The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest

Increased anthropogenic pressure including intensification of agricultural activities leads to long-term decline of natural biotopes, with planted forests often considered as promising compensatory response, although reduced biodiversity and ecosystem stability represent their common drawbacks. Here we present a complex investigation of the impact of a large Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation in a planted Scots pine forest representing a natural in situ experiment on an engineered ecosystem. After settling around 2006, the colony expanded for 15 years, leading to the intensive deposition of nutrients with feces, food remains and feather thereby considerably altering the local soil biogeochemistry. Thus, lower pH levels around 4.5, 10- and 2-fold higher concentrations of phosphorous and nitrogen, as well as 1.2-fold discrepancies in K, Li, Mn, Zn and Co., respectively, compared to the surrounding control forest area could be observed. Unaltered total organic carbon (Corg) suggests repressed vegetation, as also reflected in the vegetation indices obtained by remote sensing. Moreover, reduced soil microbial diversity with considerable alternations in the relative abundance of Proteobacteria, Firmicutes, Acidobacteriota, Actinobacteriota, Verrucomicrobiota, Gemmatimonadota, Chujaibacter, Rhodanobacter, and Bacillus has been detected. The above alterations to the ecosystem also affected climate stress resilience of the trees indicated by their limited recovery from the major 2010 drought stress, in marked contrast to the surrounding forest (p = 3∙10−5). The complex interplay between geographical, geochemical, microbiological and dendrological characteristics, as well as their manifestation in the vegetation indices is explicitly reflected in the Bayesian network model. Using the Bayesian inference approach, we have confirmed the predictability of biodiversity patterns and trees growth dynamics given the concentrations of keynote soil biogeochemical alternations with correlations R > 0.8 between observations and predictions, indicating the capability of risk assessment that could be further employed for an informed forest management

Непараметрические байесовские сети как инструмент комплексирования данных мультимасштабного анализа временных рядов и дистанционного зондирования

Introduction. Nonparametric Bayesian networks are a promising tool for analyzing, visualizing, interpreting and predicting the structural and dynamic characteristics of complex systems. Modern interdisciplinary research involves the complex processing of heterogeneous data obtained using sensors of various physical nature. In the study of the forest fund, both methods of direct dendrological measurements and methods of remote observation using unmanned aerial vehicles are widely used. Information obtained using these methods must be analyzed in conjunction with hydrometeorological monitoring data.Aim. Investigation of the possibility of automating the monitoring of the well-being of the forest fund based on the integration of ground survey data, remote multispectral measurements and hydrometeorological observations using the mathematical apparatus of nonparametric Bayesian networks.Materials and methods. To assess the long-term joint dynamics of natural and climatic indicators and the radial growth of trees, a modified method of multiscale cross-correlation analysis was used with the removal of the background trend described by the moving average model. Relationships between various indicators were estimated based on the unconditional and conditional nonparametric Spearman correlation coefficients, which were used to reconstruct and parameterize the nonparametric Bayesian network.Results. A multiscale nonparametric Bayesian network was constructed to characterize both unconditional and conditional statistical relationships between parameters obtained from remote sensing, hydroclimatic and dendrological measurements. The proposed model showed a good quality of the plant fund state forecasting. The correlation coefficients between the observed and predicted indicators exceed 0.6, with the correlation coefficient comprising 0.77 when predicting the growth trend of annual tree rings.Conclusion. The proposed nonparametric Bayesian network model reflects the relationship between various factors that affect the forest ecosystem. The Bayesian network can be used to assess risks and improve environmental management planning.Введение. Непараметрические байесовские сети представляют собой перспективный инструмент для анализа, визуализации, интерпретации и прогнозирования структурных и динамических характеристик сложных систем. Современные междисциплинарные исследования подразумевают комплексную обработку разнородных данных, получаемых с помощью датчиков различной физической природы. При исследовании лесного фонда широко применяются методы как непосредственных дендрологических измерений, так и дистанционного наблюдения с использованием беспилотных летательных аппаратов. Информацию, полученную с помощью этих методов, необходимо анализировать во взаимосвязи с данными гидрометеорологического мониторинга.Цель работы. Исследование возможности автоматизации мониторинга благополучия лесного фонда на основе комплексирования данных наземных исследований, дистанционных мультиспектральных измерений и гидрометеорологических наблюдений с использованием математического аппарата непараметрических байесовских сетей.Материалы и методы. Для оценки долговременной совместной динамики природно-климатических показателей и радиального прироста деревьев использован модифицированный метод мультимасштабного взаимного корреляционного анализа с удалением фонового тренда, описываемого моделью скользящего среднего. Взаимосвязи между различными показателями оценивались на основе безусловных и условных непараметрических коэффициентов корреляции Спирмена, которые использовались для реконструкции и параметризации непараметрической байесовской сети.Результаты. Построена мультимасштабная непараметрическая байесовская сеть, характеризующая безусловные и условные статистические взаимосвязи между параметрами, полученными в результате дистанционного зондирования, гидроклиматических и дендрологических измерений. Предложенная модель показала хорошее качество прогнозирования состояния растительного фонда. Коэффициенты корреляции между наблюдаемыми и предсказываемыми показателями превышают значения 0.6, а при предсказании тренда прироста годичных колец деревьев коэффициент корреляции составляет 0.77.Заключение. Предложенная непараметрическая байесовская сетевая модель отражает взаимосвязи между различными факторами, влияющими на лесную экосистему. Байесовская сеть может использоваться для оценки рисков и улучшения планирования экологического управления

