Diversity of Mountainous Lawn Grasses

Mountainous grasslands harbour a diverse range of habitats and biological niches as a result of altitude variability, often dominated by herbaceous and non-woody plants at high elevations (Schirpke et al., 2017). The Norwegian Scandes Mountains stretch to the Arctic Circle, where increased temperatures are melting the ice. The Scandinavian montane grasslands are a tundra ecoregion, regarded as high priority for conservation (WWF, 2000). As rising subarctic temperatures cause these mountains to warm faster than the global average (Lind et al., 2022), the impacts of climate change on their landscape biodiversity are becoming increasingly relevant.

Elevation gradients can explain biodiversity in terms of temperature lapse rate (TLR), whereby temperature decreases with elevation (Rolland, 2003). However the spatial distribution of biodiversity along elevation gradients is also affected by various factors including topography and interspecific interactions (Lomolino, 2001). Therefore, elevation gradients should be assessed alongside biogeographic factors to gain a comprehensive understanding of multivariate effects on regional and landscape biodiversity. For instance, topographic variation in tundra habitats (e.g. aspect, landscape ruggedness, urban planning) can drive localised temperature differences (Kiedrzyński et al., 2024; Aalto et al., 2022). Global warming is also causing higher shifts in precipitation from snowfall to rainfall in the Scandes (Lind et al., 2022), modifying the selective pressures faced by native species. This includes changes to soil moisture, temperature, and nutrient availability which influences the distribution of plant and soil microbiota communities (Zádrapová et al., 2024). The symbiotic endophytes associated with common florae provide essential metabolic resources to hosts, promoting plant growth and immunity (Omomowo et al., 2019). In particular, a group of endophytic fungi called Dark Septate Endophytes (DSE) has been identified in elevations up to 6150m (Kotilínek et al., 2017). The relative abundance, distribution, and diversity of plants and endophytic microbiota in such habitats can inform us of soil quality and ecosystem health. Growing up to altitudes of 3350m (St. John et al., 2012), Red Fescue (Festuca rubra) is a highly resilient cool season lawn grass, widespread across arctic and temperate zones of the Northern Hemisphere (Barkworth et al., 2007; POWO Kew, 2020). F. rubra can thrive in nutrient poor conditions (Shamim et al., 2021), inhabiting a range of climates from moist meadows to drier road verges and pastures. It is known to colonise disturbed habitats by spreading rhizomes (St. John et al., 2012) and outcompete other Festuca species, achieving greater land coverage over time. (Ferschl et al., 2024; Wallace et al., 2017). Drought tolerance exhibited by F. rubra (St John et al., 2012) suggests that diversity of associated bacteria and dicot species decreases with moisture, whereas endophytic fungi require higher moisture for growth and metabolism (Talley et al., 2002). The high prevalence of F. rubra is useful for data extrapolation across different grassland habitats.

Microclimate evaluation is a practical way to explain wider macroclimate heterogeneity (Dorigo et al., 2021). Little research exists regarding the effect of elevation gradients on biodiversity while also considering other correlates. Therefore we have used a microclimate model to examine the variable effects of precipitation, moisture and temperature on landscape and regional diversity. This can inform agricultural practice (Seoane et al., 2006), which is beneficial to local farming economies. Hence, there is both financial and ecological value to understanding complex relationships within montane grassland communities, and responses to environmental shifts under the growing threat of climate change.

Genomic data of endophytic symbionts associated with Festuca rubra (red fescue) were provided by a Norwegian research team, collected at 12 sites in the Scandes Mountains. Metagenomic sequences on 16S ribosomal RNAs and Internal Transcribed Spacers (ITS) were performed to identify taxonomic groups for bacteria and fungi, respectively. The output was 200 Amplicon Sequence Values (ASVs) for each location and domain. ASV abundances were estimated using a rarefaction analysis to account for varying sequence depths. A factorial design allowed integration of 2 IVs as discrete categories; Temperature (6.5, 8.5, 10.5 °C), and Precipitation (600, 1200, 2000, 2700 mm) using expected values at each site. Data was collected for each combination. For bacterial and fungal diversity, linear models were used to assess correlations between Shannon’s Diversity Index (H’) and temp or precip. Differences between categories, including precip-temp interactions, were analysed using 2-way ANOVA. TukeyHSD was used post-hoc for pairwise comparison. Assumptions of a 2-way ANOVA: (i) observations are independent, (ii) observations are normally distributed in every sample. (iii) variances of the samples are the same. Homoscedasticity (iii) and linearity of the model were checked visually using residuals vs fitted plots, and (ii) using a Shapiro-Wilk test. Taxonomies were then grouped by class instead of phylum to investigate correlations in precip / temp for specific subgroups. Multiple linear regressions were performed to identify these groups. P-values were adjusted for multiple comparisons. Correlations were visually assessed by examining residuals either side of a reference line (y = x).

