Sugar maple (Acer saccharum Marsh.) shoot architecture reveals coordinated ontogenetic changes between shoot specialization and branching pattern

Taugourdeau, Olivier; Delagrange, Sylvain; Lecigne, Bastien; Sousa-Silva, Rita; Messier, Christian

doi:10.1007/s00468-019-01884-9

Cited by 5 publications

(7 citation statements)

References 49 publications

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“…A physiological age is then assigned to each segmented AS based on its length. A previous attempt to automatically retrieve AS types (a concept derived from the concept of PA) from field measurements was based on a combination of topological information and morphological measurements [5]. Other research has shown that morphological and functional variables scale with the AS length [2,3].…”

Section: Classification Of Physiological Agesmentioning

confidence: 99%

“…Therefore, the ability of our method to classify different elements of a tree structure into a finite number of features could help to improve, for example, the phenotyping of fruit trees where fruits are borne on specialized axes types. This method could also be used in fundamental research on tree architecture as the composition of a tree crown in terms of axes types express the state of functioning of the tree [1,5,63]. This method could also constitute a first step toward the combination of TLS-derived tree reconstruction and functional and structural tree models that often consider a finite set of structural elements as a building block [50,67].…”

Section: Real-life Examplementioning

confidence: 99%

“…We demonstrate that distribution mixture models (DMM) are a promising tool for recognizing PAs based on very simple measurements of the tree architecture (i.e., measuring only AS length) and botanical knowledge (the number of different PAs that can occur). The annual shoots classified using a DMM present morphological and functional differences that reflect well the morphogenic gradient that exists among tree axes [1,5]. Using only the AS length also greatly reduces data collection in the field and, therefore, eases application of the model and study replication.…”

Section: On the Use Of Distribution Mixture Models To Retrieve Annual Shoot Physiological Agesmentioning

confidence: 99%

“…Trees are modular organisms that exhibit organ specialization in order to fulfill multiple functions [1]. Among these functions, the fulfillment of space exploitation (i.e., light interception) and space exploration requires the production of different types of vegetative axes, some being specialized for space exploration-long axes with a strong investment in woody structure and supporting many ramifications-and some for space exploitationshort axes with a strong investment in leaves, usually poorly or not branched [1][2][3][4][5]. These specialized axes are located on a morphogenetic gradient that can be partitioned into a finite number of "physiological ages" that express the stage of differentiation of the meristem that produced an axis.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a physiological age (PA) is characterized by a particular combination of morphological and anatomical attributes related to the function of this specific PA [1]. Studying the role, interactions, and complementarity between PAs requires cross-scale analyses (e.g., [5]). However, obtaining such data for an entire tree remains challenging as these measurements are typically very time consuming.…”

Section: Introductionmentioning

confidence: 99%

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Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Lecigne

Delagrange

Taugourdeau³

2021

Forests

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The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.

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Section: Classification Of Physiological Agesmentioning

confidence: 99%

Section: Real-life Examplementioning

confidence: 99%

Section: On the Use Of Distribution Mixture Models To Retrieve Annual Shoot Physiological Agesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Lecigne

Delagrange

Taugourdeau³

2021

Forests

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Biomass allocation and plant morphology explain the difference in shrub species abundance in a temperate forest

Markgraf,

Doyon,

Delagrange

et al. 2023

Ecology and Evolution

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In forested ecosystems, shrubs must succeed in persisting in low‐light environments, while simultaneously having the ability to rapidly expand and occupy newly created canopy openings, yet little is known about the traits that make this possible. We hypothesize that shrub species that are abundant in the understory exhibit a specific set of functional traits that define their ability to persist during unfavorable periods and to rapidly exploit newly created habitats. We tested this by comparing field‐measured functional traits such as biomass allocation, leaf display, crown morphology, and leaf traits, across individual size classes and two gap‐forest environments of five shrub species. We observed significant differences in traits between species, size classes, and gap‐forest environments. These differences were primarily related to biomass allocation traits, followed by leaf display, crown morphology, and leaf traits. Abundant shrubs like mountain maple (Acer spicatum) and hazelnut (Corylus cornuta) invested significantly more biomass in roots, had a larger total leaf area, and displayed leaves in a more efficient manner to intercept light. The high investment in root biomass can be interpreted as shrubs exploiting the persistence and colonization strategy through resprouting. Permanent sub‐canopy status likely explains the importance of efficient leaf display, wherein abundant shrubs had a large leaf area with minimal support structures.

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Branch architecture in relation to canopy positions in three Mediterranean oaks

Mediavilla

Escudero

2023

Oecologia

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Sugar maple (Acer saccharum Marsh.) shoot architecture reveals coordinated ontogenetic changes between shoot specialization and branching pattern

Cited by 5 publications

References 49 publications

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Biomass allocation and plant morphology explain the difference in shrub species abundance in a temperate forest

Branch architecture in relation to canopy positions in three Mediterranean oaks

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