Co-occurring Luzula species (Juncaceae) of different ploidies in alpine grasslands of the Eastern Alps exhibit negligible ecological differentiation at small geographic scale

Co-occurring Luzula species (Juncaceae) of different ploidies in alpine grasslands of the Eastern Alps exhibit negligible ecological differentiation at small geographic scale | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Co-occurring Luzula species (Juncaceae) of different ploidies in alpine grasslands of the Eastern Alps exhibit negligible ecological differentiation at small geographic scale Jonas Geurden, Valentin Heimer, Božo Frajman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5716596/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 May, 2025 Read the published version in Alpine Botany → Version 1 posted 4 You are reading this latest preprint version Abstract Luzula sect. Luzula is a taxonomically challenging group of angiosperms, whose evolutionary history has been shaped by polyploidy and agmatoploidy (fragmentation of holocentric chromosomes). Several species with different chromosome sizes and numbers, ranging from diploids to hexaploids, occur above timberline in the Eastern Alps. Species of different ploidies frequently co-occur in the same habitats, but the extent of ecological divergence and niche partitioning among them remains elusive, partly due to their high morphological similarity impeding reliable identification. Here, we focused on three mixed-ploidy sites in the Eastern Alps, where morphologically similar alpine species L. exspectata (diploid), L. alpina (tetraploid) and L. multiflora (its hexaploid populations) co-occur. We inferred there ploidy via flow cytometry and characterised their small-scale ecological differentiation using Landolt indicator values of accompanying species that revealed limited ecological divergence between co-occurring ploidies. While diploid L. exspectata is associated with slightly more basophilic microsite conditions, as it mostly occurs over limestone, no such differentiation was observed between tetraploid L. alpina and hexaploid L. multiflora . Our results indicate that small-scale co-occurrence of different cytotypes within Luzula sect. Luzula in alpine habitats is accompanied by only a slight niche partitioning, whereas there were significant differences in ecological parameters among the sites. These findings emphasise the influence of geography and geology on ecological microsite conditions and suggest that local niche divergence between ploidies is negligible compared to site-specific effects. Different ploidies thus likely have more divergent ecology at a distribution-wide scale than at a local scale Alpine grasslands ecological indicators microsite ecology niche partitioning polyploidy small-scale vegetation surveys Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction With more than 4,000 vascular plant species, the Alps are one of the hotspots of European biodiversity, harbouring many endemics (Merxmüller 1954 ; Pawłowski 1970 ; Tribsch and Schönswetter 2003 ; Aeschimann et al. 2004 , 2011 ; Kadereit 2017 ). One of the processes that contributed significantly to diversification of the Alpine biota is polyploidisation (Burnier et al. 2009 ; Guggisberg et al. 2009 ; Casazza et al. 2012 ; Flatscher et al. 2015 ; Skubic et al. 2023 ), which is considered one of the most important evolutionary mechanisms in vascular plants (Soltis et al. 2009 ; Wood et al. 2009 ; Madlung 2013 ; Weiss-Schneeweiss et al. 2013 ). Populations with multiple ploidy levels have been detected in several species; they often can co-occur in the same habitats, and it has been evidenced that this co-existence is possible due to differences in niche optima (Kolář et al. 2017 ). The same principles that underlie co-occurrence of closely related species, such as sympatric speciation (Grant 1981 ) or secondary contact after two allopatric species geographically reunite (Hvala and Wood 2012 ), also apply for the formation of mixed-ploidy populations within single species. Within-population cytotype diversity can either arise in situ through primary contact, or, alternatively, as the result of secondary contact following allopatric divergence of geographically isolated cytotypes (Kolář et al. 2017 ). Even though secondary contact is likely the prevalent mode of formation of mixed-ploidy populations, primary contact is not uncommon and there is evidence for both mechanisms operating in different parts of a species’ distribution range (Kolář et al. 2017 ). According to the competitive exclusion principle (Hardin 1960 ), species or cytotypes can coexist in sympatry only if they exhibit sufficient niche differentiation to reduce competition (Tilman 1982 ; Silvertown and Charlesworth 2007 ). While niche shift seems to be the predominant pattern in the establishment of new polyploids (Muñoz Pajares et al. 2017 ; López-Jurado et al. 2019 ; Castro et al. 2020 ), alternative processes such as niche conservatism and contraction may also be involved (Glennon et al. 2014 ; Castro et al. 2019 ). Generally, polyploids, especially allopolyploids (polyploids resulting from interspecific hybridization), have been suggested to have broader ecological niches than their lower-ploid ancestors due to the combination of diverged genomes, and empirical data largely support this hypothesis (McIntyre 2012 ; Kirchheimer et al. 2016 ; Zielińska et al. 2024 ). On the other hand, polyploids may not directly expand their ecological niches compared to diploids but increase the diversity of occupied habitats in some genera, simply by increasing taxonomic diversity (Petit and Thompson 1999 ). In a similar line, polyploids have been shown to often exhibit at least partial geographic sympatry and niche overlap with their progenitors (Blaine Marchant et al. 2016 ). To describe the ecology and in particular ecological niche preferences of plant species, different indicators that describe microsite characteristics have been commonly used (e.g. Čertner et al. 2015 ; Bertel et al. 2018 ; Büren and Hiltbrunner 2022 ; Ramírez et al. 2024 ). Microsite characteristics namely influence recruitment in plants, including germination as well as seedling survival and growth and thus importantly affect their small-scale distribution (Harper et al. 1961 ; Eriksson and Ehrlén 1992 , 2008 ; Elmarsdottir et al. 2003 ). Knowledge of microsite characteristics also has practical applications, for instance in nature conservation, where consideration of species-specific microsite preferences has been shown to considerably increase the success of realized conservation measures (Dunwiddie and Martin 2016 ). Landolt indicator values (Landolt et al. 2010 ) were specifically developed for the Alpine flora and have been successfully used to characterize ecological niches of Alpine species (Scherrer et al. 2019; Ivanova and Zolotova 2023 ). Mean Landolt indicator values were also effectively applied in di-polyploid systems to show ecological niche expansion of polyploid populations within a single species (Kirchheimer et al. 2016 ) and to clarify ecological preferences of species within polyploid complexes (Hülber et al. 2015 ; Sonnleitner et al. 2016 ; Silbernagl and Schönswetter, 2019 ). In addition to indicator values, strategy types, implemented into ecological research with the universal adaptive strategy theory, can be used to characterize plant communities and therefore indirectly infer ecological conditions at small scales (Grime 1979 ). One of the genera with high species diversity in the Alpine grasslands is Luzula (Juncaceae; Aeschimann et al. 2004 , 2011 ). Especially within Luzula sect. Luzula that encompasses 57 species worldwide, polyploidisation has been an important diversification mechanism (Kirschner 1992 , 1993 ; Záveská Drábková 2013 ), also in the Alps (Bačič et al. 2019 ). In addition to true polyploidy in which genome size (GS) of polyploids is expected to increase in direct proportion with ploidy level (Renny-Byfield et al. 2013 ), agmatoploidy – fragmentation of chromosomes without increase in GS – represents another important evolutionary mechanism in this section of Luzula (Malheiros-Gardé and Gardé 1950 ; Bačič et al. 2007a , 2007b ; Guerra 2016 ). Due to agmatoploidy, different species of Luzula have chromosomes of three different sizes. The largest, full-size chromosomes are designated as AL-type, the intermediate, half-size chromosomes as BL-type, and the smallest, quarter-size chromosomes as CL-type (Nordenskiöld 1951 , Bozek et al. 2012 ). In the (sub)alpine grasslands of the Eastern Alps four species of Luzula sect. Luzula are frequent: Luzula alpina Hoppe, L. exspectata Bačič et Jogan, L. multiflora (Ehrh.) Lej. and L. sudetica (Willd.) Schult. (Bačič et al. 2019 ). Whereas the latter, occurring in wet grasslands and bogs, is ecologically, morphologically and genetically most divergent and thus relatively easy to recognize, the other three species are morphologically extremely similar and often co-occur in the same localities (Bačič et al. 2019 ; Pungaršek et al. 2023 ). Luzula exspectata is karyologically divergent, as it is diploid with 24 fragmented BL-type chromosomes (2 n = 2 x = 24 BL). On the other hand, L. multiflora is polyploid with complete, AL-type chromosomes: it includes tetraploid (2 n = 4 x = 24 AL) and hexaploid (2 n = 6 x = 36 AL) individuals. Finally, L. alpina is a partial agmatoploid with 12 full-sized and 24 fragmented chromosomes (2 n = 4 x = 12 AL + 24 BL; Barlow and Nevin 1976 ; Kirschner et al. 1988 ; Bačič et al. 2007a , 2019 ). Whereas the hexaploid L. multiflora appears to have a single origin, L. alpina and tetraploid L. multiflora were intermingled within the same clusters inferred by amplified fragment length polymorphisms, suggesting that L. alpina might have originated multiple times from L. multiflora by partial fragmentation of chromosomes (Pungaršek et al. 2023 ). Alternatively, L. exspectata might have been involved in the origin of L. alpina , explaining its partial-agmatoploid karyotype (Bačič et al. 2019 ). The latter scenario appears to be supported also by restriction-site associated DNA sequencing (RADseq; Heimer et al. unpublished), which indicates that the tetraploid L. multiflora occurs only in the easternmost Eastern Alps, whereas L. alpina is parapatric and thrives in the central and western Eastern Alps. Their close relatedness and shared habitats are thus likely the reasons for the high morphological similarity among L. alpina , L. exspectata and L. multiflora ; especially L. alpina and L. multiflora are mostly impossible to distinguish morphologically (Bačič et al. 2019 ). Ecologically, L. alpina and L. multiflora are more acidophilic, growing on siliceous bedrock, whereas L. exspectata is slightly more basophilic, occurring mostly over calcareous substrates. Whereas L. multiflora , especially its hexaploid cytotype, occupies the widest range of habitats from lowland bogs to alpine meadows, L. alpina and L. exspectata mainly occur at higher elevations above timberline (Bačič et al. 2019 ). Given the common co-occurrence of the three closely related species of different ploidies in Alpine grasslands, our aim was to characterise their microsite characteristics and investigate whether their ecological niches differ at a local scale in three different localities in the western parts of the Eastern Alps. In particular, we were interested in whether there is any niche differentiation among the three ploidies, namely diploid L. exspectata , tetraploid L. alpina and hexaploid L. multiflora . To achieve this aim, we (1) sampled 100 individuals of the three species per plot at three different locations in the Eastern Alps, (2) estimated their ploidy levels via relative genome size estimation with flow cytometry, and (3) performed statistical analyses of small-scale vegetation surveys of accompanying vascular plants to characterise their niches via Landolt indicator values and additional ecological parameters recorded at plot-scale. Material and methods Sampling localities and identification of Luzula samples Three localities with co-occurrence of at least two Luzula species of different ploidy level in (sub)alpine grasslands in the Eastern Alps were selected based on preliminary investigations: 1) Austria, Vorarlberg, Rätikon, between Geißspitze and Lindauer Hütte, 1,951–1,968 m a.s.l.; 2) Austria, North Tyrol, Ötztaler Alpen, between Wildspitze and Vent, 2,310–2,333 m a.s.l.; 3) Italy, South Tyrol, Dolomites, between Langkofel and the Passo di Sella, 2,237–2,260 m a.s.l. A map indicating the localities within the European Alps (Fig. 1a) was produced in R v.4.3.3 (R Core Team 2024) with the use of GTOPO30 (EROS Center 2018) and the spatial distribution of sampled individuals within each locality (Online Resource 1) was visualised in QGIS v.3.36.0 (QGIS.org 2024) with the use of different WMS-services: for Vorarlberg “VoGIS” (Land Vorarlberg 2022) for North Tyrol “tiris” (Land Tirol 2024), and for South Tyrol “GeoKatalog” (Autonome Provinz Bozen - Südtirol 2024). At each locality we sampled 100 individuals of Luzula sect. Luzula within plots of approximately 50 × 50 m. Leaf tissue was collected for each individual and immediately dried in silica gel for relative genome size (RGS) estimation. In addition, herbarium vouchers were produced for all samples and later deposited at