Dendroclimatic Investigations of Pinus Sylvestris L. on Keretsky Archipelago Islands, the White Sea

Приведены результаты анализа динамики радиального прироста сосны обыкновенной, произрастающей в различных местообитаниях островов Керетского архипелага Белого моря (Карелия). Построены две древесно-кольцевые хронологии для суходольных участков и одна - для болотного. Установлен положительный отклик радиального прироста сосны суходольных участков на динамику климатических факторов зимне-весеннего периода. Показано, что связь радиального прироста с климатом не постоянна во времени, что, вероятно, связано с изменением сроков начала ростовых процессов из-за увеличения количества зимних осадков, наблюдаемого в последние десятилетия по данным метеостанции.The radial growth dynamics of Pinus sylvestris from different habitats of Keretsky archipelago, the White Sea, was analyzed. Two tree-ring chronologies for dry pine forest and the third one for swamp habitat were built. A positive link of radial growth and climatic factors of winter and spring period was detected. It was shown that relationship of radial growth with climate varies in time. This fact probably is related with winter precipitation increase during last decades detected by weather station, which could alter time of grow process initiation

A simple mechanistic model of the invasive species Heracleum sosnowskyi propagule dispersal by wind

Background Invasive species are one of the key elements of human-mediated ecosystem degradation and ecosystem services impairment worldwide. Dispersal of propagules is the first stage of plant species spread and strongly influences the dynamics of biological invasion. Therefore, distance prediction for invasive species spread is critical for invasion management. Heracleum sosnowskyi is one of the most dangerous invasive species with wind-dispersed propagules (seeds) across Eastern Europe. This study developed a simple mechanistic model for H. sosnowskyi propagule dispersal and their distances with an accuracy comparable to that of empirical measurements. Methods We measured and compared the propagule traits (terminal velocity, mass, area, and wing loading) and release height for H. sosnowskyi populations from two geographically distant regions of European Russia. We tested two simple mechanistic models: a ballistic model and a wind gradient model using identical artificial propagules. The artificial propagules were made of colored paper with a mass, area, wing loading, and terminal velocity close to those of natural H. sosnowskyi mericarps. Results The wind gradient model produced the best results. The first calculations of maximum possible propagule transfer distance by wind using the model and data from weather stations showed that the role of wind as a vector of long-distance dispersal for invasive Heracleum species was strongly underestimated. The published dataset with H. sosnowskyi propagule traits and release heights allows for modeling of the propagules’ dispersal distances by wind at any geographical point within their entire invasion range using data from the closest weather stations. The proposed simple model for the prediction of H. sosnowskyi propagule dispersal by wind may be included in planning processes for managing invasion of this species

Dendroclimatic Investigations of Pinus Sylvestris L. on Keretsky Archipelago Islands, the White Sea

Приведены результаты анализа динамики радиального прироста сосны обыкновенной, произрастающей в различных местообитаниях островов Керетского архипелага Белого моря (Карелия). Построены две древесно-кольцевые хронологии для суходольных участков и одна - для болотного. Установлен положительный отклик радиального прироста сосны суходольных участков на динамику климатических факторов зимне-весеннего периода. Показано, что связь радиального прироста с климатом не постоянна во времени, что, вероятно, связано с изменением сроков начала ростовых процессов из-за увеличения количества зимних осадков, наблюдаемого в последние десятилетия по данным метеостанции.The radial growth dynamics of Pinus sylvestris from different habitats of Keretsky archipelago, the White Sea, was analyzed. Two tree-ring chronologies for dry pine forest and the third one for swamp habitat were built. A positive link of radial growth and climatic factors of winter and spring period was detected. It was shown that relationship of radial growth with climate varies in time. This fact probably is related with winter precipitation increase during last decades detected by weather station, which could alter time of grow process initiation

