ORCID Profile
0000-0002-4410-507X
Current Organisations
Centre National de la Recherche Scientifique
,
Brookhaven National Laboratory
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Publisher: Wiley
Date: 18-02-2022
DOI: 10.1111/GCB.16103
Abstract: Stomata play a central role in surface–atmosphere exchange by controlling the flux of water and CO 2 between the leaf and the atmosphere. Representation of stomatal conductance ( g sw ) is therefore an essential component of models that seek to simulate water and CO 2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO 2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between g sw and photosynthetic CO 2 assimilation ( A ). However, measurement of leaf‐level g sw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of g sw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between g sw and A was not linear, challenging the key assumption upon which optimality theory is based—that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in g sw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g 0 and g 1 . Our modification of the USO model improved the goodness‐of‐fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g 1 and other leaf traits. We also observed nonlinear behavior between A and g sw in independent data sets that included data collected from attached and detached leaves, and from plants grown at elevated CO 2 concentration. We propose that this empirical modification of the USO model can improve the measurement of g sw parameters and the estimation of plant and ecosystem‐scale water and CO 2 fluxes.
Publisher: Wiley
Date: 06-04-2023
DOI: 10.1111/NPH.18901
Abstract: Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy‐scale CO 2 and water fluxes. Comparison between observed and TBM trait gradients showed ergence that impacted canopy‐scale simulations of water vapor and CO 2 exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top‐of‐canopy, leaf‐level water‐use efficiency was markedly higher at the top‐of‐canopy, and the decrease in maximum carboxylation rate from the top‐of‐canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within‐in idual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species‐rich, complex tropical forests.
Publisher: Oxford University Press (OUP)
Date: 15-06-2021
DOI: 10.1093/JXB/ERAB295
Abstract: Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.
Publisher: Springer Science and Business Media LLC
Date: 28-11-2019
Publisher: Springer Science and Business Media LLC
Date: 07-10-2020
Publisher: Wiley
Date: 09-01-2023
DOI: 10.1111/NPH.18684
Abstract: The representation of stomatal regulation of transpiration and CO 2 assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance ( g s ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g 1 (stomatal slope, inversely proportional to water use efficiency) and g 0 (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf‐level response curves of six tropical broadleaf evergreen tree species. g 1 estimated from ex situ response curves averaged 50% less than g 1 estimated from survey data. While g 0 and g 1 varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g 1 and g 0 that significantly improved model projections of diurnal trends in transpiration. The accuracy of modeled g s can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g 1 will be required to account for this phenomenon in land‐surface models.
Publisher: Wiley
Date: 16-10-2021
DOI: 10.1111/NPH.17762
Publisher: Wiley
Date: 14-08-2022
Abstract: The Arctic is warming at a faster rate than any other biome on Earth, resulting in widespread changes in vegetation composition, structure and function that have important feedbacks to the global climate system. The heterogeneous nature of arctic landscapes creates challenges for monitoring and improving understanding of these ecosystems, as current efforts typically rely on ground, airborne or satellite‐based observations that are limited in space, time or pixel resolution. The use of remote sensing instruments on small unoccupied aerial systems (UASs) has emerged as an important tool to bridge the gap between detailed, but spatially limited ground‐level measurements, and lower resolution, but spatially extensive high‐altitude airborne and satellite observations. UASs allow researchers to view, describe and quantify vegetation dynamics at fine spatial scales (1–10 cm) over areas much larger than typical field plots. UASs can be deployed with a high degree of temporal flexibility, enabling observation across diurnal, seasonal and annual time‐scales. Here we review how established and emerging UAS remote sensing technologies can enhance arctic plant ecological research by quantifying fine‐scale vegetation patterns and processes, and by enhancing the ability to link ground‐based measurements with broader‐scale information obtained from airborne and satellite platforms. Synthesis . Improved ecological understanding and model representation of arctic vegetation is needed to forecast the fate of the Arctic in a rapidly changing climate. Observations from UASs provide an approach to address this need, however, the use of this technology in the Arctic currently remains limited. Here we share recommendations to better enable and encourage the use of UASs to improve the description, scaling and model representation of arctic vegetation.
Publisher: MDPI AG
Date: 30-05-2018
Location: No location found
No related grants have been discovered for Julien LAMOUR.