IB (Herbarium of the Department of Botany of the University of Innsbruck). The identification of the sampled individuals was carried out as a combination of field observation regarding morphology and ploidy-level estimation in the lab. In this way, diploid L. exspectata and hexaploid L. multiflora could be reliably identified and distinguished from all tetraploid individuals. Tetraploid L. alpina can neither be distinguished from tetraploid L. multiflora by RGS nor reliably identified based on morphology (Bačič et al. 2019; Pungaršek et al. 2023). However, since our RADseq data and chromosome counts (Heimer et al., unpublished) indicated that most tetraploid populations in the western parts of the Eastern Alps, where our three study sites are located, correspond to L. alpina , we refer to our tetraploid samples as L. alpina hereafter. Vegetation surveys and collection of plant material We performed vegetation surveys on a microsite scale to characterise ecological preferences of each sampled individual of Luzula . Niche comparisons using Landolt indicator values are commonly achieved through calculation of mean indicator values for defined areas; when describing the niche of single individuals, usually Landolt indicator values are computed from for accompanying plant species growing in near vicinity of the studied individual (Landolt et al. 2010). For each Luzula individual that we sampled, a vegetation relevé was therefore performed in a circle of 40 cm diameter with the sampled specimen in the centre. Within this circle, all accompanying vascular plant species, and their estimated cover were recorded. Plant species were identified using regional floras (Aeschimann et al. 2004; Fischer et al. 2008; Eggenberg and Möhl 2020), and we applied the nomenclature of Landolt et al. (2010). Additionally, we estimated slope and aspect as well as coverage of rock, soil, cryptogams, dead organic material and overall cover of vascular plants. Coverage was recorded in exact percentages for values equal to and above 5%, whereas those below 5% were grouped into a single class. For the statistical analyses, exact coverage values were transformed into classes with ranges of 5 to 10% (Online Resource 2). The slope aspect was transformed into “Northness” and “Eastness”, using the absolute distance in degree to either north or east, thus following Sonnleitner et al. (2010). Additional explanatory variables were computed from the accompanying plant species using R. Those encompassed “Richness” (= total species count) as well as mean Landolt indicator values (climate indicators: T = temperature, K = continentality, L = light availability; soil indicators: F = soil moisture, W = alternating wetness, R = reaction, N = nutrient availability, H = humus content, D = soil aeration; Landolt et al. 2010) and percentage values of Grime’s (1979) strategy types (c = competitors, r = ruderals, s = stress tolerators; Landolt et al. 2010). Mean Landolt indicator values and strategy types were not weighted by coverage because this would fail to account for species that never reach high coverage but have a high specificity. It has been shown that weighing abundance works well for both extremes of an indicator but performs less efficiently in the medium range (Tölgyesi et al. 2014). Indicator values were weighted by specificity, which was achieved by giving double the weight to species with narrow requirements (Landolt et al. 2010). To avoid bias, registered accompanying species belonging to Luzula sect. Luzula were not included in the computation of indicator values and strategy types. Estimation of relative genome size (RGS) and ploidy We estimated the RGS of each sampled Luzula individual via flow cytometry of 4’,6-diamidino-2-phenylindole (DAPI)-stained nuclei following Suda and Trávníček (2006). We used Bellis perennis as a reference standard (2C = 3.38 pg DNA; Schönswetter et al. 2007). Relative fluorescence intensity of 3,000 particles was recorded using a CyFlow Space flow cytometer (Sysmec Partec GmbH, Münster, Germany). Evaluation of the results was done with FloMax software (Sysmec Partec GmbH, Münster, Germany). To assess the reliability of the recorded RGS values, we calculated coefficients of variation (CV) for samples and standards and in general accepted measurements with CV < 5%. Analyses of ecological data Statistical analyses were performed using R v.4.3.3 (R Core Team 2024) and results were visualised using the package ggplot2 (Wickham 2016). The environmental predictors that most strongly differentiated between ploidy levels were identified by analysing differences in variable means for both the entire data and for each locality separately. Within each data set, a Kolmogorov-Smirnov-test was used to test every explanatory variable for normal distribution. We then assessed differences in variable means with either an unpaired two-sample t-test or an unpaired two-samples Wilcoxon-test. To compare ecological niche breadths among ploidies, we tested for differences in standard deviations (SD) of all mean Landolt indicator values per ploidy at each locality using another unpaired two-sample t-test after confirming normal distribution. As there was only a single hexaploid individual recorded at locality 1 and a single diploid individual at locality 2 (see Results), they were excluded from within-locality analyses. Correlations between all possible combinations of explanatory variables within different data sets were assessed with Pearson’s correlation coefficients computed for the entire data, per ploidy level (3 subsets) and per locality (3 subsets). The consistency of significant correlations between each pair of data subsets (per ploidy level and per locality) was analysed by calculating the ratio of variables that were significantly correlated ( p < 0.05) in both. Small-scale ecological differentiation among ploidy levels was assessed by multivariate analyses: Detrended Correspondence Analysis (DCA), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). All analyses were performed to investigate the differences in ecological preferences among the three ploidies within the entire dataset as well as between different ploidies at each single locality. In addition, we performed LDA to investigate the differentiation among the three localities as well as in pairwise comparisons between all ploidy-pairs across the complete dataset (Online Resource 3). Variables that did not differ significantly between the groups were excluded from the respective multivariate analysis (Online Resource 3). At locality 2, the variables Northness, Eastness and Inclination were invariant, so they were excluded from tests on this locality. When those variables were included in tests across all localities (LDA, PCA), this invariance led to a strong divergence of the individuals from locality 2 in the ordination. LDA was performed with the R-package “MASS” (Venables and Ripley 2002). We used either ploidy level or locality as response variables. For each analysis, we used 80% of the data for training and the remaining 20% as test samples. As a basis for the assessment of the LDA, accuracy, i.e. percentage of observations in the test samples which the model predicted correctly, was computed. As splitting the data is pseudo-random and can therefore create differing outcomes, LDA and computation of accuracy was repeated 10,000 times with different seeds using the “set.seed( a )”-function of R with a being every integer between 1 and 10,000. We also tested a different combination of pseudo-randomly generated values (e.g. 10,000 values between 1 and 1,000,000) to ensure consistency of results. To maximize differences between ploidy levels, only the most differentiating explanatory variables that differed significantly ( p < 0.05) between ploidy levels, were included. In analyses that included more than two classes (e.g. ploidy levels of 2 x , 4 x and 6 x ), variables were included if they were significantly different between ploidies in at least one comparison. Lastly, LDA performance was evaluated via mean accuracy, standard deviation (SD) of all accuracies and accuracy range (difference of minimum and maximum accuracy). DCA was performed using the R-package vegan (Oksanen et al. 2022), unless otherwise specified. To describe the resulting model, species scores and fitted explanatory variables were used. Every explanatory variable was fitted, but only those that had a significant goodness of fit were used in the visualisation of the DCA. Euclidean distance of the first two DCA components were used for hierarchical clustering of the scores of the accompanying species using the ward.D2 method (Ward 1963; Murtagh and Legendre 2014). To find the most suitable number of clusters, silhouette width means (Rousseeuw 1987) were computed using the R-package cluster (Maechler et al. 2023). When a specific clustering was chosen, scores within every cluster were averaged. This resulted in a mean cluster score for every cluster that was then used in the visualisation of the model. For each cluster, the two species with a species score closest to the mean cluster score were used as representatives. PCA was performed using the base package stats in R (R Core Team 2024) and evaluated with the package factoextra (Kassambara and Mundt 2020). As for LDA, only explanatory variables, which differed significantly among the tested groups ( p < 0.05), were included. Additionally, PCA site scores (PCA scores of the first two principal components; scores of the individuals) of each data set within single localities were clustered via k-means clustering (MacQueen 1967). To find the most suitable number of clusters, silhouette (Rousseeuw 1987) and gap statistics (Tibshirani et al. 2001) were used. For clearer visualisation, explanatory variables which contributed less than 5% to the first two axes of the PCA were excluded from being displayed in the PCA. Results Relative genome size and ploidy distribution We could not produce reliable RGS measurements for two individuals, which were excluded from further analyses. Also excluded were two individuals at locality 2 that had an RGS of 0.65 and 0.66, suggesting pentaploidy. Of the remaining 296 individuals, 27 were diploids ( L. exspectata ) with an RGS of 0.25–0.27, 224 tetraploids ( L. alpina ) with an RGS of 0.44–0.56, and 45 hexaploids ( L. multiflora ) with an RGS of 0.70–0.82 (Online Resource 1). At locality 1, 86 individuals were tetraploid, 13 diploid and 1 hexaploid, at locality 2, 52 were tetraploid, 44 hexaploid and 1 diploid, and at locality 3, 85 were tetraploid and 14 diploid. The spatial distribution of ploidies within each locality is shown in Fig. 1b–d. Ecological microsite differentiation We registered 105 accompanying vascular plant species at locality 1, 74 species at locality 2, and 84 at locality 3 (Online Resource 4), excluding accompanying species of Luzula sect. Luzula . The following pairs of explanatory variables were significantly correlated ( p < 0.05) based on the significance test for Pearson correlation coefficient across all tested datasets: T-D, K-F, L-D, F-N, R-s, D-s, c-r and c-s. Many other variables were significantly correlated throughout some of the tested datasets, especially the indicator values (Online Resource 5). Regarding consistency of significant correlations between individuals of two different ploidy levels, 54.76% of all variables were consistently correlated between diploids and tetraploids (2 x , 4 x ), 66.19% between diploids and hexaploids (2 x , 6 x ), and 59.05% between tetraploids and hexaploids (4 x , 6 x ). For different localities, 60.00% of variables were consistently correlated between locality 1 and 2, 70.00% between locality 1 and 3, and 64.29% between locality 2 and 3. Results of the t-test/ Wilcoxon-test (Online Resource 6) showed that many variable means were significantly different between co-occurring ploidy levels at each locality. Within localities 1 and 2 there were four (RockCover, c, L, T) and five (RockCover, CryptogamCover, D, H, L) significantly differentiating variables for individuals with different ploidy levels, respectively. At locality 3, all but one (H) mean Landolt indicator values and seven additional values (Eastness, Inclination, OrganicCover, PlantCover, Richness, c, r) were significantly different (Fig. 2). At each locality SDs were not significantly different between the co-occurring ploidies, indicating no differences in niche breadths (locality 1: p = 0.41; locality 2: p = 0.61; locality 3: p = 0.88) and higher-ploidies only had a broader indicator value range due to higher sample numbers. In the LDA, the highest mean accuracy was achieved for the data sets “Entire data (Locality)” and “Ploidy (2 x , 6 x )” with mean accuracies of 95.16 and 96.09%, respectively (Fig. 3). LDA for diploids versus tetraploids (“Ploidy (2 x , 4 x )“) had a mean accuracy of 88.66%. Tests on locality 1 and 3 (2 x versus 4 x ) had mean accuracies of 88.32 and 89.46% while the “Entire data (Ploidy)” yielded a mean accuracy of 77.17%. Since all but one hexaploids occurred at locality 2, where only a single diploid was found, we additionally tested whether the high accuracy of “Ploidy (2 x , 6 x )” resulted from an additive effect of different ploidy level and locality. Therefore, we performed an additional LDA on all diploids from all localities and only tetraploids