Landform and lithospheric development contribute to the assembly of mountain floras in China

Domain: earth_science
Published: 2024-06-17
Editorial outcome: accept

Abstract
Although it is well documented that mountains tend to exhibit high biodiversity, how geological processes affect the assemblage of montane floras is a matter of ongoing research. Here, we explore landform-specific differences among montane floras based on a dataset comprising 17,576 angiosperm species representing 140 Chinese mountain floras, which we define as the collection of all angiosperm species growing on a specific mountain. Our results show that igneous bedrock (granitic and karst-granitic landforms) is correlated with higher species richness and phylogenetic overdispersion, while the opposite is true for sedimentary bedrock (karst, Danxia, and desert landforms), which is correlated with phylogenetic clustering. Furthermore, we show that landform type was the primary determinant of the assembly of evolutionarily older species within floras, while climate was a greater determinant for younger species. Our study indicates that landform type not only affects montane species richness, but also contributes to the composition of montane floras. To explain the assembly and differentiation of mountain floras, we propose the ‘floristic geo-lithology hypothesis’, which highlights the role of bedrock and landform processes in montane floristic assembly and provides insights for future research on speciation, migration, and biodiversity in montane regions.

Introduction
Globally, mountains play dual roles as museums and cradles of species diversity. It is therefore unsurprising that much of the global biodiversity is concentrated among mountains, especially those within the tropics. Worldwide, mountains harbor 39% of the global terrestrial vertebrate biodiversity, with 2.9 times more richness per unit area than lowlands. In China, ten mountainous hotspot ecoregions were found to contain 92% of the plant genera and 91% of the terrestrial mammal species present within the entire country. How such extraordinary diversity occurs across mountains has remained an open question since Humboldt’s time.

Numerous hypotheses have been proposed to explain both montane and global biodiversity, such as those pertaining to climate stability, habitat heterogeneity, and energetics. Along latitudinal gradients, current evidence suggests that biodiversity is affected by environmental energetics, particularly potential evapotranspiration (PET) and average annual temperature. At a finer scale, plant alpha diversity in some extratropical mountain regions (such as Cape Region and East Australia Region) does not substantially differ from that in the tropics. Contemporary climatic regimes do not sufficiently explain the pantropical diversity disparity in Neotropical and Indo-Malayan moist forests. These results strongly suggest that montane species diversity is largely affected by habitat heterogeneity, or so-called geodiversity. Moreover, the unique evolutionary history of each biological taxon in mountainous regions could exert a profound influence on local biodiversity. It is clear that an integrated framework is needed for the prediction of montane biodiversity, and it should include ecological processes (e.g., survival, competition, and niche differentiation), evolutionary processes (e.g., species divergence and extinction), and geological processes (e.g., orogeny and lithosphere cycling). A recent attempt at such a framework, the ‘mountain geobiodiversity hypothesis’ (MGH), was first proposed to explain the biodiversity of the Tibeto-Himalayan region and then extended to explain the origin of montane plant diversity at a global scale. The MGH proposes that the evolution of montane biodiversity results from a combination of mountain uplift, geodiversity evolution, and Neogene and Pleistocene climate changes.

The key to explaining montane biodiversity is understanding the links between biotic processes and topographic erosion, which could contribute to increasing regional habitat heterogeneity. Geological and lithological processes, particularly uplift and erosion, are known to strongly impact montane biodiversity, likely through effects on species formation, immigration, and extinction. Mountains are cradles of species diversity largely because their formation and subsequent bedrock erosion yield topographic complexities and produce new niches for a wide variety of organisms. Additionally, mountainous geographies facilitate the formation of endemic species specialized to certain types of rock or derived soils, especially on limestone (sedimentary rock) or ophiolites (igneous rock). For example, previous studies have reported that at least 5%-10% of species exhibited edaphic specialization and, thus, are dependent on specific types of underlying bedrock. Although it is well known that climate change forces plant species to migrate, those species that are adapted to local bedrock are constrained in their ability to migrate. The geochemical characteristics of bedrock are on par with climate as regulators of vegetation in granitic mountains. Some studies also suggest that local species diversification processes are consistent with edaphic rather than climatic filtration, such as in the Cape flora, Teesdale flora and New Caledonian flora, in which approximately 50% of the endemic floristic elements are ultramafic-obligate species. However, empirical studies establishing a relationship between the diversity of edaphic conditions and plant species diversity are still scarce. The unique contributions of geological and lithological processes to local species assembly are often eclipsed by ecological factors (i.e., local climate) and are thus often overlooked.

At the continental scale, montane floras sharing the same underlying bedrock are often highly similar in terms of their plant family and genus compositions. This is the case for the Wuyi, Nanling, and Qinling Mountains in eastern Asia when compared among themselves and to the Appalachian Mountains of eastern North America. The underlying bedrock of these mountains is primarily igneous and metamorphic rocks, which are partially responsible for their granitic landforms. The relationship between landform type and floristic composition suggests that the developmental processes of mountains may constrain floristic assembly. Thus, studying the relationship between species diversity and landform type may elucidate the process of floristic assembly in some of the most biodiverse regions of the world.

Here we explore the relationship between montane floristic assemblages and the types of landforms in which they occur. To accomplish this, we gather a dataset including 17,576 angiosperm species from 140 Chinese mountain floras representing five landforms categorized based on bedrock: karst, granitic-karst, granitic, Danxia, and desert. Here, we use the term ‘flora’ to refer to the collection of all angiosperm species growing on a specific mountain or in a well-delimited area. Then, we calculate species richness, phylogenetic diversity (Faith’s PD), phylogenetic structure indices (PDI, NRI, NTI), and mean divergence times (MDT) for each of the 140 floras. Finally, we construct regression models (1) using landform as a predictor (landform model hereafter) and (2) using landform along with tectonic, climatic, and geographic explanatory variables as predictors (full model hereafter, see Methods). These estimations enable us to compare the relative importance of landform type, after accounting for other predictors, in explaining the landform process–floristic assembly relationships.

Results
Landform effects on species richness and phylogenetic diversity
The angiosperm species richness in the granitic (median = 1456) and karst-granitic (median = 1458) mountain floras was higher than that in the karst (median = 1137), Danxia (median = 1132), and desert (median = 721) floras. The landform model explained 28.95% of the observed species richness deviance according to the generalized linear model (GLM) framework (Akaike Information Criterion, AIC = 116.21), and 31.40% according to the spatial error model (SEM) (AIC = 115). The full model explained 62.8% of the observed species richness deviance in the GLM and 63.7% in the SEM, and strong interaction effects between landform and mean temperature of the coldest quarter (TCQ) were detected. We also found weak interactions between landform and annual precipitation (PREC), as well as precipitation of coldest quarter (PCQ). The models that integrate landform with any other variable significantly enhanced the explanatory capacity for the deviance of species richness, indicating that species richness is affected by landform effects. Specifically, mountains of sedimentary bedrock (desert, Danxia, and karst landforms) exhibited lower species richness than mountains of igneous bedrock (granitic and karst-granitic landforms).

