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Using Data on Species Diversity in Predicting Meadow Ecosystem Biomass

Authors
  • Rogova, T. V.1
  • Sautkin, I. S.1
  • Shaykhutdinova, G. A.1
  • Chizhikova, N. A.1
  • 1 Kazan Federal University, Kazan, 420008, Russia , Kazan (Russia)
Type
Published Article
Journal
Contemporary Problems of Ecology
Publisher
Pleiades Publishing
Publication Date
Sep 01, 2021
Volume
14
Issue
5
Pages
483–491
Identifiers
DOI: 10.1134/S1995425521050115
Source
Springer Nature
Keywords
Disciplines
  • Article
License
Yellow

Abstract

Abstract—The assessment and reliable prediction of the productivity of grassland communities are largely determined by the approaches and methods used. The use of information on the species composition of the plant community and its functional structure in determining the primary production expands the possibilities of using modern information databases of geobotanical data. Selecting practically significant functional groups of species (graminoids, motley grasses, and legumes) in the composition of grassland communities of hayfields and pastures and determining dominant species allows one to include indicators of biodiversity in estimating the productivity of agricultural lands. Experience in predicting the amount of aboveground phytomass of grassland ecosystems using the data on the functional composition and projective cover of species is discussed. Cluster analysis has confirmed the assumption of the relationship between community biodiversity and its productivity. Based on the main provisions of the dominance hypothesis, by building a statistical linear model, the possibility of predicting the value of aboveground biomass from data on the species composition of communities and the abundance of the dominant functional groups of plants, which act as universal evaluation criteria, has been tested. The predictive statistical model is constructed on the basis of processing experimental data received from 32 sample geobotanical areas. The model shows the relationship between the value of the predicted biomass for the community and the abundance of the main functional groups of plants, the way they are used, and the result of assigning 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 a component of biodiversity in the further evaluation of community productivity. The use of the developed linear regression model makes it possible to estimate the productivity of grassland communities similar in species composition and belonging to the same classification categories with a sufficiently high degree of reliability without the direct collection of data on the produced biomass. The model makes it possible to take into account the contribution of plant species composition to the provision of productive ecosystem services, providing the development of an accessible technique for their evaluation.

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