from locality 2. Included variables were the same as in “Ploidy (2 x , 4 x )”. This achieved a mean accuracy of 96.72%, which is in line with “Ploidy (2 x , 6 x )”. LDA performed worst at locality 2 (“Locality 2 (4 x , 6 x )”) with a mean accuracy of 59.02%, a SD of 9.95%. Minimum and maximum LDA accuracy of every other tested dataset differed by less than 36.84%, resulting in a SD of 2.61–4.60%, except for “Locality 3 (2x, 4x)” which had a SD of 6.61%, a SD of 2.61–4.60%. With ploidy as response variable, LDA largely failed to separate individuals of different ploidies into well-defined clusters; instead, they were strongly intermixed along the first two LDA axes (Fig. 4a). When locality was used as response variable, LDA performed considerably better, separating individuals according to sampling locality (Fig. 4b). Localities were mainly differentiated by Richness (locality 1), L and T (locality 2) as well as K, R and F (locality 3; Fig 4b). This differentiation was not visible in the LDA with ploidy as response variable but in both cases, diploids were positively correlated with a higher reaction value R. When DCA was performed for the entire data, individuals were clearly grouped by locality (Fig. 5a). Differentiation between di- and tetraploids was most pronounced at locality 3 (Fig. 5d). The variables that contributed most to this differentiation were OrganicCover, D, K, L and R. On the other hand, individuals of different ploidies were intermixed at locality 1 (Fig. 5b) and 2 (Fig. 5c). Hierarchical clustering of the scores of accompanying vascular plant species resulted in three to four species groups (SG) within each of the four analyses (Fig. 5; orange arrows show the mean group score of each SG). The SGs did not have very high support, i.e. the overall mean silhouette width was between 0.4 and 0.6 (Online Resource 7). Alternative numbers of SGs only had slightly lower support. For the entire data, SGs (Online Resource 8) clearly corresponded to different localities (Fig. 5a): SG 1 corresponded to locality 3, SG 2 to locality 1 and SGs 3 and 4 to locality 2. As an example, the four to SG 1 belonging species Antennaria carpatica , Pedicularis elongata , Pedicularis verticillata and Ranunculus oreophilus were exclusively recorded at the corresponding locality 3). SG 4 contained only four species ( Arctostaphylos uva-ursi , Silene rupestris , Trifolium alpinum and Vaccinium vitis-idaea ) and corresponded to a few tetraploid and a single hexaploid Luzula individual at locality 2, that were separated from the rest along the second DCA axis. At locality 1 (Fig. 5b), SGs did not correspond to different ploidies and differentiated individuals along both DCA axes. Even though different ploidies were not separated by DCA at locality 2 (Fig. 5c), there was a clear differentiation along the first axis via SG 1 (e.g., Achillea millefolium aggr., Alchemilla vulgaris aggr., Arctostaphylos uva-ursi , Carex sempervirens , Potentilla crantzii , Silene rupestris , Trifolium alpinum and Vaccinium vitis-idaea ), reflecting the differentiation of few individuals similar to SG 4 for the entire data. SGs 2, 3 and 4 differentiated individuals along the second axis. At locality 3 (Fig. 5d), diploid individuals ( L. exspectata ) corresponded positively to species of SG 1 (e.g., Achillea clavenae , Agrostis rupestris , Antennaria carpatica , Anthyllis alpestris , Biscutella laevigata , Carex capillaris , Dryas octopetala , Elyna myosuroides , Leontopodium alpinum , Pulsatilla vernalis , Silene acaulis and Vaccinium gaultherioides ) and to very small extent to SG 2, but negatively to species of SG 3 (e.g., Anthoxanthum alpinum , Arnica montana , Carlina acaulis, Festuca nigrescens , Gentiana pilosa , Geum montanum , Nardus stricta , Phleum rhaeticum , Poa alpina , Potentilla aurea and Trollius europaeus ) and SG 4 (e.g., Anemone baldensis , Aster bellidiastrum , Bartsia alpina , Carduus defloratus , Helianthemum alpestre , Hieracium murorum aggr., Homogyne alpina , Pedicularis verticillata , Salix breviserrata , Salix retusa , Sesleria caerulea , Tofieldia calyculata and Vaccinium vitis-idaea ). In the PCA of the entire dataset, individuals were again mostly separated according to their sampling locality (Fig. 6a). Individuals from localities 1 and 3 were mainly separated along the first component, whereas those from locality 2 were separated from them along the second component. There was no visible separation of different ploidies. Within single localities, a slight differentiation trend of diploids and tetraploids at locality 1 (Fig. 6b) and 3 (Fig. 6d) was visible, whereas there was no clear differentiation at locality 2 (Fig. 6c). The variables that mainly contributed to differentiation trend of diploids from tetraploids at locality 1 were L and to a lesser extent c and T, and at locality 3 OrganicCover, D, K, L, R (positively correlated with the occurrence of diploids) as well as T, N and F (negatively correlated with the occurrence of diploids). Silhouette and gap statistics based on clustering of PCA site scores both suggested two clusters for each locality (large and small symbols in Fig. 6b–d), which were clearly correlated with the first PCA-axis. Ten out of thirteen diploids at locality 1 and twelve out of thirteen at locality 3 were classified into the same group, however, both groups also contained many tetraploids. The clustering at locality 2 was more random and did not correspond to ploidy levels. Discussion In partial support of the competitive exclusion principle (Hardin 1960; Tilman 1982; Silvertown and Charlesworth 2007), we found only a limited degree of ecological differentiation between co-occurring ploidies within Luzula sect. Luzula . All three multivariate analyses (LDA, DCA and PCA; Figs. 4, 5 and 6) revealed slight differences in ecological microsite characteristics between diploid L. exspectata and polyploid L. alpina and L. multiflora , but also a high overlap in ecological parameters. LDA consistently inferred individuals to be of the correct ploidy for the entire dataset and for each individual locality, with particularly high accuracy in case of localities 1 and 3 (Fig. 3). DCA showed slight differences in ecological preferences between diploids and tetraploids at locality 3, whereas no such pattern was revealed at the other two localities. Similarly, PCA revealed some degree of ecological differentiation between cytotypes at localities 1 and 3 but not at locality 2. Along the same line, we found the highest number (twelve) of significant differences in ecological predictor values between co-occurring ploidies at locality 3, while only five and four such differences were found at locality 2 and 1, respectively (Fig. 2). Overall, these results suggest at least a certain degree of niche divergence between diploids ( L. exspectata ) and tetraploids ( L. alpina ), whereas tetraploids and hexaploids ( L. multiflora ) have very similar ecological niches. Although niche shifts were suggested to be the predominant pattern in the establishment of new polyploids (Muñoz Pajares et al. 2017; López-Jurado et al. 2019; Castro et al. 2020), our study rather suggests niche conservatism in the polyploid series of the Alpine Luzula sect. Luzula , a pattern observed also in other species groups (e.g., Glennon et al. 2014; Castro et al. 2019). Our observations, especially at locality 2, thus do not support theoretical assumptions that a certain degree of niche differentiation of species or cytotypes in sympatry is needed to avoid competitive exclusion (Tilman 1982; Silvertown and Charlesworth 2007). Given the high topographic and microecological heterogeneity of alpine habitats (Körner 2021) we, however, cannot exclude the possibility that the differentiation between ploidies is more pronounced at a larger geographic scale. Despite the limited degree of ecological divergence revealed by multivariate analyses, there were significant differences in single Landolt indicator value between the ploidies at all three localities (Fig. 2). Diploid L. exspectata thus differ from tetraploid L. alpina in eight of the nine indicators at locality 3, but only in two at locality 1. This indicates varying degree of niche differentiation among localities and implies that the niches of both taxa might be more divergent on a distribution-wide scale than suggested by our data. Our results support that L. exspectata is the most basophilic of the three investigated species, which is congruent with its predominant distribution in the calcareous mountain ranges of the Eastern Alps (Bačič et al. 2019). Additionally, L. exspectata appears to need better light availability (L) and soil aeration (D), but lower temperature (T), soil moisture (F) and nutrient availability (N). This may indicate lower competitive ability of L. exspectata in denser vegetation and thus adaptation to more open microsites with lower competition. In the DCA performed for locality 3 (Fig. 5d, Online Resource 8), diploid L. exspectata correlated positively with SG 1, which predominantly contained typical species of the Seslerietalia Br.-Bl. in Br.-Bl. et Jenny 1926 em. Oberd. 1957, a phytosociological unit (Grabherr and Mucina 1993) that encompasses alpine meadows on calcareous substrate. Conspicuously, SG 1 also contained typical species of the Oxytropido-Elynion Br.-Bl. 1950, alpine vegetation of wind-exposed ridges, slopes and cliffs on intermediate to calcareous substrate, as well as one typical species ( Agrostis rupestris ) of the Caricion curvulae Br.-Bl. in Br.-Bl. et Jenny 1926 and one ( Vaccinium gaultherioides ) of the Loiseleurio-Vaccinion Br.-Bl. In Br.-Bl. Et Jenny 1926. Arguably, these two species can be interpreted as typical species for more acidophilic (sub-)units within the Oxytropido-Elynion . On the other hand, diploid L. exspectata correlated negatively to the SGs that contained many more species typical for the Seslerietalia Br.-Bl. in Br.-Bl. et Jenny 1926 em. Oberd. 1957 (SG 2–4), species that are typical for the Nardetalia Oberd. ex Prsg. 1949, nutrient-poor meadows on acidic or decalcified substrates (SG 3), and the Poion alpinae Oberd. 1950, more nutrient rich alpine meadows and pastures (SG 2, mainly). Altogether, this can be interpreted as a tendency of L. exspectata , opposed to L. alpina , towards habitats with overall harsher climate conditions and therefore lower competition. These findings are in line with previous case studies highlighting greater competitive abilities of polyploids (Lumaret et al. 1987; Sonnleitner et al. 2010) and a preference of diploids towards more open and less competitive habitats (Ståhlberg 2009). Moreover, a similar divergence of diploids and tetraploids in relation to hexaploids towards the lower end of the temperature indicator and the upper end of the light availability indicator was recently revealed in a study on Senecio carniolicus s.l. (Asteraceae), another polyploid complex within the Eastern Alps (Sonnleitner et al. 2016) that partly shares its habitats with our study species. The relative scarcity of L. exspectata in our study , even at localities 1 and 3 that are situated in calcareous parts of the Alps, may additionally suggest lower competitiveness, at least at localities where it co-occurs with higher ploidies. Our study also revealed that the hexaploid L. multiflora grows in sites with slightly, yet significantly, higher soil aeration and light availability, but lower humus content compared to the tetraploid L. alpina (Fig. 2), even though in general, ecological niches of these taxa highly overlap at locality 2 (Figs. 5, 6). At this locality two individuals were putative pentaploids (5 x ) based on RGS measurements. It has been shown that odd ploidies such as pentaploids can act as a bridge for gene flow across ploidies and thus also for the exchange of alleles that might confer adaptation to similar environments (Ptáček et al. 2023; Bartolić et al. 2024). Nevertheless, among the Alpine members of Luzula sect. Luzula , hexaploids possess a much broader elevational range than tetraploid L. alpina (Bačič et al. 2019; Pungaršek et al. 2023), indicating that both species likely have more divergent ecology at a distribution-wide scale than at a local scale. Further highlighting the significance of geographic scale in investigating ecological niches, we observed pronounced differences in environmental conditions across our three study sites, located up to 160 km apart. These differences in ecological differentiation between ploidies are likely associated with biogeographic differentiation among different ranges of the Eastern Alps (Merxmüller 1954; Aeschimann et al. 2004, 2011; Hartmann and Moosdorf 2012; Schuster et al. 2013), leading to variation in accompanying plant species (Fig. 5; Online Resource 4 and 8). Our results show that ecological microsite conditions differ significantly among the three sites, with this divergence surpassing the differences observed between ploidies. Interestingly, localities 1 and 3, which are geographically the furthest apart, were most similar, likely due to the presence of limestone at both sites, whereas the more divergent intermediate locality, site 2, is situated in the predominantly siliceous Central Alps (Aeschimann et al. 2004, 2011; Hartmann and Moosdorf 2012). These findings emphasize the influence of geography and geology on ecological microsite conditions and suggest that local niche divergence between ploidies is negligible compared to site-specific effects. Finally, although polyploids have been hypothesized and empirically shown to exhibit broader ecological niches than their lower-ploid ancestors (McIntyre 2012; Kirchheimer et al. 2016; Zielińska et al. 2024), our data do not support this hypothesis at a local scale. We found no differences in niche breadths between co-occurring ploidies. These findings suggest that patterns of ecological differentiation between ploidies at local scales may differ from those observed at larger geographic scales, underscoring the need for further studies on local co-occurrence of divergent ploidies. Declarations Acknowledgements and funding The study was funded by Euregio (project IPN 133-B to BF). 