The global distribution of grass functional traits within grassy biomes

Jardine, Emma; Thomas, Gavin H.; Forrestel, Elisabeth J.; Lehmann, Caroline; Osborne, Colin P.

doi:10.1111/jbi.13764

Cited by 30 publications

(33 citation statements)

References 56 publications

(93 reference statements)

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“…The scheme makes it possible to translate the observed interspecific differences into practical guidance in ways that are not currently captured by the heuristic literature. Whilst quantitative associations between climate and traits were not part of this study, the traits recorded in this study appear to support hypotheses generated by Jardine et al (2020) and Watkins et al (2020): for example, in a study of 5 bottomland species, Nash and Graves (1993) found that M. virginiana was well-suited to urban environments with high oxygen and nutrient availability. A wider implication from this scheme is that it appears many of the species currently recommended for urban planting may need to be investigated in greater detail, with comparatively few species appearing to be suitable for planting in stressful, paved environments.…”

Section: Toward a Functional Trait Scheme For Urban Forestrysupporting

confidence: 68%

Can Trait-Based Schemes Be Used to Select Species in Urban Forestry?

Watkins

Hirons

Sjöman

et al. 2021

Front. Sustain. Cities

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Urban forests in northern Europe are threatened by climate change and biosecurity risks, and in response, city planners are urged to select a wider portfolio of tree species to mitigate the risks of species die-off. However, selecting the right species is a challenge, as most guidance available to specifiers focuses on ecosystem service delivery rather than the information most critical to tree establishment: the ability of a species to tolerate the stresses found in a given place. In this paper, we investigate the potential of using ecological techniques to describe ecological traits at the level of species selection, and the potential of functional ecology theories to identify species that are not widely discussed or specified at present but might be suitable. We collected trait data on 167 tree species across 37 genera, including 38 species within a case study genus, Magnolia L., and tested four theories that posit ways in which traits trade off against each other in predictable ways. We found that at this scale, most species recommended for urban forestry tend to be ordinated along an axis of variation describing pace of life and stress tolerance, and that most Magnolia species are described as being fast-growing rather than stress-tolerant, although there is a degree of inter-specific variation. Further, we found that only one theory offers a succinct and reliable way of describing physiological strategies but translating ecological theory into a form appropriate for urban forestry will require further work.

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Section: Toward a Functional Trait Scheme For Urban Forestrysupporting

confidence: 68%

Can Trait-Based Schemes Be Used to Select Species in Urban Forestry?

Watkins

Hirons

Sjöman

et al. 2021

Front. Sustain. Cities

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“…Ignoring ITV and considering only the influence of turnover in species composition, there was nearly no AP–Height relationship. This contrasts with results for all plants at global and continental scales (Bruelheide et al., 2018; Moles et al., 2009; Šímová et al, 2018), and with grasses at a global scale (Jardine et al., 2020; Sandel et al., 2016) and regional scale (Forrestel et al, 2017). However, it is consistent with a previous study of California grasses (Sandel & Dangremond, 2012) and grasses within the San Francisco Bay Area (Sandel & Low, 2019).…”

Section: Discussionmentioning

confidence: 73%

“…At a global scale, taller grass species with larger leaves tend to be found in warmer and wetter climates (Jardine et al., 2020; Sandel et al., 2016). Globally, high SLA has also been weakly associated with warmer temperatures (Sandel et al., 2016), although a different approach revealed essentially no relationship (Jardine et al., 2020). In contrast, within California we found that temperature was consistently more important than precipitation, and that SLA increases with temperature at both inter‐ and intraspecific levels.…”

