Skip to main content
Download PDF

Insight into the phylogeny and responses of species from the genus Sergia (Campanulaceae) to the climate changes predicted for the Mountains of Central Asia (a world biodiversity hotspot)

BMC Plant Biology volume 24, Article number: 228 (2024) Cite this article

Abstract

Background

Together with other elevated areas, the Mountains of Central Asia are significantly threatened by ongoing climate change. The presence of refuges during the glaciations makes the region extremely rich in species, especially endemic ones. However, the limited potential for colonisation of other habitats makes rocky-related species with ‘island‐like’ distribution, particularly vulnerable to climate change. To understand the processes underlying species response to climate warming, we assessed differences in ecological niches and phylogenetic relationship of two geographically disjunctive alpine species belonging to the genus Sergia. The taxa are considered Tertiary relicts, endemic to the Tian Shan and Pamir-Alai Mountains. To illustrate range dynamics and differences in occupied niches of Sergia species, we used Ecological Niche Modelling of current and future distribution. Whereas, to reconstruct the phylogenetic relationship within and between Sergia and other related Campanulaceae species from the region we used molecular data (ITS, cpDNA, DArTseq-derived SNPs).

Results

The results reveal that the genus Sergia is a polyphyletic group, and its representatives differ geographically, ecologically and genetically. Both S. regelii and S. sewerzowii constitute a common clade with Asyneuma group, however, S. sewerzowii is more closely related to Campanula alberti (a species that has never previously been considered closely related to the genus Asyneuma or Sergia) than to S. regelii. Sergia sewerzowii is adapted to lower elevations with higher temperatures, while S. regelii prefers higher elevations with lower temperatures. The future distribution models demonstrate a dramatic loss of S. regelii range with a shift to suitable habitats in higher elevations, while the potential range of S. sewerzowii increases and shifts to the north.

Conclusions

This study shows that S. regelii and S. sewerzowii have a long and independent evolution history. Sergia regelii and S. sewerzowii significantly differ in realised niches. These differences are mirrored in the response of the studied endemics to future climate warming. As suitable habitats shrink, rapid changes in distribution can lead to species' range loss, which is also directly related to declines in genetic variability. The outcomes of this paper will help to more precisely assess the impact of climate changes on rocky-related plant species found in this world’s biodiversity hotspot.

Peer Review reports

Background

In this century, climate change is one of the strongest factors affecting biodiversity loss as well as largely determining the geographical distribution of species [1, 2], their phenology, physiology, community structures, and ecosystem functions [3]. The most rapid species response to climate changes is observed in alpine regions in the form of altitudinal range shifts [4]. Along with increasing temperature, cold-adapted mountain species are forced to move to cooler areas, which in this case means to the north or higher elevations, depending on their dispersal capacities and the availability of suitable habitats [5,6,7]. In extreme cases, upslope movement results in species extinction because they reach the highest elevations and have no place to escape [8].

The Mountains of Central Asia known also as a biodiversity hotspot, is one of the world’s major centers of plant diversity [9] and among the most elevated areas highly susceptible to climate change [10, 11]. The region is extremely rich in species due to a complex of highly heterogeneous and isolated habitats with diverse topographies, soil types, and microclimates relating to altitude, slope exposure, and precipitation [12,13,14]. In addition, the considerable richness of taxa is linked to the fact that during the Quaternary glaciations, ice sheets did not reach the Mountains of Central Asia, and local mountain glaciers did not cover the patches of all ecosystems, saving areas that have become refuges for Tertiary flora [15].

Among the most interesting habitats in the mountains of Central Asia are rocky habitats, which include vertical rock faces, fissures, clefts, crevices, and rock ledges. They are characterised by great edaphic drought as a result of the soil’s reduced capacity for water retention [16]. Rocky habitats may also differ from each other considerably in terms of humidity, type of substrate rock, insolation, temperature, and inclination [17]. It is an ‘island‐like’ environment, presenting discrete patches of habitats surrounded by strongly contrasting areas in terms of environmental conditions. The harsh environment and patchy structure of chasmophytic habitats make it a suitable biotope for many endemic and specialised plant taxa with narrow niches [18, 19]. The considerable structural differences of chasmophytic flora are highlighted by including the communities of rocky habitats into a separate class named Asplenietea trichomanis [20, 21], with the Campanuletalia incanescentis order of phytocoenoses distinguished within the chasmophytic vegetation in the Mountains of Central Asia [22,23,24,25]. The limited potential for the colonisation of other habitats makes rocky vegetation particularly vulnerable to changing environmental conditions [26], and thus, there is an urgent need to estimate the impact of climate change on chasmophytic plants by creating models of its future potential distribution [3].

The genus Sergia Fed. comprises two geographically disjunctive, alpine plant species associated with rocky habitats, S. regelii (Trautv.) Fed. and S. sewerzowii (Regel) Fed. These species are considered Tertiary relicts, endemic to the Mountains of Central Asia [27, 28]. Sergia regelii occurs mainly in the western Pamir-Alai Mountains (NW Tajikistan, SE Uzbekistan, and E Turkmenistan) in fissures, crevices and ledges within steep rock walls, stones, and loamy soils at an altitude between (1400-)1600 and 2400(-3200) m [29, 30] (Supplementary Material Fig. S1). Sergia sewerzowii also occurs in similar habitats, but is distributed in the north-western Tian Shan Mountains (SE Karatau and Talass Alatau ridges in S Kazakhstan) at an altitude of 600–1400(-2000) m (Supplementary Material Fig. S1). Although the two taxa are placed in the genus Sergia, they differ considerably in distribution and morphology. Sergia regelii is generally characterised by taller stems and flowers (petals, calyx, sepals) and densely hairy leaves, whereas S. sewerzowii is a smaller and glabrous plant (Supplementary Material Table S1). Although phylogenetic studies of the Campanulaceae family have a long tradition [26, 30,31,32,33,34], to our knowledge, with the exception of the work of Xu and Hong [35], representatives of the genus Sergia have not been examined. Based on the combined dataset of six chloroplast regions, Xu and Hong [35] presented a phylogenetic tree, in which the genus appears to be non-monophyletic, similarly as most of the other genera analysed within Campanulaceae analysed with using a molecular approach [31,32,33,34]

Sergia regelii and S. sewerzowii have not been studied in detail in terms of phylogeny and ecology, although they belong to the most beautiful and narrow-range group of chasmophytic plants, threatened in the face of future climate change. To better understand the processes underlying species response to future climate change we will combine the results of phylogenetic analyses and Ecological Niche Modelling of those two endemic representatives of the genus Sergia. In particular, we would like to (i) verify the hypothesis of the monophyly of the genus Sergia and analysis of the phylogenetic relations with the other Campanulaceae species occurring in the mountains of Central Asia; (ii) define the differences in realised niches and predict the spatial and temporal patterns of S. regelii and S. sewerzowii distribution; as well as (iii) evaluate the vulnerability of these alpine species to predicted climate change. The findings will contribute to a better understanding of the climatic factors affecting the distribution of chasmophytic endemics and provide a theoretical basis for their conservation and restoration purposes by identifying areas susceptible to climate change and establishing unified conservation strategies aimed at preserving the extraordinary rates of endemism of this mountain biodiversity hotspot.

Results

Chloroplast and ribosomal DNA variation

The total alignment of ITS across 12 analysed individuals (including species belonging to Asyneuma Griseb. & Schenk, Sergia and Campanula alberti Trautv.) was 683 bp. The alignment revealed differences in sequence length between the studied samples. The sequences only slightly differ in length and range, from 678 bp (Campanula alberti Trautv.) up to 682 bp (Asyneuma argutum (Regel) Bornm. and S. sewerzowii). The alignment contains 71 parsimony informative sites (67 with two variants and 4 with three variants), 3 singleton variable sites and 9 indel (insertion/deletion) events. We failed to amplify the Astho3 sample of A. thomsonii (Hook.f.) Bornm. for the petD region, Asarg2, Astra8, Sesew4, Sereg5 samples (A. argutum, A. trautvetteri (B.Fedtsch.) Bornm., S. sewerzowii and S. regelii respectively) for psbD-trnT as well as part (in one direction) of Astra8 of A. trautvetteri for trnK-psbA (for geographical origin of the samples see Supplementary Material Table S2). The concatenated alignment of petD, trnK-psbA, rpl16, psbD-trnT, and trnS-trnfM regions for the 12 studied individuals of Asyneuma, Sergia and C. alberti, without an outgroup, was 6385 bp. The concatenated alignment contains 102 parsimony informative sites with two variants, 5 singleton variable sites and 89 insertion/deletions.

