Predictive Models of the Species Diversity and Biomass of the Herb Layer During Changes in the Stands of Dark Coniferous Forests

Introduction

The study of forest biodiversity, productivity and dynamics is one of the priority areas of forest ecology and forest science in many countries. At the same time, the understorey layers are still insufficiently studied. The aim of this work is to study the interactions between the stand and the herb layer in order to develop predictive graphical models.

Methods

Genetic forest typology [1] and generally accepted methods of studying forest vegetation [2] are chosen as the methodological basis. We have studied the most widespread and productive forest type in the South Urals (Russia): moss spruce forest. Primary dark coniferous forests and secondary birch and aspen forests of different compositions and ages were studied.

Results

The studies carried out confirmed our null hypothesis regarding the possibility of modelling the species diversity and biomass of the herb layer according to the structure and age of the stand. All the criteria studied: the species richness of the plant community, the biomass of the herb layer, as well as the α-diversity estimated on the basis of diversity indices, showed a high sensitivity to the age and composition of the stand.

Conclusions

The models we have developed can be combined with remote sensing data to provide large-scale data on difficult-to-measure but important characteristics: the biodiversity and biomass of the herb layer.

Funding

This research was funded by the state assignment of the Institute Botanic Garden, the Ural Branch of the Russian Academy of Sciences.

References

1. Ivanova, N.; Fomin, V.; Kusbach, A. Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics. Sustainability 2022, 14, 3384. https://doi.org/10.3390/su14063384
2. Ivanova, N. Research Methods of Timber-Yielding Plants (in the Example of Boreal Forests). In Biology, Productivity and Bioenergy of Timber-Yielding Plants; Springer: Cham, Switzerland, 2017; pp. 121–137. https://doi.org/10.1007/978-3-319-61798-5_2