to present: the humid, stable southeast acted as a long-term biodiversity refugial regime preserving ancient lineages, whereas the arid northwest served as a diversifying regime driven by environmental heterogeneity and stress. These coexisting evolutionary regimes illustrate how contrasting diversification processes can operate simultaneously within a group. Therefore, perhaps it is common that the biodiversity hotspots defined by richness do not necessarily align with centers of ongoing speciation. Traditional conservation strategies that focus primarily on species richness may inadvertently overlook the evolutionary engines found in harsher, rapidly diversifying regions. Under ongoing climate change, lineages in these diversifying regimes may hold greater adaptive potential. Therefore, we emphasize that effective biodiversity conservation must integrate evolutionary history and contemporary ecology to protect both the climatically stable refugia and environmentally dynamic zones that drive ongoing divergence. Reference Akaike H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 : 716–723. Anderson JC, Gerbing, DW. 1988. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 103 : 411–423. Ao H, Rohling EJ, Zhang R, Roberts AP, Holbourn AE, Ladant JB, Dupont-Nivet G, Kuhnt W, Zhang P, Wu F et al. 2021. Global warming-induced Asian hydrological climate transition across the Miocene–Pliocene boundary. Nature Communications 12 : 6935. Beaulieu JM, O’Meara BC. 2016. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Systematic Biology 65 : 583–601. Blomberg SP, Garland T Jr, Ives AR. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57 : 717–745. Boschman LM, Condamine FL. 2022. Mountain radiations are not only rapid and recent: Ancient diversification of South American frog and lizard families related to Paleogene Andean orogeny and Cenozoic climate variations. Global and Planetary Change 208 : 103704. Brodribb TJ, McAdam SAM, Jordan GJ, Feild TS. 2009. Evolution of stomatal responsiveness to CO(2) and optimization of water‐use efficiency among land plants. New Phytologist 183 : 839–847. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. 2004. Toward a metabolic theory of ecology. Ecology 85 : 1771–1789. Cao GL, Li XQ, Zhang J, Peng HW, Li HL, Erst AS, Jabbour F, Ortiz RDC, Soltis PS, Soltis DE et al. 2025. Cenozoic geoclimatic changes drove the evolutionary dynamics of floristic endemism on the Qinghai–Tibet Plateau. Proceedings of the National Academy of Sciences 122 : e2426017122. Chang J, Rabosky DL, Alfaro ME. 2020. Estimating diversification rates on incompletely sampled phylogenies: theoretical concerns and practical solutions. Systematic Biology 69 : 602–611. Clarke A. 2025. The contribution of metabolic theory to ecology. Ecological Monographs 95 : e70030. Condamine FL, Toussaint EF, Clamens AL, Genson G, Sperling FA, Kergoat GJ. 2015. Deciphering the evolution of birdwing butterflies 150 years after Alfred Russel Wallace. Scientific Reports 5 :11860. Condamine FL, Rolland J, Morlon H. 2019. Assessing the causes of diversification slowdowns: temperature‐dependent and diversity‐dependent models receive equivalent support. Ecology Letters 22 : 1900–1912. Cusimano N, Stadler T, Renner SS. 2012. A new method for handling missing species in diversification analysis applicable to randomly or nonrandomly sampled phylogenies. Systematic Biology 61 : 785–792. Dagallier LMJ, Janssens SB, Dauby G, Blach-Overgaard A, Mackinder BA, Droissart V, Svenning JC, Sosef MSM, Stévart T, Harris DJ et al. 2020. Cradles and museums of generic plant diversity across tropical Africa. New Phytologist 225 : 2196-2213. Ding J, Cheng C, Zhang W, Tian Y. 2021. The ideological origins and geographical demarcation significance of Hu Huanyong Line. Acta Geographica Sinice 76 : 1317–1333. Ding WN, Ree RH, Spicer RA, Xing YW. 2020. Ancient orogenic and monsoon-driven assembly of the world’s richest temperate alpine flora. Science 369 : 578–581. Drummond CS, Eastwood RJ, Miotto ST, Hughes CE. 2012. Multiple continental radiations and correlates of diversification in Lupinus (Leguminosae): testing for key innovation with incomplete taxon sampling. Systematic Biology 61 : 443–460. Farnsworth A, Lunt DJ, Robinson SA, Valdes PJ, Roberts WHG, Clift PD, Markwick P, Su T, Wrobel N, Bragg F et al. 2019. Past East Asian monsoon evolution controlled by paleogeography, not CO 2 . Science Advances 5 : eaax1697 Fernández-Mazuecos M, Blanco-Pastor JL, Juan A, Carnicero P, Forrest A, Alarcón M, Vargas P, Glover BJ. 2019. Macroevolutionary dynamics of nectar spurs, a key evolutionary innovation. New Phytologist 222 : 1123–1138. FitzJohn RG. 2012. Diversitree: comparative phylogenetic analyses of diversification in R. Methods in Ecology and Evolution 3 : 1084–1092. FitzJohn RG. 2010. Quantitative traits and diversification. Systematic Biology 59 : 619–633. Flexas J, Carriquí M. 2020. Photosynthesis and photosynthetic efficiencies along the terrestrial plant’s phylogeny: lessons for improving crop photosynthesis. The Plant Journal 101 : 964–978. Givnish TJ, Barfuss MH, Van Ee B, Riina R, Schulte K, Horres R, Gonsiska PA, Jabaily RS, Crayn DM, Smith JA et al. 2014. Adaptive radiation, correlated and contingent evolution, and net species diversification in Bromeliaceae. Molecular Phylogenetics and Evolution 71 : 55–78. Gavrilets S, Losos JB. 2009. Adaptive radiation: contrasting theory with data. Science 323 : 732–737. GBIF.org. 2023. GBIF occurrence download.doi: 10.15468/dl.z5zcjd. GBIF.org. 2024. GBIF occurrence download. doi: 10.15468/dl.rwarpr. Gopal A, Bharti DK, Page N, Dexter KG, Krishnamani R, Kumar A, Joshi J. 2023. Range restricted old and young lineages show the southern Western Ghats to be both a museum and a cradle of diversity for woody plants. Proceedings of the Royal Society B: Biological Sciences 290 : 20222513. Harris I, Osborn TJ, Jones P, Lister D. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7 :109. Harvey MG, Rabosky DL. 2017. Continuous traits and speciation rates: Alternatives to state‐dependent diversification models. Methods in Ecology and Evolution 9 : 984–993. He J, Lyu R, Luo Y, Lin L, Yao M, Xiao J, Xie L, Wen J, Pei L, Yan S et al. 2021. An updated phylogenetic and biogeographic analysis based on genome skimming data reveals convergent evolution of shrubby habit in Clematis in the Pliocene and Pleistocene. Molecular Phylogenetics and Evolution 164 : 107259. He J, Li M, Xiao J, Luo Y, Li W, Wu H, Luo Y, Xie L. 2025. Ancient hybridization and combinatorial mechanism: drivers of adaptive evolution in Clematis sect. Fruticella (Ranunculaceae). New Phytologist . doi: 10.1111/nph.70658 Herbert TD, Lawrence KT, Tzanova A, Peterson LC, Caballero-Gill R, Kelly CS. 2016. Late Miocene global cooling and the rise of modern ecosystems. Nature Geoscience 9 : 843–847. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25 : 1965–1978. Hu H. 1935. The distribution of population in China, with statistics and maps. Acta Geographica Sinica 2 : 33–74. Jiang N, Zhou Z, Yang JB, Zhang SD, Guan KY, Tan YH, Yu WB. 2017. Phylogenetic reassessment of tribe Anemoneae (Ranunculaceae): Non-monophyly of Anemone s.l. revealed by plastid datasets. PLOS ONE 12 : e0174792. Karger DN, Conrad O, Böhner J, Kawohl T, Kreft H, Soria-Auza RW, Zimmermann NE, Linder HP, Kessler M. 2021. Climatologies at high resolution for the earth’s land surface areas. EnviDat . doi: 10.16904/envidat.228. Kong H, Condamine FL, Yang L, Harris AJ, Feng C, Wen F, Kang M. 2021. Phylogenomic and macroevolutionary evidence for an explosive radiation of a plant genus in the Miocene. Systematic Biology 71 : 589–609. Kong H, Condamine FL, Harris AJ, Chen J, Pan B, Möller M, Hoang VS, Kang M. 2017. Both temperature fluctuations and East Asian monsoons have driven plant diversification in the karst ecosystems from southern China. Molecular Ecology 26 : 6414–6429. Kergoat GJ, Condamine FL, Toussaint EFA, Capdevielle-Dulac C, Clamens AL, Barbut J, Goldstein PZ, Le Ru B. 2018. Opposite macroevolutionary responses to environmental changes in grasses and insects during the Neogene grassland expansion. Nature Communications 9 :5089. Kreft H, Jetz W. 2007. Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences 104 : 5925–5930. Lagomarsino LP, Condamine FL, Antonelli A, Mulch A, Davis CC. 2016. The abiotic and biotic drivers of rapid diversification in Andean bellflowers (Campanulaceae). New Phytologist 210 : 1430–1442. Lan T, Pimm SL, Chase JM., Gao L, Ge J, Deng Y, Xu W, Xie Z, Shen G. 2025. Revealing hidden extinction risks in China’s flora through dynamic habitat assessment. One Earth 8 : 101429. Leakey ADB, Ferguson JN, Pignon CP, Wu A, Jin Z, Hammer GL, Lobell DB. 2019. Water use efficiency as a constraint and target for improving the resilience and productivity of C 3 and C 4 crops. Annual Review of Plant Biology 70 : 781–808. Lehtonen S, Christenhusz MJM, Falck D. 2016. Sensitive phylogenetics of Clematis and its position in Ranunculaceae. Botanical Journal of the Linnean Society 182 : 825–867. Lewitus E, Bittner L, Malviya S, Bowler C, Morlon H. 2018. Clade-specific diversification dynamics of marine diatoms since the Jurassic. Nature Ecology & Evolution 2 : 1715–1723. Li H, Wiens JJ. 2019. Time explains regional richness patterns within clades more often than diversification rates or area. The American Naturalist 193 : 514–529. Li SF, Valdes PJ, Farnsworth A, Davies-Barnard T, Su T, Lunt DJ, Spicer RA, Liu J, Deng WY, Huang J et al. 2021. Orographic evolution of northern Tibet shaped vegetation and plant diversity in eastern Asia. Science Advances 7 : eabc7741. Li Y, Tian Q, Fu X, Condamine FL, Yi T, Onstein RE. 2025 . Accelerated speciation in temperate Cannabaceae: The role of pre‐adaptation, ecological opportunities and niche divergence. Global Ecology and Biogeography 34 : e70183. Lin C, Liu B, Zhao M, Ma K, Ji L. 2025. Catalogue of life China: Towards an index of known species present in China. The Innovation Life 3 : 100140. Liu R, Wang WJ, Wang H, Ree RH, Li DZ, Yu WB. 2024. Plant species diversification in the Himalaya–Hengduan Mountains region: an example from an endemic lineage of Pedicularis (Orobanchaceae) in the role of floral specializations and rapid range expansions. Cladistics 40 : 636–652. Lu LM, Mao LF, Yang T, Ye JF, Liu B, Li HL, Sun M, Miller JT, Mathews S, Hu HH et al. 2018. Evolutionary history of the angiosperm flora of China. Nature 554 : 234–238. Lyu R, Xiao J, Li M, Luo Y, He J, Cheng J, Xie L. 2023. Phylogeny and historical biogeography of the East Asian Clematis droup, sect. Tubulosae , inferred from phylogenomic data. International Journal of Molecular Sciences 24 : 3056. Magallón S, Sánchez-Reyes LL, Gómez-Acevedo SL. 2018. Thirty clues to the exceptional diversification of flowering plants. Annals of Botany 123 : 491–503. Maddison WP, Midford PE, Otto SP. 2007. Estimating a binary character’s effect on speciation and extinction. Systematic Biology 56 : 701–710. Maliet O, Hartig F, Morlon H. 2019. A model with many small shifts for estimating species-specific diversification rates. Nature Ecology & Evolution 3 : 1086–1092. Maliet O, Morlon H. 2021. Fast and accurate estimation of species-specific diversification rates using data augmentation. Systematic Biology 71 : 353–366. Matzke NJ. 2014. Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades . Systematic Biology 63 : 951–970. McPeek MA, Brown JM. 2007. Clade age and not diversification rate explains species richness among animal taxa. The American Naturalist 169 : E97–E106. Miao Y, Fang X, Sun J, Xiao W, Yang Y, Wang X, Farnsworth A, Huang K, Ren Y, Wu F et al. 2022. A new biologic paleoaltimetry indicating Late Miocene rapid uplift of northern Tibet Plateau. Science 378 : 1074–1079. Miikeda O, Kita K, Handa T, Yukawa T. 2006. Phylogenetic relationships of Clematis (Ranunculaceae) based on chloroplast and nuclear DNA sequences. Botanical Journal of the Linnean Society 152 : 153–168. Miller AH, Stroud JT, Losos JB. 2023. The ecology and evolution of key innovations. Trends in Ecology & Evolution 38 : 122–131. Mo ZQ, Fu CN, Zhu MS, Milne RI, Yang JB, Cai J, Qin HT, Zheng W, Hollingsworth PM, Li DZ et al. 2022. Resolution, conflict and rate shifts: insights from a densely sampled plastome phylogeny for Rhododendron (Ericaceae). Annals of Botany 130 : 687–701. Morlon H, Lewitus E, Condamine FL, Manceau M, Clavel J, Drury J. 2016. RPANDA: an R package for macroevolutionary analyses on phylogenetic trees. Methods in Ecology and Evolution 7 : 589–597. O’brien RM. 2007. A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41: 673–690. Olofsson JK, Cantera I, Van de Paer C, Hong-Wa C, Zedane L, Dunning LT, Alberti A, Christin PA, Besnard G. 2019. Phylogenomics using low‐depth whole genome sequencing: A case study with the olive tribe. Molecular Ecology Resources 19 : 877–892. Pagel M. 1999. Inferring the historical patterns of biological evolution. Nature 401 : 877–884. Paradis E, Schliep K. 2018. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35 : 526–528. Peng HW, Ling YY, Xiang KL, Erst AS, Li XQ, Lian L, Liu B, Erst TV, Del C Ortiz R, Jabbour F et al. 2025. Repeated evolution of storage root and invasions of alpine biome drove replicated radiations of the megadiverse Corydalis (Papaveraceae) in the Qinghai–Tibet Plateau. Systematic Biology 74 : 359–372. Plummer M, Best N, Cowles K, Vines K. 2006. CODA: Convergence diagnosis and output analysis for MCMC. R News 6 : 7–11. Price CA, Gilooly JF, Allen AP, Weitz JS, Niklas KJ. 2010. The metabolic theory of ecology: prospects and challenges for plant biology. New Phytologist 188 : 696–710. Qiang XK, Li ZX, Powell CMA., Zheng HB. 2001. Magnetostratigraphic record of the Late Miocene onset of the East Asian monsoon, and Pliocene uplift of northern Tibet. Earth and Planetary Science Letters 187 : 83–93. Rabosky DL. 2009. Ecological limits and diversification rate: alternative paradigms to explain the variation in species richness among clades and regions. Ecology Letters 12 : 735–743. Rabosky DL. 2014. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLOS ONE 9 : e89543. Rabosky DL, Grundler M, Anderson C, Title P, Shi JJ, Brown JW, Huang H, Larson JG. 2014. BAMMtools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods in Ecology and Evolution 5 : 701–707. Rae JWB., Zhang YG, Liu X, Foster GL, Stoll HM, Whiteford RDM. 2021. Atmospheric CO 2 over the Past 66 Million Years from Marine Archives. Annual Review of Earth and Planetary Sciences 49 : 609–641. Revell LJ. 2024. phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ 12 : e16505. Ringelberg JJ, Koenen EJM, Sauter B, Aebli A, Rando JG, Iganci JR, de Queiroz LP, Murphy DJ, Gaudeul M, Bruneau A et al. 2023. Precipitation is the main axis of tropical plant phylogenetic turnover across space and time. Science Advances 9 : eade4954. Rosseel Y. 2012. lavaan: An R package for structural equation modeling. Journal of Statistical Software 48 : 1–36. Satorra A, Bentler PM. 2001. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika 66 : 507–514. Schluter D, Pennell MW. 2017. Speciation gradients and the distribution of biodiversity. Nature 546 : 48–55. Shalabi LA, Shaaban Z, Kasasbeh B. 2006. Data mining: A preprocessing engine. Journal of Computer Science 2 : 735–739. Simpson GG. The major features of evolution. New York: Columbia University Press, 1953. Slomba JM, Garey JR, Essig FB. 2004. The Actin I intron - A phylogenetically informative DNA region in Clematis (Ranunculaceae). SIDA, Contributions to Botany 21 : 879–886. Smith SA, Walker JF. 2018. PyPHLAWD: A python tool for phylogenetic dataset construction. Methods in Ecology and Evolution 10 : 104–108. Song W, Li Y, Luo A, Su X, Wang Q, Liu Y, Lyu T, Chen Y, Peng S, Sandanov D, Wang Z. 2024 . Historical and contemporary climate jointly determine angiosperm plant diversity patterns across east Eurasia. Ecography 2024 : e07062. Soltis PS, Folk RA, Soltis DE. 2019. Darwin review: angiosperm phylogeny and evolutionary radiations. Proceedings of the Royal Society B: Biological Sciences 286 : 20190099. Swofford DL. 2003. PAUP* . Phylogenetic Analysis Using