Phylogenetic relationships

The trees revealed by the Bayesian Inference (BI) based on cpDNA as well as Neighbor-Joining (NJ) based on DArTseq derived SNPs yielded similar topologies (Fig. 1A, B), showing clear and well-supported (in the case of BI) divergence between both studied Sergia species. In all trees, samples of C. alberti group together with samples of S. sewerzowii, indicating that the taxa split from a common ancestor. The current range of S. sewerzowii and C. alberti partially overlap and are characterised by an altitudinal disjunction, where C. alberti prefers higher-situated habitats (Supplementary Material Fig. S2).

Fig. 1
figure 1

Phylogenetic relationship and similarity in flower morphology of the studied group of species. A Bayesian Inference of phylogeny based on concatenated cpDNA data (25 individuals, 6710 bp); numbers at the nodes indicates the Bayesian posterior probability values, B Neighbor Joining tree based on DArTseq derived SNPs (24 individuals, 1047 SNPs), C Flower morphology. Photographs of A. argutum, A. trautvetteri, C. alberti and S. regelii by M. Nobis & A. Nowak, photograph of S. sewerzowii by A. Ebel. An explanation of abbreviations of samples used in analyses is given in the Supplementary Material Table S2

Full size image

In all analyses, both Sergia species as well as C. alberti form a common clade with representatives of the genus Asyneuma. Analyses revealed considerable uncertainty in the case of S. regelii (Sereg1, Sereg5), which was assigned either as a sister group to the Asyneuma (Asarg1, Asarg2, Astho3, Astho4, Astra7, Astra8) or Sergia sewerzowii-Campanula alberti group (Sesew6, Calb24, Calb124) (Fig. 1 A, B). Only the ITS analysis indicates that both species of Sergia share the common ancestor (Supplementary Material Fig. S3), however in this analysis, particular clades referring to Sergia, Campanula alberti and Asyneuma are organised in a polytomy.

Performance of the models and potential current species distribution

The values of average test AUC obtained from models were high (0.991 ± 0.003 for S. regelii and 0.989 ± 0.012 for S. sewerzowii), which confirms high levels of the model’s predictive performance. According to the results of Maxent modelling, variables bio7, srh, bio19, crf, cec, and bio17 were the major contributors to the distribution model of S. regelii (Table 1), with a cumulative contribution of 97.6%. In the case of S. sewerzowii, the main contributors were bio19, srl, bio17, snd, crf and elev. Their cumulative contribution was 98.4%. The suitable area for S. regelii (probability of presence > 0.1313) was 27,226 km2 (Fig. 2). The 3,658 km2 (13.4% of the total suitable area) was identified as area of high probability of occurrence (0.6–1). In the case of S. sewerzowii, the suitable area was almost twice time lower, 14,554 km2 (probability of presence > 0.2263), however the area of high potential suitability (0.6–1) was 2,989 km2, which accounted for over 20% of the total suitable area.

Table 1 Contribution of environmental variables used for modelling the potential distribution of S. regelii and S. sewerzowii
Full size table
Fig. 2
figure 2

Potential current distribution of S. regelii and S. sewerzowii. The present suitable area is defined as an area with a probability of species presence higher than the Equal test sensitivity and specificity logistic threshold. A highly suitable area is one with a probability of presence from 0.6 to 1

Full size image

The current potential distribution of S. regelii was predicted in the Pamir-Alai Mts, particularly in the Kuhitang, Baisun-tau, Hissar, Turkestan, and Zeravshan ridges, from where its occurrence was already known (Supplementary Material Fig. S1). These results confirm the accuracy of the model because the potential and actual distribution of S. regelii significantly overlap. In the case of the potential distribution of S. sewerzowii, besides known localities in the Karatau and Talas Alatau ridges (south Kazakhstan), suitable habitat was also predicted in east Uzbekistan, from where the species has not been listed yet.

Comparison of ecological niches

The PCA biplot shows that the realised niches of S. regelii and S. sewerzowii slightly overlap (Fig. 3). Among the analysed variables, bio7, bio8, bio19, srl, and cec differentiate the niches of the examined species the most (Supplementary Material Fig. S4). The cumulative contribution of these variables according to the Maxent model is 71.8% for S. regelii and 67.1% for S. sewerzowii. Despite significant overlap of the other environmental variables (e.g. bio17, crs, ocs, and pH), the realised niches of the studied species differ noticeably.

Fig. 3
figure 3

PCA of niche overlap (green shading – realised niche of S. regelii, red shading – realised niche of S. sewerzowii, blue shading – niches overlap). Solid and dashed lines – 100% and 75% of the available (background) environment for S. regelii (green) and S. sewerzowii (red)

Full size image

Schoener’s D index and the I statistic are 0.0085 and 0.0505, respectively, reflecting a low level of niche similarity (Supplementary Material Table S3). The test for niche divergence (alternative = “lower”) shows significant distinctions between the S. regelii and S. sewerzowii niches, indicating that they are less equivalent than expected by chance (p < 0.05). The niche conservatism test (alternative = “greater”) indicates that the studied species’ niches are less equivalent (similar) than expected by chance (p > 0.05). Therefore, we can conclude that there is no significant niche conservatism between S. regelii and S. sewerzowii, however, there is significant niche divergence between them.

Potential future species distribution under different scenarios of global warming

The projected future models for both 2.6 and 8.5RCP emissions scenarios showed a progressive reduction of the extent of suitable areas of occurrence for S. regelii, in comparison to the potential current distribution (Supplementary Material Table S4, Fig. 4). According to the ‘optimistic’ emissions scenario (+ 2.6 W/km2), up to 2060 the potentially suitable areas for S. regelii will decrease by 32.8% and up to 2080, by 41.8%. Considering a ‘pessimistic’ scenario (+ 8.5 W/km2), the predicted reduction of suitable areas is even more significant (especially in the later period): 31.8% by 2060 and 64.4% by 2080. Changes in the potential geographic distribution of S. sewerzowii up to 2060 under the ‘optimistic’ emissions scenario start with habitat loss along the southern border of the distribution and gradually move to the north by 2080. According to ‘pessimistic’ scenario, S. sewerzowii will expand its range by 45.5% up to 2080. As in the optimistic scenario, the species may find new suitable niches north of its current range (Supplementary Material Fig. S5).

Fig. 4
figure 4

Changes in the potential niche distribution of S. regelii and S. sewerzowii during periods 2041–2060 and 2061–2080 under different climate change scenarios (blue – gain, red – loss, yellow – unchanged). Numbers in the left angles – percent of change in the potential distribution area compared with current distribution

Full size image

Discussion

In the face of ongoing climate change and progressive habitat decrease, understanding the range dynamics of alpine plants vulnerable to extinction, restricted today to mid- and lower-elevated regions, is of pivotal importance [13]. The chasmophytic vegetation of Central Asian mountains consists of many endemics, significantly differing from other types of phytocoenoses [23,24,25]. Current distribution patterns, genetic structure, and the phylogenetic relationships of endemic species in the study area are primarily determined by past climate changes and the Pleistocene glaciation cycles [6, 36, 37]. During glaciation periods, most of the lower- and mid-elevated areas in Central Asia were not covered by ice sheets. They most likely experienced a cooling effect through the albedo from high-altitude glaciers, which advanced beyond their present positions (ca. 600–1000 m lower than today) in the Pamir and Tian Shan mountain regions [38, 39]. Many endemic species survived in the interglacial refugia in deep, wide valleys (e.g. Ostrowskia magnifica Regel, Potentilla kulabensis Th.Wolf, Ranunculus chodzhamastonicus Ovcz. & Junussov) or on steep rock faces in the montane belt, such as S. regelii and S. sewerzowii. These species adapted to the harsh, but relatively stable conditions in the highest elevations and have thrived in these habitats until now [40].

