Browsing by Author "Wright, I J"
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Item Correlations among leaf traits provide a significant constraint on the estimate of global gross primary production(2012) Wang, Y P; Lu, X J; Wright, I J; Dai, Y J; Rayner, P J; Reich, Peter BCurrent estimates of gross primary productivity (GPP) of the terrestrial biosphere vary widely, from 100 to 175 Gt C year−1. Ecosystem GPP cannot be measured directly, and is commonly estimated using models. Among the many parameters in those models, three leaf parameters have strong influences on the modelled GPP: leaf mass per area, leaf lifespan and leaf nitrogen concentration. The first two parameters affect the modelled canopy leaf area and the last two determine the maximal leaf photosynthetic rate. Ecological studies have firmly established that these three parameters are significantly correlated at regional to global scales, but this knowledge is yet to be used in predicting global GPP. We hypothesize that incorporating multi-trait covariance can reduce uncertainties of model predictions in a way likely to provide improved realism. Using the Australian community land surface model (CABLE), we find that correlations among these three parameters reduce the variance among GPP estimates by CABLE by over 20% for shrub, C4 grassland and tundra, and by between 5% and 20% for most other PFTs, as compared with the simulated GPP without considering the correlations. Globally the correlations do not alter the mean but reduce the variance of modeled GPP by CABLE by 28% and result in fewer extremely high or extremely low (and unlikely) global GPP predictions. Therefore correlations among the three leaf parameters, and possibly other parameters, can be used as a significant constraint on the estimates of model parameters or predictions by those models.Item The evolution of plant functional variation: traits, spectra, and strategies(University of Chicago Press, 2003) Reich, Peter B; Wright, I J; Cavender‐Bares, J; Craine, J M; Oleksyn, J; Westoby, M; Walters, M BVariation in plant functional traits results from evolutionary and environmental drivers that operate at a variety of different scales, which makes it a challenge to differentiate among them. In this article we describe patterns of functional trait variation and trait correlations within and among habitats in relation to several environmental and trade‐off axes. We then ask whether such patterns reflect natural selection and can be considered plant strategies. In so doing we highlight evidence that demonstrates that (1) patterns of trait variation across resource and environmental gradients (light, water, nutrients, and temperature) probably reflect adaptation, (2) plant trait variation typically involves multiple‐correlated traits that arise because of inevitable trade‐offs among traits and across levels of whole‐plant integration and that must be understood from a whole‐plant perspective, and (3) such adaptation may be globally generalizable for like conditions; i.e., the set of traits (collections of traits in syndromes) of taxa can be considered as “plant strategies.”Item Impacts of trait variation through observed trait-climate relationships of performance of a representative Earth System Model: A conceptual analysis(2013) Verheijen, L; Brovkin, V; Aerts, R; Bönish, G; Cornelissen, J; Kattge, J; Reich, Peter B; Wright, I J; van Bodegom, P MIn many current dynamic global vegetation models (DGVMs), including those incorporated into Earth system models (ESMs), terrestrial vegetation is represented by a small number of plant functional types (PFTs), each with fixed properties irrespective of their predicted occurrence. This contrasts with natural vegetation, in which many plant traits vary systematically along geographic and environmental gradients. In the JSBACH DGVM, which is part of the MPI-ESM, we allowed three traits (specific leaf area (SLA), maximum carboxylation rate at 25 °C (Vcmax25) and maximum electron transport rate at 25 °C (Jmax25)) to vary within PFTs via trait–climate relationships based on a large trait database. The R2adjusted of these relationships were up to 0.83 and 0.71 for Vcmax25 and Jmax25, respectively. For SLA, more variance remained unexplained, with a maximum R2adjusted of 0.40. Compared to the default simulation, allowing trait variation within PFTs resulted in gross primary productivity differences of up to 50% in the tropics, in > 35% different dominant vegetation cover, and a closer match with a natural vegetation map. The discrepancy between default trait values and natural trait variation, combined with the substantial changes in simulated vegetation properties, together emphasize that incorporating climate-driven trait variation, calibrated on observational data and based on ecological concepts, allows more variation in vegetation responses in DGVMs and as such is likely to enable more reliable projections in unknown climates.Item New handbook for standardised measurement of plant functional traits worldwide(CSIRO, 2013) Pérez-Harguindeguy, N; Díaz, S; Garnier, E; Lavorel, S; Poorter, H; Jaureguiberry, P; Bret-Harte, M S; Cornwell, W K; Craine, J M; Gurvich, D E; Urcelay, C; Veneklaas, E J; Reich, Peter B; Poorter, L; Wright, I J; Ray, P; Enrico, L; Pausas, J G; de Vos, A C; Buchmann, N; Funes, G; Quétier, F; Hodgson, J G; Thompson, K; Morgan, H D; ter Steege, H; van der Heijden, M G A; Sack, L; Blonder, B; Poschlod, P; Vaieretti, M V; Conti, G; Staver, A C; Aquino, S; Cornelissen, J H CPlant functional traits are the features (morphological, physiological, phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels and influence ecosystem properties. Variation in plant functional traits, and trait syndromes, has proven useful for tackling many important ecological questions at a range of scales, giving rise to a demand for standardised ways to measure ecologically meaningful plant traits. This line of research has been among the most fruitful avenues for understanding ecological and evolutionary patterns and processes. It also has the potential both to build a predictive set of local, regional and global relationships between plants and environment and to quantify a wide range of natural and human-driven processes, including changes in biodiversity, the impacts of species invasions, alterations in biogeochemical processes and vegetation–atmosphere interactions. The importance of these topics dictates the urgent need for more and better data, and increases the value of standardised protocols for quantifying trait variation of different species, in particular for traits with power to predict plant- and ecosystemlevel processes, and for traits that can be measured relatively easily. Updated and expanded from the widely used previous version, this handbook retains the focus on clearly presented, widely applicable, step-by-step recipes, with a minimum of text on theory, and not only includes updated methods for the traits previously covered, but also introduces many new protocols for further traits. This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species’ effects on key ecosystem properties.We hope this new handbook becomes a standard companion in local and global efforts to learn about the responses and impacts of different plant species with respect to environmental changes in the present, past and future.