Contribution of epiphytes and fog to patterns of atmospheric fluxes in mountainous forests (Picea abies [L.] and Pinus cembra [L.]) - Lemenkova, Polina (Acta herbologica, 2025)
Water balance in coniferous forest dominated by Picea abies [L.] and Pinus cembra [L.]) is a central process contributing to global carbon and water cycling. Quantifying the role of the major biotic and abiotic agents of water balance, i.e. lichens and fog, is thus important for a better understanding of this process. Methods to quantify water balance, such as evapotranspiration, precipitation, temperature suffer from several shortcomings, such as destructive sampling or subsampling. We developed and tested a Python-based statistical approach based on computed environmental and climate parameters obtained from Eddy covariance measurements of coniferous forests from a field experiment with dominated Swiss pine and spruce as major tree species. We quantified the volume of key meteorological parameters in forest canopies with old (>200 y.o.) and young (< 30 y.o.) trees and relative water vapour volume showing signs of contribution from fog. The data were compared using Matplotlib library of Python for statistical analysis for both types of trees. Fog and lichens were identified with high accuracy and strongly correlated with water content in coniferous forests. Our data show that this is a powerful approach in silviculture to quantify water balance by Python and statistical analysis of datasets. In contrast to other methods, programming libraries of Python present flexible yet powerful approach to data analysis. Besides, a non-destructive field measurements were performed, covering entire study area and providing spatially explicit information of forest health. Such integrated approach opens a wide range of research options in nature conservation and land management in protected areas of mountainous coniferous forests.
Keywords: Data modelling; Python; data analysis; environmental monitoring; forest; landscapes