TreeNet–The Biological Drought and Growth Indicator Network

Zweifel, Roman; Etzold, Sophia; Basler, David; Bischoff, Reinhard; Braun, Sabine; Buchmann, Nina; Conedera, Marco; Fonti, Patrick; Geßler, Arthur; Haeni, Matthias; Hoch, Günter; Kahmen, Ansgar; Köchli, Roger; Maeder, Marcus; Nievergelt, Daniel; Peter, Martina; Peters, Richard L.; Schaub, Marcus; Trotsiuk, Volodymyr; Walthert, Lorenz; Wilhelm, Micah; Eugster, Werner

doi:10.3389/ffgc.2021.776905

Cited by 16 publications

(16 citation statements)

References 68 publications

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“…The 47 study sites of the network TreeNet (Zweifel et al, 2021a ) represent a strong climate gradient within Switzerland (Figure S2 ), with mean annual temperature (MAT) ranging from 4.5 to 12.1°C and mean annual precipitation (MAP) from 600 to 1580 mm (Table S1 , Figure S3 ). The species studied include three evergreen, coniferous species ( Picea abies (L.) H. Karst, Pinus sylvestris L., Abies alba Mill.)…”

Section: Methodsmentioning

confidence: 99%

“…Trees equipped with dendrometers were mature, dominant trees. Details about the installation of sensors in the field, data acquisition and processing, as well as extraction of the growth component and associated uncertainties are described in Zweifel et al ( 2021a ). Briefly, the growth component from the stem radius changes was extracted based on the ‘zero growth’ concept (Zweifel et al, 2021a ; Zweifel et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…Details about the installation of sensors in the field, data acquisition and processing, as well as extraction of the growth component and associated uncertainties are described in Zweifel et al ( 2021a ). Briefly, the growth component from the stem radius changes was extracted based on the ‘zero growth’ concept (Zweifel et al, 2021a ; Zweifel et al, 2016 ). Hereby, the accumulated growth (GRO) appears as stepped line, which increases in periods when the stem radius exceeds a former maximum.…”

Section: Methodsmentioning

confidence: 99%

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Number of growth days and not length of the growth period determines radial stem growth of temperate trees

et al. 2021

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Radial stem growth dynamics at seasonal resolution are essential to understand how forests respond to climate change. We studied daily radial growth of 160 individuals of seven temperate tree species at 47 sites across Switzerland over 8 years.Growth of all species peaked in the early part of the growth season and commenced shortly before the summer solstice, but with species-specific seasonal patterns.Day length set a window of opportunity for radial growth. Within this window, the probability of daily growth was constrained particularly by air and soil moisture, resulting in intermittent growth to occur only on 29 to 77 days (30% to 80%) within the growth period. The number of days with growth largely determined annual growth, whereas the growth period length contributed less. We call for accounting for these non-linear intra-annual and species-specific growth dynamics in tree and forest models to reduce uncertainties in predictions under climate change.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Number of growth days and not length of the growth period determines radial stem growth of temperate trees

et al. 2021

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“…This is further advantaged by increasingly ubiquitous connectivity and the extensive coverage provided by the latest generations of communication networks [5]. Thus, such technologies offer new opportunities for collecting continuously consistent environmental and ecophysiological parameters with high temporal frequency across broad spatialized networks [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Toward a Unified TreeTalker Data Curation Process

Tomelleri

Marchesini

Yaroslavtsev

et al. 2022

Forests

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The Internet of Things (IoT) development is revolutionizing environmental monitoring and research in macroecology. This technology allows for the deployment of sizeable diffuse sensing networks capable of continuous monitoring. Because of this property, the data collected from IoT networks can provide a testbed for scientific hypotheses across large spatial and temporal scales. Nevertheless, data curation is a necessary step to make large and heterogeneous datasets exploitable for synthesis analyses. This process includes data retrieval, quality assurance, standardized formatting, storage, and documentation. TreeTalkers are an excellent example of IoT applied to ecology. These are smart devices for synchronously measuring trees’ physiological and environmental parameters. A set of devices can be organized in a mesh and permit data collection from a single tree to plot or transect scale. The deployment of such devices over large-scale networks needs a standardized approach for data curation. For this reason, we developed a unified processing workflow according to the user manual. In this paper, we first introduce the concept of a unified TreeTalker data curation process. The idea was formalized into an R-package, and it is freely available as open software. Secondly, we present the different functions available in “ttalkR”, and, lastly, we illustrate the application with a demonstration dataset. With such a unified processing approach, we propose a necessary data curation step to establish a new environmental cyberinfrastructure and allow for synthesis activities across environmental monitoring networks. Our data curation concept is the first step for supporting the TreeTalker data life cycle by improving accessibility and thus creating unprecedented opportunities for TreeTalker-based macroecological analyses.

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“…in the case of a drought event like in the summer of 2018. Machine-learning algorithms have a great potential to do so [7].…”

Section: Introductionmentioning

confidence: 99%

Reconstructing radial stem size changes of trees with machine learning

Luković

Zweifel

Thiry

et al. 2022

J. R. Soc. Interface.

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Like many scientists, ecologists depend heavily on continuous uninterrupted data in order to understand better the object of their study. Although this might be straightforward to achieve under controlled laboratory conditions, the situation is easily complicated under field conditions where sensors and data transmission are affected by harsh weather, living organisms, changes in atmospheric conditions etc. This often results in parts of the data being corrupted or missing altogether. We propose the use of the most recent machine-learning techniques to reverse such data losses in multi-channel time series. In particular, we focus on tree stem growth data obtained from the TreeNet project, which monitors the changes in stem radius and environmental conditions of a few hundred trees across Switzerland. In the first part of the study, we test the performance of five architectures based on encoders and recurrent and convolutional neural networks, and we show that a deep neural network combining long short-term memory with one-dimensional convolutional layers performs the best. In the second part, we adopt this model to reconstruct the original TreeNet dataset, which we then use in a separate classification problem to show the effect of the proposed gap-filling procedure.

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TreeNet–The Biological Drought and Growth Indicator Network

Cited by 16 publications

References 68 publications

Number of growth days and not length of the growth period determines radial stem growth of temperate trees

Number of growth days and not length of the growth period determines radial stem growth of temperate trees

Toward a Unified TreeTalker Data Curation Process

Reconstructing radial stem size changes of trees with machine learning

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