Modeling of various life processes of <i>Juniperus excelsa</i> M. Bieb to determine optimal growing conditions in the southern coast of Crimea, Russia-Reference-Cited by-同舟云学术

Modeling of various life processes of <i>Juniperus excelsa</i> M. Bieb to determine optimal growing conditions in the southern coast of Crimea, Russia

Published:2022-12-29 Issue:4 Volume:17 Page:50-60
ISSN:2413-0958
Container-title:South of Russia: ecology, development
language:
Short-container-title:Ûg Ross.: èkol. razvit.

Author:

Pashtetsky A. V.¹^ORCID,Ilnitsky O. A.¹^ORCID

Affiliation:

1. National Scientific Centre, Labour Red Banner Order Nikita Botanical Gardens, Russian Academy of Sciences

Abstract

Aim. In connection with global climate change and an increase in the intensity of aridisation of the region of the southern coast of Crimea (SCC), the aim is to study the ecophysiological response of Juniperus excelsa M. Bieb is, during its intensive vegetative phase, and the impact of external environmental factors that greatly influence the characteristics of the water regime, which would allow the possible establishment of optimal and unfavorable conditions for the growth of the species.Material and Methods. Measurements of environmental parameters were carried out using a wireless phytomonitoring system. Applied computer programs were used for statistical data processing. Modeling and smoothing of two‐dimensional data was carried out using the least squares method, robust locally weighted regression and a mathematical model of stepwise regression analysis.Results. To assess the ecophysiological response to the impact of external environmental factors during the growing season of Juniperus excelsa M. Bieb, we applied a mathematical model of stepwise regression analysis. As dependent variables, we used the relative water flow velocity in the shoot (Sf, r.u.) and shoot diameter (d, mm), data were obtained from SF‐5P water flow sensors and SD‐10z sensors. The independent variables were the main environmental factors. The share of dispersion of the dependent variable, explained by the applied models, was determined as 98–99%.Conclusions. The development of a model based on a database of plant functions with appropriate quantitative characteristics will make it possible in the future to predict the ecological state of a particular area or region as a whole.

Publisher

Institute of Applied Ecology

Subject

Ecology,Geography, Planning and Development,Ecology, Evolution, Behavior and Systematics

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