Growth rings in tropical trees : role of functional traits, environment, and phylogeny

Acknowledgments Financial support of the Centre National de la Recherche Scientifique (USR 3330), France, and from the Rufford Small Grants Foundation (UK) is acknowledged. We thank the private farmers and coffee plantation companies of Kodagu for providing permissions and logistical support for this project. We are grateful to N. Barathan for assistance with slide preparation and data entry, S. Aravajy for botanical assistance, S. Prasad and G. Orukaimoni for technical inputs, and A. Prathap, S. Shiva, B. Saravana, and P. Shiva for field assistance. The corresponding editor and three anonymous reviewers provided insightful comments that improved the manuscript.Peer reviewedPostprin

Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study

Characterizing the tissue morphology and anatomy of seagrasses is essential to predicting their acoustic behavior. In this pilot study, we use histology techniques and whole slide imaging (WSI) to describe the composition and topology of the aerenchyma of an entire leaf blade in an automatic way combining the advantages of X-ray microtomography and optical microscopy. Paraffin blocks are prepared in such a way that microtome slices contain an arbitrarily large number of cross sections distributed along the full length of a blade. The sample organization in the paraffin block coupled with whole slide image analysis allows high throughput data extraction and an exhaustive characterization along the whole blade length. The core of the work are image processing algorithms that can identify cells and air lacunae (or void) from fiber strand, epidermis, mesophyll and vascular system. A set of specific features is developed to adequately describe the convexity of cells and voids where standard descriptors fail. The features scrutinize the local curvature of the object borders to allow an accurate discrimination between void and cell through machine learning. The algorithm allows to reconstruct the cells and cell membrane features that are relevant to tissue density, compressibility and rigidity. Size distribution of the different cell types and gas spaces, total biomass and total void volume fraction are then extracted from the high resolution slices to provide a complete characterization of the tissue along the leave from its base to the apex

Advancement of field-deployable, computer-vision wood identification technology

Globally, illegal logging poses a significant threat. This results in environmental damage as well as lost profits for legitimate wood product producers and taxes for governments. A global value of $30 to$100 billion is estimated to be associated with illegal logging and processing. Field identification of wood species is fundamental to combating species fraud and misrepresentation in global wood trade. Using computer vision wood identification (CVWID) systems, wood can be identified without the need for time-consuming and costly offsite visual inspections by trained wood anatomists. While CVWID research has received significant attention, most studies have not considered the generalization capabilities of the models by testing them on a field sample, and only report overall accuracy without considering misclassifications. The aim of this dissertation is to advance the design and development of CVWID systems by addressing three objectives: 1) to develop functional, field-deployable CVWID models for Peruvian and North American hardwoods, 2) test the ability of CVWID to solve increasingly challenging problems (e.g., larger class sizes, lower anatomical diversity, and spatial heterogeneity in the context of porosity), and 3) to evaluate the generalization capabilities by testing models on independent specimens not included in training and analyzing misclassifications. This research features four main sections: 1) an introduction summarizing each chapter, 2) a chapter (Chapter 2) developing a 24-class model for Peruvian hardwoods and testing its generalization capabilities with independent specimens not used in training, 3) a chapter (Chapter 3) on the design and implementation of a continental scale 22-class model for North American diffuse-porous hardwoods using wood anatomy-driven model performance evaluation, and 3) a chapter (Chapter 4) on the development of a 17-class models for North American ring-porous hardwoods, in particular examining the model\u27s effectiveness in dealing with the greater spatial heterogeneity of ring-porous hardwoods

Detection and visualization of encoded local features as anatomical predictors in cross-sectional images of Lauraceae

This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency–inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific

The Methodological and Diagnostic Applications of Micro-CT to Palaeopathology: A Quantitative Study of Porotic Hyperostosis