Diversity of dicot species associated with lawn grass was assessed in a local sloped microclimate (F.rubra grassland, hypotenuse 12m), with elevation quantified as distance from road (m). Long-term records of soil moisture and temperature allowed correlation modelling between elevation (E), soil moisture (M) and temperature (T) to predict M and T at any given location. Random sampling with 0.5m quadrats provided data on the abundance (% cover) of 11 target herb species (Species A-K) using morphological identification. A specimen from each species was analysed using PCR and Sanger sequencing. Electropherogram outputs were analysed using BLAST to identify taxonomies. Regression analysis assessed the predictive models for the York study site. H’ was then calculated for each sample position. Linear regression analysis and model fitting was conducted to assess correlations between response (H’) and explanatory variables (E, M, T). Outliers were removed prior. Linear modelling was additionally used to assess correlations between (i) abundance of individual species and E, and (ii) abundance of species-species to identify relationships indicating shared environmental preferences.

Results from a 2-way ANOVA [Table 1.] found precip significantly affects fungal diversity (F(3)=4.7265; p<.005). There was insufficient evidence for temp affecting H’, however there were significant temp-precip interactions (F(6)=2.6862; p<.05). This is likely because temp affects evaporation rates and in turn, atmospheric moisture. TukeyHSD post-hocs found H’ was significantly higher when temp=10.5, precip=2000 versus precip=1200 [Fig. 1b]. No such differences were observed in lower temperatures. This suggests root symbiont fungi are more sensitive to changes in precipitation at high temperatures, and that fungal diversity is maximal in warm, moist conditions. Although fungal diversity was significantly affected by changes to precip / temp, we could not identify any specific fungal classes significantly associated with precip or temp. However, ASV abundance of the Exophiala genera was positively associated with temp (F(2,91)=10.16; R^2 = 0.1645; p<.0005).

2-way ANOVA [Table 2] found no significant effects of precip or temp on bacterial diversity alone, however interactions were significant at a 5% level. As normality was only partially met, this finding should be interpreted with a degree of caution. Bacilli (p<.05) and Alphaproteobacteria (p=0.02755189) were significantly correlated with precip and / or temp. Overall, precip and temp explained a significant amount of variation in Bacilli abundance (F(2,91) =6.931; R^2 =0.1131; p<.005 **) and Alphaproteobacteria (F(2,91) =6.678; R^2 =0.1088; p <.005). Bacilli was significantly positively correlated with temp, and significantly inversely correlated with precip [Table 3a.], suggesting a preference for warm, moderately dry conditions. However, visual assessment of the residuals plot for Bacilli suggested a linear model was not viable; an exponential model may have been a better fit. Hence, these correlations should be interpreted with caution as we cannot deduce meaningful relationships.

Alphaproteobacteria abundance shared no significant relationship with temp, however it was significantly inversely correlated with precip [Table 3b.]. Residuals supported the model fit. Subgroup order Rhizobiales was the most abundant, followed by Sphingomonadales. Both orders are frequently observed in Poaceae rhizospheres (Blaine et al., 2017; Jha et al., 2019). Examination of residuals suggested both linear models were accurate fits. Rhizobiales was negatively correlated with precip as seen in Table 3c. (F(2,91) =6.58; R2 =0.1071; p<.005). Although Sphingomonadales was inversely correlated with temp [Table 3d.], the regression model was not significant (F(2,91) =2.043; R2 = 0.02194; p>.05). It is therefore reasonable to assume order Rhizobiales contributes to the trend in precip observed in Alphaproteobacteria. Rhizobiales family Xanthobacteraceae occupied the same significant trend in precip (F(2,91) =5.705, t =-3.360, p<.005, R^2 adj=0.09189), suggesting this family of bacteria has a preference for, or is tolerant of, drier habitats.

Linear microclimatic models of E and M explained 46.48% of variance in plant diversity data (F(1,77) = 68.74; p <.005), T explains 42.93% (F(1,77) = 59.67; p <.005). T was weakly positively correlated with H’, with a gradient of 0.1003, 95% CI [0.1258, 0.0742]. Correlations between H’ and E / M were comparably weaker. E against H’ exhibited a positive gradient of 0.04908, 95% CI [0.03729, 0.06087], while for M this was -0.03108, 95% CI [-0.02362, -0.03855]. This suggests temperature has the greatest effect on dicot diversity. However, given the interaction between E, T and M seen in the predictive correlation models [Fig. 4.], E and M may still contribute to this combined effect (e.g. greater elevation may increase sunlight exposure for photosynthesis, increasing plant metabolism). Such confounding variables may also explain the variation in data.