According to the full model, higher values of longitude, greater differences in elevation (elevdiff), and higher mean TCQ positively affected species richness. The mountain floras with higher species richness were mainly located in the monsoon climatic zone of eastern China. These observed positive relationships were generally consistent with the notion that habitat heterogeneity and precipitation are strong predictors of species richness. Notably, high TCQ has a negative effect on species richness in desert landforms. In fact, when considering the interaction with the landform, high TCQ only positively affected the species richness in granitic landforms.

We also found that high precipitation seasonality (Pvar) and high mean temperature of the warmest quarter (TWQ) were negatively correlated with species richness, according to the full model. TWQ is a proxy for environmental energy flux, which has long been regarded as a driver of species richness at continental scales. Interestingly, TWQ was not a significant predictor and explained only 0.9% of the variation in species richness. The observed negative correlation between species richness and high TWQ may be the result of incorporating landform effects into the regression models. In particular, the karst, Danxia, and desert landforms are characterized by sedimentary bedrock (e.g., limestone and glutenite), which are more permeable than igneous granite. This may result in faster water loss from the soil in non-granitic landforms and, thus, a greater moisture deficit yielding lower species richness.

The correlational pattern between phylogenetic diversity and the five landforms was similar to the pattern for species richness, largely because phylogenetic diversity was positively correlated with species richness. The phylogenetic diversity index (PDI) of the desert (median = −16.59) landform was the lowest, followed by that of the Danxia (median = −3.23) landform, which was consistent with species richness. Unexpectedly, the PDI was highest for the karst (median = −1.03) landform, which exhibited lower species richness than granitic (median = −1.77) and karst-granitic (median = −1.27) landforms. The landform effect on PDI was significant in both the landform (explaining 70.89% of deviance in GLM, 77.15% in SEM) and full (explaining 88.09% of deviance in GLM, 88.25% in SEM) models. Thus, species with the deepest phylogenetic divergences occur in karst landforms, while species with the shallowest divergences occur in desert landforms. The PDI results serve as an indicator of the level of floristic stability. Here, we would like to indicate that the species inhabiting the arid limestone mountains in our data exhibit an earlier divergence age and possess a remarkable capacity for long-term survival. For example, an Oligocene fossil flora discovered in Wenshan basin located in Yunnan, China, revealed a fossil assemblage (e.g., Burretiodendron, Ficus microtrivia), which clearly indicates that the current local karst vegetation may have existed since the early Oligocene.

According to the full model, orogenic, high latitude, high temperature annual range (TAR), and high TCQ were negatively correlated with PDI. High TWQ was the only variable positively correlated with PDI. This suggests that mountains composed of igneous rocks, such as those with granitic landforms, may have recently undergone higher rates of evolution related to orogeny. Furthermore, higher TWQ may have led to higher rates of extinction in mountains composed of sedimentary rock, such as karst landforms. Extinction rates in karst landforms are likely to be higher among closely related species due to their conserved ecological niches, thus yielding higher PDI. A low TAR tends to be related to environmental stability and consequently higher species richness, especially in tropical areas. Nevertheless, the TAR of karst landforms was quite low, while the karst flora was characterized by lower species richness and higher PDI. Therefore, the coupling of landform effects and climatic factors in karst landforms may represent a unique mountain environment with a strong environmental filter, sustaining only highly persistent species.

Landform effects on species age structure of floras
The assembly history of mountain floras is reflected in both species composition and species age structure. To determine these distinctions between landforms, we calculated the MDTs of all species, mean divergence times of the youngest 25% of species (MDT.youngest), and mean divergence times of the oldest 25% of species (MDT.oldest) contained within the 140 floras. Among landforms, MDT, MDT.oldest, and MDT.youngest exhibited the same patterns, with the median age of karst being highest, followed by karst-granitic, granitic, Danxia, and desert landforms. For MDT.oldest, the results are similar for median age, while the mean age for karst-granitic landforms (24.66 Mya) is higher than that for karst (24.42 Mya). This may imply that species on karst landforms often survive during the transition to granitic despite limestone being a strong driver of the richness of endemic species and increased limestone erosion tend to exacerbate the extinction of ancient local endemic species. The survival of species within karst-granitic landforms may be due to niche evolution, with species adapting from alkaline soil to acidic soil, although this may be infrequent. Our results also show that the MDT of floras on mountains in northern China, in particular desert floras, have significantly younger ages than floras in other landforms. This may have resulted from the strong influence of glacial periods in the Pleistocene and necessitated relatively recent recolonizations for the northern mountain floras.

We further used regression models to compare the effects of landform and climate variables on the divergence times of floras. The results show that the landform type had significant effects on MDT, MDT.oldest, and MDT.youngest in both the landform and full models. Furthermore, landform effects had greater explanatory power for MDT.oldest than for MDT and MDT.youngest. The full model indicated that high TAR and high TCQ were the most important climatic variables and were negatively correlated with the divergence times of floras. The standardized coefficient of TAR was higher for MDT.oldest than for MDT and MDT.youngest. These results suggest that landform effects have a greater impact on the assembly of ancient species (the oldest 25% of species) in mountain floras, while modern climates have a greater impact on the assembly of younger species (the youngest 25% of species).

Overall, our results are consistent with several prior studies suggesting that the species age structure of mountain floras is closely related to landform processes. For example, most Chinese floras, including both montane and lowland floras, differentiated during the Miocene when the East Asian monsoon climate intensified. Accordingly, this period also saw high rates of development of modern karst, Danxia, and granitic landforms in China. At the intercontinental scale, the ages of floras were largely consistent with regional landform developmental processes. For example, the floras of eastern Asia are older than those of the Andes and Amazonia (Alpine orogeny in the late Cretaceous to Cenozoic), since the former’s landform processes are much older while the latter’s have occurred more recently. Thus, the relationship between the floristic assembly of mountains and landform developmental processes seems to be global in scope, at least for angiosperms.

