Increasing abundance of an invasive C4 grass is associated with larger community changes away than at home
Hábenczyus, Alida Anna et al. (2022), Increasing abundance of an invasive C4 grass is associated with larger community changes away than at home, Dryad, Dataset, https://doi.org/10.5061/dryad.612jm645g
Aim: We evaluated the stands of the invasive grass, Sporobolus cryptandrus in its native North American and non-native European range, where it is a recent invader. Our aim was to reveal how the species’ increasing abundance affects functional diversity and ecosystem service provisioning capacities of plant communities in both ranges.
Location: Sand grasslands in the Kiskunság, Hungary, and in Montana, USA.
Methods: All vascular plant species and their relative abundances were recorded in a stratified random manner in 1m×1m plots in each range, using the following cover categories of Sporobolus as strata: 1–25%, 26–50%, 50–75%, and 75–100%. The functional characteristics of the plant communities of the two continents were compared. We performed the comparisons of the communities both with and without including Sporobolus.
Results: Increasing Sporobolus cover resulted in a lower functional diversity and species richness, reduced average specific leaf area, and increased height of the whole plant communities in both ranges but these effects were significantly stronger in the non-native stands. Sporobolus also negatively affected the cover of insect-pollinated plant species and the proportion of native perennials, switching the rest of the community from perennial-dominated to annual-dominated. In the plant communities without Sporobolus, increasing Sporobolus cover led to higher specific leaf area and seed mass in both ranges, but average height was decreasing along the Sporobolus abundance gradient in the native range, while it was increasing in the non-native range.
Conclusions: The spread of Sporobolus, away from its native range, leads to the impoverishment of host communities and compromises the biomass and floral resource provisioning capacity of the vegetation to higher trophic levels. Tackling the spread of this new invader should therefore be a priority task.
We selected four sites both in the native (Montana, USA) and non-native (Kiskunság, Hungary) Sporobolus cryptandrus habitats between July and September 2019. We established 1 m × 1 m plots in each site in a stratified random manner, using the following cover gradient categories of S. cryptandrus as strata: 1–25%, 26–50%, 50–75%, and 75–100%. We aimed to select ten plots for each cover category in all sites; however, as some categories were underrepresented, we obtained a total of 101 plots in the USA and 153 plots in Hungary. We recorded the percentage cover of all vascular plant species in the plots, including S. cryptandrus.
Since the functional role of organisms in ecosystems depends on their traits rather than on their taxonomical affiliation, we used a trait-based approach to study the effects of increasing S. cryptandrus cover on the vegetation. We considered a wide range of traits, encompassing the entire life history of the species and their ecosystem-level effects (Table 1). LHS (Leaf–Height–Seed) traits, including leaf area (LA), specific leaf area (SLA), vegetative plant height, and thousand-seed mass (hereafter seed mass) provide information on interspecific interactions (Westoby, 1998; Klimešová & Pyšek 2011); SLA also correlates with photosynthetic efficacy and palatable biomass for herbivores (Wilson et al. 1999). Pollination type, complemented with the mean range of flowering period makes a link between plants and pollinators by providing information about floral resources. We used growth form (a simplified version of the original Raunkiaer’s life forms; Raunkiaer, 1934) as a proxy to describe the general appearance of the vegetation. In the case of species missing from the available databases, we used trait values of the most closely related, morphologically similar species published in the database. If multiple records were provided for a species, we used the average value.
To characterize the diversity of the surveyed grasslands, we used plot-level species richness and Rao’s quadratic entropy, including all the collected traits in the analysis. The latter is a measure of functional diversity, expressing the mean functional distance between two randomly chosen species in the plots, while also accounting for the abundances of the species (Botta-Dukát, 2005). For the continuous traits, we calculated community-weighted means (CWMs) for each plot, while for categorical variables we used the cumulative cover of each category without any transformation. We prepared linear mixed-effect models to quantify the effects of S. cryptandrus cover, region (i.e. Montana and Hungary) and their interaction on the vegetation descriptors. We used site identity within a region as a random factor. The values of the response variables and the cover values of S. cryptandrus were scaled prior to fitting the linear mixed-effect models. We involved traits that are widely used to trace community-level changes or responses, and supposedly reflect ecosystem functions. Ecosystem functions are determined by whole plant communities but the direct effects of S. cryptandrus may be better understood if scrutinizing only the rest of the vegetation along the S. cryptandrus cover gradient. Therefore, we performed the analysis in two ways: (i) using whole vegetation datasets together with S. cryptandrus, and (ii) reduced datasets by omitting S. cryptandrus.
All analyses were performed in R version 4.0.3. (R Core Team 2020). Rao's quadratic entropy and CWMs were calculated with the ‘dbFD’ function of the FD package (Laliberté et al. 2014). We used the ‘lmer’ function of the lme4 package (Bates et al. 2015) to build the linear mixed-effect models, and the ‘Anova’ function of the car package (Fox & Weisberg 2019) to test the effect of the predictors. We used the ‘rsq’ function of the ‘rsq’ package (Zhang, 2021) to obtain marginal R2 values, which represent the proportion of variation explained by the fixed-effects factors.
Labels of spreadsheet 'Plot Records' are supposed to be evident after reading the 'Methods'. For additional information on the content of spreadsheet 'Traits x Spp', see 'Read Me'.
Ministry for Innovation and Technology of Hungary, Award: UNKP-19-2-SZTE-47
Hungarian Scientific Research Fund, Award: PD132131
Hungarian Academy of Sciences, Award: Bolyai János Research Scholarship
Ministry for Innovation and Technology of Hungary, Award: UNKP-21-5-SZTE-591
Hungarian Academy of Sciences, Award: PD137747
Hungarian Academy of Sciences, Award: K124796
Hungarian Academy of Sciences, Award: K119225
Hungarian Academy of Sciences, Award: K137573