The purpose of this dissertation was to assess the value of micro-CT to palaeopathology for the non-destructive analysis of orbital and cranial porotic hyperostosis, common lesions observed in many archaeological skeletal collections. The objectives of this study were to: 1) identify palaeoepidemiological trends in the prevalence of porotic hyperostosis that may support differential diagnoses, 2) evaluate the reproducibility and reliability of two-dimensional (2D) and three-dimensional (3D) methods of micro-CT data collection for the quantitative analysis of bone microarchitecture, and 3) quantitatively evaluate orbital and cranial porotic hyperostosis to determine the value of micro-CT methods for understanding disease pathogenesis and improving the differential diagnosis of these lesions. Sixty-six individuals obtained from four skeletal collections were assessed macroscopically using published methods of visual analysis as well as quantitatively using micro-CT methods. Structural indices used to quantify bone microarchitecture included bone volume density (BV/TV), specific bone surface (BS/BV), trabecular thickness (Tb.Th.), trabecular number (Tb.N.), and trabecular spacing (Tb.Sp.) (Hildebrand et al. 1999). The results of the visual analysis demonstrated an age-related trend in the prevalence of porotic hyperostosis, supporting previous hypotheses that this condition has an onset in childhood (e.g. Stuart-Macadam 1985). The micro-CT results illustrated that the most reliable and reproducible method for quantifying bone microarchitecture was a 3D volume of interest (VOI) method that maximized VOI size. Three-dimensional methods using VOIs of a uniform size were recommended with caution, and 2D VOI methods did not provide consistent observer agreement. The analysis of orbital porotic hyperostosis demonstrated significant changes (p \u3c 0.05) to bone microarchitecture in the advanced stages of disease pathogenesis, but not in the early or healing stages. The results for cranial porotic hyperostosis demonstrated significant changes only in the light stage. These results suggest that orbits are differentially and more significantly affected than the cranial vault likely due to structural differences between the bones of the skull. Changes to bone microarchitecture included an overall loss of trabecular bone and an increase in thinned, gracile trabeculae. Considering these findings within the clinical literature a differential diagnosis that includes anaemic conditions was supported. The identified palaeoepidemiological context also supported this differential diagnosis. Overall, the application of 3D micro-CT methods is of significant value for elucidating the process of disease pathogenesis and supporting current differential diagnoses of porotic hyperostosis in archaeological skeletal remains

Variation of measured cross-sectional cell dimensions and calculated water vapor permeability across a single growth ring of spruce wood

A statistical study of the cell dimensions in a growth ring of spruce along the radial and tangential directions is performed. The data are used to study the variation of the cell vapor permeability in the growth ring. Studying cell rows within one growth ring, the frequency distributions of the cell wall thickness in the radial direction and of the lumen dimension in the tangential direction are found to be both unimodal. In contrast, the frequency distributions of these dimensions in the other directions are bimodal, where the different modes can be attributed to earlywood and latewood. Analysis of the bimodal distributions results in the determination of threshold values of cell wall thickness and the lumen dimension for earlywood and latewood tracheids. The cell dimensions are used to predict cell porosity and water vapor permeability distribution within a growth ring. The bimodal frequency distributions of the tangential cell wall thickness and the radial lumen dimension provide an explanation for the observed bimodal frequency distribution of the cell water vapor permeability both in radial and in tangential directions. Contrary to measured macroscopic vapor permeability results, the tracheid geometry results in lower cell vapor permeability in radial than in tangential direction. This confirms that rays play an important role in the vapor permeability of wood, as they can be considered as pathways for vapor transport in radial direction. The dataset analyzed in this paper leads to a set of parameters characterizing the earlywood and latewood cell dimensions. Such characterization can be used, for example, for producing synthetic data for computational modeling studie

Open tools for dendrochronology. Advances in sample digitization and deep learning methods for image segmentation