Parsimony (*and other methods), Version 4 . Sunderland, MA, USA: Sinauer Associates. Stadler T. 2009. On incomplete sampling under birth–death models and connections to the sampling-based coalescent. Journal of Theoretical Biology 261 : 58–66. Stadler T. 2011. Mammalian phylogeny reveals recent diversification rate shifts. Proceedings of the National Academy of Sciences 108 : 6187–6192. Stephens PR, Wiens JJ. 2003. Explaining species richness from continents to communities: the time-for-speciation effect in emydid turtles. The American Naturalist 161 : 112–128. Sun M, Folk RA, Gitzendanner MA, Soltis PS, Chen Z, Soltis DE, Guralnick RP. 2020. Recent accelerated diversification in rosids occurred outside the tropics. Nature Communications 11 : 3333. Sun ZJ, Sun BJ, Wang YP, Su GH, Li JT, Jiang JP, Su SQ, Zhao T. 2025a. Environmental and evolutionary factors jointly shape life-history trait diversity of terrestrial vertebrates across China. Zoological Research 46 : 983–995. Sun Y, Deng Y, Yao S, Sun Y, Degen AA, Dong L, Luo J, Xie S, Hou Q, Tang D, Sun Y, Xiong J, Peng J, Hu W, Ran J, Deng J. 2025b. Distribution Range and Richness of Plant Species Are Predicted to Increase by 2100 due to a Warmer and Wetter Climate in Northern China. Global Change Biology 31 :e70334. Tamura M. 1987. A classification of genus Clematis . Acta Phytotaxonomica Geobotanica 38 : 33–44. Tamura M. 1995. Ranunculaceae. In: Hiepko P, ed. Die Natürlichen Pflanzenfamilien . 2nd edn. Vol. 17a, No. 4. Berlin, Germany: Duncker & Humblot, 366–387. Tietje M, Antonelli A, Baker WJ, Govaerts R, Smith SA, Eiserhardt WL. 2022. Global variation in diversification rate and species richness are unlinked in plants. Proceedings of the National Academy of Sciences 119 : e2120662119. Thomas GH, Hartmann K, Jetz W, Joy JB. Mimoto A, Mooers AO. 2013. PASTIS: an R package to facilitate phylogenetic assembly with soft taxonomic inferences. Methods in Ecology and Evolution 4 : 1011–1017. Wagenmakers EJ, Farrell S. 2004. AIC model selection using Akaike weights. Psychonomic Bulletin & Review 11 : 192–196. Wang L, Schneider H, Zhang XC, Xiang QP. 2012. The rise of the Himalaya enforced the diversification of SE Asian ferns by altering the monsoon regimes. BMC Plant Biology 12 : 210. Wang WT. 1980. Clematis . In: Editorial Committee of the Flora of China, Chinese Academy of Sciences, ed. Flora Reipublicae Popularis Sinicae . Vol. 28. Beijing: Science Press, 74–238. Wang WT, Bartholomew B. 2001. Clematis Linnaeus. In: Wu ZY, Raven PH, eds. Flora of China. Vol. 6. Beijing: Science Press; St. Louis: Missouri Botanical Garden Press, 333–380. Wang WT, Li LQ. 2005. A new system of classification of the genus Clematis (Ranunculaceae). Journal of Systematics and Evolution 43 : 431–488. Wang Z, Zhang P, Liu X, Liu Y. 1995. On the ecological sensitive zone in China. Acta Ecologica Sinica 15 : 319–326. Wan S, Zhao D, Jin H, Sha Y, Shi Z, Clift PD, Jian Z, Liu C, Carlos AZ, France-Lanord C et al. 2025. Interactive forces of temperature and topographic uplift shaped the East Asian monsoon rainfall evolution since the Oligocene. The Innovation Geoscience 3 : 100141. Weir JT, Schluter D. 2007. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315 : 1574–1576. Westerhold T, Marwan N, Drury AJ, Liebrand D, Agnini C, Anagnostou E, Barnet JSK, Bohaty SM, De Vleeschouwer D, Florindo F et al. 2020. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science 369 : 1383–1387. Wiens JJ. 2011. The causes of species richness patterns across space, time, and clades and the role of “ecological limits”. The Quarterly Review of Biology 86 : 75–96. Xia XM, Yang MQ, Li CL, Huang SX, Jin WT, Shen TT, Wang F, Li XH, Yoichi W, Zhang LH et al. 2021. Spatiotemporal Evolution of the Global Species Diversity of Rhododendron . Molecular Biology and Evolution 39 : msab314. Xiao JM, Lyu R, He J, Li M, Ji J, Cheng J, Xie L. 2022. Genome-partitioning strategy, plastid and nuclear phylogenomic discordance, and its evolutionary implications of Clematis (Ranunculaceae). Frontiers in Plant