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Trait covariance: the functional warp of plant diversity?

2017; Wiley; Volume: 216; Issue: 4 Linguagem: Inglês

10.1111/nph.14853

1469-8137

Anthony P. Walker, Michael McCormack, Julie Messier, Isla H. Myers‐Smith, Stan D. Wullschleger,

Species Distribution and Climate Change

New PhytologistVolume 216, Issue 4 p. 976-980 MeetingsFree Access Trait covariance: the functional warp of plant diversity? Anthony P. Walker, Corresponding Author Anthony P. Walker walkerap@ornl.gov Environmental Sciences Division & Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USA(Author for correspondence: tel: +1 865 576 9365; email walkerap@ornl.gov)Search for more papers by this authorM. Luke McCormack, M. Luke McCormack Department of Plant and Microbial Biology, University of Minnesota, St Paul, MN, 55108 USASearch for more papers by this authorJulie Messier, Julie Messier Biology Department, University of Sherbrooke, Sherbrooke, QC, J1K 2R1 CanadaSearch for more papers by this authorIsla H. Myers-Smith, Isla H. Myers-Smith School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3FF UKSearch for more papers by this authorStan D. Wullschleger, Stan D. Wullschleger Environmental Sciences Division & Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this author Anthony P. Walker, Corresponding Author Anthony P. Walker walkerap@ornl.gov Environmental Sciences Division & Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USA(Author for correspondence: tel: +1 865 576 9365; email walkerap@ornl.gov)Search for more papers by this authorM. Luke McCormack, M. Luke McCormack Department of Plant and Microbial Biology, University of Minnesota, St Paul, MN, 55108 USASearch for more papers by this authorJulie Messier, Julie Messier Biology Department, University of Sherbrooke, Sherbrooke, QC, J1K 2R1 CanadaSearch for more papers by this authorIsla H. Myers-Smith, Isla H. Myers-Smith School of GeoSciences, University of Edinburgh, Edinburgh, EH9 3FF UKSearch for more papers by this authorStan D. Wullschleger, Stan D. Wullschleger Environmental Sciences Division & Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, 37831 USASearch for more papers by this author First published: 07 November 2017 https://doi.org/10.1111/nph.14853Citations: 15AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat 39th New Phytologist Symposium 'Trait covariation: structural and functional relationships in plant ecology', Exeter, UK, June 2017 In 300 bc Ancient Greece, Theophrastus was one of the first to organize the diversity of plant life on Earth into categories of function and use (Theophrastus, 1916). Today, scientists are still working to simplify the vast array of plant species and forms in order to distill general features of plant function, structure, and strategy (e.g. Westoby et al., 2002; Wright et al., 2005; Anderegg, 2014; Niinemets et al., 2015; Iversen et al., 2017; Laliberté, 2017; Messier et al., 2017). The search for generalities can come at the cost of overlooked nuance and interesting detail, but nonetheless provides a valuable starting point to understand patterns, causes, and consequences of biodiversity (Peter Reich, University of Minnesota, USA). The study of traits is an active and fruitful area of study that combines field research, experiments, theory, and modelling. The growing interest in traits as a unifying tool in plant functional ecology prompted the 39th New Phytologist Symposium on 'Trait covariation: structural and functional relationships in plant ecology', held in Exeter, UK on the 27–29 June 2017. The goals of the meeting were: (1) to assess the progress made in the study and use of plant trait variation and covariation to reveal trade-offs, constraints, and strategy in plant function; (2) to discuss the best way to use traits for modelling and predictive purposes; and, (3) to identify the current frontiers in trait-based ecology. In this article we report on the symposium, primarily goals (1) and (2), summarizing themes that wove through the presentations, posters, and discussions. We refer readers to the abstract book (www.newphytologist.org/symposia/39) and New Phytologist YouTube channel (www.youtube.com/user/NewPhytologist) for the referenced and unreferenced presentations and posters upon which this report is based. The warp of ecological diversity A major theme of the symposium was how trait covariance can identify common dimensions of phenotypic variation, whether they arise from biophysical or genetic constraints or selection for adaptive trait combinations. These axes of co-varying traits can represent common axes of strategy that make up the 'warp' (major tendencies) of plant functional diversity upon which is woven the 'weft' (secondary variation) of Earth's rich tapestry of plant form and function (Díaz et al., 2016). Early work by Robert MacArthur and Edward O. Wilson proposed the canonical r vs K selection, which imposes a strategic axis of high reproduction vs high survival (MacArthur & Wilson, 1967). MacArthur and Wilson's theory was further developed by the pioneering quantitative comparative plant ecology of J. Phillip Grime and collaborators in the 1970s. By analysing many plant traits – including traits related to morphology, resource allocation, and phenology – of species in a temperate UK grassland, Grime (1977) suggested that plant strategies aligned along two environmental axes: resources and disturbance. Grime suggested that low-disturbance, high-resource environments select for traits that allow organisms to compete (C