Le but de cette thèse était d'analyser la relation entre l’économie de course à pied (ECO) et l'efficiencemécanique (Eff) dans la performance des coureurs spécialistes en moyenne et longue distance, utilisantdes modèles allométriques. Basé dans les résultats de trois études originales, nous avons conclu quel’échelle allométrique peut améliorer la relation entre ECO et performance dans la course à pied demoyenne et longue distance, principalement en coureurs amateurs, pour raison morpho-fonctionnelles.Également, pour cette même population, des travaux mécaniques, principalement le externe, peut êtreconsidérées comme prédicteurs de la performance de la course à pied de sujets spécialistes en longuedistance, et un exposant allométrique peut améliorer cette prédiction. En ce qui concerne à l’Eff, lesrésultats ont montré que cette variable est, aussi, une important variable de prédiction de laperformance. Toutefois, quand appliqué des exposant allométriques, il n'y avait aucune améliorationdans cette prédiction, principalement en coureurs de haut niveau. Les résultats ont montré, aussi, quepour le calcul de l’Eff, la contribution de la dépense énergétique anaérobie est important, parce que,contrairement, les résultats peuvent être surestimés. En général, lorsque l’objectif est prédit laperformance des coureurs amateurs de moyenne ou de longue distance, à travers des puissancesmétaboliques ou mécaniques, est suggéré d'adopter un exposant allométrique spécifique du groupeétudié. Toutefois, lorsque cette prédiction est réalisée avec la utilisation de l’Eff en un groupe decoureurs de haut niveau, l’échelle allométrique n’est pas indiquée. / The aim of this thesis was to use allometric models to analyze the relationship that running economy(ECO) and mechanical efficiency (Ef) have on the performance of middle- and long-distance runners.Based on the results of three original studies, we concluded that allometric scaling can improve therelationship between ECO and performance in recreational middle- and long-distance runners, mainlydue to morphofunctional aspects. Similarly, the mechanical works, especially the external mechanicalwork, may be considered to be predictive of running performance and a specific allometric exponentcan improve these predictions. The results also showed that Ef is an important predictor of theperformance of long-distance runners. However, when the specific allometric exponents were applied,there was no improvement in the prediction of this performance. The results also showed that it isimportant to consider the contribution of anaerobic energy expenditure when calculating Ef, becauseotherwise the results may be overestimated, as already reported in other studies. In general, when theobjective is to predict the performance of middle- or long-distance runners through metabolic ormechanical powers, it is useful to adopt a specific allometric exponent of the group investigated.However, when this prediction is performed considering the Ef, particularly in high-performance longdistancerunners, the allometric application is not necessary.

京都大学 / 新制・課程博士 / 博士(農学) / 甲第23950号 / 農博第2499号 / 新制||農||1091(附属図書館) / 学位論文||R4||N5385(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)准教授 岡田 直紀, 教授 井鷺 裕司, 教授 柴田 昌三 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Tropical forests store more than half of the world’s forest carbon and are particularly threatened by deforestation and degradation processes, which together represent the second largest source of anthropogenic CO2 emissions. Consequently, tropical forests are the focus of international climate policies (i.e. Reducing emissions from deforestation and forest degradation, REDD) aiming at reducing forest-related CO2 emissions. The REDD initiative lies on our ability to map forest carbon stocks (i.e. spatial dynamics) and to detect deforestation and degradations (i.e. temporal dynamics) at large spatial scales (e.g. national, forested basin), with accuracy and precision. Remote-sensing is as a key tool for this purpose, but numerous sources of error along the carbon mapping chain makes meeting REDD criteria an outstanding challenge. In the present thesis, we assessed carbon (quantified through aboveground biomass, AGB) estimation error at the tree- and plot-level using a widely used pantropical AGB model, and at the landscape-level using a remote sensing method based on canopy texture features from very high resolution (VHR) optical data. Our objective was to better understand and reduce AGB estimation error at each level using information on large canopy tree structure, distribution and spatial organization. Although large trees disproportionally contributed to forest carbon stock, they are under-represented in destructive datasets and subject to an under-estimation bias with the pantropical AGB model. We destructively sampled 77 very large tropical trees and assembled a large (pantropical) dataset to study how variation in tree form (through crown sizes and crown mass ratio) contributed to this error pattern. We showed that the source of bias in the pantropical model was a systematic increase in the proportion of tree mass allocated to the crown in canopy trees. An alternative AGB model accounting for this phenomenon was proposed. We also propagated the AGB model bias at the plot-level and showed that the interaction between forest structure and model bias, although often overlooked, might in fact be substantial. We further analyzed the structural properties of crown branching networks in light of the assumptions and predictions of the Metabolic Theory of Ecology, which supports the power-form of the pantropical AGB model. Important deviations were observed, notably from Leonardo’s rule (i.e. the principle of area conservation), which, all else being equal, could support the higher proportion of mass in large tree crowns. A second part of the thesis dealt with the extrapolation of field-plot AGB via canopy texture features of VHR optical data. A major barrier for the development of a broad-scale forest carbon monitoring method based on canopy texture is that relationships between canopy texture and stand structure parameters (including AGB) vary among forest types and regions of the world. We investigated this discrepancy using a simulation approach: virtual canopy scenes were generated for 279 1-ha plots distributed on contrasted forest types across the tropics. We showed that complementing FOTO texture with additional descriptors of forest structure, notably on canopy openness (from a lacunarity analysis) and tree slenderness (from a bioclimatic proxy) allows developing a stable inversion frame for forest AGB at large scale. Although the approach we proposed requires further empirical validation, a first case study on a forests mosaic in the Congo basin gave promising results. Overall, this work increased our understanding of mechanisms behind AGB estimation errors at the tree-, plot- and landscape-level. It stresses the need to better account for variation patterns in tree structure (e.g. ontogenetic pattern of carbon allocation) and forest structural organization (across forest types, under different environmental conditions) to improve general AGB models, and in fine our ability to accurately map forest AGB at large scale.