Are endemic species necessarily ecological specialists? Functional variability and niche differentiation of two threatened Dianthus species in the montane steppes of northeastern Iran

Plant functional variability

In total, 78 species occurred (cover ≥ 5%) at the different sites, creating the set of species over which CSR strategies were assessed (Fig. 2; Table S2). A clear dominance of relatively stress-tolerant strategies was evident across the sites; indeed, most species showed a proportion of S exceeding 50% (Fig. 2, Supplementary Figs. S1, S2).

Dianthus pseudocrinitus was the only Dianthus species that exhibited general functional divergence, ranging from strong ruderalism at the Rein site (R; C:S:R = 12.0:7.2:80.8%), an intermediate strategy at Rakhtian and Misino (S/SR; C:S:R = 2.8:75.9:21.3%; and C:S:R = 7.4:70.5:22.1%, respectively), to strong stress-tolerance at the Biu Pass site (S; C:S:R = 6.8:82.3:10.9%) (Fig. 2). Differences among D. pseudocrinitus populations at different sites were apparent for S-selection (ANOVA on arcsine transformed data, predictor variables were sites and response variables were the percentage CSR-scores; f = 34.386, df_numerator = 3, df_denominator = 37, p = 0.000) and R-selection (f = 43.707, df_numerator = 3, df_denominator = 37, p = 0.000) but not for C-selection (f = 2.801, df_numerator = 3, df_denominator = 37, p = 0.054), with a Tukey’s post-hoc multiple comparison on data for R-selection (i.e. the highest f-value), suggesting that populations at all sites differed from one another, except for those at Misino and Rakhtian.

In terms of interspecific differences, analysis of variance (ANOVA) showed that D. pseudocrinitus differed significantly from the community mean at the Rein site in terms of R-selection (f = 46.982, df_numerator = 16, df_denominator = 146, p = 0.000) and S-selection (f = 44.601, df_numerator = 16, df_denominator = 146, p = 0.000; arcsine transformed data, with species (i.e. taxa present in the plant community) as the predictor variables and percentage CSR-scores as the response variables). Crucially, that D. pseudocrinitus exhibited extensive intraspecific variability was evident as extreme values of strategy variance (s²) compared to the intraspecific variability of sympatric species at the Rakhtian and Rein sites (Table 1). Note that the CSR strategy variability evident for sympatric species is presented in greater detail in Fig. S3.

Dianthus polylepis subsp. polylepis exhibited an extreme stress-tolerant strategy (C:S:R = 0.1:99.1:0.8%) across all sites (Fig. S1). Most sympatric species at sites of D. polylepis subsp. polylepis represented a broadly stress-tolerant strategy (Fig. S1), but interspecific functional variability was evident, including subordinate species (mean cover percentage 5.5–9.0%) with relatively generalist, intermediate strategies (Fig. S1). Intraspecific differences in Dianthus polylepis subsp. polylepis between sites were apparent for C-selection (ANOVA on arcsine transformed data, predictor variables were sites and response variables the percentage CSR-scores; f = 7.599, df_numerator = 5, df_denominator = 48, p = 0.000) and S-selection (f = 6.686, df_numerator = 5, df_denominator = 48, p = 0.000) and R-selection (f = 8.099, df_numerator = 5, df_denominator = 48, p = 0.000), with a Tukey’s post-hoc multiple comparison on data for R-selection (i.e. the highest f-value) suggesting that the population at Bezd was distinct from other sites.

Dianthus polylepis subsp. binaludensis exhibited an extremely stress-tolerant strategy (C:S:R = 0.5:99.5:0.0%) at all sites except Zoshk, where it exhibited an intermediate S/SR strategy (Fig. S2). Intraspecific differences in D. polylepis subsp. binaludensis between sites were apparent for C-selection (ANOVA on arcsine transformed data, predictor variables were sites and response variables the percentage CSR-scores; f = 2.801, df_numerator = 4, df_denominator = 46, p = 0.054), S-selection (f = 25.796, df_numerator = 4, df_denominator = 46, p = 0.000) and R-selection (f = 18.476, df_numerator = 4, df_denominator = 46, p = 0.000), with a Tukey’s post-hoc multiple comparison on data for S-selection (i.e. the highest f-value) suggesting that the population at Zoshk was distinct from other sites. At Zoshk, Dahane Jaji and Dizbad, D. polylepis subsp. binaludensis exhibited significantly lower C-selection (p ≤ 0.05) with respect to the community mean (t tests within site on arcsine-transformed data).