A moderate positive correlation was identified between Species J (Centaurea nigra, Table 4.) and elevation [Fig. 6.]. The null hypothesis was rejected and the alternative accepted. This suggests growth of C. nigra improves at higher elevations, likely resulting from increased light availability and moderate-low moisture, its preferential conditions (Hill et al., 1999). Significant correlations were also identified between Species D-F, D-J, and C-I [Fig. 5.], indicating shared environmental preferences.

The notion that fungi are more sensitive to changes in precipitation at high temperatures is supported by a meta-analysis (Yadav et al., 2025), which found significant seasonal differences in fungal colonisation frequencies; monsoon (80.9%), winter (61.8%) and summer (51.4%). Aligning with our findings, this is likely due to higher resource availability and decreased risk of desiccation in warm, moist versus warm, dry climates. The Exophilia genus, positively correlated with temp, is known to comprise DSE species tolerant of extreme temperatures (Oliveira et al., 2025; de León et al., 2026). However, this does not indicate high temperatures are preferential. An alternative explanation could be decreased competition with other fungi due to increased salinity, an indirect result of increased temperature due to evaporation (Khamidov et al., 2022). Existing literature does not align with our findings on bacteria. Longitudinal research (Zádrapová et al., 2024) has found significantly higher nutrient levels (Al, Fe, P, S) and Proteobacteria abundance in soils exposed to high versus low precipitation. This contrasts our findings of Alphaproteobacteria, indicating that nutrient availability induced by precip contributes to changes in microbial diversity. Alternatively, herbivory gradients may explain the variation in soil microbiota. All 3 Festuca species are major food sources for Scandinavian reindeer herds (Rangifer tarandus) during the feeding season (Granath et al., 2007). Seed dispersal brought about by this mutualism may cause distribution variance, which was not factored in this experiment.

Factorial design limited the parameters under which temp and precip could be examined. Consequently, the macroclimate model fails to predict changes in microbial abundance under extreme conditions. Genomic data also contained taxonomic errors such that 2 ASVs belonging to family Xanthobacteraceae were incorrectly classified as Rhodoplanes, indicating potential for critical Type I and II errors. Moreover, existing literature describes Epichlӧe festucae, a common fungal symbiont found in aerial organs of 65.7% of F.rubra (Pereira et al., 2019; Wallace et al. 2017). As genomic data came purely from root endophytes, no E. festucae ASVs were identified. Investigating aboveground microbiota such as E. festucae may have provided greater insight into how important symbioses are affected along environmental gradients.

Microclimate analysis provided interesting small-scale findings, using predictive correlations. Species J (C. nigra) dominates British NVC community MG5; grassland communities characterized by a preference for well-drained soils (Wallace et al., 2017). This aligns with correlates indicated by the elevation gradient [Fig. 6.]. However, moistures and temperatures exceeding optimum for C.nigra growth still reveal interacting confounders, including CO2 concentration, soil pH, and CO2:NO2 ratios (Quaderi et al., 2013). Both J and F were significantly correlated with D, but not one another, despite being known to share similar elevation and habitat preferences (Hill et al., 1999; ADHB, 2008). It is more likely their coexistence with D is due to shared resources than plant-plant interspecific mutualism (Li et al., 2025). Future investigation should assess effects of environmental variables in J, D, and F on a larger scale to explore these relationships more comprehensively. Elevation only explained 48.46% of microclimatic variance. The remainder is often an indirect result of broader factors (eg. precip influencing nutrient availability, temp influencing light availability). The model was also limited in that it could not uniformly explain and predict topographic variation in the Scandes; although temperature was correlated with elevation in the local microclimate, TLR may have the opposite large-scale impact (Rolland, 2003). The extent of this is particularly relevant as global warming is increasing upper elevation thresholds for montane grassland communities (Lamprecht et al., 2018). Future research should employ factorial design with wider parameters to examine variables independently and identify causal relationships. Herbivory gradients could be monitored via herd tracking. It is challenging and tedious to replicate and predict changes in mountainous landscapes without confounding influence. To overcome this, simulated microclimate modelling could be used (Hungerford et al., 1989) whereby statistical algorithms are developed to represent the actual conditions faced by organisms. The output can then be used to explain and predict large-scale biodiversity responses to climate change.

Overall, this study suggests diversity of rhizospheric bacteria and plants associated with lawn grass decreases with moisture. It revealed that while fungal endophytes prefer higher soil moisture contents, this can be limited by temperature, demonstrating the importance of multivariate analysis to understand changes in micro and macroclimate biodiversity along environmental gradients.

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