Landform effects on the phylogenetic structure of floras
Landform effects can also be observed in the phylogenetic structure of Chinese mountain floras. For example, the net relatedness index (NRI) for desert landforms (median = 9.45) was considerably higher (i.e., largely non-overlapping) than that of other landforms. This is especially true for granitic landforms, which generally have the lowest NRIs (median = −1.47). In fact, landform effects explained 64.91% (GLM) and 77.43% (SEM) of the variance in NRI in the landform model. In the full model, landform effects still had a unique contribution of 1.22% (out of 81.51% of the total variance explained in SEM). Among floras, the NRI results indicate that phylogenetic overdispersion (NRI < 0) occurred mainly in the southeast monsoon region of mainland China, as has been inferred in prior studies. Our results also show that the Danxia (median = −0.41) and karst (median = −0.42) landforms in the southeast monsoon region have higher NRIs than granitic (median = −1.47) and karst-granitic (median = −0.94) landforms. This means that granitic floras exhibited more phylogenetic overdispersion, a pattern also observed in the negative correlation with the nearest taxon index (NTI) in both the landform and full models. Phylogenetic clustering also occurs in some granitic landform floras, which possibly can be attributed to their occurrence in orogenic belts or colder climate zones. Orogeny can facilitate rapid in situ evolutionary radiations, thereby promoting the co-occurrence of closely related species in mountains. Moreover, environmental filtering effects further contribute to the aggregation of those species which could tolerant of cold and alpine environmental conditions, as anticipated by phylogenetic niche conservatism (PNC).

Additionally, in the full model, TAR, which represents climatic instability of the habitat, was positively correlated with NRI. Highly unstable habitats are known to act as a strong ecological filter and lead to phylogenetic clustering. This is because speciation often occurs ex situ within a regional species pool, from which only certain immigrants can successfully establish. Aside from their phylogenetic structures, differing landforms also exhibit differences in their floristic compositions. For example, karst floras have more Malvales, Rosales, and Lamiales species; desert floras are dominated by Poales, Asterales, and Caryophyllales; and granitic floras are dominated by Magnoliales, Saxifragales, and Ericales. In particular, karst-granitic floras represent a stage of transformation from karst to granitic floras and contain many relict lineages, such as Torricellia in Apiales, Rhoiptelea and Platycarya in Fagales, and Davidia and Nyssa in Cornales.

Landforms play an important role in shaping floristic diversity
Our results showed the unique effects of landforms on species richness and phylogenetic diversity. In general, higher habitat heterogeneity in mountain environments is a key driver of higher species richness. Montane species richness is also recognized to be positively correlated with high temperature (e.g., TWQ, TCQ) and precipitation, which is consistent with the spatial heat and mass distribution within China. However, our full model indicated that the TWQ is negatively related to species richness. Specifically, TWQ and TCQ are only positively correlated with species richness in granitic landforms, but negatively in other landforms. The special contribution of landform effects was difficult to detect due to interactions with temperature and precipitation. Landform developmental processes are associated with local bedrock and regional climate. The assembly of a local flora is impacted by a combination of geological and climatic processes, as well as biological processes. However, the species richness in mountains of igneous bedrock (granitic and karst-granitic) is clearly higher than mountains of sedimentary bedrock (karst and Danxia). The underlying reason is that water cycle processes and rock erosion rates differ between igneous and sedimentary mountain ecosystems. In limestone mountains, water can easily be lost through underground river systems, while a greater quantity of overland runoff is available for plants in mountains of igneous bedrock. In extreme cases, high temperature can accelerate water loss, further reducing species richness. This occurs in desert, Danxia, and karst landforms. Although our results highlight the impact of landform on floristic diversity, we do not intend to deny the importance of climate. We hold the opinion that montane species richness is determined by the availability of water and energy (which is combined result of bedrock, temperature, and precipitation) in mountain ecosystems.

Bedrock promotes local speciation resulting in floristic differentiation between landforms
Based on comparisons between individual floristic phylogenetic structures and MDT, we found that landform effects partially determine the final composition of montane floras. Except in desert landforms, which are dominated by drought, the phylogenetic structure of floras associated with mountains of sedimentary bedrock (karst and Danxia) were more clustered than mountains of igneous bedrock (granitic and karst-granitic). This may have resulted from both local speciation and strong habitat filtering effects. Bedrocks, from which landforms are derived, promote the evolution of endemic edaphic specialists. For example, the rapid development of karst landforms since the Miocene is thought to have triggered adaptive diversification in several genera, e.g., Begonia and Primulina. Similarly, the local richness of endemic species in Mount Kinabalu (Malaysia) is primarily made up of preadapted and locally derived species on high-elevation granite pluton. Radiative evolution results in the aggregation of average relatedness among species within a flora. Such effects further promote the phylogenetic relatedness (clustering) of mountain floras because new lineages tend to maintain their ancestral ecological niche.

On the other hand, adaptive evolution, encompassing both morphological and physiological traits, could play a pivotal role in facilitating the diversification of plants in novel environments. In plants, radiative evolution often accompanies habitat and landform shifts, as seen in Old World gesneriads and North American desert rock daisies (Compositae tribe Perityleae). Similar patterns can also be observed in insects. For example, two radiating clades (nodes 14 to 16 and nodes 31 to 33) of Exocelina has been suggested as a resulted of ecological niche transition from the transition from uplifted Australian Plate bedrock to ultramafic/ophiolite. Although, many previous studies have documented climate fluctuations as an important driver of species radiations. The contribution of bedrock type on plants adaptive radiation of should not be ignored. For example, the development of a key innovation (lime-secreting hydathodes) may have made Saxifraga sect. Porphyrion better suited to limestone habitats, and the low specific leaf area (SLA) exhibited by Erica maybe an adaptation to oligotrophic habitats (quartzite/sandstone) in the Cape. These landform and bedrock effects could strongly promote both species and floristic differentiation between different regions.

Restricted dispersal with establishment between landforms as the result of environment filtering
For many biogeographers, mountains are regarded as both barriers and bridges of species dispersal. The role of mountains as corridors has been documented in those of North-South orientation, such as the Andes and Hengduan Mountains. However, the contribution of dispersal to montane floristic diversity largely depends on the ecological and physiological requirements of the species, since dispersal only affects regional species diversity if it is followed by successful establishment. Our research demonstrates the role of landform constraints on the interaction of different landform floras, which is shown in their species richness, phylogenetic structures, and species age structures. The dispersal process is generally less constrained during the initial stages of mountain landform development, which are characterized by gentle slopes and limited geographical barriers. This scenario is well demonstrated by the assembly of alpine biotas. Since the Miocene, the colonization rate in the gentle elevation gradient Qinghai-Tibet Plateau (QTP) (0.06-0.25) is always larger than that in the QHM (<0.05). The role of local species recruitment is most important during the early stages of mountain floristic assembly, subsequently being supplanted by local adaptation or in situ speciation due to the emergence of heterogeneous mountain environments. Mountains in different landforms will recruit different plant species as a result of environmental filtering caused by differences in bedrock exposure. For example, mountains composed of limestone bedrock contain more species which are physiologically tolerant of drought and high calcium stress than mountains composed of metamorphic rocks and granites. The landform restriction effect on species diffusion gradually strengthens when more bedrock is exposed and the connectivity between mountains of different landforms is greatly reduced. Variation in the species composition of mountain floras between different landforms increases under the combined effects of landform, environmental filtering, and local endemic speciation. Therefore, we propose that the patchy spatial distribution of different mountain landforms is an important factor in shaping biogeographical zoning.

