Meadow biomass contributes to understanding the potential of carbon sequestration

Assessing the functional structure of plant communities and their productivity helps to determine the contribution of biological diversity and primary productivity to ecosystem services, the most significant of which are provisioning and regulating services.

The former determine the amount of biomass produced in the future available for use as biological resources by humans, the latter - the formation of the habitat of natural and anthropogenically modified ecosystems. The assessment and reliable forecast of the productivity of meadow communities are largely determined by the approaches and methods used, most of which require laborious field and laboratory research.

The use of information on the species composition of the plant community and its functional structure in determining the primary productivity can be expanded by using modern information databases of geobotanical data.

The identification of practically significant functional groups of species (cereals, forbs and legumes) in the composition of meadow communities of hayfields and pastures and the determination of the dominant species make it possible to include indicators of biological diversity in the procedure of assessing the productivity of agricultural lands of pastures and hayfields.

The paper discusses the experience of predicting the value of the aboveground phytomass of meadow ecosystems using data on the functional composition and projective cover of species. The performed statistical analysis of the data confirms the assumption that there is a relationship between the community species composition and its productivity.

Based on the main provisions of the dominance hypothesis, by constructing a statistical linear model, the possibility of predicting the value of aboveground biomass was tested based on the data on the species composition of communities and the abundance of dominant species and functional groups of plants, which act as universal evaluation criteria.

The team studied secondary meadows formed on the site of subtaiga coniferous-deciduous forests, deciduous forests and forest-steppe. With regards to anthropogenic load, the studied meadow communities are built in a gradient from not currently used, overgrown hayfields within the boundaries of the reserve, to active hayfields and pasture meadows.

Using the Flora database which includes more than 18,000 plots, the JUICE v7.0 software was utilized to carry out the classification of communities and habitats. The investigated meadows are classified as vegetation classes of the Braun-Blanquet system (EVC), as well as habitat types of the EUNIS Habitat Classification [Davies et al., 2004].

To form an empirical training sample on ten of the 32 investigated trial plots in the peaks of the growing seasons of 2017-2019, the registration of the aboveground phytomass was performed. Statistical data processing and model building were undertaken in R software.

To identify the relationship between the biomass value and the type of habitat, vegetation class and intensity of use, cluster analysis was carried out and communities were ordinated using the principal component analysis method. To predict biomass, regression analysis was used - building a linear model using empirically obtained data as a training sample.

To assess the quality, the model was cross-validated (leave-one-out cross-validation). The predictive statistical model shows the relationship between the predicted biomass value for a community with the abundance of the main functional groups of plants, the nature of use, and the result of assigning the community to the classification categories of the EVC and EUNIS systems. The applied classifications, based on species lists and indicators of the projective cover of species, bring the biodiversity component into the further assessment of the productivity of communities.

The use of the developed linear regression model allows, with a sufficiently high degree of reliability, to assess the productivity of meadow communities that are similar in species composition and belong to the same classification categories, without directly collecting data on the biomass produced.

The model is adjusted to take account of the contribution of the species composition of plants to the provisioning ecosystem services, providing the development of an accessible method for their economic assessment. Thus, the study found a way to include the indicators of species diversity both in the assessment of provisioning ecosystem services and in the successful forecast of the biomass of individual communities, relying only on data on their species composition.

When assigning the observed community to a certain class of vegetation and type of habitat, and in the presence of coefficients reflecting the abundance of functional groups of species that form a community, a large-scale assessment of the production potential of the vegetation cover of territories is possible when planning environmental management scenarios.

Keeping in mind the ongoing climate change, there is a need for a more detailed assessment of carbon sequestration by natural communities. The use of indices of the functional diversity of communities and additional plant functional traits will make it possible to more accurately assess the accumulation of carbon in tree crowns and in the ground cover, which solves the practically significant problem of assessing the ability of natural ecosystems to sequester excess atmospheric carbon.

The use of remote sensing methods for accounting for vegetation cover in combination with ground-based studies in key areas of the allocated classification units and the development of spatial statistical models should become a methodological basis for monitoring observations of climate change.

Research Report: "Using Data on Species Diversity in Predicting Meadow Ecosystem Biomass"

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