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Stat Methodol 63:411–423. https://doi.org/10.1111/1467-9868.00293 Tilman D (1982) Resource competition and community structure. In May RM (ed) Monographs in Population Biology 17. Princeton University Press, Princeton. https://doi.org/10.2307/j.ctvx5wb72 Tölgyesi C, Bátori Z, Erdös L (2014) Using statistical tests on relative ecological indicator values to compare vegetation units - Different approaches and weighting methods. Ecol Indic 36:441–446. https://doi.org/10.1016/j.ecolind.2013.09.002 Tribsch A, Schönswetter P (2003) Patterns of endemism and comparative phylogeography confirm palaeo-environmental evidence for Pleistocene refugia in the Eastern Alps. Taxon 52:477–497 Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New York Ward JH (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58:236–244. https://doi.org/10.1080/01621459.1963.10500845 Weiss-Schneeweiss H, Emadzade K, Jang TS, Schneeweiss GM (2013) Evolutionary consequences, constraints and potential of polyploidy in plants. Cytogenet Genome Res 140:137–50. https://doi.org/10.1159/000351727 Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York Wood TE, Takebayashi N, Barker MS, Mayrose I, Greenspoon PB, Rieseberg LH (2009) The frequency of polyploid speciation in vascular plants. P Natl Acad Sci USA 106:13875–13879. https://doi.org/10.1073/pnas.0811575106 Záveská Drábková LZ (2013) A survey of karyological phenomena in the Juncaceae with emphasis on chromosome number variation and evolution. Bot Rev 79:401–446. https://doi.org/10.1007/s12229-013-9127-6 Zielińska KM, Kiedrzyński M, Tołoczko W, Kiedrzyńska E, Mętrak M (2024) The edaphic niche of ploidy-different grasses in the light of the coarse-grained data modeling and direct soil sampling. Ecol Indic 158, 111548. https://doi.org/10.1016/j.ecolind.2024.111548 Supplementary Files Supplementarymateriallegends.docx OR1.xlsx OR4.xlsx OR5.xlsx SupplementaryMaterials.pdf Cite Share Download PDF Status: Published Journal Publication published 20 May, 2025 Read the published version in Alpine Botany → Version 1 posted Reviewers agreed at journal 06 Jan, 2025 Reviewers invited by journal 06 Jan, 2025 Editor assigned by journal 27 Dec, 2024 First submitted to journal 26 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5716596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398385228,"identity":"5a1d045e-631a-4353-9f67-0df759ecb637","order_by":0,"name":"Jonas Geurden","email":"","orcid":"","institution":"Universität Innsbruck: Universitat Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Jonas","middleName":"","lastName":"Geurden","suffix":""},{"id":398385229,"identity":"9096900d-5819-46f5-a418-dab77af96a95","order_by":1,"name":"Valentin Heimer","email":"","orcid":"","institution":"Universität Innsbruck: Universitat Innsbruck","correspondingAuthor":false,"prefix":"","firstName":"Valentin","middleName":"","lastName":"Heimer","suffix":""},{"id":398385230,"identity":"981c5330-62a0-4c44-8725-2eae669d9b63","order_by":2,"name":"Božo Frajman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACAzBpw8DA2ACkPzBIJEgQpyUNooVxBkKLAWEtIMDMw8BAWIs5e/vDBwwJ9+SZpx1+9ti2zSJPsoH54QeGmj84tVj2HEg2YEgoNmycnWZunNsmUSzNwGYswXAMj8NuJByTYPyRwNg4O8FMGqglcR4DgxkDAxs+LYltEgwJCfaNs9O/SVuCtbB/Y2D4h09LMhtIS2Lj7BwzaUagltkMPGYMjG24tVj2HGM2SEhISAZqKZPsOSdRLNnMUyyR2GeMUws4xD4kJNhunJ2+TeJHWV2exPH2jR8+fJPDqQUMEoDYsAHGY4aKEATyxCgaBaNgFIyCkQkArLFMfJA8E5UAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-3667-1135","institution":"Universitat Innsbruck","correspondingAuthor":true,"prefix":"","firstName":"Božo","middleName":"","lastName":"Frajman","suffix":""}],"badges":[],"createdAt":"2024-12-26 14:03:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5716596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5716596/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00035-025-00331-5","type":"published","date":"2025-05-20T15:58:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73272856,"identity":"05d93a7b-cf37-4271-b5dc-6740f60ebc85","added_by":"auto","created_at":"2025-01-08 11:14:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4613067,"visible":true,"origin":"","legend":"\u003cp\u003eSampled localities within the Eastern Alps (a) and distribution of individuals within single localities (b, Geißspitze; c, Wildspitze; d, Langkofel). Colours correspond to different ploidies. Insets in (a) show position of the sampling area within the Alps, a typical specimen voucher of \u003cem\u003eLuzula multiflora\u003c/em\u003e, an enlarged image of the inflorescence of an individual of \u003cem\u003eLuzula\u003c/em\u003e \u003cem\u003eexspectata\u003c/em\u003eand an example of a vegetation relevé\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/cb4d64ad51d078411a81a011.png"},{"id":73271579,"identity":"8fe0a9bf-8603-4a65-afcb-cb1a623b9793","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105424,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of mean Landolt indicator values (D = soil aeration, F = soil moisture, H = humus content, K = continentality, L = light availability, N = nutrient availability, R = reaction, T = temperature, W = alternating wetness) represented by boxplots between different ploidies at locality 1 (Geißspitze; a), locality 2 (Wildspitze; b) and locality 3 (Langkofel; c). Orange stars indicate variables which were significantly different (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05) between ploidies at each locality\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/42bf0907147618d5172796e9.png"},{"id":73271575,"identity":"79f2023f-8b79-4029-9da6-7febb779c848","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36284,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of Linear Discriminant Analysis (LDA) for each tested data set. Boxplots showing the distribution of accuracy values of 10,000 LDA iterations with different splits of the data. Blue diamonds represent mean accuracy across all iterations and green numbers indicate standard deviation (SD)\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/3bf882ca2ec24a375633422f.png"},{"id":73271576,"identity":"0bc59d97-ae9c-421f-a193-cfca70fe5529","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77705,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of the best-fitted Linear Discriminant Analysis (LDA) with (a) ploidy as response variable with an accuracy of 91.38% and (b) locality as response variable with an accuracy of 100%. The proportion of the between-class explained variance of the first two LDA axes is shown in the axes labels\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/7058ac1b42ee9e24657ee6d1.png"},{"id":73271873,"identity":"2be8e34b-f487-45eb-b7db-c2242bbb15b4","added_by":"auto","created_at":"2025-01-08 11:06:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":130142,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of Detrended Correspondence Analysis (DCA) of (a) the entire dataset (species groups: 1 = \u003cem\u003eAndrosace chamaejasme\u003c/em\u003e \u0026amp; \u003cem\u003eRhinanthus glacialis\u003c/em\u003e, 2 = \u003cem\u003eFestuca nigrescens\u003c/em\u003e \u0026amp; \u003cem\u003ePoa alpina\u003c/em\u003e, 3 = \u003cem\u003eThesium alpinum\u003c/em\u003e\u0026amp; \u003cem\u003eTrifolium repens\u003c/em\u003e, 4 = \u003cem\u003eTrifolium alpinum\u003c/em\u003e \u0026amp; \u003cem\u003eVaccinium vitis-idaea\u003c/em\u003e), (b) locality 1 (Geißspitze; species groups: 1 = \u003cem\u003eMyosotis alpestris\u003c/em\u003e \u0026amp; \u003cem\u003ePimpinella major\u003c/em\u003e aggr., 2 = \u003cem\u003eHomogyne alpina \u003c/em\u003e\u0026amp; \u003cem\u003eLuzula sylvatica\u003c/em\u003e, 3 = \u003cem\u003eCarex ornithopoda\u003c/em\u003e \u0026amp; \u003cem\u003ePrimula veris\u003c/em\u003e), (c) locality 2 (Wildspitze; species groups: 1 = \u003cem\u003eAchillea millefolium\u003c/em\u003eaggr. \u0026amp; \u003cem\u003eAlchemilla vulgaris\u003c/em\u003e aggr., 2 = \u003cem\u003eAvenella flexuosa\u003c/em\u003e\u0026amp; \u003cem\u003eTrifolium pratense\u003c/em\u003e, 3 = \u003cem\u003eBiscutella laevigata\u003c/em\u003e \u0026amp; \u003cem\u003eHieracium pilosella\u003c/em\u003e, 4 = \u003cem\u003eHieracium hoppeanum\u003c/em\u003e \u0026amp; \u003cem\u003eMinuartia verna\u003c/em\u003e) and (d) locality 3 (Langkofel; species groups: 1 = \u003cem\u003eAgrostis rupestris\u003c/em\u003e \u0026amp; \u003cem\u003ePulsatilla vernalis\u003c/em\u003e, 2 = \u003cem\u003eCarex ornithopoda\u003c/em\u003e \u0026amp; \u003cem\u003eTrifolium repens\u003c/em\u003e, 3 = \u003cem\u003eFestuca nigrescens\u003c/em\u003e \u0026amp; \u003cem\u003eGentiana pilosa\u003c/em\u003e, 4 = \u003cem\u003eAster bellidiastrum \u003c/em\u003e\u0026amp; \u003cem\u003eHieracium murorum \u003c/em\u003eaggr.). Orange arrows (numbers) represent mean group scores of all species within each species group resulting from k-means clustering using mean silhouette widths (Online Resource 8). For each group, the two species with a species score closest to the mean group score were used as representatives. Black arrows represent fitted explanatory variables. All explanatory variables that had a significant “goodness of fit” were included. The proportion of explained variance of the first two DCA axes is shown in the axes labels.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/95f260cd73ba4e9673fc7f9f.png"},{"id":73271581,"identity":"82d9e392-ea75-4cbd-b5f7-1fc29b73498e","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":109459,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of Principal Component Analysis (PCA) for the entire dataset (a), locality 1 (Geißspitze; b), locality 2 (Wildspitze; c) and locality 3 (Langkofel; d). For individual localities (b–d), the two major clusters inferred with k-means clustering of the first two principal components are indicated by small and large symbols. Explanatory variables that contributed less than 5% to the first two components are not displayed. The proportion of explained variance of the first two PCA axes is shown in the axes labels.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/72f189248945d2333e188a1c.png"},{"id":83460253,"identity":"43990ed9-bc36-4808-b531-a2257f2e7d12","added_by":"auto","created_at":"2025-05-26 16:12:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6067592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/8c6d7f7f-e22c-4a4f-ac5a-a8c5e342bd91.pdf"},{"id":73271872,"identity":"79456a0b-d866-4912-8c61-7ca8ce0d4c6c","added_by":"auto","created_at":"2025-01-08 11:06:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14747,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymateriallegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/a85eba3469bfe4e2907104ee.docx"},{"id":73271577,"identity":"402d876b-a30f-49b9-9ae2-3a451c2e0615","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39992,"visible":true,"origin":"","legend":"","description":"","filename":"OR1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/96fe30b23754479b9ab3616b.xlsx"},{"id":73271583,"identity":"8b714c81-9fab-4dd3-bfb7-1dd6cbf17327","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":118351,"visible":true,"origin":"","legend":"","description":"","filename":"OR4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/b71b4b810db3b0a9810da678.xlsx"},{"id":73271586,"identity":"37afd7cd-29f2-424e-ae2c-17c5c117734b","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":34008,"visible":true,"origin":"","legend":"","description":"","filename":"OR5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/1643d2e63e49239a51f62d02.xlsx"},{"id":73271584,"identity":"d38fd673-ebf1-4bd6-8a11-36bbbe32db78","added_by":"auto","created_at":"2025-01-08 10:58:35","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":319131,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5716596/v1/c87a1295090ec4de72ad81ab.pdf"}],"financialInterests":"","formattedTitle":"Co-occurring Luzula species (Juncaceae) of different ploidies in alpine grasslands of the Eastern Alps exhibit negligible ecological differentiation at small geographic scale","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith more than 4,000 vascular plant species, the Alps are one of the hotspots of European biodiversity, harbouring many endemics (Merxm\u0026uuml;ller \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1954\u003c/span\u003e; Pawłowski \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Tribsch and Sch\u0026ouml;nswetter \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Aeschimann et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kadereit \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One of the processes that contributed significantly to diversification of the Alpine biota is polyploidisation (Burnier et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Guggisberg et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Casazza et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Flatscher et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Skubic et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is considered one of the most important evolutionary mechanisms in vascular plants (Soltis et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wood et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Madlung \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Weiss-Schneeweiss et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Populations with multiple ploidy levels have been detected in several species; they often can co-occur in the same habitats, and it has been evidenced that this co-existence is possible due to differences in niche optima (Kol\u0026aacute;ř et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The same principles that underlie co-occurrence of closely related species, such as sympatric speciation (Grant \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) or secondary contact after two allopatric species geographically reunite (Hvala and Wood \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), also apply for the formation of mixed-ploidy populations within single species. Within-population cytotype diversity can either arise \u003cem\u003ein situ\u003c/em\u003e through primary contact, or, alternatively, as the result of secondary contact following allopatric divergence of geographically isolated cytotypes (Kol\u0026aacute;ř et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though secondary contact is likely the prevalent mode of formation of mixed-ploidy populations, primary contact is not uncommon and there is evidence for both mechanisms operating in different parts of a species\u0026rsquo; distribution range (Kol\u0026aacute;ř et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the competitive exclusion principle (Hardin \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1960\u003c/span\u003e), species or cytotypes can coexist in sympatry only if they exhibit sufficient niche differentiation to reduce competition (Tilman \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Silvertown and Charlesworth \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While niche shift seems to be the predominant pattern in the establishment of new polyploids (Mu\u0026ntilde;oz Pajares et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; L\u0026oacute;pez-Jurado et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Castro et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), alternative processes such as niche conservatism and contraction may also be involved (Glennon et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Castro et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Generally, polyploids, especially allopolyploids (polyploids resulting from interspecific hybridization), have been suggested to have broader ecological niches than their lower-ploid ancestors due to the combination of diverged genomes, and empirical data largely support this hypothesis (McIntyre \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kirchheimer et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zielińska et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, polyploids may not directly expand their ecological niches compared to diploids but increase the diversity of occupied habitats in some genera, simply by increasing taxonomic diversity (Petit and Thompson \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In a similar line, polyploids have been shown to often exhibit at least partial geographic sympatry and niche overlap with their progenitors (Blaine Marchant et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo describe the ecology and in particular ecological niche preferences of plant species, different indicators that describe microsite characteristics have been commonly used (e.g. Čertner et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bertel et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; B\u0026uuml;ren and Hiltbrunner \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ram\u0026iacute;rez et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Microsite characteristics namely influence recruitment in plants, including germination as well as seedling survival and growth and thus importantly affect their small-scale distribution (Harper et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1961\u003c/span\u003e; Eriksson and Ehrl\u0026eacute;n \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1992\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Elmarsdottir et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Knowledge of microsite characteristics also has practical applications, for instance in nature conservation, where consideration of species-specific microsite preferences has been shown to considerably increase the success of realized conservation measures (Dunwiddie and Martin \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Landolt indicator values (Landolt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) were specifically developed for the Alpine flora and have been successfully used to characterize ecological niches of Alpine species (Scherrer et al. 2019; Ivanova and Zolotova \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mean Landolt indicator values were also effectively applied in di-polyploid systems to show ecological niche expansion of polyploid populations within a single species (Kirchheimer et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and to clarify ecological preferences of species within polyploid complexes (H\u0026uuml;lber et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sonnleitner et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silbernagl and Sch\u0026ouml;nswetter, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition to indicator values, strategy types, implemented into ecological research with the universal adaptive strategy theory, can be used to characterize plant communities and therefore indirectly infer ecological conditions at small scales (Grime \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1979\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the genera with high species diversity in the Alpine grasslands is \u003cem\u003eLuzula\u003c/em\u003e (Juncaceae; Aeschimann et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Especially within \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e that encompasses 57 species worldwide, polyploidisation has been an important diversification mechanism (Kirschner \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1992\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Z\u0026aacute;vesk\u0026aacute; Dr\u0026aacute;bkov\u0026aacute; \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), also in the Alps (Bačič et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition to true polyploidy in which genome size (GS) of polyploids is expected to increase in direct proportion with ploidy level (Renny-Byfield et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), agmatoploidy \u0026ndash; fragmentation of chromosomes without increase in GS \u0026ndash; represents another important evolutionary mechanism in this section of \u003cem\u003eLuzula\u003c/em\u003e (Malheiros-Gard\u0026eacute; and Gard\u0026eacute; \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Bačič et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007b\u003c/span\u003e; Guerra \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to agmatoploidy, different species of \u003cem\u003eLuzula\u003c/em\u003e have chromosomes of three different sizes. The largest, full-size chromosomes are designated as AL-type, the intermediate, half-size chromosomes as BL-type, and the smallest, quarter-size chromosomes as CL-type (Nordenski\u0026ouml;ld \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1951\u003c/span\u003e, Bozek et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the (sub)alpine grasslands of the Eastern Alps four species of \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e are frequent: \u003cem\u003eLuzula alpina\u003c/em\u003e Hoppe, \u003cem\u003eL. exspectata\u003c/em\u003e Bačič et Jogan, \u003cem\u003eL. multiflora\u003c/em\u003e (Ehrh.) Lej. and \u003cem\u003eL. sudetica\u003c/em\u003e (Willd.) Schult. (Bačič et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whereas the latter, occurring in wet grasslands and bogs, is ecologically, morphologically and genetically most divergent and thus relatively easy to recognize, the other three species are morphologically extremely similar and often co-occur in the same localities (Bačič et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pungaršek et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eLuzula exspectata\u003c/em\u003e is karyologically divergent, as it is diploid with 24 fragmented BL-type chromosomes (2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24 BL). On the other hand, \u003cem\u003eL. multiflora\u003c/em\u003e is polyploid with complete, AL-type chromosomes: it includes tetraploid (2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24 AL) and hexaploid (2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;36 AL) individuals. Finally, \u003cem\u003eL. alpina\u003c/em\u003e is a partial agmatoploid with 12 full-sized and 24 fragmented chromosomes (2\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12 AL\u0026thinsp;+\u0026thinsp;24 BL; Barlow and Nevin \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Kirschner et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Bačič et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whereas the hexaploid \u003cem\u003eL. multiflora\u003c/em\u003e appears to have a single origin, \u003cem\u003eL. alpina\u003c/em\u003e and tetraploid \u003cem\u003eL. multiflora\u003c/em\u003e were intermingled within the same clusters inferred by amplified fragment length polymorphisms, suggesting that \u003cem\u003eL. alpina\u003c/em\u003e might have originated multiple times from \u003cem\u003eL. multiflora\u003c/em\u003e by partial fragmentation of chromosomes (Pungaršek et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Alternatively, \u003cem\u003eL. exspectata\u003c/em\u003e might have been involved in the origin of \u003cem\u003eL. alpina\u003c/em\u003e, explaining its partial-agmatoploid karyotype (Bačič et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The latter scenario appears to be supported also by restriction-site associated DNA sequencing (RADseq; Heimer et al. unpublished), which indicates that the tetraploid \u003cem\u003eL. multiflora\u003c/em\u003e occurs only in the easternmost Eastern Alps, whereas \u003cem\u003eL. alpina\u003c/em\u003e is parapatric and thrives in the central and western Eastern Alps. Their close relatedness and shared habitats are thus likely the reasons for the high morphological similarity among \u003cem\u003eL. alpina\u003c/em\u003e, \u003cem\u003eL. exspectata\u003c/em\u003e and \u003cem\u003eL. multiflora\u003c/em\u003e; especially \u003cem\u003eL. alpina\u003c/em\u003e and \u003cem\u003eL. multiflora\u003c/em\u003e are mostly impossible to distinguish morphologically (Bačič et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ecologically, \u003cem\u003eL. alpina\u003c/em\u003e and \u003cem\u003eL. multiflora\u003c/em\u003e are more acidophilic, growing on siliceous bedrock, whereas \u003cem\u003eL. exspectata\u003c/em\u003e is slightly more basophilic, occurring mostly over calcareous substrates. Whereas \u003cem\u003eL. multiflora\u003c/em\u003e, especially its hexaploid cytotype, occupies the widest range of habitats from lowland bogs to alpine meadows, \u003cem\u003eL. alpina\u003c/em\u003e and \u003cem\u003eL. exspectata\u003c/em\u003e mainly occur at higher elevations above timberline (Bačič et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the common co-occurrence of the three closely related species of different ploidies in Alpine grasslands, our aim was to characterise their microsite characteristics and investigate whether their ecological niches differ at a local scale in three different localities in the western parts of the Eastern Alps. In particular, we were interested in whether there is any niche differentiation among the three ploidies, namely diploid \u003cem\u003eL. exspectata\u003c/em\u003e, tetraploid \u003cem\u003eL. alpina\u003c/em\u003e and hexaploid \u003cem\u003eL. multiflora\u003c/em\u003e. To achieve this aim, we (1) sampled 100 individuals of the three species per plot at three different locations in the Eastern Alps, (2) estimated their ploidy levels via relative genome size estimation with flow cytometry, and (3) performed statistical analyses of small-scale vegetation surveys of accompanying vascular plants to characterise their niches via Landolt indicator values and additional ecological parameters recorded at plot-scale.