Section: Discussionmentioning

confidence: 99%

Predicting intraspecific trait variation among California's grasses

et al. 2021

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Plant species can show considerable morphological and functional variation along environmental gradients. This intraspecific trait variation (ITV) can have important consequences for community assembly, biotic interactions, ecosystem functions and responses to global change. However, directly measuring ITV across many species and wide geographic areas is often infeasible. Thus, a method to predict spatial variation in a species’ functional traits could be valuable. We measured specific leaf area (SLA), height and leaf area (LA) of grasses across California, covering 59 species at 230 sampling locations. We asked how these traits change along climate gradients within each species and used machine learning to predict local trait values for any species at any location based on phylogenetic position, local climate and that species’ mean traits. We then examined how much these local predictions alter patterns of assemblage‐level trait variation across the state. Most species exhibited higher SLA and grew taller at higher temperatures and produced larger leaves in drier conditions. The random forests predicted spatial variation in functional traits very accurately, with correlations up to 0.97. Because trait records were spatially biased towards warmer areas, and these areas tend to have higher SLA individuals within each species, species means of SLA were upwardly biased. As a result, using species means over‐estimates SLA in the cooler regions of the state. Our results also suggest that height may be substantially under‐predicted in the warmest areas. Synthesis. Using only species mean traits to characterize the functional composition of communities risks introducing substantial error into trait‐based estimates of ecosystem properties including decomposition rates or NPP. The high performance of random forests in predicting local trait values provides a way forward for estimating high‐resolution patterns of ITV without a massive data collection effort.

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“…Hietz et al 2011;Willis et al 2017;Koski et al 2020;reviewed in Lang et al 2018); to fill in gaps in sparse trait databases (Queenborough and Porras 2014;Perez et al 2020); or to conduct comparative studies of clades whose species are hard to sample all at once (e.g. Jardine et al 2020). Measuring functional traits on herbarium specimens carries the promise of letting us reach the inaccessible, including the past or distant parts of the world.…”

Section: Introductionmentioning

confidence: 99%

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

Kothari

Beauchamp‐Rioux

Laliberté

et al. 2021

Preprint

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More than ever, ecologists seek to employ herbarium collections as tools to estimate plant functional traits from the past or from places that are hard to sample. However, many functional trait measurements are destructive, which may preclude their use on valuable herbarium specimens. Reflectance spectroscopy is increasingly used to estimate traits rapidly from fresh or dried, ground leaves, as well as to differentiate and identify taxa. Here, we extend this body of work to pressed, intact leaves such as those in herbarium collections. Using a dataset with 619 plant samples belonging to 70 woody and herbaceous species, we used partial least-squares regression to build validated models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), carbon (Cmass) and nitrogen (Nmass) concentrations, and carbon fractions such as cellulose and lignin. We compared the accuracy of these trait estimates to those from fresh- or ground-leaf spectra of the same samples. Our pressed-leaf models predicted these traits with moderate-to-high accuracy (R2 = 0.586-0.924; %RMSE = 5.7-11.7%). For estimating chemical traits, pressed-leaf models performed better than fresh-leaf models but slightly worse than ground-leaf models. Pressed-leaf models did not perform as well as fresh-leaf models for estimating LMA and LDMC, but outperformed ground-leaf models for LMA. Accuracy was no worse for pressed leaves that underwent discoloration in storage. Finally, on a subset of common species in the dataset, we used partial least-squares discriminant analysis to classify specimens to species with near-perfect accuracy from pressed-(>97%), and ground-leaf (>96%) spectra and slightly lower accuracy from fresh-leaf spectra (>89%). The success of trait estimation and species classification from pressed-leaf spectra may owe to the fact that they combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of internal leaf structure; like ground leaves, they reveal minor absorption features of chemical constituents that would otherwise be masked by water. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits non-destructively. Our study has far-reaching implications for capturing the wide range of functional, phenotypic, and taxonomic information in the world’s preserved plant collections.

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The global distribution of grass functional traits within grassy biomes

Cited by 30 publications

References 56 publications

Can Trait-Based Schemes Be Used to Select Species in Urban Forestry?

Can Trait-Based Schemes Be Used to Select Species in Urban Forestry?

Predicting intraspecific trait variation among California's grasses

Reflectance spectroscopy allows rapid, accurate, and non-destructive estimates of functional traits from pressed leaves

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