The distribution of S. regelii and S. sewerzowii is restricted to the western outcrops of the Central Asian mountains, however, they are characterised by significant differences in distribution patterns (horizontal and vertical geographical disjunction). Our molecular analyses, based on both combined five cpDNA regions as well as DArT SNPs, confirm that they share a common ancestor with species of Asyneuma and C. alberti, however afterwards, they evolve independently. Our results support also the earlier findings of Xu & Hong [35] that the genus cannot be considered monophyletic. Based on the character of corolla, deeply (until the base) divided into 5 lobes, S. regelii was described at first as Phyteuma regelii Trautv., being later transferred to Asyneuma as A. regelii (Trautv.) Bornm. and subsequently to Podanthum Boiss., P. regelii (Trautv.) O.Fedtsch. & B.Fedtsch. While S. sewerzowii was described as Campanula sewerzowii Regel [41]. In accordance with the results of our phylogenetic analyses, S. regelii exhibits an inconclusive locality within the tree and is grouped either as a sister clade to the Asyneuma or Sergia sewerzowii-Campanula alberti clades. Whereas Sergia sewerzowii, in all analyses except ITS, is grouped with C. alberti, to which it exhibits much more morphological similarity (considering the corolla morphology) than to S. regelii. The ITS-based phylogenetic tree indicates that both Sergia species split from the common ancestor, however, it failed to resolve phylogenetic relationships between Sergia, C. alberti and Asyneuma. Our findings are related to the results of Jones et al. [34], where C. alberti is grouped within a common clade with Asyneuma and some other taxa from Petromarula Vent. ex R.Hedw., Physoplexis Schur and Campanula L. Whereas, they differ from those of Xu & Hong [35], where C. alberti is placed within a common subclade with, e.g., C. stevenii M.Bieb., C. wolgensis P.A.Smirn., and C. persicifolia L. Our results therefore contribute to establishing the taxonomic position of C. alberti and confirm that it should not be included to the C. stevenii species-group [42]. In our studies, samples of C. alberti are grouped together with S. sewerzowii in both cpDNA and DArT analyses. These two mentioned above taxa split probably in the late Pliocene-early Pleistocene similarly as the other species of rocky-related endemic plants of this region, i.e., C. lehmanniana and C. eugeniae [7], and can be also regarded altitudinal vicariants within the Tian Shan Mts. Based on the morphology of the flowers, the corolla of S. sewerzowii, that is deeply divided into 5 longer than wide lobes, fused in the lower part (Fig. 1C), is more similar to the corolla of C. alberti than to S. regelii (which corolla lobes are much narrow and divided into 5, fused only at the base, lobes). The three species are also phylogenetically closely related to Cylindrocarpa sewerzowii (Regel) Regel, occurring mainly in southern Kazakhstan [34, 35, 43]. Having in mind that S. regelii has an inconsistent position within the Sergia-Asyneuma clade, it can not be excluded that during Pleistocene glaciations the range of these two Sergia species and C. alberti could be much broader, and similarly as in the case of other cold-adapted alpine species [6, 7], gene flow events between these taxa may have occurred in the past. Since all of the above-mentioned species (except C. alberti) were grouped by Xu & Hong [35] within one common clade, they suggest merging them (together with selected species from such genera as Asyneuma, Campanula or Phyteuma L., etc.) into the genus Phyteuma with the appropriate new circumscription. On the other hand, taking into account that the phylogenetic relationship within Campanulaceae is still unresolved [31,32,33,34,35], it is worth considering a monophyletic approach to the genus Campanula, as was recently proposed by Sennikov in Tojibaev et al. [44], who transferred all the Tian-Shanian species of Adenophora Fisch., Asyneuma, Cylindrocarpa, Phyteuma and Sergia to Campanula. It is worth mentioning that a similar monophyletic approach was applied also for other genera, such as Carex L. or Prunus L., with confused phylogenetic relations between their members [45,46,47,48,49].

Additional support for the view of the long and independent evolution of S. regelii and S. sewerzowii is the significant difference in their realised niches. Both species grow on rocky substrate, but S. regelii prefers much higher elevations. Consequently, the habitat of S. regelii is characterised by lower temperatures, especially during summer, and lower precipitation. Among the bioclimatic variables, temperature (bio7) mainly influences the distribution of S. regelii, whereas precipitation (bio17, bio19) for S. sewerzowii. All of these ecological differences are mirrored in the response of the studied endemics to future climate warming.

A remarkable increase in temperature (0.4 °C/decade) and moderate changes in precipitation, with a wetting trend in spring and a drying trend in summer, are expected in future in Central Asia [50]. Although the future forecasts for both studied species are similar, their responses to predicted climate change differ. Up to 64.4% of potential future range loss is expected for S. regelii. According to the model, the species will try to escape to higher elevations with cooler climates (statistically significant altitude shift observed for the RPC8.5 model in 2061–2080 with the lower boundary of the distribution moving upward to ca. 700 m). However, upward migration in the mountains is limited because S. regelii could reach the limits of its tolerance to such environmental conditions as precipitation, especially in the summer, which could significantly decrease its fitness. Additional stress may occur in the long-term perspective from potential competition with other species escaping from climate warming into new areas at higher elevations [51]. Finally, the species will remain only in small high-mountain refugia, and thus become critically threatened with extinction in the near future. The described consequences, with severe range contractions as a response to recent climate change, are already being observed [52, 53] and/or predicted for many alpine species [54,55,56], including chasmophytic plant species from the Mountains of Central Asia, such as Stipa zeravshanica [6] and C. lehmanniana [7].

Sergia sewerzowii, which is distributed farther north than S. regelii and at lower elevations, shows another possible scenario of alpine species’ response to predicted climate warming that includes a gradual range extension to the north with previous loss of habitats in the south 6. The level of expansion is higher when the more pessimistic scenario (RCP8.5) is applied, informing that S. sewerzowii possesses high tolerance to warmer temperatures. However, due to the island character of rocky habitats and other intrinsic (restricted dispersal ability) and extrinsic (human activity) factors, the optimistic scenario predicted for S. sewerzowii is no guarantee that the species will outlast future climate changes successfully. Although its overall potential habitat area would increase in response to climate change, the species could lose part of its habitat in the Chatkal (NE Uzbekistan), Karzhan-tau, and Ugam Mts (S Kazakhstan). Sergia regelii, in turn, will face a high risk of habitat loss in the Kuhitang Mts (E Turkmenistan), Baisun-tau, Sarykya, Chulbair, Tubere-Oland (S Uzbekistan), Nuratau, Aktau (E Uzbekistan), and Kuraminian Mts (N Tajikistan), particularly under the pessimistic scenario in 2061–2080. Moreover, such fast changes in distribution under climatic pressure may lead to the loss of genetic variation and, consequently, to decreased adaptive ability. Thus, it is important to protect these shrinking habitats of Sergia species in the Central Asian mountains, where future warming will impose severe stress.

Another important output of the study is the mapping of the areas with highly suitable habitats for S. regelii and S. sewerzowii, from which they have not been reported yet (W Turkestan range in the case of S. regelii, and the Karzhan-tau, Ugam, and Pskemsky ranges for S. sewerzowii). Since both Sergia species are narrow specialists restricted in their distribution to rocky habitats, additional field studies in the newly described areas of potentially high probability of occurrence will contribute to a better understanding of their environmental preferences and limits, which is important from a conservation point of view and will help to calibrate the model. The model-predicted range shift to the north or higher elevations under climate change can be extrapolated to other unique mountain plant species associated with rocky habitats from the Pamir-Alai and western Tian Shan, part of the Mountains of Central Asia biodiversity hotspot. A whole group of cold-adapted chasmophytic species [18, 23] are vulnerable to potential extinction. Therefore, future conservation activities should be complex and cover not one particular species, but the whole complex of alpine plant communities associated with rocky habitats.