strategists); low-disturbance, low-resource environments select for traits associated with tolerance to abiotic stress (S strategists), while; high-disturbance, high-resource environments select for traits enabling a ruderal life-history (R strategists). While Grime's CSR triangular ordination was the beginning of strategy identification through dimension reduction of multi-variate trait space, it was interesting to see that the current research being presented did not attempt to link back to CSR strategies. A recurrent theme at the symposium was the discussion of how many common axes of plant strategy exist; for an in-depth analysis of this question see Laughlin (2014). Three axes of plant strategy were focused on: resource acquisition vs conservation, quality vs quantity of propagules, and hydraulic efficiency vs safety (Fig. 1). Two further axes of leaf strategy to maximize net carbon gain (Prentice et al., 2014; Colin Prentice, Imperial College London, UK) and regulate leaf temperature (Wright et al., 2017) were also discussed. There were also multiple discussions on fine-root strategies, mycorrhizal strategies, and their interaction for belowground resource acquisition (Luke McCormack, University of Minnesota, USA) and defense (Etienne Laliberté, University of Montréal, Canada). Figure 1Open in figure viewerPowerPoint An illustration summarizing various common axes of plant strategy (rectangles) and their associated traits (circles and ellipses) discussed at the symposium. Several traits associated with more than one strategy are shown to illustrate the potential for connections, but the illustration of all relevant connections is not intended to be comprehensive. The coupling referred to in the leaf temperature strategy is the coupling of the leaf to the atmosphere, large leaves are less coupled and thus can overheat during the day in hot, dry environments or can become too cold on clear, cold nights (Wright et al., 2017). Ci : Ca is the ratio of internal leaf CO2 concentration to the concentration outside the leaf, also known as χ. g1 is the slope of the stomatal conductance relationship to carbon assimilation rate and environment. SLA is specific leaf area. P50 is the organ water potential at 50% loss of conductivity. The most discussed axis was that of resource acquisition vs conservation across multiple scales (organs and whole plant). This resource acquisition/conservation axis is exemplified by the 'leaf economics spectrum' (Wright et al., 2004), which shows that specific leaf area (SLA), leaf nitrogen concentration (%N), and leaf longevity (LL) are correlated. High values of SLA and %N lead to rapid acquisition of light and CO2, while low values of SLA and high values of LL lead to conservation of resources invested in leaves. For a given unit of leaf dry matter a plant could make a thin, broad leaf with a high surface area that can capture light but is likely more susceptible to herbivory and physical damage, or a plant could make a thicker leaf that intercepts less light but is tough and less appealing to herbivores. It has been suggested that such 'fast' or 'slow' resource use could be a whole-plant strategy (Reich, 2014), but a whole-plant coordination of resource-use has conflicting support, primarily because this trade-off remains to be shown broadly in roots (see Root traits in resource acquisition and conservation section for more detail). Díaz et al. (2016) found that at the global scale, the 'leaf economic spectrum' is largely orthogonal to a size axis characterized by the maximum plant height and seed mass. Seed mass represents the trade-off of quality against quantity of progeny, the competition vs colonization (dispersal) trade-off. The hydraulic safety vs efficiency trade-off was also the subject of much discussion. Large diameter xylem are more efficient at transporting water than small diameter xylem but they are also more vulnerable to embolism and cavitation (Bartlett et al., 2016; Pratt & Jacobsen, 2016). The talks highlighted that, in contrast to the common assumption that hydraulic efficiency and safety are incompatible, these two properties can be expressed independently from each other. The winner of the poster competition, Teresa Rosas (CREAF, Spain), showed coupling of hydraulic strategy and resource acquisition at the leaf scale through a positive correlation of SLA with leaf conductance and negative correlation with water potential at 50% loss of conductivity (P50). However, compensatory responses at the whole-plant scale meant that the correlation was not apparent at the organism level. Root traits in resource acquisition and conservation Recent research into root traits has aimed to uncover whether root traits are similarly correlated with leaf traits on common axes of strategy. For example, on the resource acquisition/conservation axis, root traits like diameter and specific root length (SRL) and root longevity (RL) were hypothesized to correlate with similar leaf traits SLA and LL. While consistent in some instances, evidence suggests that this is not always the case (Roumet et al., 2016; Weemstra et al., 2016; Laliberté, 2017; Isabelle Aubin, CEF, Canada). In fine roots, it is likely that acquisition or conservation of resources will not be as simply related to SRL as leaf resource acquisition/conservation is related to SLA. Root mycorrhizal associations can substantially alter the acquisitive capability of a root, thus SRL may not be the only indicator of the soil volume that can be exploited by a unit of root mass. Data on this are very hard won, and consequently data on hyphal mass, length, and