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eSampling localities and identification of \u003cem\u003eLuzula\u003c/em\u003e samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree localities with co-occurrence of at least two \u003cem\u003eLuzula\u003c/em\u003e species of different ploidy level in (sub)alpine grasslands in the Eastern Alps were selected based on preliminary investigations: 1) Austria, Vorarlberg, R\u0026auml;tikon, between Gei\u0026szlig;spitze and Lindauer H\u0026uuml;tte, 1,951\u0026ndash;1,968 m a.s.l.; 2) Austria, North Tyrol, \u0026Ouml;tztaler Alpen, between Wildspitze and Vent, 2,310\u0026ndash;2,333 m a.s.l.; 3) Italy, South Tyrol, Dolomites, between Langkofel and the Passo di Sella, 2,237\u0026ndash;2,260 m a.s.l. A map indicating the localities within the European Alps (Fig. 1a) was produced in R v.4.3.3 (R Core Team 2024) with the use of GTOPO30 (EROS Center 2018) and the spatial distribution of sampled individuals within each locality (Online Resource 1) was visualised in QGIS v.3.36.0 (QGIS.org 2024) with the use of different WMS-services: for Vorarlberg \u0026ldquo;VoGIS\u0026rdquo; (Land Vorarlberg 2022) for North Tyrol \u0026ldquo;tiris\u0026rdquo; (Land Tirol 2024), and for South Tyrol \u0026ldquo;GeoKatalog\u0026rdquo; (Autonome Provinz Bozen - S\u0026uuml;dtirol 2024).\u003c/p\u003e\n\u003cp\u003eAt each locality we sampled 100 individuals of \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e within plots of approximately 50 \u0026times; 50 m. Leaf tissue was collected for each individual and immediately dried in silica gel for relative genome size (RGS) estimation. In addition, herbarium vouchers were produced for all samples and later deposited at IB (Herbarium of the Department of Botany of the University of Innsbruck). The identification of the sampled individuals was carried out as a combination of field observation regarding morphology and ploidy-level estimation in the lab. In this way, diploid \u003cem\u003eL. exspectata\u003c/em\u003e and hexaploid \u003cem\u003eL. multiflora\u003c/em\u003e could be reliably identified and distinguished from all tetraploid individuals. Tetraploid \u003cem\u003eL. alpina\u003c/em\u003e can neither be distinguished from tetraploid \u003cem\u003eL. multiflora\u003c/em\u003e by RGS nor reliably identified based on morphology (Bačič et al. 2019; Pungar\u0026scaron;ek et al. 2023). However, since our RADseq data and chromosome counts (Heimer et al., unpublished) indicated that most tetraploid populations in the western parts of the Eastern Alps, where our three study sites are located, correspond to \u003cem\u003eL. alpina\u003c/em\u003e, we refer to our tetraploid samples as \u003cem\u003eL. alpina\u003c/em\u003e hereafter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVegetation surveys and collection of plant material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed vegetation surveys on a microsite scale to characterise ecological preferences of each sampled individual of \u003cem\u003eLuzula\u003c/em\u003e. Niche comparisons using Landolt indicator values are commonly achieved through calculation of mean indicator values for defined areas; when describing the niche of single individuals, usually Landolt indicator values are computed from for accompanying plant species growing in near vicinity of the studied individual (Landolt et al. 2010). For each \u003cem\u003eLuzula\u003c/em\u003e individual that we sampled, a vegetation relev\u0026eacute; was therefore performed in a circle of 40 cm diameter with the sampled specimen in the centre. Within this circle, all accompanying vascular plant species, and their estimated cover were recorded. Plant species were identified using regional floras (Aeschimann et al. 2004; Fischer et al. 2008; Eggenberg and M\u0026ouml;hl 2020), and we applied the nomenclature of Landolt et al. (2010). Additionally, we estimated slope and aspect as well as coverage of rock, soil, cryptogams, dead organic material and overall cover of vascular plants. Coverage was recorded in exact percentages for values equal to and above 5%, whereas those below 5% were grouped into a single class. For the statistical analyses, exact coverage values were transformed into classes with ranges of 5 to 10% (Online Resource 2). The slope aspect was transformed into \u0026ldquo;Northness\u0026rdquo; and \u0026ldquo;Eastness\u0026rdquo;, using the absolute distance in degree to either north or east, thus following Sonnleitner et al. (2010). Additional explanatory variables were computed from the accompanying plant species using R. Those encompassed \u0026ldquo;Richness\u0026rdquo; (= total species count) as well as mean Landolt indicator values (climate indicators: T = temperature, K = continentality, L = light availability; soil indicators: F = soil moisture, W = alternating wetness, R = reaction, N = nutrient availability, H = humus content, D = soil aeration; Landolt et al. 2010) and percentage values of Grime\u0026rsquo;s (1979) strategy types (c = competitors, r = ruderals, s = stress tolerators; Landolt et al. 2010). Mean Landolt indicator values and strategy types were not weighted by coverage because this would fail to account for species that never reach high coverage but have a high specificity. It has been shown that weighing abundance works well for both extremes of an indicator but performs less efficiently in the medium range (T\u0026ouml;lgyesi et al. 2014). Indicator values were weighted by specificity, which was achieved by giving double the weight to species with narrow requirements (Landolt et al. 2010). To avoid bias, registered accompanying species belonging to \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e were not included in the computation of indicator values and strategy types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimation of relative genome size (RGS) and ploidy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated the RGS of each sampled \u003cem\u003eLuzula\u003c/em\u003e individual via flow cytometry of 4\u0026rsquo;,6-diamidino-2-phenylindole (DAPI)-stained nuclei following Suda and Tr\u0026aacute;vn\u0026iacute;ček (2006). We used\u003cem\u003e\u0026nbsp;Bellis perennis\u003c/em\u003e as a reference standard (2C = 3.38 pg DNA; Sch\u0026ouml;nswetter et al. 2007). Relative fluorescence intensity of 3,000 particles was recorded using a CyFlow Space flow cytometer (Sysmec Partec GmbH, M\u0026uuml;nster, Germany). Evaluation of the results was done with FloMax software (Sysmec Partec GmbH, M\u0026uuml;nster, Germany). To assess the reliability of the recorded RGS values, we calculated coefficients of variation (CV) for samples and standards and in general accepted measurements with CV \u0026lt; 5%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalyses of ecological data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R v.4.3.3 (R Core Team 2024) and results were visualised using the package \u003cem\u003eggplot2\u003c/em\u003e (Wickham 2016). The environmental predictors that most strongly differentiated between ploidy levels were identified by analysing differences in variable means for both the entire data and for each locality separately. Within each data set, a Kolmogorov-Smirnov-test was used to test every explanatory variable for normal distribution. We then assessed differences in variable means with either an unpaired two-sample t-test or an unpaired two-samples Wilcoxon-test. To compare ecological niche breadths among ploidies, we tested for differences in standard deviations (SD) of all mean Landolt indicator values per ploidy at each locality using another unpaired two-sample t-test after confirming normal distribution. As there was only a single hexaploid individual recorded at locality 1 and a single diploid individual at locality 2 (see Results), they were excluded from within-locality analyses.\u003c/p\u003e\n\u003cp\u003eCorrelations between all possible combinations of explanatory variables within different data sets were assessed with Pearson\u0026rsquo;s correlation coefficients computed for the entire data, per ploidy level (3 subsets) and per locality (3 subsets). The consistency of significant correlations between each pair of data subsets (per ploidy level and per locality) was analysed by calculating the ratio of variables that were significantly correlated (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) in both.\u003c/p\u003e\n\u003cp\u003eSmall-scale ecological differentiation among ploidy levels was assessed by multivariate analyses: Detrended Correspondence Analysis (DCA), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). All analyses were performed to investigate the differences in ecological preferences among the three ploidies within the entire dataset as well as between different ploidies at each single locality. In addition, we performed LDA to investigate the differentiation among the three localities as well as in pairwise comparisons between all ploidy-pairs across the complete dataset (Online Resource 3). Variables that did not differ significantly between the groups were excluded from the respective multivariate analysis (Online Resource 3). At locality 2, the variables Northness, Eastness and Inclination were invariant, so they were excluded from tests on this locality. When those variables were included in tests across all localities (LDA, PCA), this invariance led to a strong divergence of the individuals from locality 2 in the ordination. LDA was performed with the R-package \u0026ldquo;MASS\u0026rdquo; (Venables and Ripley 2002). We used either ploidy level or locality as response variables. For each analysis, we used 80% of the data for training and the remaining 20% as test samples. As a basis for the assessment of the LDA, accuracy, i.e. percentage of observations in the test samples which the model predicted correctly, was computed. As splitting the data is pseudo-random and can therefore create differing outcomes, LDA and computation of accuracy was repeated 10,000 times with different seeds using the \u0026ldquo;set.seed(\u003cem\u003ea\u003c/em\u003e)\u0026rdquo;-function of R with \u003cem\u003ea\u003c/em\u003e being every integer between 1 and 10,000. We also tested a different combination of pseudo-randomly generated values (e.g. 10,000 values between 1 and 1,000,000) to ensure consistency of results. To maximize differences between ploidy levels, only the most differentiating explanatory variables that differed significantly (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) between ploidy levels, were included. In analyses that included more than two classes (e.g. ploidy levels of 2\u003cem\u003ex\u003c/em\u003e, 4\u003cem\u003ex\u003c/em\u003e and 6\u003cem\u003ex\u003c/em\u003e), variables were included if they were significantly different between ploidies in at least one comparison. Lastly, LDA performance was evaluated via mean accuracy, standard deviation (SD) of all accuracies and accuracy range (difference of minimum and maximum accuracy).\u003c/p\u003e\n\u003cp\u003eDCA was performed using the R-package \u003cem\u003evegan\u003c/em\u003e (Oksanen et al. 2022), unless otherwise specified. To describe the resulting model, species scores and fitted explanatory variables were used. Every explanatory variable was fitted, but only those that had a significant goodness of fit were used in the visualisation of the DCA. Euclidean distance of the first two DCA components were used for hierarchical clustering of the scores of the accompanying species using the \u003cem\u003eward.D2\u003c/em\u003e method (Ward 1963; Murtagh and Legendre 2014). To find the most suitable number of clusters, silhouette width means (Rousseeuw 1987) were computed using the R-package \u003cem\u003ecluster\u003c/em\u003e (Maechler et al. 2023). When a specific clustering was chosen, scores within every cluster were averaged. This resulted in a mean cluster score for every cluster that was then used in the visualisation of the model. For each cluster, the two species with a species score closest to the mean cluster score were used as representatives.\u003c/p\u003e\n\u003cp\u003ePCA was performed using the base package \u003cem\u003estats\u003c/em\u003e in R (R Core Team 2024) and evaluated with the package \u003cem\u003efactoextra\u003c/em\u003e (Kassambara and Mundt 2020). As for LDA, only explanatory variables, which differed significantly among the tested groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), were included. Additionally, PCA site scores (PCA scores of the first two principal components; scores of the individuals) of each data set within single localities were clustered via k-means clustering (MacQueen 1967). To find the most suitable number of clusters, silhouette (Rousseeuw 1987) and gap statistics (Tibshirani et al. 2001) were used. For clearer visualisation, explanatory variables which contributed less than 5% to the first two axes of the PCA were excluded from being displayed in the PCA.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eRelative genome size and ploidy distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe could not produce reliable RGS measurements for two individuals, which were excluded from further analyses. Also excluded were two individuals at locality 2 that had an RGS of 0.65 and 0.66, suggesting pentaploidy. Of the remaining 296 individuals, 27 were diploids (\u003cem\u003eL. exspectata\u003c/em\u003e) with an RGS of 0.25\u0026ndash;0.27, 224 tetraploids (\u003cem\u003eL. alpina\u003c/em\u003e) with an RGS of 0.44\u0026ndash;0.56, and 45 hexaploids (\u003cem\u003eL. multiflora\u003c/em\u003e) with an RGS of 0.70\u0026ndash;0.82 (Online Resource 1). At locality 1, 86 individuals were tetraploid, 13 diploid and 1 hexaploid, at locality 2, 52 were tetraploid, 44 hexaploid and 1 diploid, and at locality 3, 85 were tetraploid and 14 diploid. The spatial distribution of ploidies within each locality is shown in Fig. 1b\u0026ndash;d. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEcological microsite differentiation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe registered 105 accompanying vascular plant species at locality 1, 74 species at locality 2, and 84 at locality 3 (Online Resource 4), excluding accompanying species of \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e. The following pairs of explanatory variables were significantly correlated (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) based on the significance test for Pearson correlation coefficient across all tested datasets: T-D, K-F, L-D, F-N, R-s, D-s, c-r and c-s. Many other variables were significantly correlated throughout some of the tested datasets, especially the indicator values (Online Resource 5). Regarding consistency of significant correlations between individuals of two different ploidy levels, 54.76% of all variables were consistently correlated between diploids and tetraploids (2\u003cem\u003ex\u003c/em\u003e, 4\u003cem\u003ex\u003c/em\u003e), 66.19% between diploids and hexaploids (2\u003cem\u003ex\u003c/em\u003e, 6\u003cem\u003ex\u003c/em\u003e), and 59.05% between tetraploids and hexaploids (4\u003cem\u003ex\u003c/em\u003e, 6\u003cem\u003ex\u003c/em\u003e). For different localities, 60.00% of variables were consistently correlated between locality 1 and 2, 70.00% between locality 1 and 3, and 64.29% between locality 2 and 3.\u003c/p\u003e\n\u003cp\u003eResults of the t-test/ Wilcoxon-test (Online Resource 6) showed that many variable means were significantly different between co-occurring ploidy levels at each locality. Within localities 1 and 2 there were four (RockCover, c, L, T) and five (RockCover, CryptogamCover, D, H, L) significantly differentiating variables for individuals with different ploidy levels, respectively. At locality 3, all but one (H) mean Landolt indicator values and seven additional values (Eastness, Inclination, OrganicCover, PlantCover, Richness, c, r) were significantly different (Fig. 2). At each locality SDs were not significantly different between the co-occurring ploidies, indicating no differences in niche breadths (locality 1: \u003cem\u003ep\u003c/em\u003e = 0.41; locality 2: \u003cem\u003ep\u003c/em\u003e = 0.61; locality 3: \u003cem\u003ep\u003c/em\u003e = 0.88) and higher-ploidies only had a broader indicator value range due to higher sample numbers.\u003c/p\u003e\n\u003cp\u003eIn the LDA, the highest mean accuracy was achieved for the data sets \u0026ldquo;Entire data (Locality)\u0026rdquo; and \u0026ldquo;Ploidy (2\u003cem\u003ex\u003c/em\u003e, 6\u003cem\u003ex\u003c/em\u003e)\u0026rdquo; with mean accuracies of 95.16 and 96.09%, respectively (Fig. 3). LDA for diploids versus tetraploids (\u0026ldquo;Ploidy (2\u003cem\u003ex\u003c/em\u003e, 4\u003cem\u003ex\u003c/em\u003e)\u0026ldquo;) had a mean accuracy of 88.66%. Tests on locality 1 and 3 (2\u003cem\u003ex\u003c/em\u003e versus 4\u003cem\u003ex\u003c/em\u003e) had mean accuracies of 88.32 and 89.46% while the \u0026ldquo;Entire data (Ploidy)\u0026rdquo; yielded a mean accuracy of 77.17%. Since all but one hexaploids occurred at locality 2, where only a single diploid was found, we additionally tested whether the high accuracy of \u0026ldquo;Ploidy (2\u003cem\u003ex\u003c/em\u003e, 6\u003cem\u003ex\u003c/em\u003e)\u0026rdquo; resulted from an additive effect of different ploidy level and locality. Therefore, we performed an additional LDA on all diploids from all localities and only tetraploids from locality 2. Included variables were the same as in \u0026ldquo;Ploidy (2\u003cem\u003ex\u003c/em\u003e, 4\u003cem\u003ex\u003c/em\u003e)\u0026rdquo;. This achieved a mean accuracy of 96.72%, which is in line with \u0026ldquo;Ploidy (2\u003cem\u003ex\u003c/em\u003e, 6\u003cem\u003ex\u003c/em\u003e)\u0026rdquo;. LDA performed worst at locality 2 (\u0026ldquo;Locality 2 (4\u003cem\u003ex\u003c/em\u003e, 6\u003cem\u003ex\u003c/em\u003e)\u0026rdquo;) with a mean accuracy of 59.02%, a SD of 9.95%. Minimum and maximum LDA accuracy of every other tested dataset differed by less than 36.84%, resulting in a SD of 2.61\u0026ndash;4.60%, except for \u0026ldquo;Locality 3 (2x, 4x)\u0026rdquo; which had a SD of 6.61%, a SD of 2.61\u0026ndash;4.60%. With ploidy as response variable, LDA largely failed to separate individuals of different ploidies into well-defined clusters; instead, they were strongly intermixed along the first two LDA axes (Fig. 4a). When locality was used as response variable, LDA performed considerably better, separating individuals according to sampling locality (Fig. 4b). Localities were mainly differentiated by Richness (locality 1), L and T (locality 2) as well as K, R and F (locality 3; Fig 4b). This differentiation was not visible in the LDA with ploidy as response variable but in both cases, diploids were positively correlated with a higher reaction value R.\u003c/p\u003e\n\u003cp\u003eWhen DCA was performed for the entire data, individuals were clearly grouped by locality (Fig. 5a). Differentiation between di- and tetraploids was most pronounced at locality 3 (Fig. 5d). The variables that contributed most to this differentiation were OrganicCover, D, K, L and R. On the other hand, individuals of different ploidies were intermixed at locality 1 (Fig. 5b) and 2 (Fig. 5c). Hierarchical clustering of the scores of accompanying vascular plant species resulted in three to four species groups (SG) within each of the four analyses (Fig. 5; orange arrows show the mean group score of each SG). The SGs did not have very high support, i.e. the overall mean silhouette width was between 0.4 and 0.6 (Online Resource 7). Alternative numbers of SGs only had slightly lower support. For the entire data, SGs (Online Resource 8) clearly corresponded to different localities (Fig. 5a): SG 1 corresponded to locality 3, SG 2 to locality 1 and SGs 3 and 4 to locality 2. As an example, the four to SG 1 belonging species \u003cem\u003eAntennaria carpatica\u003c/em\u003e, \u003cem\u003ePedicularis elongata\u003c/em\u003e, \u003cem\u003ePedicularis verticillata\u003c/em\u003e and \u003cem\u003eRanunculus oreophilus\u003c/em\u003e were exclusively recorded at the corresponding locality 3). SG 4 contained only four species (\u003cem\u003eArctostaphylos uva-ursi\u003c/em\u003e, \u003cem\u003eSilene rupestris\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Trifolium alpinum\u003c/em\u003e and \u003cem\u003eVaccinium vitis-idaea\u003c/em\u003e) and corresponded to a few tetraploid and a single hexaploid \u003cem\u003eLuzula\u003c/em\u003e individual at locality 2, that were separated from the rest along the second DCA axis. At locality 1 (Fig. 5b), SGs did not correspond to different ploidies and differentiated individuals along both DCA axes. Even though different ploidies were not separated by DCA at locality 2 (Fig. 5c), there was a clear differentiation along the first axis via SG 1 (e.g., \u003cem\u003eAchillea millefolium\u003c/em\u003e aggr., \u003cem\u003eAlchemilla vulgaris\u003c/em\u003e aggr., \u003cem\u003eArctostaphylos uva-ursi\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Carex sempervirens\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Potentilla crantzii\u003c/em\u003e, \u003cem\u003eSilene rupestris\u003c/em\u003e, \u003cem\u003eTrifolium alpinum\u003c/em\u003e and \u003cem\u003eVaccinium vitis-idaea\u003c/em\u003e), reflecting the differentiation of few individuals similar to SG 4 for the entire data. SGs 2, 3 and 4 differentiated individuals along the second axis. At locality 3 (Fig. 5d), diploid individuals (\u003cem\u003eL. exspectata\u003c/em\u003e) corresponded positively to species of SG 1 (e.g., \u003cem\u003eAchillea clavenae\u003c/em\u003e, \u003cem\u003eAgrostis rupestris\u003c/em\u003e, \u003cem\u003eAntennaria carpatica\u003c/em\u003e, \u003cem\u003eAnthyllis alpestris\u003c/em\u003e, \u003cem\u003eBiscutella laevigata\u003c/em\u003e, \u003cem\u003eCarex capillaris\u003c/em\u003e, \u003cem\u003eDryas octopetala\u003c/em\u003e, \u003cem\u003eElyna myosuroides\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Leontopodium alpinum\u003c/em\u003e, \u003cem\u003ePulsatilla vernalis\u003c/em\u003e, \u003cem\u003eSilene acaulis\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Vaccinium gaultherioides\u003c/em\u003e) and to very small extent to SG 2, but negatively to species of SG 3 (e.g., \u003cem\u003eAnthoxanthum alpinum\u003c/em\u003e, \u003cem\u003eArnica montana\u003c/em\u003e, \u003cem\u003eCarlina acaulis, Festuca nigrescens\u003c/em\u003e, \u003cem\u003eGentiana pilosa\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Geum montanum\u003c/em\u003e, \u003cem\u003eNardus stricta\u003c/em\u003e, \u003cem\u003ePhleum rhaeticum\u003c/em\u003e, \u003cem\u003ePoa alpina\u003c/em\u003e, \u003cem\u003ePotentilla aurea\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Trollius europaeus\u003c/em\u003e) and SG 4 (e.g., \u003cem\u003eAnemone baldensis\u003c/em\u003e, \u003cem\u003eAster bellidiastrum\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Bartsia alpina\u003c/em\u003e, \u003cem\u003eCarduus defloratus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Helianthemum alpestre\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Hieracium murorum\u0026nbsp;\u003c/em\u003eaggr.,\u003cem\u003e\u0026nbsp;Homogyne alpina\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Pedicularis verticillata\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Salix breviserrata\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Salix retusa\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Sesleria caerulea\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Tofieldia calyculata\u003c/em\u003e and \u003cem\u003eVaccinium vitis-idaea\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eIn the PCA of the entire dataset, individuals were again mostly separated according to their sampling locality (Fig. 6a). Individuals from localities 1 and 3 were mainly separated along the first component, whereas those from locality 2 were separated from them along the second component. There was no visible separation of different ploidies. Within single localities, a slight differentiation trend of diploids and tetraploids at locality 1 (Fig. 6b) and 3 (Fig. 6d) was visible, whereas there was no clear differentiation at locality 2 (Fig. 6c). The variables that mainly contributed to differentiation trend of diploids from tetraploids at locality 1 were L and to a lesser extent c and T, and at locality 3 OrganicCover, D, K, L, R (positively correlated with the occurrence of diploids) as well as T, N and F (negatively correlated with the occurrence of diploids). Silhouette and gap statistics based on clustering of PCA site scores both suggested two clusters for each locality (large and small symbols in Fig. 6b\u0026ndash;d), which were clearly correlated with the first PCA-axis. Ten out of thirteen diploids at locality 1 and twelve out of thirteen at locality 3 were classified into the same group, however, both groups also contained many tetraploids. The clustering at locality 2 was more random and did not correspond to ploidy levels.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn partial support of the competitive exclusion principle (Hardin 1960; Tilman 1982; Silvertown and Charlesworth 2007), we found only a limited degree of ecological differentiation between co-occurring ploidies within \u003cem\u003eLuzula\u0026nbsp;\u003c/em\u003esect. \u003cem\u003eLuzula\u003c/em\u003e. All three multivariate analyses (LDA, DCA and PCA; Figs. 4, 5 and 6) revealed slight differences in ecological microsite characteristics between diploid \u003cem\u003eL. exspectata\u003c/em\u003e and polyploid \u003cem\u003eL. alpina\u003c/em\u003e and \u003cem\u003eL. multiflora\u003c/em\u003e, but also a high overlap in ecological parameters. LDA consistently inferred individuals to be of the correct ploidy for the entire dataset and for each individual locality, with particularly high accuracy in case of localities 1 and 3 (Fig. 3). DCA showed slight differences in ecological preferences between diploids and tetraploids at locality 3, whereas no such pattern was revealed at the other two localities. Similarly, PCA revealed some degree of ecological differentiation between cytotypes at localities 1 and 3 but not at locality 2. Along the same line, we found the highest number (twelve) of significant differences in ecological predictor values between co-occurring ploidies at locality 3, while only five and four such differences were found at locality 2 and 1, respectively (Fig. 2). Overall, these results suggest at least a certain degree of niche divergence between diploids (\u003cem\u003eL. exspectata\u003c/em\u003e) and tetraploids (\u003cem\u003eL. alpina\u003c/em\u003e), whereas tetraploids and hexaploids (\u003cem\u003eL. multiflora\u003c/em\u003e) have very similar ecological niches. Although niche shifts were suggested to be the predominant pattern in the establishment of new polyploids (Mu\u0026ntilde;oz Pajares et al. 2017; L\u0026oacute;pez-Jurado et al. 2019; Castro et al. 2020), our study rather suggests niche conservatism in the polyploid series of the Alpine \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e, a pattern observed also in other species groups (e.g., Glennon et al. 2014; Castro et al. 2019). Our observations, especially at locality 2, thus do not support theoretical assumptions that a certain degree of niche differentiation of species or cytotypes in sympatry is needed to avoid competitive exclusion (Tilman 1982; Silvertown and Charlesworth 2007). Given the high topographic and microecological heterogeneity of alpine habitats (K\u0026ouml;rner 2021) we, however, cannot exclude the possibility that the differentiation between ploidies is more pronounced at a larger geographic scale.