Plant Strategies in Relation to Resource Supply in Mesic to Wet Environments: Does Theory Mirror Nature?

Ordóñez, Jenny C.; Bodegom, Peter M. van; Witte, J.P.M.; Bartholomeus, R.P.; Hal, Jurgen van; Aerts, Rien

doi:10.1086/649582

Cited by 83 publications

(81 citation statements)

References 60 publications

(85 reference statements)

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“…1A), which reflects the multiple drivers acting on LMA, like drought, fertility, and shade (26). Across the globe, LMA was predicted to be lowest in fertile (temperate) regions and highest in dry tropical environments, which coincides with earlier findings (28).…”

Section: Resultssupporting

confidence: 86%

“…A combination of soil and climate variables explained SSD best (R 2 adj = 0.52) followed by seed mass (R 2 adj = 0.41) and LMA (R 2 adj = 0.35). The R 2 values of LMA are lower than in a previous study using locally observed soil C and soil N (26), but the use of local observations (and the lack of such observations in concert with trait observations) comes at the cost of fewer data points and a potential selection bias.…”

Section: Resultscontrasting

confidence: 66%

See 1 more Smart Citation

A fully traits-based approach to modeling global vegetation distribution

Bodegom

Douma

Verheijen

2014

Proc. Natl. Acad. Sci. U.S.A.

196

213

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Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.T o understand and predict the impacts of climate change on system Earth, it is essential to predict global vegetation distribution and its attributes. Vegetation determines the fluxes of energy, water, and CO 2 to and from terrestrial ecosystems. Socalled Dynamic Global Vegetation Models (DGVMs) (reviewed in ref. 1) are indispensable tools to make predictions on such biosphere-climate interactions. Despite their importance, DGVMs are among the most uncertain components of earth system models when predicting climate change (2).DGVMs have been built around the concept of Plant Functional Types (PFTs) (3). Traditionally, various functional attributes (or traits) were assumed to be constant for a given PFT. This assumption has various drawbacks (reviewed in ref. 4). For instance, it implies assuming that trait values used to parameterize PFTs are valid under past environmental conditions and will be valid under future conditions. As such, this assumption neglects acclimation and adaptation (5), nonrandom species extinction (6), and major differences in dispersal rates among species and within PFTs (7). Moreover, this assumption strongly hampers quantifying feedback mechanisms between vegetation and its environment.For these reasons, the application of traits in DGVMs is increasingly refined. Trait responses to, for example, different soil fertility conditions are desc...

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Section: Resultssupporting

confidence: 86%

Section: Resultscontrasting

confidence: 66%

A fully traits-based approach to modeling global vegetation distribution

Bodegom

Douma

Verheijen

2014

Proc. Natl. Acad. Sci. U.S.A.

196

213

View full text Add to dashboard Cite

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“…Chapin et al 1993;Grime et al 1997;Reich et al 1997;Aerts and Chapin 1999;Westoby et al 2002;Díaz et al 2004;Wright et al 2004;Cornwell et al 2008;Baraloto et al 2010a;Freschet et al 2010;Ordoñez et al 2010;Kattge et al 2011). Ample evidence indicates that plant traits and trait syndromes significantly affect ecosystem processes and services (for overviews, see Lavorel and Garnier 2002;Díaz et al 2007;Chapin et al 2008;De Bello et al 2010;Cardinale et al 2012).…”

Section: Introduction and Discussionmentioning

confidence: 99%

Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide

Pérez‐Harguindeguy¹,

Díaz²,

Garnier³

et al. 2016

Aust. J. Bot.

409

275

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The authors regret that elements of Appendix 1 were incorrect in the original publication. The correct version of Appendix 1 is given below. Appendix 1. Summary of plant traits Summary of plant traits included in the handbookThe range of values corresponds to those generally reported for field-grown plants. Ranges of values are based on the literature and the authors' datasets and do not always necessarily correspond to the widest ranges that exist in nature or are theoretically possible. Recommended sample size indicates the minimum and preferred number of individuals to be sampled, so as to obtain an appropriate indication of the values for the trait of interest; when only one value is given, it corresponds to the number of individuals ( = replicates); when two values are given, the first one corresponds to the number of individuals and the second one to the number of organs to be measured per individual. Note that one replicate can be compounded from several individuals (for smaller species), whereas one individual cannot be used for different replicates. The expected coefficient of variation (CV) range gives the 20th and the 80th percentile of the CV ( = s.d. scaled to the mean) as observed in several datasets obtained for a range of field plants for different biomes. Numbering of plant traits corresponds with the numbering of the chapters in the handbook Abstract. Plant 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 ...

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“…PFT-specific β root based on Schaphoff et al (2018b) and minimum and maximum leaf C : N ratios based on the TRY database (Kattge et al, 2011) with data from Kurokawa and Nakashizuka (2008), Garnier et al (2007), Penuelas et al (2010a, b), Fyllas et al (2009), Loveys et al (2003), Han et al (2005), Ordonez et al (2010), Atkin et al (1999), White et al (2000), Xu and Baldocchi (2003), Freschet et al (2010a, b), Laughlin et al (2010), Niinemets (2001Niinemets ( , 1999, Willis et al (2010), Baker et al (2009), Patiño et al (2009), Pakeman et al (2009Pakeman et al ( , 2008, Fortunel et al (2009), Cornelissen et al (1996Cornelissen et al ( , 1997Cornelissen et al ( , 2004, Quested et al (2003), Sardans et al (2008b, a), and Ogaya and Penuelas (2003). The C : N ratios for C 3 and C 4 grasses and crops are based on White et al (2000).…”

Section: Photosynthesis and Gross And Net Primary Production Under N mentioning

confidence: 99%

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

et al. 2018

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Abstract. The well-established dynamical global vegetation, hydrology, and crop growth model LPJmL is extended with a terrestrial nitrogen cycle to account for nutrient limitations. In particular, processes of soil nitrogen dynamics, plant uptake, nitrogen allocation, response of photosynthesis and maintenance respiration to varying nitrogen concentrations in plant organs, and agricultural nitrogen management are included in the model. All new model features are described in full detail and the results of a global simulation of the historic past ) are presented for evaluation of the model performance. We find that the implementation of nitrogen limitation significantly improves the simulation of global patterns of crop productivity. Regional differences in crop productivity, which had to be calibrated via a scaling of the maximum leaf area index, can now largely be reproduced by the model, except for regions where fertilizer inputs and climate conditions are not the yield-limiting factors. Furthermore, it can be shown that land use has a strong influence on nitrogen losses, increasing leaching by 93 %.

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Plant Strategies in Relation to Resource Supply in Mesic to Wet Environments: Does Theory Mirror Nature?

Cited by 83 publications

References 60 publications

A fully traits-based approach to modeling global vegetation distribution

A fully traits-based approach to modeling global vegetation distribution

Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide

Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0)

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