density are scarce. Initial analysis suggests that mycorrhizal association could compensate for root surface area variability caused by variability in SRL (Luke McCormack, University of Minnesota, USA). Furthermore, while most commonly thought to represent a cost in exchange for acquisitive capacity, mycorrhizal associations may also protect roots from predation or pathogens and density dependent plant mortality (Albornoz et al., 2017). This is not to say that roots do not conform to the common axes of plant strategy, just that the aboveground and belowground environments are very different and that it is not always clear which root traits can best indicate function (Weemstra et al., 2016). For example, is root %N as strong an indicator of root physiological function as is leaf %N? Or, is root acquisitiveness well defined by root traits alone, or are root and mycorrhizal traits required to best indicate root function? These questions identified at the symposium remain to be answered. A quality of an entity Like any good scientific meeting there was some lively debate surrounding definitions of terms. Violle et al. (2007) discussed in depth the definition of 'trait' and its various classes (e.g. functional, performance, etc.; Box 1). The discussion at the symposium centred around the fluctuation vs stasis of a trait, i.e. is a rate a trait? The strong environmental control of physiological rates, as well as rates of growth and mortality, mean that rates can fluctuate strongly over process relevant timescales. Thus, can rates measured over any particular period be thought of as a trait? Clearly many elements of the plant phenotype (traits) are controlled by an interaction between genotype and environment. Some traits are more genetically controlled and others more environmentally controlled. The definition of 'trait' by Violle et al. (2007) was augmented to include the word 'heritable' by Garnier et al. (2017) and a useful consideration when discussing a trait may be the degree of genetic control to which the trait is subject. A key challenge is to be clear about which types of traits are being referred to and to use common and traceable trait definitions across all fields of ecology, from physiology to demography to ecological modelling. Demographic 'performance traits' Demographic 'performance traits' (growth, survival, and reproductive rates; Box 1) and how to combine them with functional traits were the subject of a number of presentations. Demographic traits have been used to demonstrate the growth–survival trade-off (i.e. anti-correlation between growth and survival) at the 50-ha plot on Barrow Colorado Island (BCI), Panama (Wright et al., 2010). At BCI, Nadja Rüger (University of Leipzig, Germany) found a correlation between the growth–survival trade-off and the fast–slow axis, where wood density is positively correlated with survival and 'slow' growth. A second strategic axis showed a positive correlation between growth and survival that was negatively correlated with recruitment to yield strategies of long-lived pioneers (relatively fast growers, high survival, few high-quality offspring) vs 'short-lived breeders' (relatively slow growers and low survival but with a high number of offspring). The connection between functional traits and demographic or performance traits is an exciting area of research that will be greatly facilitated by the work of Roberto Salguero-Gómez (University of Oxford, UK) that combines the TRY plant trait database (Kattge et al., 2011) with the COMPADRE database of plant demographic matrices (Salguero-Gómez et al., 2015). Box 1. Definition of trait and types of trait from Violle et al. (2007) '… a trait is any morphological, physiological or phenological feature measurable at the individual level, from the cell to the whole-organism level, without reference to the environment or any other level of organization''… we … define a functional trait as any [morphological, physiological, and phenological] trait which impacts fitness indirectly via its effects on performance traits [biomass, reproductive output, survival].' Trait–environment relationships Predicting how ecosystem function will respond to global environmental change is a grand challenge in ecology due to many functions and processes operating across many timescales. Trait-based research has been advocated as a unifying theme that can integrate the necessary components of community ecology, ecosystem ecology, and evolutionary biology to meet this grand challenge (Lavorel & Garnier, 2002; McGill et al., 2006). More broadly, how trait space is shaped and traits are filtered by environment was a question that featured frequently during the meeting. Evidence of strong biogeographic patterns in plant traits was presented from global to biome-level analyses (Julia Joswig, Max Planck Institute, Germany; Haydn Thomas, University of Edinburgh, UK; Isabelle Aubin, CEF, Canada; Isla Myers-Smith, University of Edinburgh). And several optimization approaches were presented to predict how traits might change with environment (Belinda Medlyn, Western Sydney University, Australia; Colin Prentice, Imperial College London, UK). It was argued that a better theoretical understanding of how traits respond to, and are filtered by, environment is needed to move trait-based ecology towards a more predictive science (Colin Prentice). Plant traits in ecosystem models Plant traits have always been a key component of ecosystem models (Stephen Sitch, University of Exeter, UK; Peter Thornton, ORNL, USA). Since their inception, land-surface and terrestrial