\u003c/p\u003e\n\u003cp\u003eDespite the limited degree of ecological divergence revealed by multivariate analyses, there were significant differences in single Landolt indicator value between the ploidies at all three localities (Fig. 2). Diploid \u003cem\u003eL. exspectata\u003c/em\u003e thus differ from tetraploid \u003cem\u003eL. alpina\u003c/em\u003e in eight of the nine indicators at locality 3, but only in two at locality 1. This indicates varying degree of niche differentiation among localities and implies that the niches of both taxa might be more divergent on a distribution-wide scale than suggested by our data. Our results support that \u003cem\u003eL. exspectata\u003c/em\u003e is the most basophilic of the three investigated species, which is congruent with its predominant distribution in the calcareous mountain ranges of the Eastern Alps (Bačič et al. 2019). Additionally,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cem\u003eL. exspectata\u003c/em\u003e appears to need better light availability (L) and soil aeration (D), but lower temperature (T), soil moisture (F) and nutrient availability (N). This may indicate lower competitive ability of \u003cem\u003eL. exspectata\u003c/em\u003e in denser vegetation and thus adaptation to more open microsites with lower competition. In the DCA performed for locality 3 (Fig. 5d, Online Resource 8), diploid \u003cem\u003eL. exspectata\u003c/em\u003e correlated positively with SG 1, which predominantly contained typical species of the \u003cem\u003eSeslerietalia\u003c/em\u003e Br.-Bl. in Br.-Bl. et Jenny 1926 em. Oberd. 1957, a phytosociological unit (Grabherr and Mucina 1993) that encompasses alpine meadows on calcareous substrate. Conspicuously, SG 1 also contained typical species of the \u003cem\u003eOxytropido-Elynion\u003c/em\u003e Br.-Bl. 1950, alpine vegetation of wind-exposed ridges, slopes and cliffs on intermediate to calcareous substrate, as well as one typical species (\u003cem\u003eAgrostis rupestris\u003c/em\u003e) of the \u003cem\u003eCaricion curvulae\u003c/em\u003e Br.-Bl. in Br.-Bl. et Jenny 1926 and one (\u003cem\u003eVaccinium gaultherioides\u003c/em\u003e) of the \u003cem\u003eLoiseleurio-Vaccinion\u003c/em\u003e Br.-Bl. In Br.-Bl. Et Jenny 1926. Arguably, these two species can be interpreted as typical species for more acidophilic (sub-)units within the \u003cem\u003eOxytropido-Elynion\u003c/em\u003e. On the other hand, diploid \u003cem\u003eL. exspectata\u003c/em\u003e correlated negatively to the SGs that contained many more species typical for the \u003cem\u003eSeslerietalia\u003c/em\u003e Br.-Bl. in Br.-Bl. et Jenny 1926 em. Oberd. 1957 (SG 2\u0026ndash;4), species that are typical for the \u003cem\u003eNardetalia\u003c/em\u003e Oberd. ex Prsg. 1949, nutrient-poor meadows on acidic or decalcified substrates (SG 3), and the \u003cem\u003ePoion alpinae\u0026nbsp;\u003c/em\u003eOberd. 1950, more nutrient rich alpine meadows and pastures (SG 2, mainly). Altogether, this can be interpreted as a tendency of \u003cem\u003eL. exspectata\u003c/em\u003e, opposed to \u003cem\u003eL. alpina\u003c/em\u003e, towards habitats with overall harsher climate conditions and therefore lower competition. These findings are in line with previous case studies highlighting greater competitive abilities of polyploids (Lumaret et al. 1987; Sonnleitner et al. 2010) and a preference of diploids towards more open and less competitive habitats (St\u0026aring;hlberg 2009). Moreover, a similar divergence of diploids and tetraploids in relation to hexaploids towards the lower end of the temperature indicator and the upper end of the light availability indicator was recently revealed in a study on \u003cem\u003eSenecio carniolicus\u003c/em\u003e s.l. (Asteraceae), another polyploid complex within the Eastern Alps (Sonnleitner et al. 2016) that partly shares its habitats with our study species. The relative scarcity of \u003cem\u003eL. exspectata\u003c/em\u003e in our study\u003cem\u003e,\u003c/em\u003e even at localities 1 and 3 that are situated in calcareous parts of the Alps, may additionally suggest lower competitiveness, at least at localities where it co-occurs with higher ploidies.\u003c/p\u003e\n\u003cp\u003eOur study also revealed that the hexaploid \u003cem\u003eL. multiflora\u003c/em\u003e grows in sites with slightly, yet significantly, higher soil aeration and light availability, but lower humus content compared to the tetraploid \u003cem\u003eL. alpina\u003c/em\u003e (Fig. 2), even though in general, ecological niches of these taxa highly overlap at locality 2 (Figs. 5, 6). At this locality two individuals were putative pentaploids (5\u003cem\u003ex\u003c/em\u003e) based on RGS measurements. It has been shown that odd ploidies such as pentaploids can act as a bridge for gene flow across ploidies and thus also for the exchange of alleles that might confer adaptation to similar environments (Pt\u0026aacute;ček et al. 2023; Bartolić et al. 2024). Nevertheless, among the Alpine members of \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e, hexaploids possess a much broader elevational range than tetraploid \u003cem\u003eL. alpina\u0026nbsp;\u003c/em\u003e(Bačič et al. 2019; Pungar\u0026scaron;ek et al. 2023), indicating that both species likely have more divergent ecology at a distribution-wide scale than at a local scale.\u003c/p\u003e\n\u003cp\u003eFurther highlighting the significance of geographic scale in investigating ecological niches, we observed pronounced differences in environmental conditions across our three study sites, located up to 160 km apart. These differences in ecological differentiation between ploidies are likely associated with biogeographic differentiation among different ranges of the Eastern Alps (Merxm\u0026uuml;ller 1954; Aeschimann et al. 2004, 2011; Hartmann and Moosdorf 2012; Schuster et al. 2013), leading to variation in accompanying plant species (Fig. 5; Online Resource 4 and 8). Our results show that ecological microsite conditions differ significantly among the three sites, with this divergence surpassing the differences observed between ploidies. Interestingly, localities 1 and 3, which are geographically the furthest apart, were most similar, likely due to the presence of limestone at both sites, whereas the more divergent intermediate locality, site 2, is situated in the predominantly siliceous Central Alps (Aeschimann et al. 2004, 2011; Hartmann and Moosdorf 2012). These findings emphasize the influence of geography and geology on ecological microsite conditions and suggest that local niche divergence between ploidies is negligible compared to site-specific effects.\u003c/p\u003e\n\u003cp\u003eFinally, although polyploids have been hypothesized and empirically shown to exhibit broader ecological niches than their lower-ploid ancestors (McIntyre 2012; Kirchheimer et al. 2016; Zielińska et al. 2024), our data do not support this hypothesis at a local scale. We found no differences in niche breadths between co-occurring ploidies. These findings suggest that patterns of ecological differentiation between ploidies at local scales may differ from those observed at larger geographic scales, underscoring the need for further studies on local co-occurrence of divergent ploidies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements and funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Euregio (project IPN 133-B to BF). We thank Marie Heck and Tatjana Noah Steixner for the help in the field as well as Marie Heck, Marianne Magauer and Daniela Pirkebner for their helpful assistance in the lab.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding acquisition (BF), Conceptualization (BF, VH), Investigation (JG, VH), Formal analyses (JG), Supervision (BF), Visualization (JG), Writing \u0026ndash; original draft (JG); review \u0026amp; editing (VH, BF).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAeschimann D, Lauber K, Moser DM, Theurillat J-P (2004) Flora Alpina, Band 1\u0026ndash;3. 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Ecol Indic 158, 111548. https://doi.org/10.1016/j.ecolind.2024.111548\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"alpine-botany","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"albo","sideBox":"Learn more about [Alpine Botany](http://link.springer.com/journal/35)","snPcode":"35","submissionUrl":"https://www.editorialmanager.com/albo/default2.aspx","title":"Alpine Botany","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Alpine grasslands, ecological indicators, microsite ecology, niche partitioning, polyploidy, small-scale vegetation surveys","lastPublishedDoi":"10.21203/rs.3.rs-5716596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5716596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e is a taxonomically challenging group of angiosperms, whose evolutionary history has been shaped by polyploidy and agmatoploidy (fragmentation of holocentric chromosomes). Several species with different chromosome sizes and numbers, ranging from diploids to hexaploids, occur above timberline in the Eastern Alps. Species of different ploidies frequently co-occur in the same habitats, but the extent of ecological divergence and niche partitioning among them remains elusive, partly due to their high morphological similarity impeding reliable identification. Here, we focused on three mixed-ploidy sites in the Eastern Alps, where morphologically similar alpine species \u003cem\u003eL. exspectata\u003c/em\u003e (diploid), \u003cem\u003eL. alpina\u003c/em\u003e (tetraploid) and \u003cem\u003eL. multiflora\u003c/em\u003e (its hexaploid populations) co-occur. We inferred there ploidy via flow cytometry and characterised their small-scale ecological differentiation using Landolt indicator values of accompanying species that revealed limited ecological divergence between co-occurring ploidies. While diploid \u003cem\u003eL. exspectata\u003c/em\u003e is associated with slightly more basophilic microsite conditions, as it mostly occurs over limestone, no such differentiation was observed between tetraploid \u003cem\u003eL. alpina\u003c/em\u003e and hexaploid \u003cem\u003eL. multiflora\u003c/em\u003e. Our results indicate that small-scale co-occurrence of different cytotypes within \u003cem\u003eLuzula\u003c/em\u003e sect. \u003cem\u003eLuzula\u003c/em\u003e in alpine habitats is accompanied by only a slight niche partitioning, whereas there were significant differences in ecological parameters among the sites. These findings emphasise the influence of geography and geology on ecological microsite conditions and suggest that local niche divergence between ploidies is negligible compared to site-specific effects. Different ploidies thus likely have more divergent ecology at a distribution-wide scale than at a local scale\u003c/p\u003e","manuscriptTitle":"Co-occurring Luzula species (Juncaceae) of different ploidies in alpine grasslands of the Eastern Alps exhibit negligible ecological differentiation at small geographic scale","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 10:58:30","doi":"10.21203/rs.3.rs-5716596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-01-06T15:35:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-06T15:30:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-28T01:03:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Alpine Botany","date":"2024-12-26T09:00:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"alpine-botany","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"albo","sideBox":"Learn more about [Alpine Botany](http://link.springer.com/journal/35)","snPcode":"35","submissionUrl":"https://www.editorialmanager.com/albo/default2.aspx","title":"Alpine Botany","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6a7a5e6d-d073-4ad9-8bf9-e9e9ff06db3c","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:07:27+00:00","versionOfRecord":{"articleIdentity":"rs-5716596","link":"https://doi.org/10.1007/s00035-025-00331-5","journal":{"identity":"alpine-botany","isVorOnly":false,"title":"Alpine Botany"},"publishedOn":"2025-05-20 15:58:22","publishedOnDateReadable":"May 20th, 2025"},"versionCreatedAt":"2025-01-08 10:58:30","video":"","vorDoi":"10.1007/s00035-025-00331-5","vorDoiUrl":"https://doi.org/10.1007/s00035-025-00331-5","workflowStages":[]},"version":"v1","identity":"rs-5716596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5716596","identity":"rs-5716596","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}