Concluding remarks
Our results highlight that bedrock and landform effects play a key role in floristic assembly, especially considering that Earth’s bedrock is unevenly distributed. The primary reason why landforms might explain floristic assembly is that they represent or underlie major aspects of environmental filters to which plants respond via the processes of speciation, local extinction, and immigration from the regional species pool. Here, we propose the ‘floristic geo-lithology hypothesis’ to explain the assembly and differentiation of mountain floras. In this theoretical framework, floristic assembly in mountains is driven by the lithospheric cycle, which refers to the bedrock-constrained developmental processes of landforms. Specifically, under this hypothesis, montane species differentiation is closely related to the type of bedrock and degree of erosion. Both the species richness and species composition of mountain floras result from interactions between the landform and the environment. In addition, the dispersal of plants between different landform types is more restricted than that within the same landform type. Successful diffusion across a landform is often accompanied by the emergence of adaptive traits or speciation.

To explain montane species diversity, this hypothesis differs from those such as the MGH, which focuses more on the origination of high levels of biodiversity found in mountain systems. The MGH is invoked to explain the cause of alpha diversity. In contrast, our hypothesis is more concerned with the process of mountain flora differentiation, which is a hypothesis of beta diversity. Here, we would like to introduce the concept of ‘landform flora’ for mountain biodiversity studies, meaning a unique flora formed under the influence of bedrock erosion and mountain landform development processes. Recognizing the differences that exist between different ‘landform flora’ (e.g., granitic flora, karst flora, Danxia flora) will benefit future studies in the prediction of mountain biodiversity and speciation, and also in species protection. We argue that the ‘floristic geo-lithology hypothesis’ presented here could serve as a general explanation for global diversity patterns, as the formation of mountains on the Earth’s surface is the result of the cycling of sedimentary, igneous, and metamorphic rocks. In conclusion, our study highlights the floristic patterns of different landforms and provides a framework for studying the mechanisms of plant species diversification within mountains and the distributional patterns of mountain floras worldwide.

Methods
Study area and sampling units
The physical geographic environment of modern China has experienced four major orogenic events since the Palaeozoic: the Caledonian, Indosinian, Yanshan, and Himalayan orogenies. These events represent cycles of uplift of marine strata followed by geologic erosion and subsequent formation of a variety of landform types. These Chinese landforms are home to more than 30,000 species of vascular plants. China is a natural laboratory for investigating patterns of biodiversity due to its heterogeneous physical geography and range of habitats as well as its large geographic size and considerable biological diversity. In this study, the geographic sampling units were the montane floras of protected areas, such as nature reserves and forest parks (Supplementary Table 1). Comprehensive scientific surveys encompassing physical geography and biodiversity have been conducted in these areas, serving as the fundamental cornerstone of our study. A total of 140 mountain floras were included in this study. Maps of China used in this study were adapted from DataV. GeoAtlas (http://datav.aliyun.com/portal/school/atlas/area_selector) and visualized in ArcGIS 10.8 (http://www.esri.com/).

Dataset generation and reconciliation
We compiled checklists of angiosperm species for each mountain flora from previously published, comprehensive species checklists, white papers, and research papers (Supplementary Table 1). From our initial checklists, we excluded all non-native species and reconciled the taxonomy with the Leipzig Catalogue of Vascular Plants (LCVP) using the R 4.1.0 (http://www.r-project.org/) package lcvplants, with infraspecific taxa combined under their respective species. To determine the generic, familial, and ordinal affinities of the species, we used APG IV and the Angiosperm Phylogeny Website (http://www.mobot.org/MOBOT/research/APweb). Following taxonomic reconciliation and categorization within higher ranks, our dataset comprised a total of 17,576 species in 2,585 genera belonging to 251 families and 56 orders. We used these data to generate a presence-absence (1/0) matrix with 140 mountain sites in columns and the species represented in rows.

Phylogenetic reconstruction
Using the recently published, dated megaphylogenetic tree GBOTB.extended.LCVP.tre as a backbone, we generated a phylogeny of the study species using the R package V.PhyloMaker2. Of the 2,585 genera and 17,576 species studied here, 2,349 genera and 8,663 species were included in GBOTB.extended.LCVP.tre. Based on previously published megaphylogenies, we treated each of the 236 missing genera as sisters to their most closely related genera in GBOTB.extended.LCVP.tre using R package V.PhyloMaker2. Although this method resulted in more robust phylogenetic relationships than Phylocom, the ultimate phylogenetic relationships should still be considered relative, as complete phylogenetic data are still lacking for many families and genera. We added study species that were absent from GBOTB.extended.LCVP.tre to their respective genera using Phylomatic and generated their branch lengths with BLADJ, as implemented in the R package V.PhyloMaker2. Subsequently, in package V.PhyloMaker2, we used build.nodes.1 to extract genus and family information for downstream uses within the algorithm and generated our final megaphylogeny (Fig. S2) using Scenario 3, in which species were added to the backbone topology at the phylogenetic midpoint within their respective genera. Scenario 3 is regarded as the most robust of the three available approaches within the software package. We visualized the resulting megaphylogeny using iTOL v6 (https://itol.embl.de/).

Indices of phylogenetic diversity and structure
To measure the phylogenetic diversity of each mountain flora, we employed Faith’s phylogenetic diversity (PD Faith), which is the sum of all phylogenetic branch lengths within the subtree representing the flora and is known to be positively correlated with species richness (SR). We also used the phylogenetic diversity index (PDI), which standardizes PD Faith using null models. Thus, the PDI allows comparisons among floras with different underlying species richness. To calculate the PDI, we used package PhyloMeasures in R, in which the null model was set as uniform, and the following typical algorithm was implemented:
PDI = (PD observed − PD randomized) / (sd PD randomized) (1)

We also applied the net relatedness index (NRI) and the nearest taxon index (NTI), which are widely used to investigate the phylogenetic structure of species assemblages, i.e., clustered or overdispersed. NRI is a measure of the standardized effect size of mean phylogenetic distance (MPD) and primarily reflects the structure at deeper nodes of the phylogeny. NTI is based on the mean nearest taxon distance (MNTD), which is the mean distance between each terminal taxon and its sister lineage and reflects shallower nodes within the phylogeny. NRI and NTI were determined as follows:
NRI = −(MPD observed − MPD randomized) / (sd MPD randomized) (2)
NTI = −(MNTD observed − MNTD randomized) / (sd MNTD randomized) (3)

In these equations, MPD observed and MNTD observed are the observed MPD and MNTD, MPD randomized and MNTD randomized are the expected (i.e., average) MPD and MNTD of the randomized assemblages, which were calculated based on the null model uniform in the R package PhyloMeasures, and sd MPD randomized and sd MNTD randomized are the SD of the MPD and MNTD for the randomized assemblages. A positive value of NRI or NTI indicates phylogenetic clustering, whereas negative values indicate phylogenetic overdispersion.

