TY - JOUR

T1 - Contribution of epiphytes and fog to patterns of atmospheric fluxes in mountainous forests (Picea abies L. and Pinus cembra L.)

AU - Леменкова, Полина Алексеевна

PY - 2025/8/4

Y1 - 2025/8/4

N2 - 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 roles of the major biotic and abiotic agents that influence 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, and 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 by 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 for quantifying water balance using Python and statistical analysis of datasets. In contrast to other methods, Python programming libraries offer a flexible yet powerful toolset for data analysis. Additionally, non-destructive field measurements were performed across the entire study area, providing spatially explicit information on forest health. This integrated approach opens a wide range of research opportunities in nature conservation and land management within protected areas of mountainous coniferous forests.

AB - 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 roles of the major biotic and abiotic agents that influence 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, and 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 by 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 for quantifying water balance using Python and statistical analysis of datasets. In contrast to other methods, Python programming libraries offer a flexible yet powerful toolset for data analysis. Additionally, non-destructive field measurements were performed across the entire study area, providing spatially explicit information on forest health. This integrated approach opens a wide range of research opportunities in nature conservation and land management within protected areas of mountainous coniferous forests.

KW - data modeling

KW - Python

KW - data analysis

KW - environmental monitoring

KW - forest

KW - landscapes

U2 - 10.5281/zenodo.16735570

DO - 10.5281/zenodo.16735570

M3 - Article

VL - 34

SP - 7

EP - 21

JO - Acta herbologica

JF - Acta herbologica

SN - 0354-4311

IS - 1

ER -