Dendrochronological techniques are paramount in forest research. The current climate change scenario and the central role of forests in biogeophysical cycles enforce the importance of novel techniques to get accurate data from trees and their relationship with the environment in faster ways. Recent technological advances and the place of open source software and hardware are making free, user-developed tools for forest research available to the research community. The aim of this Ph.D. thesis is the development of tools for image acquisition and data collection in dendrochronology based on open source software and hardware. Thus, four different tools for dendrochronological research are presented in five different chapters. The first chapter focuses on the development of a do-it-yourself tool based on open source hardware for image acquisition and wood sample digitization at high resolution. We used open hardware equipment from Arduino and Python programming to develop CaptuRING and published the entire free open source tool as: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" in Methods in Ecology and Evolution, 2022; 13:1185-1191. Furthermore, the original software was registered in the Registro General de Propiedad Intelectual (00/2022/737) of Ministerio de Cultura y Deporte (Spain). The second chapter presents "How to build and install your own CaptuRING". This contribution presents a series of videos with a step-by-step guide to promote the use of CaptuRING in the research community. The manuscript and related videos have been submitted for publication. The third chapter describes ρ-MtreeRing. This free and open-source software, which is written in R, analyzes X-ray films from dendrochronological samples to get microdensity values automatically segmented through a graphical user interface. The open source tool and manuscript are published as: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" in Forests. 2021; 12(10):1405. The fourth chapter describes the potential of deep learning methods to automatically segment xylem vessels. We trained three different convolutional neural networks to segment vessels in stained wood microsections using the Keras framework in Python. Our results demonstrate the potential of these techniques to automatically segment xylem vessels and overcome derived problems from image illumination, which hamper segmentation using classical image segmentation methods. The manuscript is published as "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" in: Neural Computing & Applications, 2020; 32:17927-17939. The fifth chapter develops an algorithm to delineate annual ring limits in stained wood microsections of a species with diffuse porous wood using convolutional neural networks. We used Python for image processing and the Keras framework for the algorithm training. The results show the ability of this techniques to obtain accurate tree ring segmentation for quantitative wood anatomy, reaching similar or even outperforming conventional manual delimitation in most of the evaluated cases. The results of this chapter will be presented in the manuscript "Deep Learning for ring bordering in stained cross-sectional images". This PhD Thesis presents four open source tools to get accurate information from wood features to unveil how trees respond to the environment. From digitization at macroscopic perspective, automatic data collection and the development of feature segmentation on microscopic samples. The presented four novel dendrochronological tools based on open source software facilitates forest research in the current climate change scenario.Las técnicas dendrocronológicas son fundamentales en la investigación forestal. El escenario actual de cambio climático y el papel central de los bosques en los ciclos biogeofísicos subrayan la necesidad de nuevas técnicas para obtener de un modo ágil datos precisos de los árboles y de su relación con el medio ambiente. Los recientes avances tecnológicos, además de la disponibilidad actual del software y el hardware de código abierto están poniendo a disposición de la comunidad investigadora herramientas gratuitas desarrolladas por los usuarios para la investigación forestal. El objetivo de esta tesis doctoral es el desarrollo de herramientas para la adquisición de imágenes y la recogida de datos basadas en software y hardware de código abierto para el estudio dendrocronológico. Esta tesis presenta cuatro herramientas diferentes para esta rama científica en cinco capítulos diferentes. El primer capítulo se centra en el desarrollo de una herramienta "hágalo usted mismo" basada en hardware de código abierto para la adquisición de imágenes y la digitalización de muestras de madera a alta resolución. Usamos equipos de hardware abierto de Arduino y programación de Python para desarrollar CaptuRING y publicamos la herramienta completa de código abierto como: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" en Methods in Ecology and Evolution, 2022; 13:1185-1191. Además, el software original fue registrado en el Registro General de Propiedad Intelectual (00/2022/737) del Ministerio de Cultura y Deporte (España). El segundo capítulo presenta "Cómo construir e instalar su propio CaptuRING" ("How to build and install your own CaptuRING"). Esta contribución presenta una serie de vídeos con una guía paso a paso para promover el uso de CaptuRING en la comunidad investigadora. El manuscrito y los vídeos relacionados se han enviado para su publicación. El tercer capítulo describe ρ-MtreeRing. Este software gratuito y de código abierto, que está escrito en R, analiza imágenes de rayos X de muestras dendrocronológicas para obtener valores de microdensidad automáticamente segmentados a través de una sencilla interfaz gráfica de usuario. La herramienta de código abierto y el manuscrito se publicaron como: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" en Forests. 2021; 12(10):1405. El cuarto capítulo describe el potencial de los métodos de aprendizaje profundo para segmentar automáticamente los vasos del xilema. Entrenamos tres redes neuronales convolucionales diferentes para segmentar vasos en cortes histológicos de madera utilizando el marco Keras en Python. Nuestros resultados demuestran el potencial de estas técnicas para segmentar automáticamente los vasos del xilema y superar los problemas derivados de la iluminación de la imagen, que dificultan la labor de métodos clásicos de segmentación de imágenes. El manuscrito se publicó como "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" en: Neural Computing & Applications. 2020; 32:17927-17939. El quinto capítulo desarrolla un algoritmo para delinear los límites anuales de los anillos en cortes histológicos de una especie con madera difuso-porosa utilizando redes neuronales convolucionales. Se utilizó Python para el procesamiento de imágenes y el marco Keras para el entrenamiento del algoritmo. Los resultados muestran la capacidad de estas técnicas para obtener una segmentación precisa de los anillos de los árboles para la anatomía cuantitativa de la madera alcanzando, en la mayoría de los casos evaluados, un rendimiento similar o incluso superior a la delimitación manual convencional. Los resultados de este capítulo se presentarán en el manuscrito "Deep Learning for ring bordering in stained cross-sectional images". Esta Tesis Doctoral presenta cuatro herramientas de código abierto para obtener información precisa de las características de la madera investigar cómo los árboles responden al entorno facilitando la investigación en el actual escenario de cambio climático.Escuela de DoctoradoDoctorado en Conservación y Uso Sostenible de Sistemas Forestale