Species divergence time estimation
We calculated the mean divergence time (MDT) for each flora using the mean ages of its species according to the dated phylogenetic tree generated in R package V.PhyloMaker2. The divergence time of each species used to calculate MDT was not the absolute age, as they were extracted from the megatree generated in R package V.PhyloMaker2. In this approach, divergence time of species is expected to be overestimated, as the branch of some species in a local phylogeny is usually longer than that in the global phylogeny (including all species). For instance, if a lineage became extinct, the divergence time of its existing closest relative species would be dated at the point of their last common ancestor. To assess the robustness and the effect of this sampling bias on the final results of species age structure of a mountain flora, we used four divergence time datasets and found similar MDT patterns between mountains of different landforms (Supplementary Fig. 8). This result is consistent with a study, which found that “in large-scale biodiversity and phylogenetic analyzes, sources of noise in divergence time estimation are to be expected, but they did not affect the reliability of the results”. We believe that our dated megaphylogenetic tree was suitable for this study because our aim was to reveal the general patterns of landform influence on the formation of mountain flora rather than focusing on the age of each species.

We facilitated comparisons among landforms by mapping the 140 floras to their landform type through the integration of spatial data in ArcGIS v.10.8. Each species within a flora was assembled at a different time. Previous studies have shown that the species ages within floras are quite different and that environmental variables have better explanatory power for herbaceous species. Because herbaceous species have shorter generation times than woody species and, consequently, tend to be evolutionarily younger. To investigate whether old species and young species exhibit different patterns of assembly, we partitioned all species into quartiles based on their divergence times and, in addition to computing the MDT of all species, we also calculated the MDT of the oldest 25% of species (MDT.oldest) and of the youngest 25% of species (MDT.youngest) for each mountain flora following the method in Lu et al.

Predictor variables
The diversity and phylogenetic structure of mountain floras are influenced by many factors, including climate, regional geologic history, and geographic heterogeneity. To take into account aspects of these factors, we obtained data representing a total of 18 predictor variables (Supplementary Table 2). For each of the 140 mountain floras, these variables included geographic characteristics, type of landform and tectonic plate, and climatic features. The 18 variables were obtained as follows:
Geographic information included longitude, which reflects the distribution pattern of rainfall on the Chinese mainland; latitude, which reflects the temperature gradient; area; median elevation (elevmid) of species; and elevational range of each mountain (elevdiff), which is related to local heterogeneity. We obtained these geographic information data from local governmental reports on physical geography (Supplementary Table 1).

We assigned a landform type to each mountain based on relevant regional geological and geomorphological survey reports (Supplementary Table 1), as well as a world geological map (http://portal.onegeology.org/OnegeologyGlobal/). In general, the developmental stage of a mountain landform depends on the stage of its rock-stratigraphic denudation. Therefore, based on the sequence of stratigraphic denudation and bedrock type occurring in a mountain, we defined five types of landforms: karst, karst-granitic, granitic, desert, and Danxia (Fig. 1).

Karst develops from a high-carbonate limestone stratum with a marine sedimentary origin. Granitic landforms are characterized by igneous bedrocks, which are crystalline and poorly soluble in water compared to limestone. Granitic landforms are formed when the intrusion of acidic magmatic rocks causes overlying strata to become denuded and exposed. In cases where the bedrock of a mountain is composed of both limestone and granite, it is defined here as a ‘karst-granitic’ landform and represents the intermediate state of karst evolving into a granitic landform. Danxia landforms are made up of non-marine clastic rock and characterized by red walls and cliffs, which are usually Mesozoic continental sediment strata and were developed along with the Himalayan orogeny in China. The development of desert landforms is usually closely related to aridification. In China, desert landforms are mainly located in the northern and western provinces and consist of flat, arid plains and exposed, rocky mountains resulting from the strengthening of the winter monsoon on the mainland after the Neogene. Therefore, ‘desert’ in this study represents the mountain floras located in the arid region. We used these five types of landforms to classify the 140 mountain floras included in this study, which comprised 19 karst floras, 14 karst-granitic floras, 84 granitic floras, 13 desert floras, and 10 Danxia floras (Supplementary Table 1, Fig. 1).

We distinguished the tectonic plate to which each mountain belonged by referring to the Plate Tectonic Regionalization of China. The mountains on cratons, such as the Yangtze or North China cratons, represent stable geological regions and were coded as ‘craton’. In contrast, mountains located in orogenic belts were coded as ‘orogenic’.

For each of the 140 mountain floras, we downloaded CHELSA climate data (v. 1.2, available at http://chelsa-climate.org/) at a spatial resolution of 30 arc-seconds, and extracted the climatic variable mean values of each mountain layer using the zonal statistics function in ArcGIS 10.8. CHELSA is a high resolution climatology dataset widely used in recent years for modeling species distributions and inferring the evolution of climatic niches. The 19 bioclimatic variables of CHELSA, BIO1-BIO19, describe temperature, precipitation, and fluctuations in temperature and precipitation at various timescales. Of the 19 variables, we excluded eight that had pairwise Pearson correlation coefficients > 0.95 to avoid collinearity. We included the remaining 11 bioclimatic variables in our analyzes (Supplementary Table 2).

Data analysis
A total of 140 mountain floras were included in our analysis. These mountains were representative of major Chinese climate regions and showed a high degree of variation in plant species richness, climate, and geology (Fig. 2; Supplementary Table 1). For each mountain, we log-transformed geographic area to account for power relationships to species diversity, phylogenetic diversity, and MDT. The other 15 numerical variables included all 11 climatic variables, longitude, latitude, elevmid, and elevdiff. We standardized these 15 variables from 0-1 according to the formula (x − x min) / (x max − x min), following Ricklefs & He.