ecosystem models have relied on plant traits as parameters to simulate ecosystem function and land–atmosphere interactions and to distill the functional diversity of plant life into representative classes known as plant functional types (PFTs). Traditionally, PFTs have been treated as bundles of traits representing model parameters relevant to plant processes in land–atmosphere interactions (e.g. leaf transmittance and reflectance, maximum height, stomatal conductance related traits). Commonly in models, trait values are varied across PFTs but not dynamically within PFTs. However, in nature traits vary strongly within PFTs (van Bodegom et al., 2012), and the consideration of how to make traits more dynamic within models has been an active area of research in recent years. The key element of these sometimes called 'trait-based' approaches is how to select for traits using selection rules based on environmental filtering and competition. A range of implicit environmental filtering and competition approaches were presented to include trait variation in models: empirical trait–environment relationships (van Bodegom et al., 2014), optimality based approaches (Colin Prentice), empirical trait–trait correlations (Fyllas et al., 2014; Sakschewski et al., 2016), and using the mass ratio hypothesis (Pavlick et al., 2013). More explicit competition (Sakschewski et al., 2016) and natural selection (Scheiter et al., 2013) based approaches were also presented. Discussion centered on covariation among traits and trait covariation with environment and how or whether these should be encoded within models. Allowing trait covariance to be predicted by a model, by competition or selection based rules, would enable the use of trait covariance as a model evaluation dataset. However, it is likely unrealistic to expect a model to reproduce accurate trait variation and covariation when all processes that affect plant strategy are not included in the model (e.g. herbivory and its interaction with SLA and LL). On the other hand, models that specify trait covariances and optimality approaches allow instantaneous change when, in reality, change is affected by processes that act on multiple timescales: acclimation, plasticity, community shifts, and adaptation (Peter van Bodegom, Leiden University, the Netherlands). How can rates of change in a trait be accurately encoded into models? Which trait covariances should be hard coded into models? Would empirical trait covariation hold under non-analogue climates? Which trait covariances ought we expect models to predict? All are key questions. In summary, there are many ways to approach 'trait-based' modelling, each with their own difficulties, complexities, and uncertainties (Peter van Bodegom), and a single 'trait-based' modelling method is yet to be agreed upon. In part, our inability to properly quantify and understand the inherent uncertainty that is added to models with additional processes and complexity is to blame. Tools are now available, and in development, to better quantify model uncertainty and to identify its sources (e.g. Dietze et al., 2014). In the coming years modellers and experimentalists will be able to utilize these tools to identify uncertainty in 'trait-based' modelling methods and to develop a targeted research agenda to improve our predictive understanding of terrestrial ecosystems. The 39th New Phytologist Trait covariation Symposium brought together researchers to highlight current research on trait covariances, trade-offs, and plant strategies that make up the warp of plant form and function. Plant traits link species and individual phenotypes to ecophysiology, ecological life history strategy, coexistence, evolution, and ecosystem functioning. Progress is being made in understanding the relationships between traits, plant strategy, and environment as well as the incorporation of more realistic trait variation in ecosystem models. The presence of major axes of trait covariance, the common axes of strategy they may represent, and when and why they do not hold were discussed at the symposium. While it is still unclear how best to define trait axes for plant root systems, it is increasingly clear that trait frameworks should incorporate complementary roles played by mycorrhizal fungi to capture belowground plant strategies. More remains to be done: to illuminate common trait covariances, trade-offs, strategies, and their mechanistic causes; to understand how these relationships vary across scales; and to understand how traits relate to function. In summary, plant trait research has a long history of yielding insights into plant functional diversity that continues rapidly at present and promises many useful advances and syntheses in the years to come. Acknowledgements The authors would like to thank all the participants at the 39th New Phytologist symposium, the symposium organizers, Exeter University, and the New Phytologist Trust. This manuscript has been authored by UT-Battelle, LLC under Contract no. DE-AC05-00OR22725 with the US Department of Energy. References Albornoz FE, Burgess TI, Lambers H, Etchells H, Laliberté E. 2017. Native soilborne pathogens equalize differences in competitive ability between plants of contrasting nutrient-acquisition strategies. Journal of Ecology 105: 549– 557. Anderegg WRL. 2014. Spatial and temporal variation in plant hydraulic traits and their relevance for climate change impacts on vegetation. New Phytologist 205: 1008– 1014. Bartlett MK, Klein T, Jansen S, Choat B, Sack L. 2016. 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