Conclusions

The present study highlights the relationship between climate change-induced range dynamics, niche distinctiveness, and evolutionary history by considering the example of two chasmophytic endemic plant species, S. regelii and S. sewerzowii, from the Mountains of Central Asia (a biodiversity hotspot). Genome-wide SNP genotyping, together with analysis of ITS and cpDNA regions showed the polyphyletic nature of the genus Sergia and revealed that the taxa constitute a common clade with Asyneuma species-group. However, S. sewerzowii is more closely related to Campanula alberti (a species that has never been included in the genus Asyneuma nor in Sergia) than S. regelii. At the same time, we provide that both S. regelii and S. sewerzowii have long and independent evolution within different areas of Central Asian mountains. The distinctiveness of species is also reflected in the ranges of occurrence and occupied niches. Also, the future distribution model of studied endemics notably differs: S. regelii demonstrates a dramatic loss of habitats with a shift of suitable habitats to higher elevations, while the potential range of S. sewerzowii increases and shifts to the north. The character of changes in the predicted distribution depends on the occupied ecological niche and is explained by the fact that S. regelii prefers much higher elevations with lower temperatures while S. sewerzowii adapted to considerably lower elevations with higher temperatures. The results of presented future distribution modelling can be applied to the whole complex of alpine plant communities associated with rocky habitats of the studied biodiversity hotspot, that are threatened by the ongoing climate changes.

Methods

Plant material

Data on the current distribution of S. regelii and S. sewerzowii are based on (1) phytosociological records and plant specimens collected in Tajikistan during fieldwork in 2008–2019 and deposited in the KRA herbarium (all acronyms following Thiers [57]), (2) herbarium specimens deposited in the LE herbarium, (3) GBIF database and other online sources. Material for the genetic study was collected during previously mentioned fieldwork and from herbarium specimens deposited in the KRA and LE herbaria. The list of samples used in this study is presented in Supplementary Material Table S2. The research complies with relevant institutional, national, and international guidelines and legislation.

Study area

The study area is located in Central Asia and comprises the Pamir-Alai (NW Tajikistan, SE Uzbekistan, and E Turkmenistan) and western Tian Shan Mountains (S Kazakhstan), characterised by complex climatic conditions due to variable altitudes and orographic effects (Supplementary Material Fig. S6).

The territory of Pamir-Alai belongs to the temperate climatic zone. It is characterised by relatively high insolation, low percentage of cloud cover, high amplitude of annual temperatures, low humidity, and low precipitation. The climate is controlled by two pressure systems: a north-easterly inflow of cold air originating from the Siberian High during winter and a north-westerly inflow of dry air from the Azores High in summer [58]. Therefore, the climate is extremely continental with cold winters, hot and dry summers, and a maximum precipitation in spring. The average annual temperature is 13˚C − 14˚C, the average temperature of January is 0˚C − 2˚C, the average temperature of July is 26˚C − 28˚C, annual precipitation is 400 − 600 mm [59]. In addition, at the highest elevations (> ~ 2500 m a.s.l.), the climate is strongly affected by altitude displaying a more alpine climate regime [58]. In contrast to the lowlands, at alpine elevations, the average temperatures during mid-summer are lower (between 9.7 °C and 13.5 °C) and the annual precipitation is higher [60]. The limit of perpetual snow is at an altitude of 3,500–3,600 m in the western Pamir Alai Mts [61].

The climate of western Tian Shan is typical of the Central Asian mountains, where summer is hot and dry, with air temperatures up to + 30 °C in the mid-mountains and a cold, snowy winter. The average annual precipitation for the mid-mountain zone is about 700 mm, for the high mountain − about 1000 mm. Precipitation is distributed by season as follows: winter − 28%, spring − 38%, summer − 9%, autumn − 25%. Average annual air temperature is + 9.6 °C, the number of frost-free days is 185, average temperature in January is − 3.1 °C, while in July it is + 22.3 °C (Tasaryk meteorological station, 1936–2000, Kazakhstan) [62]. The relief of the western Tian Shan is characterised by very steep slopes in the high- and mid-mountain belts (down to ca. 1400 m) that are replaced by a low-mountain belt with less steep slopes, except for canyons near streams and rivers [63].

DNA extraction

The genomic DNA was extracted from dried leaf tissues. After grounding plant tissues to a fine powder by using a mixer mill (MM400 Retsch) with 3–5 mm glass beads, genomic DNA was isolated using the Genomic Mini AX Plant kit (A&A Biotechnology, Poland). The purity and concentration of extracted DNA were checked using a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, USA), whereas the quality was verified by 1% agarose gel electrophoresis. Due to the DArTseq genotyping procedure, the samples were diluted to the concentration of 70 ng/μL, whereas for PCR (ITS and cpDNA) samples were adjusted to 10 ng/μL.

ITS and cpDNA amplification and sequencing

The nuclear ribosomal Internal Transcribed Spacer region (including ITS1, 5.8S, and ITS2) was amplified using standard ITS5 forward and ITS4 reverse primers [64]. Amplification reactions were performed in a total volume of 25 µl, containing 2.5 μl of 10X DreamTaq Green Buffer (Thermo Scientific, USA), 0.3 μl of 10 mM dNTPs MIX (A&A Biotechnology, Poland), 0.3 μl of 5 U/μl DreamTaq Green DNA Polymerase (Thermo Scientific, USA), 0.5 μl of each primer (10 pmol/µl), 0.1 µl of BSA (20 mg/ml; Thermo Scientific, USA) and 1 μl of template DNA (10 ng/µl). Ultrapure H2O was added to obtain a total volume of 25 μl. Amplification of the ITS region was performed in a programmable T100 Thermal Cycler (Bio-Rad, USA) under the following thermal conditions: initial denaturation for 3 min at 94 °C followed by 25 cycles of denaturation for 1 min at 94 °C, annealing for 2 min at 50–56 °C and elongation for 2 min at 72 °C, with final elongation for 7 min at 72 °C.

For cpDNA analysis, we chose five regions used in previously published studies on the family Campanulaceae [34, 65, 66], namely petD, trnK-psbA, rpl16, psbD-trnT, and trnS-trnfM. All regions were amplified using the previously described primers (Supplementary Material Table S5).

PCR conditions in the total volume of 25 μL were: 2.5 μl of 10X DreamTaq Green Buffer (Thermo Scientific, USA), 0.3 μl of 10 mM dNTPs MIX (A&A Biotechnology, Poland), 0.3 μl of 5 U/μl DreamTaq Green DNA Polymerase (Thermo Scientific, USA), 0.2 μl of each primer (10 pmol/µl), 0.2 µl of BSA (20 mg/ml; Thermo Scientific, USA) and 1 μl of template DNA (10 ng/µl). Ultrapure H2O was added to obtain a total volume of 25 μl. Amplification of the cpDNA regions was performed in a programmable Eppendorf® Mastercycler. For the psbD-trnT, trnS-trnfM, trnK-psbA, and rpl16 regions, we slightly modified the parameters described in Ronikier et al. [66]: initial denaturation for 10 min at 94 °C, followed by 30 cycles that include: 45 s denaturation at 94 °C, 1 min of annealing at 52–60 °C (57–60 °C for psbD-trnT, 55 °C for rpl16, 58 °C for trnS-trnfM, 52–55 °C for trnK-psbA), 2 min of extension at 72 °C, and a final extension of 10 min at 72 °C. For the petD region, we used the following parameters: initial denaturation for 1.5 min at 96 °C, followed by 35 cycles of 30 s at 95 °C, 1 min at 50 °C and 1.5 min at 72 °C, and a final extension of 20 min at 72 °C [67].

In both cases, agarose gel electrophoresis was used to detect the presence or absence of the target sequence and the length of the fragment. Prior to sequencing, PCR products were purified using the Exo-BAP Kit (EURx, Poland). PCR products were sent to an external company (Genomed, Poland) for paired-end Sanger sequencing. The resulting sequences were manually verified and aligned using BioEdit ver. 7.0.5.3 [68].

Genomic library preparation, DArT sequencing and DArT data filtering

DArTseq is a genome complexity reduction method which implements the sequencing of representations on the Next Generation Sequencing (NGS) platform, optimised for each organism and application in order to select the most appropriate complexity reduction method [69,70,71]. Therefore, the method has been used successfully in many ecological, evolutionary, population genomic, phylogenetic, and phylogeographic studies [72,73,74,75,76,77]. Genome complexity reduction using restriction enzymes and high-throughput polymorphism detection [69] was performed by Diversity Arrays Technology Pty Ltd (Canberra, Australia). Based on testing several enzyme combinations for complexity reduction, Diversity Arrays Technology Pty Ltd selected the PstI-MseI method for Campanula.