We used generalized linear models (GLMs) to model log-transformed species richness as the response variable and landforms, tectonics, and climate variables as predictors. Initially, we modeled species richness as a function landform only (i.e., landform model) because, in this study, we focused primarily on the roles of landforms in floristic assembly. However, we also extended the landform model to include all other variables to assess the effect of climate on species diversity (i.e., full model). The determinants of species richness might change with landform type, and we therefore test for interactions between landform and other predictor variables (only significant variables are shown in the full model). We further used GLM to determine the effects of landform, tectonics, and climate on phylogenetic diversity, NRI, NTI, PDI, MDT, MDT.oldest, and MDT.youngest (Supplementary Table 2). As with species richness, we initially modeled landform as a single predicting factor (i.e., landform model) before extending to all variables within a full model. We performed all GLM analyzes in R version 4.1 (https://www.r-project.org/) using the glm function in the package MASS.

The step function in package MASS is used for Akaike Information Criterion (AIC) model selection to derive a minimum adequate model. We further applied the ‘leave one out’ approach to find the best model for each full model. Under this approach, the importance of a variable is evaluated by comparing a full model that includes the variable to a reduced model that excludes it. We also analyzed the standardized coefficients of predictors to compare the importance of each predictor variable based on Antonelli et al.

Spatial autocorrelation is a general feature of macroecological data and may lead to erroneous interpretations. We used Moran’s I values to quantify residual spatial autocorrelation. These are considered the spatial equivalent of Pearson´s correlation coefficients and normally vary between 1 and −1, with values close to 0 indicating a lack of spatial autocorrelation. Because of spatial autocorrelation present in our dataset, we performed a spatial error model (SEM) to account for residual spatial autocorrelation. The expected Moran´s I values for the response variable, as well as for GLM and SEM residuals, are shown in the appendix (Supplementary Tables 3–9). Spatial statistics were performed with the package spdep in R version 4.1 (https://www.r-project.org/).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a well structured and put together paper. I'm certain you're right that the factors you examined explain patterns of montane diversity and phylogenetic community structure in these mountains in China. I'll be happy to see this excellent work published somewhere. And I do think this is the first time something like this has been attempted, at this scale, with montane plant diversity in China. However, the overall concept and approach is not novel. Others, sometimes working on other taxa and in other locations, have taken the approach of combining contemporary environmental variables with various suites of geological variables to explain spatial variation in diversity. As one key example, I’d point to Antonelli et al. (2018) in Nature Geoscience. In that paper, Antonelli and colleagues analyze “…how erosion, relief, soil, and climate relate to the geographical distribution of terrestrial tetrapods, which include amphibians, birds and mammals. [They] find that centres of species richness correlate with areas of high temperatures, annual rainfall and topographic relief, supporting previous studies. We unveil additional links between mountain-building processes and biodiversity: species richness correlates with erosion rates and heterogeneity of soil types, with a varying response across continents. These additional links are prominent but under-explored, and probably relate to the interplay between surface uplift, climate change and atmospheric circulation through time.”\nNote that Antonelli et al.’s paper is global in scale and examines multiple taxa. Catherine Badgley and colleagues have also done similar work, focused on mammals in mountains.\nAll of that is to say that the work is solid, and I believe it. It’s just not that novel and limited in taxonomic and geographic scope.", "reviewer_2": "This paper explored the relationship between the type of landforms and floral diversity in mountain ranges. Floral checklists from 140 natural reserves and natural parks in Chinese mountain ranges were compiled. Plant species richness, phylogenetic diersity, species relatedness and time since divergence were modeled based landforms and geological, geographic and climatic variables, controlling for the potential area effect. All measures of floral diversity differed among landforms, with additional effects from some other variables.\nFindings in this study are important for understanding biodiversity in mountain ranges which is a central topic in biodiversity and biogeographic research. In particular, the significant role of landforms in generating and maintaining mountain biodiversity across a broad spatial scale is a novel finding. I do have some concerns and suggestions listed below, but if the results are robust, I think this study has very good potential to make a fine publication which will be of great interest for the broad readership of the journal. The writing is clear and generally easy to follow.\nOverall, I feel that the specific mechanisms of how landforms could shape the plant communities/floras could be explained further in depth (e.g. in the introduction), especially considering the broad readership of the journal. Comparing the importance of landforms and other variables in explaing diversity seems to be a logical step but theoretically, their effects can't really be separated. The importance of landforms in shaping floral diversity/composition thus could be further established by investigating their interactions with other environmental variables, as briefly discussed by the authors (L131-132). Either a boosted regression or a multi-level model with landforms as a group-level effect would be very illuminating. In addition, a lot of the variation among landforms seems to come from deserts vs others, based on fig. 3a-c -- do the other groups differ?\nA major technical concern is about how the phylogenetic data were handled. The phrasing at L416 seems to suggest that species not occurring in your studied areas are pruned, which will affect your null expectation for those phylogenetic metrics and your calculation of phylogenetic dipersion/clustering. I do not have a good answer to what would be the best way to construct a null model for phylogenetic diversity or dispersion, whether it should be the global plant phylogeny or a Chinese plant phylogeny, or for a slightly different question, a regional phylogeny just for the floras around the focal site. Any of these cases would be better than a phylogeny of only the species in your dataset. Please clarify this and explain your reasoning.\nRelated to that, the interpretation of terminal branch length as species age and all associated discussions are problematic, because the plant phylogeny presumably does not include extinct lineages. The terminal branch length thus only reflects a species' distinctiveness from extant plant species within your sampling pool. In fact, the whole section about the ages of floras is problematic and not very informative unless the formation time frame of their habitats could be provided. To some extent, these analyses could help inferring whether modern floras were shaped by radiation in-situ, but this might involve a lot of confounding factors. The outcome also heavily depends on whether you have stripped away species not included in the study, and the data coverage of higher taxa (which is not explained, unless I miss it).\nThe models do not seem to account for spatial auto-correlation, and no justification has been given on why that is not an issue.\nMinor comments:\nL67-77: this paragraph might be better for the beginning of the Intro. The current beginning completely ignores elevation and associated environmental heterogeneity, which doesn't provide a broad enough overview of the topic.\nL72-77: the two sentences are a bit contradicting with each other about whether the subject has been studied or not -- better rephrase.\nL83: add \"to\" to before \"refer to\"\nL87: change \"resolved\" to \"reflected\" or \"represented\"\nL89: better clarify what \"phylogenetic structure\" is referring to (relatedness?)\nL93: change \"additional\" to something like \"in addition to\" or \"along with\"\nL140: PDI has not been explained at this point -- given the format with Methods after everything, it would be helpful to have some technical information specified in the results (e.g. whether area and latitutude were accounted for in the models).\nL244-250: environmental filtering as a potential mechanism should probably be discussed here, especially given L235-237.\nL246: TAR reflects \"instability\", right?\nL260: change \"maybe\" to \"may be\"\nL398: \"a total of\"\nL343: better use \"allows\" than \"facilitates\"\nL440: is NRI driven by sample coverage, i.e. the number of artificial sister species/genera in the floras?\nL461: \"is different\" or \"differs\"\nL509: please clarify whether the resolution of the climatic layers matches your sampling areas -- are most floras much larger than the units of the climatic datasets?", "reviewer_3": "Review of the manuscript “Landform and lithosphere development drive the assembly of mountain floras” by Zhao Wan-Yi et al.\nThe authors investigated 140 mountain regions (with their flora and bedrocks) in China to study the relationship between mountain floras and the types of landforms in which they occur. The authors obtained a backbone phylogeny from a recently generated time-calibrated mega-tree, including species across all floras. Based on the phylogeny, they calculated phylogenetic diversity, phylogenetic structure, and mean divergence times. They constructed regression models to predict species richness, phylogenetic diversity and structure, and mean divergence times, using landform as a predictor, and using additional tectonic, climatic, and geographic explanatory variables as predictors. Based on their findings, the authors put forward what they call \"the geological lithology hypothesis of assembly and differentiation of mountain floras”. They claim that this hypothesis “provides a novel framework rooted in geology for future research on the origins, differentiation, and migration of angiosperm assemblages and mountain floras.”\nThis study complements a growing body of literature on the close interrelationships between geological diversity (geodiversity) and biological diversity (biodiversity), on their “concerted” evolution, and on the assembly of mountain floras. It stresses edaphic factors (related to bedrock, landforms) as important for ecological filtering, species immigration, and evolution, further driving differences among landforms.\nI enjoyed reading this manuscript, and think it can potentially make an important contribution. However, at the same time I found that reference to highly relevant recent literature (pertaining to the 'mountain geobiodiversity hypothesis') was lacking and thus not considered, especially with regard to the hypothesis here by the authors. In addition, some methodological issues, e.g. pertaining to taxonomic name resolution (use of the now long outdated Plant List) and molecular dating (derivation of species divergence times from a phylogeny not sampled for taxonomic coverage), need consideration.\nIn the following, I will provide my comments which I hope will be useful for the authors. Line numbers refer to the pdf.\nTitle: As the study is confined to mountains in China, and the authors emphasize the special geological conditions in China in their manuscript, the title could be adjusted to reflect this. E.g. “Landform and lithosphere development drive the assembly of Chinese mountain floras”. Also, the word “drive” may be reviewed (“drivers” is a strong term).\nLine 30: “The biodiversity of mountains is attributable in part to their geological activities, namely uplift and erosion, as well as the habitat heterogeneity that they provide, such as along their elevational gradients4-6“. It would be appropriate to cite the work by Antonelli et al. 2018 already here.\nLine 31: “how mountain biodiversity is shaped on temporal and spatial scales remains poorly understood7-9“ This ignores a growing body of literature investigating this in detail, such as literature on the 'mountain geobiodiversity hypothesis' (MGH) (Mosbrugger et al. 2018, Muellner-Riehl 2019, Muellner-Riehl et al. 2019, and references therein). There are, by now, hundreds of papers on Chinese mountain biogeography and Chinese mountain phylogeography. This needs more to receive more attention in the manuscript, and sentences such as the one cited above (“poorly understood”) need to be rephrased to pay tribute to the growing knowledge.\nLine 38-40: “the ages of the studied floras are consistent with the geological development of the different landforms with mean divergence times being the highest…“ This is not clear - what exactly does it mean that ages are consistent, given that the authors later define „flora“ (line 83) as „sum of all angiosperm families, genera, and species“? Different taxonomic levels (species – genera – families) have vastly different ages (unless a genus or a family are both monospecific, or close to), and including only a very small fraction of the species of a genus or family, as done here (because only the species occuring on the 140 Chinese mountain regions are included in the analyses), will not allow to arrive at ages which are anywhere close to the “real” ages of the species (or higher taxonomic levels).\nLine 42: What do the authors consider as “younger” species?\nLine 42-47: “We put forward \"the geological lithology hypothesis of assembly and differentiation of mountain floras”.” “Our hypothesis provides a novel framework rooted in geology for future research on the origins, differentiation, and migration of angiosperm assemblages and mountain floras.” As mentioned before, the authors seem unaware of the MGH ('mountain geobiodiversity hypothesis'). This hypothesis was originally formulated by Mosbrugger et al. 2018, later refined by Muellner-Riehl 2019, and finally tested on a global scale by Muellner-Riehl et al. 2019. The MGH is supposed to provide an overarching framework for the investigation of the concerted evolution of mountains and their biotas, and on the assembly of mountain biodiversity, through time, and also considering climatic fluctuations which act on top of orogenic processes. Together with the flickering connectivity system by Flantua et al. (the latter which is cited by the authors), the MHG thus deals with the intricate relationship between mountains and their biotas/flora. It needs to be elaborated and explained what the “geological lithology hypothesis” would add specifically, and how it differs from the other hypothesis already brought forward and tested by empirical studies in the past few years. The authors should consult the work by Mosbrugger et al. 2018, Muellner-Riehl 2019, Muellner-Riehl et al. 2019. Is it justified to formulate yet another hypothesis, or may the hypothesis introduced here be considered under the umbrella of the MGH?\nLine 43/44: “Under this hypothesis, landforms develop according to their underlying bedrock, and their geological development drives both the assembly and subsequent differentiation of mountain floras.“ This may be considered less a hypothesis than a fact.\nGeneral comment: Missing in this first paragraph of the text is also a general reference to “geodiversity” and “geobiodiversity”. It may be worth noting here, for the information of the authors, that the recognition of the intricate relationship between geological diversity and biodiversity has led to specific research activities in the scientific community, such as e.g. the Research Activity “Geobiodiversity and Climate” which “studies the interactions between climate, Earth surface processes and biodiversity on different time scales. It further examines the impacts of climate and Earth surface processes on the evolutionary and ecological dynamics of species and communities. An important research topic are the effects of anthropogenic climate change on biodiversity and ecosystem functions.” (https://www.senckenberg.de/en/science/biodiversity-and-climate/geobiodiversity-and-climate/). What I am trying to say here is that the introductory part of the manuscript leaves the impression that the authors are not aware of an important body of literature and concepts pertaining to geobiodiversity, and this should be avoided.\nLine 67/68: “Historically, mountain biodiversity and, more broadly, global biodiversity, have been explained by numbers of hypotheses such as the climate stability23, habitat heterogeneity24-25, and energy hypotheses26-27.