Publications by authors named "I Colin Prentice"

78 Publications

Vegetation responses to climate extremes recorded by remotely sensed atmospheric formaldehyde.

Glob Chang Biol 2021 Sep 12. Epub 2021 Sep 12.

Department of Geoscience, Environment & Society-BGEOSYS, Université Libre de Bruxelles, Brussels, Belgium.

Accurate monitoring of vegetation stress is required for better modelling and forecasting of primary production, in a world where heatwaves and droughts are expected to become increasingly prevalent. Variability in formaldehyde (HCHO) concentrations in the troposphere is dominated by local emissions of short-lived biogenic (BVOC) and pyrogenic volatile organic compounds. BVOCs are emitted by plants in a rapid protective response to abiotic stress, mediated by the energetic status of leaves (the excess of reducing power when photosynthetic light and dark reactions are decoupled, as occurs when stomata close in response to water stress). Emissions also increase exponentially with leaf temperature. New analytical methods for the detection of spatiotemporally contiguous extremes in remote-sensing data are applied here to satellite-derived atmospheric HCHO columns. BVOC emissions are shown to play a central role in the formation of the largest positive HCHO anomalies. Although vegetation stress can be captured by various remotely sensed quantities, spaceborne HCHO emerges as the most consistent recorder of vegetation responses to the largest climate extremes, especially in forested regions.
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http://dx.doi.org/10.1111/gcb.15880DOI Listing
September 2021

Coordination of plant hydraulic and photosynthetic traits: confronting optimality theory with field measurements.

New Phytol 2021 Jul 29. Epub 2021 Jul 29.

Department of Biological Sciences, Macquarie University, North Ryde, NSW, 2109, Australia.

Close coupling between water loss and carbon dioxide uptake requires coordination of plant hydraulics and photosynthesis. However, there is still limited information on the quantitative relationships between hydraulic and photosynthetic traits. We propose a basis for these relationships based on optimality theory, and test its predictions by analysis of measurements on 107 species from 11 sites, distributed along a nearly 3000-m elevation gradient. Hydraulic and leaf economic traits were less plastic, and more closely associated with phylogeny, than photosynthetic traits. The two sets of traits were linked by the sapwood to leaf area ratio (Huber value, v ). The observed coordination between v and sapwood hydraulic conductivity (K ) and photosynthetic capacity (V ) conformed to the proposed quantitative theory. Substantial hydraulic diversity was related to the trade-off between K and v . Leaf drought tolerance (inferred from turgor loss point, -Ψ ) increased with wood density, but the trade-off between hydraulic efficiency (K ) and -Ψ was weak. Plant trait effects on v were dominated by variation in K , while effects of environment were dominated by variation in temperature. This research unifies hydraulics, photosynthesis and the leaf economics spectrum in a common theoretical framework, and suggests a route towards the integration of photosynthesis and hydraulics in land-surface models.
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http://dx.doi.org/10.1111/nph.17656DOI Listing
July 2021

Eco-evolutionary optimality as a means to improve vegetation and land-surface models.

New Phytol 2021 09 21;231(6):2125-2141. Epub 2021 Jul 21.

Department of Biological Sciences, Macquarie University, North Ryde, NSW, 2109, Australia.

Global vegetation and land-surface models embody interdisciplinary scientific understanding of the behaviour of plants and ecosystems, and are indispensable to project the impacts of environmental change on vegetation and the interactions between vegetation and climate. However, systematic errors and persistently large differences among carbon and water cycle projections by different models highlight the limitations of current process formulations. In this review, focusing on core plant functions in the terrestrial carbon and water cycles, we show how unifying hypotheses derived from eco-evolutionary optimality (EEO) principles can provide novel, parameter-sparse representations of plant and vegetation processes. We present case studies that demonstrate how EEO generates parsimonious representations of core, leaf-level processes that are individually testable and supported by evidence. EEO approaches to photosynthesis and primary production, dark respiration and stomatal behaviour are ripe for implementation in global models. EEO approaches to other important traits, including the leaf economics spectrum and applications of EEO at the community level are active research areas. Independently tested modules emerging from EEO studies could profitably be integrated into modelling frameworks that account for the multiple time scales on which plants and plant communities adjust to environmental change.
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http://dx.doi.org/10.1111/nph.17558DOI Listing
September 2021

Predictability of leaf traits with climate and elevation: a case study in Gongga Mountain, China.

Tree Physiol 2021 Aug;41(8):1336-1352

State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Renmin South Road, Wuhou District, Chengdu 610065, China.

Leaf mass per area (Ma), nitrogen content per unit leaf area (Narea), maximum carboxylation capacity (Vcmax) and the ratio of leaf-internal to ambient CO2 partial pressure (χ) are important traits related to photosynthetic function, and they show systematic variation along climatic and elevational gradients. Separating the effects of air pressure and climate along elevational gradients is challenging due to the covariation of elevation, pressure and climate. However, recently developed models based on optimality theory offer an independent way to predict leaf traits and thus to separate the contributions of different controls. We apply optimality theory to predict variation in leaf traits across 18 sites in the Gongga Mountain region. We show that the models explain 59% of trait variability on average, without site- or region-specific calibration. Temperature, photosynthetically active radiation, vapor pressure deficit, soil moisture and growing season length are all necessary to explain the observed patterns. The direct effect of air pressure is shown to have a relatively minor impact. These findings contribute to a growing body of research indicating that leaf-level traits vary with the physical environment in predictable ways, suggesting a promising direction for the improvement of terrestrial ecosystem models.
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http://dx.doi.org/10.1093/treephys/tpab003DOI Listing
August 2021

An improved statistical approach for reconstructing past climates from biotic assemblages.

Proc Math Phys Eng Sci 2020 Nov 25;476(2243):20200346. Epub 2020 Nov 25.

Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, People's Republic of China.

Quantitative reconstructions of past climates are an important resource for evaluating how well climate models reproduce climate changes. One widely used statistical approach for making such reconstructions from fossil biotic assemblages is weighted averaging partial least-squares regression (WA-PLS). There is however a known tendency for WA-PLS to yield reconstructions compressed towards the centre of the climate range used for calibration, potentially biasing the reconstructed past climates. We present an improvement of WA-PLS by assuming that: (i) the theoretical abundance of each taxon is unimodal with respect to the climate variable considered; (ii) observed taxon abundances follow a multinomial distribution in which the total abundance of a sample is climatically uninformative; and (iii) the estimate of the climate value at a given site and time makes the observation most probable, i.e. it maximizes the log-likelihood function. This climate estimate is approximated by weighting taxon abundances in WA-PLS by the inverse square of their climate tolerances. We further improve the approach by considering the frequency ( ) of the climate variable in the training dataset. Tolerance-weighted WA-PLS with correction greatly reduces the compression bias, compared with WA-PLS, and improves model performance in reconstructions based on an extensive modern pollen dataset.
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http://dx.doi.org/10.1098/rspa.2020.0346DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7735294PMC
November 2020
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