This section was performed according to the previously reported procedures [69]. Briefly, all DNA samples were processed in digestion/ligation reactions as described by Kilian et al. [69], but replacing a single PstI-compatible adaptor with two different adaptors corresponding to two different restriction enzyme overhangs. Only "mixed fragments" (PstI-MseI) were effectively amplified by PCR. After PCR, equimolar amounts of amplification products from each sample of the 96-well microtiter plate were bulked and applied to c-Bot (Illumina, USA) bridge PCR, followed by sequencing on the Hiseq2500 (Illumina, USA). The proprietary DArT analytical pipelines were used to generate sequences from each lane. At the beginning poor-quality sequences (the fastq files) were filter away. As a result, ca. 2.5 mln sequences per barcode/sample were identified and used in the marker call step. For the downstream analyses, we applied co-dominant single nucleotide polymorphisms (SNPs) markers processed in R v. 4.0.3 [78] with the additional package dartR v.2.3.3 [79]. For most of the analysis, we applied the following filtration steps: (1) monomorphic loci were removed, (2) loci identified (= called) in greater than 60%, (3) loci with a scoring reproducibility of 100% were kept, (4) SNPs that shared secondaries were randomly filtered out to keep only one random sequence tag, (5) SNPs were filtered based on the criteria of a minor allele frequency (MAF) threshold of 1%.

Species delimitation

We conducted a phylogenetic analysis based on ITS and concatenated cpDNA (petD, trnK-psbA, rpl16, psbD-trnT, and trnS-trnfM) regions by using Bayesian Inference (BI) analysis performed in MrBayes 3.2.6 [80]. Bayesian Inference (BI) analysis was performed on 25 individuals of representatives of Sergia, Asyneuma, and Campanula. The best-fit substitution model suggested based on the BIC/AIC score calculated in MEGA X [81] was chosen separately for each tree. For the tree based on the concatenated chloroplast regions, we chose the GTR model with a gamma (+ Γ) distributed rates, whereas for ITS data only, we chose K2P model with a gamma (+ Γ) distributed rates. An MCMC simulation was set for 2,000,000 generations, sampling one of every 1000 generations. The first 1000 of iterations were discarded as a burn-in. We also used DArTseq derived SNPs to visualise relationship among studied species (24 individuals and 1047 SNPs) by using Neighbor Joining (NJ) method implemented in dartR package [79]. Based on the results of our studies as well as phylogenetic studies of Jones et al. [33], six species (C. cervicaria L., C. incanescens Boiss., C. glomerata L., C. rapunculoides L., C. sibirica L., C. trachelium L.) distantly related to the group of interest, were selected as an outgroup to root the BI and NJ trees. All BI and NJ trees were placed into visual form using FigTree v. 1.4.0 software [82]. A complete list of individuals used in the study and an explanation of abbreviations of samples used in phylogenetic analyses can be found in the Supplementary Material Table S2.

Data sources and variables selection for ecological niche modelling (ENM)

In order to model the potential distribution of S. regelii and S. sewerzowii, 34 variables were tested as predictors (Supplementary Material Table S6). In particular, 19 bioclimatic layers, solar radiation data, and elevation were obtained from the WorldClim version 2.1 database [83], the remaining 11 soil variables from a 5–15 cm depth were downloaded from the ISRIC database [84]. The spatial resolution of all layers was 30 arc-seconds (ca. 1 × 1 km). The layers were processed using a system with identical projection (WGS84), cell size, and extent, cropped to the region of interest (Central Asia) and converted into.asc format using the Spatial Analyst and Conversion tools in ArcGIS 10.5 (ArcGIS, 2016). After eliminating redundant presences in each 1 × 1 km grid (spatial resolution of the variables used), 61 localities of S. regelii and 19 of S. sewerzowii were used for ecological niche modelling. The Pearson correlation coefficient (r) was used to eliminate cross-correlation among variables. Variables with coefficient values less than -0.8 or more than 0.8 were treated as correlated. The selection among correlated variables was based on the variables’ contribution to the model and our own experience and knowledge about the species’ ecology and the region in order to keep ecologically meaningful predictors. This reduction of predictor variables resulted in the inclusion of 10 and 11 variables in the distribution models of S. regelii and S. sewerzowii, respectively (Table 1).

To predict the future potential distribution of studied species, we used MIROC6 [85], ACCESS-CM2 [86], and HadGEM3-GC31-LL [87] climate change models from CMIP6 (Coupled Model Intercomparison Project Phase 6) that were downloaded from the World Climate Database version 2.1 (30 arc-second spatial resolution) [83]. Representative concentration pathways (RCPs) for minimum (2.6 W/m2 of the total radioactive forcing) and maximum (8.5 W/m2) emission hypotheses over the periods 2041–2060 and 2061–2080 were selected for further projections. The RCPs reflect potential radiative forcing by 2100 compared with the pre-industrial values of + 2.6 W/m2, which is optimistic, or + 8.5 W/m2, which is more pessimistic and reflects high emission levels of greenhouse gases [88].

Ecological modelling

The MaxEnt program used in this study was downloaded from biodiversityinformatics.amnh.org/open_source/maxent/ (free of charge for scientific research activities). Among the basic settings, random seed was selected, 25% of occurrence records were used to test the model and 75% for training, replicated run type is a subsample, the number of replicates is equal to 100. Among the advanced settings, a 10-percentile training presence threshold rule was applied [89]. The remaining settings were kept as default. The Maxent results were validated using the threshold-independent area under the curve (AUC) of receiver operating characteristics (ROC). Jackknife analyses were performed to assess the importance of the variables [90]. The results of the Maxent modelling in the form of potential species distribution maps ranged in value from 0 to 1, according to the probability of species occurrence in each particular area (cell on map). To produce presence/absence maps of S. regelii and S. sewerzowii potential distribution, the Equal test sensitivity and specificity logistic threshold were selected among the different threshold values produced by Maxent as the most ecologically meaningful for the studied species. The threshold equals 0.1313 for S. regelii and 0.2263 for S. sewerzowii. The reclassify tool in ArcGIS 10.5 was used to create binary maps (suitable or unsuitable areas). To produce a consensus map of potential future distribution based on the three climate models (MIROC6, ACCESS-CM2, and HadGEM3-GC31-LL), the Cell Statistics tool in ArcGIS 10.5 with a majority voting approach was applied [91]. To assess the changes in the distribution of studied species during the twenty-first century, current and future presence/absence maps were compared. The changes in area were classified as (1) gain, (2) loss, and (3) unchanged [92].

Niche model analyses

To compare the niches of S. regelii and S. sewerzowii, 25000 random background points were extracted from a buffer zone of 20 km around known localities of occurrence, using ArcMap 10.5. Then, the values selected for the modelling variables (except elev, srh, and snd) were extracted for known localities of the studied species and for the random background points. The overlap between realised niches of S. regelii and S. sewerzowii was calculated using Schoener’s D index and I statistic, ranging from zero (no overlap) to one (complete overlap). Niche equivalency and niche similarity tests were applied to test the hypotheses of niche conservatism and niche divergence [93]. All calculations were performed using the ecospat package v 3.2 [94] in R [78]. The niche equivalency and similarity hypothesis was tested by comparing the observed niche overlap to overlaps between random niches, built from random reallocations of occurrences. The process was repeated 1000 times to generate a null distribution of niche overlap values of simulated randomly permuted niches. For both the niche equivalency and similarity tests, the argument = “greater” (overlap greater than expected by chance) to test the conservatism hypothesis, and the argument = “lower” (overlap lower than expected by chance) to test the divergence hypothesis were used [93, 94]. In the case of the niche equivalency test, the null hypothesis of niche equivalency was accepted if the observed value of D/I fell within a density of 95% of the simulated values, whereas in the case of the niche similarity test, the null hypothesis was accepted when observed overlap values were greater than 95% of the simulated values. Principal Component Analysis (PCA) was performed to visualise niche overlap. The PCA scores of the two compared niches were projected onto a grid of cells bounded by minimum and maximum PCA scores in the study areas. Additionally, niche dynamics along the gradients of all variables selected for the analysis were visualised [94].