Science 13 : 1059379. Xiao JM, Li MY, Wen J, Puchałka R, Wu HY, Li WH, Li ZY, Liu BW, Luo YX, Lyu RD et al. 2025. Worldwide phylogeny and integrative taxonomy of Clematis : insights from phylogenomics. Plant Diversity . doi: 10.1016/j.pld.2025.11.004 Xie L, Wen J, Li LQ. 2011. Phylogenetic analyses of Clematis (Ranunculaceae) based on sequences of nuclear ribosomal ITS and three plastid regions. Systematic Botany 36 : 907–921. Ye XY, Ma PF, Yang GQ, Guo C, Zhang YX, Chen YM, Guo ZH, Li DZ. 2019. Rapid diversification of alpine bamboos associated with the uplift of the Hengduan Mountains. Journal of Biogeography 46 : 2678–2689. You Z, Feng ZM, Yang YZ. 2018. Relief degree of land surface dataset of China (1 km). Journal of Global Change Data & Discovery 2 : 151–155. Yu Y, Blair C, He X. 2019. RASP 4: ancestral state reconstruction tool for multiple genes and characters. Molecular Biology and Evolution 37 : 604–606. Zhang R, Guo J, Bradshaw CD, Xu X, Shen T, Li S, Nie J, Zhang C, Li X, Liu Z et al. 2025. Vegetation feedbacks accelerated the late Miocene climate transition. Science Advances 11 : eads4268. Zhang X, Landis JB, Sun Y, Zhang H, Lin N, Kuang T, Huang X, Deng T, Wang H, Sun H. 2021. Macroevolutionary pattern of Saussurea (Asteraceae) provides insights into the drivers of radiating diversification. Proceedings of the Royal Society B: Biological Sciences 288 : 20211575. Zheng HB, Powell CM, Rea DK, Wang JL, Wang PX. 2004. Late Miocene and mid-Pliocene enhancement of the East Asian monsoon as viewed from the land and sea. Global and Planetary Change 41 : 147–155. Ziman SN. 1981. Analysis of phylogenetic relations in Subtribe Clematidinae (Ranunculaceae). Ukrainskii Botanicheskii Zhurnal 38 : 4–43. Zizka A, Silvestro D, Andermann T, Azevedo J, Duarte-Ritter C, Edler D., Farooq H, Herdean A, Ariza M, Scharn R et al. 2019. CoordinateCleaner: Standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution 10 : 744–751. Zomer RJ, Xu J, Trabucco A. 2022. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Scientific Data 9 : 409. Zuntini AR, Carruthers T, Maurin O, Bailey PC, Leempoel K, Brewer GE, Epitawalage N, Françoso E, Gallego-Paramo B, McGinnie C et al. 2024. Phylogenomics and the rise of the angiosperms. Nature 629 : 843–850. Information & Authors Information Version history V1 Version 1 01 April 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords biodiversity gradients chinese clematis diversification rates ecological opportunity environmental filtering trait-environment interactions Authors Affiliations Xinru Zhang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Liguo Zhang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Mengmeng Wang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Wyckliffe Omondi Omollo National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Xueqin Wang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Yu Meng National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Mei Liang National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Lei Xie Beijing Forestry University View all articles by this author Jianfei Ye Sun Yat-Sen University School of Ecology View all articles by this author Wei Wang 0000-0001-6901-6375 Institute of Botany, Chinese Academy of Sciences View all articles by this author Miao Sun 0000-0001-5701-0478 [email protected] National Key Laboratory for Germplasm Innovation and Utilization of Horticultural Crops View all articles by this author Metrics & Citations Metrics Article Usage 167 views 98 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xinru Zhang, Liguo Zhang, Mengmeng Wang, et al. Environmental regimes and traits drive the spatial mismatch between species richness and diversification rates of Chinese Clematis. Authorea . 01 April 2026. DOI: https://doi.org/10.22541/au.177508127.72900723/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177508127.72900723/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe251bbeb9b09d6',t:'MTc3OTE4NTUwNA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();