Site and environmental variables

The canonical correspondence analysis (CCA) (Fig. 3) was constrained by a matrix of soil and topographic data and bioclimatic variables. Seven soil variables (clay, silt, sand, EC, P, CEC and organic carbon) and 15 bioclimatic variables were eliminated from the environmental data set owing to high collinearity (VIF > 10). Soil organic matter, pH, N, K, lime, elevation, and aspect were the edaphic/topographic variables exhibiting the highest levels of significance (p < 0.05; Table 2). Among the 19 bioclimatic variables, annual mean temperature (bio1), temperature seasonality (bio4), annual precipitation (bio12), and precipitation of the wettest quarter (bio16) exhibited the greatest significance. All canonical axes were significant (p = 0.001) (Fig. 3; Table 2).

Soil and topographic variables showed significant effects on species composition, with eigenvalues for the first four axes of 0.360, 0.284, 0.238, and 0.216, respectively (Fig. 3). The first four axes explained 70.1% of the total variation, with CCA1 accounting for 23.0%. The effect of elevation was greater than that of other factors (r²= 0.578). For soil factors, organic matter (r² = 0.514), lime (r²= 0.458), and total nitrogen (r²= 0.379) were the strongest explanatory variables, while pH and K showed weaker associations (Table 2). Topographic factors had greater impacts on plant species identity at sites for D. polylepis subsp. binaludensis, and elevation had the greatest r². However, these factors were negatively associated with some sites of D. polylepis subsp. polylepis and D. pseudocrinitus. Thus, elevation was a major gradient in differentiating the distributions of these species. Of the environmental variables, total N, organic matter, and lime had positive correlations with all sites of D. pseudocrinitus and with Balghur and Kardeh Dam sites for D. polylepis subsp. polylepis. Soil nutrients, particularly total N, were the main environmental factors influencing vegetation properties at these sites.

Four bioclimatic variables had significant associations with species composition (Fig. 3B, Table 2). Precipitation of the wettest quarter (bio16) was the strongest bioclimatic variable (r²= 0.824), positively correlated with most sites for D. polylepis subsp. binaludensis and the Balghur and Khowre-Kalat sites for D. polylepis subsp. polylepis. Annual precipitation (bio12) and temperature seasonality (bio4; r²= 0.806 and 0.438, respectively) were also associated with plant community variability; both variables were positively correlated with all D. pseudocrinitus sites, but negatively with some sites of D. polylepis subsp. polylepis and D. polylepis subsp. binaludensis. The explanatory power of annual mean temperature (bio1) was low (r²= 0.090). It was positively correlated with the sites of Kardeh Dam and Bezd, for D. polylepis subsp. polylepis, and the Dizbad and Moghan sites for D. polylepis subsp. binaludensis, and negatively correlated with sites where D. pseudocrinitus occurred. These findings indicate considerable effects of multiple edaphic, topographic, and bioclimatic factors on the vegetation, rather than a single overarching environmental factor.

Community CSR scores and environmental factors

These analyses revealed significant correlations between certain soil properties, bioclimatic variables, and CWM strategy scores (Table S3). We noted significant, positive correlations between the degree of C-selection (i.e. CWM-C) and several temperature and precipitation variables, as well as some negative correlations. Indeed, the strongest community-level correlations with environmental factors were between CWM-C and precipitation of driest month (bio14), precipitation seasonality (bio15), precipitation of driest quarter (bio17), and precipitation of warmest quarter (bio18), with the highest correlation coefficient (between CWM-C and temperature annual range, bio7) being 0.4428 (r²= 0.1150, p = 0.0029; Table S3). The degree of stress-tolerance in the community (CWM-S) generally showed the opposite pattern: mostly negative correlations with the same factors, although mean temperature of the wettest quarter (bio8) and precipitation of the driest month (bio14) were not significant. The extent of ruderalism in the community (CWM-R) was not correlated significantly with any climatic factors (Table S3).

Soil pH and lime content were the only soil variables that showed significant (all negative) correlations with CWM-C. In contrast, silt, pH, K, and lime showed significant positive correlations with CWM-S, and sand and P exhibited significant negative correlations (Table S3). CWM-R exhibited essentially the opposite pattern of correlations to those of CWM-S, but was positively correlated with soil N content, and not related to K and lime.

Source: Ecology - nature.com