SabaTracheid 1.0: A Novel Program for Quantitative Analysis of Conifer Wood Anatomy — A Demonstration on African Juniper From the Blue Nile Basin

Gebregeorgis, Eyob Gebrehiwot; Boniecka, Justyna; Pia̧tkowski, Marcin; Robertson, Iain; Rathgeber, Cyrille

doi:10.3389/fpls.2021.595258

“…If white-band width correlated only weakly to ring width, we would expect the fixed-width approach to outperform sectoring. Still, using relative cell position is appealing because it better accounts for variations in ring width and can help us better select in each year that were produced at the same time (Gebregeorgis et al 2021).…”

Section: Discussionmentioning

confidence: 99%

“…The first approach averaged cell measurements over an area proportional to the total ring width, referred to as sectoring (Edwards et al 2020;Gebregeorgis et al 2021) (Fig. 4b).…”

Section: Data Processing and Statistical Analysismentioning

confidence: 99%

Quantifying terminal white bands in Salix from the Yenisei river, Siberia and their relationship to late-season flooding

Thaxton

¹

,

Panyushkina

²

,

Meko

³

et al. 2023

Trees

3

0

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Key Message Wood fiber cell wall thickness best characterizes white bands found at the end of certain growth rings in Salix alba. Evidence suggests these features are related to late-season hydrology. Abstract Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic Amplification. These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the effect of these changes on riparian tree growth along the lower reaches of the Yenisei River, we collected white willow (Salix alba) cross sections from a fluvial fill flat terrace that occasionally floods when water levels are extremely high. These samples displayed bands of lighter colored wood at the end of certain annual growth rings that we hypothesized were related to flood events. To identify the characteristics and causes of these features, we use an approach known as quantitative wood anatomy (QWA) to measure variation in fiber cell dimensions across tree rings, particularly fiber lumen area (LA) and cell wall thickness (CWT). We investigate (1) which cell parameters and method to extract intra-annual data from annual tree rings best capture terminal white bands identified in Salix, and (2) if these patterns are related to flood magnitude and/or duration. We find that fiber CWT best captures terminal white bands found in Salix rings. Time series derived from CWT measurements correlate with July water-level durations, but at levels too low to be labeled flooding. Although both terminal white bands and July flooding have reduced since 1980, questions remain as to the cause of terminal white bands. Understanding how riparian vegetation responds to changes in hydrology can help us better manage riparian ecosystems and understand the impacts of a changing Arctic hydrological regime.

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“…If white-band width correlated only weakly to ring width, we would expect the fixed-width approach to outperform sectoring. Still, using relative cell position is appealing because it better accounts for variations in ring width and can help us better select cells in each year that were produced at the same time (Gebregeorgis et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Quantifying Terminal White Bands in Salix From the Yenisei River, Siberia and Their Relationship to Late-Season Flooding

Thaxton

¹

,

Meko

²

,

Panyushkina

³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Key Message Wood fiber cell wall thickness best characterizes white bands found at the end of certain growth rings in Salix alba. Evidence suggests these features are related to late-season hydrology. Abstract Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic Amplification. These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the effect of these changes on riparian tree growth along the lower reaches of the Yenisei River, we collected white willow (Salix alba) cross sections from a fluvial fill flat terrace that occasionally floods when water levels are extremely high. These samples displayed bands of lighter colored wood at the end of certain annual growth rings that we hypothesized were related to flood events. To identify the characteristics and causes of these features, we use an approach known as quantitative wood anatomy (QWA) to measure variation in fiber cell dimensions across tree rings, particularly fiber lumen area (LA) and cell wall thickness (CWT). We investigate (1) which cell parameters and method to extract intra-annual data from annual tree rings best capture terminal white bands identified in Salix, and (2) if these patterns are related to flood magnitude and/or duration. We find that fiber CWT best captures terminal white bands found in Salix rings. Time series derived from CWT measurements correlate with July water-level durations, but at levels too low to be labeled flooding. Although both terminal white bands and July flooding have reduced since 1980, questions remain as to the cause of terminal white bands. Understanding how riparian vegetation responds to changes in hydrology can help us better manage riparian ecosystems and understand the impacts of a changing Arctic hydrological regime.

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“…Quantitative wood anatomy involves quantitative analysis of anatomical features of wood and evaluation of its geographical origin and growing condition based on the anatomical features (Gebregeorgis et al, 2021;von Arx et al, 2016). To note, the anatomical structure of wood varies from tree species to species, while external environmental factors influence the size of wood cells (da Silva et al, 2021;Hwang et al, 2020;Kim et al, 2018;Seo and Eom, 2017;Seo et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Mask Region-Based Convolutional Neural Network (R-CNN) Based Image Segmentation of Rays in Softwoods

Yoo¹,

Kwon²,

Seo³

2022

J. Korean Wood Sci. Technol.

0

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The current study aimed to verify the image segmentation ability of rays in tangential thin sections of conifers using artificial intelligence technology. The applied model was Mask region-based convolutional neural network (Mask R-CNN) and softwoods (viz. Picea jezoensis, Larix gmelinii, Abies nephrolepis, Abies koreana, Ginkgo biloba, Taxus cuspidata, Cryptomeria japonica, Cedrus deodara, Pinus koraiensis) were selected for the study. To take digital pictures, thin sections of thickness 10-15 μm were cut using a microtome, and then stained using a 1:1 mixture of 0.5% astra blue and 1% safranin. In the digital images, rays were selected as detection objects, and Computer Vision Annotation Tool was used to annotate the rays in the training images taken from the tangential sections of the woods. The performance of the Mask R-CNN applied to select rays was as high as 0.837 mean average precision and saving the time more than half of that required for Ground Truth. During the image analysis process, however, division of the rays into two or more rays occurred. This caused some errors in the measurement of the ray height. To improve the image processing algorithms, further work on combining the fragments of a ray into one ray segment, and increasing the precision of the boundary between rays and the neighboring tissues is required.

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SabaTracheid 1.0: A Novel Program for Quantitative Analysis of Conifer Wood Anatomy — A Demonstration on African Juniper From the Blue Nile Basin

Cited by 6 publications

References 31 publications

Quantifying terminal white bands in Salix from the Yenisei river, Siberia and their relationship to late-season flooding

Quantifying terminal white bands in Salix from the Yenisei river, Siberia and their relationship to late-season flooding

Quantifying Terminal White Bands in Salix From the Yenisei River, Siberia and Their Relationship to Late-Season Flooding

Mask Region-Based Convolutional Neural Network (R-CNN) Based Image Segmentation of Rays in Softwoods

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