Availability of data and materials

The SNP dataset derived from the DArTseq pipeline in the genlight format as well as alignments from ITS and cpDNA in the fasta format are available via Figshare repository, https://doi.org/https://doi.org/10.6084/m9.figshare.21707294. The ITS sequences used for comparative analyses and phylogenetic trees reconstruction have been deposited in GenBank database (Accession Numbers: OP998105-OP998118).

References

  1. Pacifici M, Foden W, Visconti P, Watson J, Butchart S, Kovacs K, et al. Assessing species vulnerability to climate change. Nat Clim Chang. 2015;5:215–24.

    Article  Google Scholar 

  2. Urban MC. Accelerating extinction risk from climate change. Science. 2015;348(6234):571–3.

    Article  CAS  PubMed  Google Scholar 

  3. Bellard C, Bertelsmeier C, Leadley P, Wilfried T, Courchamp F. Impacts of climate change on the future of biodiversity. Ecol Lett. 2012;15:365–77.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Seddon AWR, Macias-fauria M, Long PR, Benz D, Willis KJ, Seddon AWR. Sensitivity of global terrestrial ecosystems to climate variability. Nature. 2016;531(7593):229–32.

    Article  CAS  PubMed  Google Scholar 

  5. Gottfried M, Pauli H, Futschik A, Akhalkatsi M, Baran P, Rosa M, et al. Continent-wide response of mountain vegetation to climate change. Nat Clim Chang. 2012;2:111–5.

    Article  Google Scholar 

  6. Vintsek L, Klichowska E, Nowak A, Nobis M. Genetic differentiation, demographic history and distribution models of high alpine endemic vicariants outline the response of species to predicted climate changes in a Central Asian biodiversity hotspot. Ecol Indic. 2022;144:109419.

    Article  Google Scholar 

  7. Nobis M, Klichowska E, Vintsek L, Wróbel A, Nobis A, Zalewska-Gałosz J, et al. Evolutionary response of cold-adapted chasmophytic plants to Quaternary climatic oscillations in the Mountains of Central Asia (a world hotspot of biodiversity). Divers Distrib. 2023;29:1458–77.

    Article  Google Scholar 

  8. Colwell RK, Brehm G, Cardelús CL, Gilman AC, Longino JT. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science. 2008;322(5899):258–61.

    Article  CAS  PubMed  Google Scholar 

  9. Mittermeier CG, Turner WR, Larsen FW, Brooks TM, Gascon C. Global biodiversity conservation: The critical role of hotspots. In: Zachos FE, Habel JC, editors. Biodiversity hotspots: distribution and protection of priority conservation areas. Berlin: Springer-Verlag; 2011. p. 3–22.

    Chapter  Google Scholar 

  10. Ajjur SB, Al-Ghamdi SG. Global hotspots for future absolute temperature extremes from CMIP6 models. Earth Sp Sci. 2021;8(9):e2021EA001817.

    Article  Google Scholar 

  11. Fay M, Block RI, Ebinger J. Adapting to climate change in Eastern Europe and Central Asia. Washington: World Bank Publications; 2010.

  12. Noroozi J, Talebi A, Doostmohammadi M, Rumpf SB, Linder HP, Schneeweiss GM. Hotspots within a global biodiversity hotspot - areas of endemism are associated with high mountain ranges. Sci Rep. 2018;8:10345.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Perrigo A, Hoorn C, Antonelli A. Why mountains matter for biodiversity. J Biogeogr. 2020;47(2):315–25.

    Article  Google Scholar 

  14. Nobis M, Gudkova PD, Nowak A, Sawicki J, Nobis A. A synopsis of the genus Stipa (Poaceae) in Middle Asia, including a key to species identification, an annotated checklist, and phytogeographic analyses. Ann Missouri Bot Gard. 2020;105:1–63.

    Article  Google Scholar 

  15. Safarov N. First National Report on Biodiversity Conservation. Dushanbe: Convention on Biological Diversity; 2003.

  16. Piñar Fuentes JC, Cano-Ortiz A, Musarella CM, Pinto-Gomes C, Spampinato G, Cano E. Rupicolous habitats of interest for conservation in the central-southern Iberian peninsula. Plant Sociol. 2017;54(2):29–42.

    Google Scholar 

  17. Nowak A, Nowak S, Nobis M. Distribution patterns, ecological characteristic and conservation status of endemic plants of Tadzhikistan – A global hotspot of diversity. J Nat Conserv. 2011;19:296–305.

    Article  Google Scholar 

  18. Nowak A, Świerszcz S, Nowak S, Plášek V, Nobis A, Klichowska E, et al. Diversity, distribution, and classification of chasmophytic vegetation in the central Asian biodiversity hotspot: alpine belt of the Eastern Pamir-Alai and Western Tian Shan mountains. Acta Soc Bot Pol. 2022;91(1):911.

    Article  Google Scholar 

  19. Agakhanjanz O, Breckle SW. Plant diversity and endemism in high mountains of Central Asia, the Caucasus and Siberia. In: Korner C, Spehn E, editors. Mountain biodiversity – A global assessment. Boca Raton: Parthenon Publication Group; 2002. p. 117–27.

    Google Scholar 

  20. Meier H, Braun-Blanquet J. Classe des asplenietales rupestres, groupements rupicoles. Prodr Pflanzenges. 1934;2:1–47.

    Google Scholar 

  21. Mucina L. Asplenietea trichomanis. In: Grabherr G, Mucina L, editors. Die Pflanzengesellschaften Österreichs Teil II Natürliche waldfreie Vegetation. Jena, Stuttgart: Fischer Verlag; 1993. p. 241–75.

    Google Scholar 

  22. Nobis M, Nowak A, Nobis A. Stipa zeravshanica sp nov (Poaceae), an endemic species from rocky walls of the western Pamir Alai Mountains (middle Asia). Nord J Bot. 2013;31:666–75.

    Article  Google Scholar 

  23. Nowak A, Nowak S, Nobis M, Nobis A. Vegetation of rock clefts and ledges in the Pamir Alai Mts, Tajikistan (Middle Asia). Cent Eur J Biol. 2014;9(4):444–60.

    Google Scholar 

  24. Nowak A, Nowak S, Nobis M, Nobis A. Vegetation of rock crevices of the montane and colline zones in the Pamir-Alai and Tian Shan Mts in Tajikistan (Middle Asia). Plant Biosyst. 2014;148(6):1199–210.

    Article  Google Scholar 

  25. Nowak A, Nowak S, Nobis M, Nobis A. Vegetation of solid rock faces and fissures of the alpine and subnival zone in the Pamir Alai Mountains (Tajikistan, Middle Asia). Phytocoenologia. 2014;44:81–101.

    Article  Google Scholar 

  26. Clavel J, Julliard R, Devictor V. Worldwide decline of specialist species: toward a global functional homogenization? Front Ecol Environ. 2011;9:222–8.

    Article  Google Scholar 

  27. Eddie WMM, Shulkina T, Gaskin J, Haberle RC, Jansen RK. Phylogeny of Campanulaceae s. str. inferred from ITS sequences of nuclear ribosomal DNA. Ann Missouri Bot Gard. 2003;90(4):554–75.

    Article  Google Scholar 

  28. Sales F, Hedge IC. Genneric endemism in South-West Asia: an overview. Rostaniha. 2013;14(1):22–35.

    Google Scholar 

  29. Ibragimov AJ. Endem types of mountainous Central Asia Province in Kuhitang floor. Texas J Multidiscip Stud. 2021;2:9–11.

    Google Scholar 

  30. Khassanov F, Kodirov UH. Campanulaceae. In: Sennikov AN, editor. Flora of Uzbekistan. Toshkent: Navroz; 2017. p. 135–53.

    Google Scholar 

  31. Conser ME, Raubeson LA, Jansen RK. Chloroplast DNA rearrangements in campanulaceae: phylogenetic utility of highly rearranged genomes. BMC Evol Biol. 2004;4:27.

    Article  Google Scholar 

  32. Roquet C, Sáez L, Aldasoro JJ, Susanna A, Alarcón ML, Garcia-Jacas N. Natural delineation, molecular phylogeny and floral evolution in Campanula. Syst Bot. 2008;33:203–17.

    Article  Google Scholar 

  33. Lakušić D, Eddie EMM, Lulezim S, Lazarević M, Barina Z. The evolving “fate” of Asyneuma comosiforme: validation of Hayekia, a new monotypic genus of Campanulaceae from Albania Willdenowia. Willdenowia. 2019;49(1):81–93.

    Article  Google Scholar 

  34. Jones KE, Korotkova N, Petersen J, Henning T, Borsch T, Kilian N. Dynamic diversification history with rate upshifts in Holarctic bell-flowers (Campanula and allies). Cladistics. 2017;6:637–66.

    Article  Google Scholar 

  35. Xu C, Hong DY. Phylogenetic analyses confirm polyphyly of the genus Campanula (Campanulaceae s. str.), leading to a proposal for generic reappraisal. J Syst Evol. 2021;59(3):475–89.

    Article  Google Scholar 

  36. Hewitt GM. Quaternary phylogeography: the roots of hybrid zones. Genetica. 2011;139(5):617–38.

    Article  PubMed  Google Scholar 

  37. Wróbel A, Klichowska E, Nowak A, Nobis M. Alpine extremophytes in evolutionary turmoil: complex diversification patterns and demographic responses of a halophilic grass in a central Asian biodiversity hotspot. Syst Biol. 2024. https://doi.org/10.1093/sysbio/syad073.

    Article  Google Scholar 

  38. Abramowski U, Bergau A, Seebach D, Zech R, Glaser B, Sosin P, et al. Pleistocene glaciations of Central Asia: results from 10Be surface exposure ages of erratic boulders from the Pamir (Tajikistan) and the Alay-Turkestan range (Kyrgyzstan). Quat Sci Rev. 2006;25:1080–96.

    Article  Google Scholar 

  39. Kuhle M. The Glaciation of High Asia: from the last ice age to the present. Cham: Springer; 2018.

  40. Raduła M, Świerszcz S, Nobis A, Nowak A. Palaeoclimate has a major effect on the diversity of endemic species in the hotspot of mountain biodiversity in Tajikistan. Sci Rep. 2021;11:18684.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Abdullaeva MN, Adylov TA, Filimonova ZN, Kovalevskaja SS, Levizev IO, Li AD, et al. Campanulaceae. In: Adylov NA, editor., et al., Key of Middle Asia flora IX. “FAN” Uzbek SR: Tashkent; 1987. p. 351–60.

    Google Scholar 

  42. Victorov VP. Taxonomic synopsis of Campanula L. (Campanulaceae) in Russia and adjacent countries. Novosti Sist Vyssh Rast. 2002;34:197–234.

    Google Scholar 

  43. Vasileva AN, Gamaunova AP, Dmitryeva AA, Goloskokov VP, Zayceva LG, Karmysheva NH. Campanulaceae. In: Pavlov NV, editor. Flora of Kazakhstan VIII. Alma-Ata: Nauka; 1965. p. 288–302.

    Google Scholar 

  44. Tojibaev K, Sennikov A, Lazkov GA, Jang C, Choi HJ, Chang KS, et al. Checklist of vascular plants of the Tian-Shan Mountain System. Pocheon: Korea National Arboretum; 2021.

    Google Scholar 

  45. Bortiri E, Oh SH, Jiang JG, Baggett S, Granger A, Weeks C, et al. Phylogeny and systematics of Prunus (Rosaceae) as determined by sequence analysis of ITS and the chloroplast trnL-trnF spacer DNA. Syst Bot. 2001;26:797–807.

    Google Scholar 

  46. Lee S, Wen J. A phylogenetic analysis of Prunus and the Amygdaloideae (Rosaceae) using ITS sequences of nuclear ribosomal DNA. Am J Bot. 2001;88:150–60.

    Article  CAS  PubMed  Google Scholar 

  47. Shi S, Li J, Sun J, Yu J, Zhou S. Phylogeny and classification of Prunus sensu lato (Rosaceae). J Integr Plant Biol. 2013;55(11):1069–79.

    Article  CAS  PubMed  Google Scholar 

  48. Global Carex Group. Making Carex monophyletic (Cyperaceae, tribe Cariceae): a new broader circumscription. Bot J Linn Soc. 2015;179(1):1–42.

    Article  Google Scholar 

  49. Roalson EH, Jiménez-Mejías P, Hipp AL, Benítez-Benítez C, Bruederle LP, Chung KS, et al. A framework infrageneric classification of Carex (Cyperaceae) and its organizing principles. J Syst Evol. 2021;59(4):726–62.

    Article  Google Scholar 

  50. Jiang J, Zhou T, Chen X, Zhang L. Future changes in precipitation over Central Asia based on CMIP6 projections. Environ Res Lett. 2020;15:054009.

    Article  Google Scholar 

  51. Losapio G, Cerabolini BEL, Maffioletti C, Tampucci D, Gobbi M, Caccianiga M. The consequences of glacier retreat are uneven between plant species. Front Ecol Evol. 2021;8:616562.

    Article  Google Scholar 

  52. Auld J, Everingham SE, Hemmings FA, Moles AT. Alpine plants are on the move: Quantifying distribution shifts of Australian alpine plants through time. Divers Distrib. 2022;28:943–55.

    Article  Google Scholar 

  53. Raxworthy CJ, Pearson RG, Rabibisoa N, Rakotondrazafy AM, Ramanamanjato JB, Raselimanana AP, et al. Extinction vulnerability of tropical montane endemism from warming and upslope displacement: a preliminary appraisal for the highest massif in Madagascar. Glob Chang Biol. 2008;14(8):1703–20.

    Article  PubMed Central  Google Scholar 

  54. Cotto O, Wessely J, Georges D, Klonner G, Schmid M, Dullinger S, et al. A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nat Commun. 2017;8:15399.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Niskanen AKJ, Niittynen P, Aalto J, Väre H, Luoto M. Lost at high latitudes: arctic and endemic plants under threat as climate warms. Divers Distrib. 2018;25:809–21.

    Article  Google Scholar 

  56. Behroozian M, Ejtehadi H, Peterson AT, Memariani F, Mesdaghi M. Climate change influences on the potential distribution of Dianthus polylepis Bien. ex Boiss. (Caryophyllaceae), an endemic species in the Irano-Turanian region. PLoS One. 2020;15(8):e0237527.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Thiers B. New York Botanical Garden’s Virtual Herbarium. Index Herbariorum: A global directory of public herbaria and associated staff. 2022. http://sweetgum.nybg.org/science/ih. Accessed 10 Oct 2022.

  58. Seim A, Omurova G, Azisov E, Musuraliev K, Aliev K, Tulyaganov T, et al. Climate change increases drought stress of juniper trees in the mountains of Central Asia. Plos One. 2016;11(4):1–14.

    Article  Google Scholar 

  59. Williams MW, Konovalov VG. Central Asia Temperature and Precipitation Data (1879–2003). Boulder, Colorado: USA National Snow and Ice Data Center; 2008.

    Google Scholar 

  60. Latipova WA. Precipitation [Kolichestvo osadkov]. In: Narzikulov IK, Stanjukovich KW, editors. Atlas Tajikskoi SSR. Dushanbe-Moskva: Akademia Nauk Tajikskoi SSR; 1968. p. 68–9.

    Google Scholar 

  61. Narzikulov IK, Stanjukovich KW. Atlas Tajikskoi SSR. Dushanbe-Moskva: Akademia Nauk Tajikskoi SSR; 1968.

  62. Dimeyeva LA, Sitpayeva GT, Sultanova BM, Ussen K, Islamgulova AF, et al. High-altitude flora and vegetation of Kazakhstan and climate change impacts. In: Öztürk M, et al., editors. Climate change impacts on high-altitude ecosystems. Switzerland: Springer International Publishing; 2015. p. 1–48.

    Google Scholar 

  63. Zhikhareva GA, Kurmangaliev АB, Sokolov AA. The soils of Chimkent Oblast. Alma-Ata: Nauka; 1969.

    Google Scholar 

  64. White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of Fungal Ribosomal Rna genes for Phylogenetics. In: Innis AM, Gelfand DH, Sninsky JJ, White TJ, editors. PCR - Protocols. A guide to methods and applications. Academic Press; 1990. p. 315–22.

    Google Scholar 

  65. Ronikier M, Zalewska-Gałosz J. Independent evolutionary history between the Balkan ranges and more northerly mountains in Campanula alpina s.l. (Campanulaceae): genetic divergence and morphological segregation of taxa. Taxon. 2014;63(1):116–31.

    Article  Google Scholar 

  66. Ronikier M, Cieślak E, Korbecka G. High genetic differentiation in the alpine plant Campanula alpina Jacq. (Campanulaceae): evidence for glacial survival in several carpathian regions and long-term isolation between the carpathians and the alps. Mol Ecol. 2008;17(7):1763–75.

    Article  CAS  PubMed  Google Scholar 

  67. Borsch T, Korotkova N, Raus T, Lobin W, Löhne C. The petD group II intron as a species level marker: utility for tree inference and species identification in the diverse genus Campanula (Campanulaceae). Willdenowia. 2009;39(1):7–33.

    Article  Google Scholar 

  68. Hall T. BioEdit: a user-friendly biological sequence alignment editor and analysis program for windows 95/98/NT. Nucleic Acids Symp Ser. 1999;41:95–8.

    CAS  Google Scholar 

  69. Kilian A, Wenzl P, Huttner E, Carling J, Xia L, Blois H, et al. Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol Biol. 2012;888:67–89.

    Article  PubMed  Google Scholar 

  70. Sansaloni C, Petroli C, Jaccoud D, Carling J, Detering F, Grattapaglia D, et al. Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proc. 2011;5(7):P54.

    Article  PubMed Central  Google Scholar 

  71. Cruz VMV, Kilian A, Dierig DA. Development of DArT marker platforms and genetic diversity assessment of the U.S. collection of the new oilseed crop lesquerella and related species. PLoS One. 2013;8(5):64062.

    Article  Google Scholar 

  72. Baiakhmetov E, Nowak A, Gudkova PD, Nobis M. Morphological and genome-wide evidence for natural hybridisation within the genus Stipa (Poaceae). Sci Rep. 2020;10(1):1–14.

    Article  Google Scholar 

  73. Melville J, Haines ML, Boysen K, Hodkinson L, Kilian A, Smith Date KL, et al. Identifying hybridization and admixture using SNPs: application of the DArTseq platform in phylogeographic research on vertebrates. R Soc Open Sci. 2017;4(7):161061.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Edet OU, Gorafi YSA, Nasuda S, Tsujimoto H. DArTseq-based analysis of genomic relationships among species of tribe Triticeae. Sci Rep. 2018;8:16397.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Smýkal P, Trnčný O, Brus J, Hanáček P, Rathore A, Das RR, et al. Genetic structure of wild pea (Pisum sativum subsp. elatius) populations in the northern part of the Fertile Crescent reflects moderate cross-pollination and strong effect of geographic but not environmental distance. Plos One. 2018;13(3):e0194056.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Sinaga P, Klichowska E, Nowak A, Nobis M. Hybridization and introgression events in cooccurring populations of closely related grasses (Poaceae: Stipa) in high mountain steppes of Central Asia. Plos One. 2024;19(2):e0298760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Ketema S, Tesfaye B, Keneni G, Fenta BA, Assefa E, Greliche N, et al. DArTSeq SNP-based markers revealed high genetic diversity and structured population in Ethiopian cowpea [Vigna unguiculata (L.) Walp] germplasms. Plos One. 2020;15(10):e0239122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2020. https://www.r-project.org. Accessed 1 Oct 2022.

  79. Gruber B, Unmack PJ, Berry OF, Georges A. dartr: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol Ecol Resour. 2018;18(3):691–9.

    Article  PubMed  Google Scholar 

  80. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: efficient bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61(3):539–42.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35(6):1547–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Rambaut A. Figtree v1.4.4. 2018. http://tree.bio.ed.ac.uk/software/figtree. Accessed 1 Oct 2022.

  83. Fick SE, Hijmans RJ. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 2017;37(12):4302–15.

    Article  Google Scholar 

  84. Batjes NH, Ribeiro E, van Oostrum A. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth Syst Sci Data. 2020;12:299–320.

    Article  Google Scholar 

  85. Shiogama H, Abe M, Tatebe H. MIROC MIROC6 model output prepared for CMIP6 ScenarioMIP. Earth System Grid Federation; 2019. https://doi.org/10.22033/ESGF/CMIP6.577110.22033/ESGF/CMIP6.5771. Accessed 1 Oct 2022.

  86. Dix M, Bi D, Dobrohotoff P, Fiedler R, Harman I, Law R, et al. CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP historical. Earth System Grid Federation; 2019. https://doi.org/10.22033/ESGF/CMIP6.4271. Accessed 1 Oct 2022.

  87. Roberts M. MOHC HadGEM3-GC31-LL model output prepared for CMIP6 HighResMIP. Earth System Grid Federation; 2017. https://doi.org/10.22033/ESGF/CMIP6.1901. Accessed 1 Oct 2022.

  88. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol. 2005;25:1965–78.

    Article  Google Scholar 

  89. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Modell. 2006;190:231–59.

    Article  Google Scholar 

  90. Pearce J, Ferrier S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Modell. 2000;133:225–45.

    Article  Google Scholar 

  91. Wang TL, Wang GY, Innes J, Nitschke C, Kang HJ. Climatic niche models and their consensus projections for future climates for four major forest tree species in the Asia-Pacific region. For Ecol Manage. 2016;360:357–66.

    Article  Google Scholar 

  92. Zhang K, Yao L, Meng J, Tao J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci Total Environ. 2018;634:1326–34.

    Article  CAS  PubMed  Google Scholar 

  93. Warren DL, Glor RE, Turelli M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution. 2008;62(11):2868–83.

    Article  PubMed  Google Scholar 

  94. Di Cola V, Broennimann O, Petitpierre B, Breiner FT, D’Amen M, Randin C, et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography. 2017;40(6):774–87.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to express our gratitude to the curators of KRA, TAD, AA, FRU and LE herbaria for their kind hospitality during our visits and for making specimens of Sergia available for research. We also thank Prof. Aleksandr Ebel (Tomsk State University) for sharing us photos of Sergia sewerzowii taken in southern Kazakhstan and permission for their publication. We would like to thank the anonymous Reviewers and the Editor for their constructive remarks to the previous version of the manuscript.

Funding

The research was funded by the National Science Centre (Poland) under the project 2018/29/B/NZ9/00313. The open-access publication of this article was funded by the programme “Excellence Initiative – Research University” at the Faculty of Biology of the Jagiellonian University in Kraków, Poland.

Author information

Authors and Affiliations

  1. Institute of Botany, Faculty of Biology, Jagiellonian University, Gronostajowa 3, Kraków, 30–387, Poland

    Lizaveta Vintsek, Ewelina Klichowska & Marcin Nobis

  2. Polish Academy of Sciences Botanical Garden, Center for Biological Diversity Conservation in Powsin, Prawdziwka 2, Warsaw, 02-973, Poland

    Arkadiusz Nowak

  3. Botanical Garden of the Wrocław University, Sienkiewicza 23, 50-335, Wrocław, Poland

    Arkadiusz Nowak

Authors
  1. Lizaveta Vintsek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ewelina Klichowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arkadiusz Nowak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marcin Nobis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.N. idea of studies and project coordination, M.N., E K. and A.N. collected plant material in the field, L.V. revised the herbarium materials, performed ENM analyses, analysed and visualised the results, E.K. performed molecular analyses with input of M.N. and visualised the results; L.V, E.K., M.N. interpreted the results and wrote the original version of the manuscript. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Lizaveta Vintsek or Marcin Nobis.

Ethics declarations

Ethics approval and consent to participate

The research complies with relevant institutional, national, and international guidelines and legislation. Plants collected in wild were formally identified by the collection authors and deposited in the herbarium (voucher numbers and further details on the specimens together with the name of the person who collected, identified and deposited them in the herbarium are given in Supplementary Table S2).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vintsek, L., Klichowska, E., Nowak, A. et al. Insight into the phylogeny and responses of species from the genus Sergia (Campanulaceae) to the climate changes predicted for the Mountains of Central Asia (a world biodiversity hotspot). BMC Plant Biol 24, 228 (2024). https://doi.org/10.1186/s12870-024-04938-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-024-04938-4

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us