The evolution of the worldwide leaf economics spectrum

Donovan, Lisa A.; Maherali, Hafiz; Caruso, Christina M.; Huber, Heidrun; Kroon, Hans de

doi:10.1016/j.tree.2010.11.011

Cited by 298 publications

(309 citation statements)

References 86 publications

Supporting

Mentioning

280

Contrasting

Unclassified

Order By: Relevance

“…A full explanation of trait values by current environmental drivers will principally be impossible given the additional influences of historical events, stochastic processes, biotic interactions, and phylogenetic constraints (although the latter only seems to have a limited influence on the traits considered here) (34). Improvements may be obtained, however, when better global and spatially explicit estimates of local soil, hydrology, and climate conditions relevant to vegetation become available.…”

Section: Challenges Of Traits-based Approaches For Predicting Vegetationmentioning

confidence: 99%

A fully traits-based approach to modeling global vegetation distribution

Bodegom

Douma

Verheijen

2014

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

206

214

View full text Add to dashboard Cite

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...

show abstract

Section: Challenges Of Traits-based Approaches For Predicting Vegetationmentioning

confidence: 99%

A fully traits-based approach to modeling global vegetation distribution

Bodegom

Douma

Verheijen

2014

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

206

214

View full text Add to dashboard Cite

show abstract

“…For example, interspecific variation in photosynthetic efficiency is not due to one trait, but is instead a result of several physiological traits each with relatively small effects [105]. Moreover, these traits are often correlated and may have synergistic effects [72]. For example, hypothetically, having low A mass and high N mass is expected to yield very unfit genotypes because the high cost of maintaining Rubisco would exceed carbon gains [72].…”

Section: Question 4: What Traits Are Favouredmentioning

confidence: 99%

Evolutionary context for understanding and manipulating plant responses to past, present and future atmospheric [CO ₂ ]

Leakey

Lau

2012

Phil. Trans. R. Soc. B

View full text Add to dashboard Cite

Variation in atmospheric [CO 2 ] is a prominent feature of the environmental history over which vascular plants have evolved. Periods of falling and low [CO 2 ] in the palaeo-record appear to have created selective pressure for important adaptations in modern plants. Today, rising [CO 2 ] is a key component of anthropogenic global environmental change that will impact plants and the ecosystem goods and services they deliver. Currently, there is limited evidence that natural plant populations have evolved in response to contemporary increases in [CO 2 ] in ways that increase plant productivity or fitness, and no evidence for incidental breeding of crop varieties to achieve greater yield enhancement from rising [CO 2 ]. Evolutionary responses to elevated [CO 2 ] have been studied by applying selection in controlled environments, quantitative genetics and traitbased approaches. Findings to date suggest that adaptive changes in plant traits in response to future [CO 2 ] will not be consistently observed across species or environments and will not be large in magnitude compared with physiological and ecological responses to future [CO 2 ]. This lack of evidence for strong evolutionary effects of elevated [CO 2 ] is surprising, given the large effects of elevated [CO 2 ] on plant phenotypes. New studies under more stressful, complex environmental conditions associated with climate change may revise this view. Efforts are underway to engineer plants to: (i) overcome the limitations to photosynthesis from today's [CO 2 ] and (ii) benefit maximally from future, greater [CO 2 ]. Targets range in scale from manipulating the function of a single enzyme (e.g. Rubisco) to adding metabolic pathways from bacteria as well as engineering the structural and functional components necessary for C 4 photosynthesis into C 3 leaves. Successfully improving plant performance will depend on combining the knowledge of the evolutionary context, cellular basis and physiological integration of plant responses to varying [CO 2 ].

show abstract

“…One implication of the LES is that evolutionary processes are constrained, with some combinations of leaf traits being either biochemically or competitively unviable (Reich et al 1997, Donovan et al 2011. A second implication is that easy-to-measure functional traits, such as LMA or leaf dry-matter content, can be collected from large numbers of species and used to infer processes that are more difficult to measure (e.g., photosynthetic rate, growth rate, life span) but correlate strongly with these functional traits across a broad range of species (Reich et al 2007 (Kattge et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Leaf traits within communities: Context may affect the mapping of traits to function

Funk

Cornwell

2013

Ecology

100

109

View full text Add to dashboard Cite

Abstract. The leaf economics spectrum (LES) has revolutionized the way many ecologists think about quantifying plant ecological trade-offs. In particular, the LES has connected a clear functional trade-off (long-lived leaves with slow carbon capture vs. short-lived leaves with fast carbon capture) to a handful of easily measured leaf traits. Building on this work, community ecologists are now able to quickly assess species carbon-capture strategies, which may have implications for community-level patterns such as competition or succession. However, there are a number of steps in this logic that require careful examination, and a potential danger arises when interpreting leaf-trait variation among species within communities where trait relationships are weak. Using data from 22 diverse communities, we show that relationships among three common functional traits (photosynthetic rate, leaf nitrogen concentration per mass, leaf mass per area) are weak in communities with low variation in leaf life span (LLS), especially communities dominated by herbaceous or deciduous woody species. However, globally there are few LLS data sets for communities dominated by herbaceous or deciduous species, and more data are needed to confirm this pattern. The context-dependent nature of trait relationships at the community level suggests that leaf-trait variation within communities, especially those dominated by herbaceous and deciduous woody species, should be interpreted with caution.

show abstract

The evolution of the worldwide leaf economics spectrum

Cited by 298 publications

References 86 publications

A fully traits-based approach to modeling global vegetation distribution

A fully traits-based approach to modeling global vegetation distribution

Evolutionary context for understanding and manipulating plant responses to past, present and future atmospheric [CO ₂ ]

Leaf traits within communities: Context may affect the mapping of traits to function

Contact Info

Product

Resources

About

The evolution of the worldwide leaf economics spectrum

Cited by 298 publications

References 86 publications

A fully traits-based approach to modeling global vegetation distribution

A fully traits-based approach to modeling global vegetation distribution

Evolutionary context for understanding and manipulating plant responses to past, present and future atmospheric [CO 2 ]

Leaf traits within communities: Context may affect the mapping of traits to function

Contact Info

Product

Resources

About

Evolutionary context for understanding and manipulating plant responses to past, present and future atmospheric [CO ₂ ]