“ Again, reference to the MGH is missing here. The sentence should be rephrased to accommodate it, as it specifically refers to mountains, which is the core topic of this manuscript, and thus especially relevant. Muellner-Riehl et al. 2019 already reflected about general hypotheses explaining high biodiversity levels, and then specifically tested the MGH, which was originally proposed for the Tibet-Himalaya-Hengduan region (thus especially relevant for Chinese mountains), and then tested for global relevance.\nLine 75-77: „Despite this, previous researches have focused primarily on the impacts of ecological factors on mountain biodiversity, while the contributions of geological and lithologic processes are largely unexplored3,9,29.“ This may have been considered true (at least, to some extent - though the field of „geobotany“ is, of course, a very old discipline) some ten years ago, but ignores recent literature on geodiversity-biodiversity relationships. See also my comments further above. But there is also a growing body of literature of several working groups and researchers specifically addressing these issues.\nLine 84/85: “growing on a specific mountain or in a well-delimited area, which is a relatively independent and self-evolving natural complex30-31.“ This is not correct. Mountains are usually not considered „independent“ and „self-evolving“, i.e. disconnected from surrounding areas (unlike true islands). There is quite some literature on the similarities, but more importantly differences, between island and mountain systems (e.g. see Mendez-Castro et al. 2021, Itescu 2018, Flantua et al. 2020). This statement needs to be re-phrased accordingly.\nLine 114: “(median = 1,462.0)“ – not entirely clear what this number represents.\nLine 122-136: “Based on the full model, longitude, elevational difference between the highest and lowest points of a flora (elevdiff hereafter), and mean temperature of the coldest quarter (TCQ hereafter) had positive effects on species richness (Extended Data Table 3).“ In this sentence, and the following ones, it should be made clear whether larger/smaller or higher/lower values have a positive or negative effect. This seems not evident from the text, as currently written. E.g. do the authors mean “a larger elevational difference“? Or later in line 128, high or low precipitation seasonality and high or low mean temperature? As a note aside, net primary productivity (NPP) might have been a better predictor for species richness.\nLine 142 f.: What about the role of dispersal from other mountain systems? (see e.g. Jiménez-Alfaro et al. 2020", "reviewer_doi": "10.1002/ecy.2788).\nRelated to that, the interpretation of terminal branch length as species age and all associated discussions are problematic, because the plant phylogeny presumably does not include extinct lineages. The terminal branch length thus only reflects a species' distinctiveness from extant plant species within your sampling pool. In fact, the whole section about the ages of floras is problematic and not very informative unless the formation time frame of their habitats could be provided. To some extent, these analyses could help inferring whether modern floras were shaped by radiation in-situ, but this might involve a lot of confounding factors. The outcome also heavily depends on whether you have stripped away species not included in the study, and the data coverage of higher taxa (which is not explained, unless I miss it).\nReviewer #3 (Remarks to the Author):\nReview of the manuscript “Landform and lithosphere development drive the assembly of mountain floras” by Zhao Wan-Yi et al.\nThe authors investigated 140 mountain regions (with their flora and bedrocks) in China to study the relationship between mountain floras and the types of landforms in which they occur. The authors obtained a backbone phylogeny from a recently generated time-calibrated mega-tree, including species across all floras. Based on the phylogeny, they calculated phylogenetic diversity, phylogenetic structure, and mean divergence times. They constructed regression models to predict species richness, phylogenetic diversity and structure, and mean divergence times, using landform as a predictor, and using additional tectonic, climatic, and geographic explanatory variables as predictors. Based on their findings, the authors put forward what they call \"the geological lithology hypothesis of assembly and differentiation of mountain floras”. They claim that this hypothesis “provides a novel framework rooted in geology for future research on the origins, differentiation, and migration of angiosperm assemblages and mountain floras.”\nThis study complements a growing body of literature on the close interrelationships between geological diversity (geodiversity) and biological diversity (biodiversity), on their “concerted” evolution, and on the assembly of mountain floras. It stresses edaphic factors (related to bedrock, landforms) as important for ecological filtering, species immigration, and evolution, further driving differences among landforms.\nI enjoyed reading this manuscript, and think it can potentially make an important contribution. However, at the same time I found that reference to highly relevant recent literature (pertaining to the 'mountain geobiodiversity hypothesis') was lacking and thus not considered, especially with regard to the hypothesis here by the authors. In addition, some methodological issues, e.g. pertaining to taxonomic name resolution (use of the now long outdated Plant List) and molecular dating (derivation of species divergence times from a phylogeny not sampled for taxonomic coverage), need consideration.", "reviewer_author_response": "Response to reviewers’ comments\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThis is a well structured and put together paper. I'm certain you're right that the factors you examined explain patterns of montane diversity and phylogenetic community structure in these mountains in China. I'll be happy to see this excellent work published somewhere. And I do think this is the first time something like this has been attempted, at this scale, with montane plant diversity in China. However, the overall concept and approach is not novel. Others, sometimes working on other taxa and in other locations, have taken the approach of combining contemporary environmental variables with various suites of geological variables to explain spatial variation in diversity. As one key example, I’d point to Antonelli et al. (2018) in Nature Geoscience. In that paper, Antonelli and colleagues analyze “…how erosion, relief, soil, and climate relate to the geographical distribution of terrestrial tetrapods, which include amphibians, birds and mammals. [They] find that centres of species richness correlate with areas of high temperatures, annual rainfall and topographic relief, supporting previous studies. We unveil additional links between mountain-building processes and biodiversity: species richness correlates with erosion rates and heterogeneity of soil types, with a varying response across continents. These additional links are prominent but under-explored, and probably relate to the interplay between surface uplift, climate change and atmospheric circulation through time.”\nNote that Antonelli et al.’s paper is global in scale and examines multiple taxa. Catherine Badgley and colleagues have also done similar work, focused on mammals in mountains.\nAll of that is to say that the work is solid, and I believe it. It’s just not that novel and limited in taxonomic and geographic scope.\nReviewer #2 (Remarks to the Author):\nThis paper explored the relationship between the type of landforms and floral diversity in mountain ranges. Floral checklists from 140 natural reserves and natural parks in Chinese mountain ranges were compiled. Plant species richness, phylogenetic diersity, species relatedness and time since divergence were modeled based landforms and geological, geographic and climatic variables, controlling for the potential area effect. All measures of floral diversity differed among landforms, with additional effects from some other variables.\nFindings in this study are important for understanding biodiversity in mountain ranges which is a central topic in biodiversity and biogeographic research. In particular, the significant role of landforms in generating and maintaining mountain biodiversity across a broad spatial scale is a novel finding. I do have some concerns and suggestions listed below, but if the results are robust, I think this study has very good potential to make a fine publication which will be of great interest for the broad readership of the journal. The writing is clear and generally easy to follow."}, "author_response": ""}]

Decision letter