Growth Characteristics of Annual Rings of Juniperus sibirica Burgsd. in the Highlands of the Southern Urals

Можжевельник – один из самых распространенных кустарниковых видов. Изучение радиального прироста его годичных колец позволит лучше понять процессы реакции (адаптации) растений на современное изменение климата в горных экосистемах Урала. Приведены результаты анализа динамики радиального прироста можжевельника сибирского (Juniperus sibirica Burgsd.), произрастающего в лесотундровом экотоне на г. Дальний Таганай на Южном Урале. Большая часть изученных растений (75 %) демонстрирует увеличение ширины годичных колец к 2019 г. Построена одна древесно-кольцевая хронология длиной с 1983 по 2019 г. Установлены положительный отклик радиального прироста J. sibirica на температуру летнего периода (июнь, июль) и отрицательная связь с суммой осадков летнего периода (июнь). Установлены основные периоды ксилогенеза 2018 г. на основании исследования сезонного роста числа клеток: дата появления первых клеток 9 июня, кульминации скорости роста 8–10 июля и дата формирования последних клеток 18–29 августа. Полученные результаты могут быть полезны для исследования динамики экосистем гор Южного Урала. Работа расширяет знания о радиальном росте растений жизненной формы кустарник, которые редко выступают объектом дендрохронологических исследований по сравнению с растениями одноствольной формы ростаJuniper is one of the most common shrubs, and the study of the radial growth of its growth rings will help elucidate the processes of plant response and adaptation to climate change in the mountain ecosystems of the Urals. The article presents the analysis of the radial growth dynamics in Siberian juniper (Juniperus sibirica Burgsd.) growing in the forest-tundra ecotone on Dalniy Taganai Mountain, the Southern Urals. The ring width of most of the study plants (75 %) increased by 2019. The tree-ring chronology created covers the period from 1983 to 2019. Analysis revealed a positive response of the radial growth of shrubs to the temperature of the summer period (June, July) and a negative response to the summer precipitation (June). The seasonal cell growth was used to estimate the main phenological dates of xylogenesis in 2018: the first cells formed on June 9, the growth rate reached a maximum on July 8–10, and the last cells formed on August 18–29. The findings obtained can be useful for studying the dynamics of mountain ecosystems in the Southern Urals. The present work expands the knowledge about the radial growth of plants of the shrub life form, which are seldom the focus of dendrochronological studies compared to plants of the single-stem growth for

The impact of Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation: a natural long-term in situ experiment in a planted pine forest

Increased anthropogenic pressure including intensification of agricultural activities leads to long-term decline of natural biotopes, with planted forests often considered as promising compensatory response, although reduced biodiversity and ecosystem stability represent their common drawbacks. Here we present a complex investigation of the impact of a large Grey Heron (Ardea cinerea L.) colony on soil biogeochemistry and vegetation in a planted Scots pine forest representing a natural in situ experiment on an engineered ecosystem. After settling around 2006, the colony expanded for 15 years, leading to the intensive deposition of nutrients with feces, food remains and feather thereby considerably altering the local soil biogeochemistry. Thus, lower pH levels around 4.5, 10- and 2-fold higher concentrations of phosphorous and nitrogen, as well as 1.2-fold discrepancies in K, Li, Mn, Zn and Co., respectively, compared to the surrounding control forest area could be observed. Unaltered total organic carbon (Corg) suggests repressed vegetation, as also reflected in the vegetation indices obtained by remote sensing. Moreover, reduced soil microbial diversity with considerable alternations in the relative abundance of Proteobacteria, Firmicutes, Acidobacteriota, Actinobacteriota, Verrucomicrobiota, Gemmatimonadota, Chujaibacter, Rhodanobacter, and Bacillus has been detected. The above alterations to the ecosystem also affected climate stress resilience of the trees indicated by their limited recovery from the major 2010 drought stress, in marked contrast to the surrounding forest (p = 3∙10-5). The complex interplay between geographical, geochemical, microbiological and dendrological characteristics, as well as their manifestation in the vegetation indices is explicitly reflected in the Bayesian network model. Using the Bayesian inference approach, we have confirmed the predictability of biodiversity patterns and trees growth dynamics given the concentrations of keynote soil biogeochemical alternations with correlations R ) 0.8 between observations and predictions, indicating the capability of risk assessment that could be further employed for an informed forest management.1-1

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities

"Flora of Russia" on iNaturalist: a dataset

The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection.Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities