SPECIAL FEATURE FORUM THE TREE OF LIFE IN ......Journal of Ecology 2014, 102, 275–301 doi:...

SPECIAL FEATURE – FORUM

THE TREE OF LIFE IN ECOSYSTEMS

The world-wide ‘fast–slow’ plant economics spectrum:a traits manifestoPeter B. Reich1,2*1Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA; and 2Hawkesbury Institute forthe Environment, University of Western Sydney, Penrith, NSW 2751, Australia

Summary

1. The leaf economics spectrum (LES) provides a useful framework for examining species strategies as shapedby their evolutionary history. However, that spectrum, as originally described, involved only two key resources(carbon and nutrients) and one of three economically important plant organs. Herein, I evaluate whether theeconomics spectrum idea can be broadly extended to water – the third key resource –stems, roots and entireplants and to individual, community and ecosystem scales. My overarching hypothesis is that strong selectionalong trait trade-off axes, in tandem with biophysical constraints, results in convergence for any taxon on a uni-formly fast, medium or slow strategy (i.e. rates of resource acquisition and processing) for all organs and allresources.2. Evidence for economic trait spectra exists for stems and roots as well as leaves, and for traits related towater as well as carbon and nutrients. These apply generally within and across scales (within and across com-munities, climate zones, biomes and lineages).3. There are linkages across organs and coupling among resources, resulting in an integrated whole-plant eco-nomics spectrum. Species capable of moving water rapidly have low tissue density, short tissue life span andhigh rates of resource acquisition and flux at organ and individual scales. The reverse is true for species withthe slow strategy. Different traits may be important in different conditions, but as being fast in one respect gen-erally requires being fast in others, being fast or slow is a general feature of species.4. Economic traits influence performance and fitness consistent with trait-based theory about underlying adap-tive mechanisms. Traits help explain differences in growth and survival across resource gradients and thus helpexplain the distribution of species and the assembly of communities across light, water and nutrient gradients.Traits scale up – fast traits are associated with faster rates of ecosystem processes such as decomposition or pri-mary productivity, and slow traits with slow process rates.5. Synthesis. Traits matter. A single ‘fast–slow’ plant economics spectrum that integrates across leaves, stemsand roots is a key feature of the plant universe and helps to explain individual ecological strategies, communityassembly processes and the functioning of ecosystems.

Key-words: carbon, ecophysiology, leaf, light, nutrient, phylogeny, root, stem, strategy, water

Introduction

Two central and intertwined goals in ecology and evolutionare to understand trade-offs that underpin ecological strate-gies and to identify attributes of species that are responsiblefor those trade-offs (Raunkiaer 1934; Grime 1979; Chapin1980; Noble & Slatyer 1980; K€orner 2003; Westoby 1998;Craine 2009). ‘Strategy’ is defined here (sensu Westoby1998) to mean ‘how a species sustains a population… (giventhat it is) operating in the presence of competing species, invaried landscapes and under regimes of disturbance’. The

‘gold standard’ in meeting these two goals is to develop ageneral theory that explains trade-offs in as many contexts aspossible using the fewest but most critical attributes of spe-cies; this paper focuses on a subset of those attributes rele-vant to higher plants, the plant functional traits directlyrelevant to resource economics at organ, individual andecosystem scales.Plants possess characteristics, or trait values (traits hereaf-

ter), at tissue-to-organismal scales that reflect their evolution-ary history and mould their performance (Grime 1977;Chapin 1980; Bond 1989; Lambers & Poorter 1992; Reich,Walters & Ellsworth 1992; Lavorel & Garnier 2002;K€orner 2003; Reich et al. 2003; Westoby & Wright 2006;*Correspondence author: E-mail: [email protected]

© 2014 The Author. Journal of Ecology © 2014 British Ecological Society

Journal of Ecology 2014, 102, 275–301 doi: 10.1111/1365-2745.12211

Cavender-Bares et al. 2009). Traits, including functionaltraits, therefore offer clues and insights regarding how andwhy a plant may behave as it does, where it grows and whereit does not, how it interacts with other plants, and how itinfluences the abiotic and biotic environment around it(Fig. 1). An explicitly economic approach to plant functionaltrait ecology has deep roots, including the work of Bloom,Chapin and Mooney (1985), Givnish (1986) and others. Suchan approach is attractive because it provides a conceptualframework that enables us to link the physiology and mor-phology of a taxon to its environmental and resource toler-ance, as well as its contributions to ecosystem function.The terms ‘trait’ and ‘plant functional trait’ are widely and

variably used, however, and encompass ecophysiological, lifehistory, demographic, response and effect attributes, at organ,individual, population, community and ecosystem scales(Violle et al. 2007). Violle et al. (2007) define ‘functionaltraits’ as ‘morpho-physio-phenological traits which impactfitness indirectly via their effects on growth, reproduction andsurvival, the three components of individual performance’.Accordingly, functional traits are relevant to life-historytheory, which addresses how selection acts to optimize fitnessof organisms.At the physiological level for individuals, trade-offs are

caused by allocation of limited resources to one purpose vs.

another; for example, individuals with lower reproductiveeffort may have a longer life span or vice versa. Herein, Ifocus on a subset of plant functional traits, those directlyinvolved in the acquisition, processing and conservation ofresources, and hence a subset that can be considered ‘eco-nomic’ from a resource analysis perspective. The resourceeconomic traits that are the focus of this piece influence lifehistory largely by influencing growth vs. survival trade-offsthat impact performance (see Figures and associated refer-ences) across the continuum of low to high levels of resources(such as light, water or nutrients). As a consequence, this piecegives short shrift to plant traits specifically related to reproduc-tion, such as seed size and dispersal, and related trade-offs,such as between colonization and competition.It may be helpful to consider at what scales (spatial, taxo-

nomic, biogeographical) ‘resource economics’ theory applies.There is substantial evidence that (i) predicted trait trade-offsoccur and explain much about species performance and com-munity assembly for different species within a given commu-nity (e.g. Reich, Walters & Ellsworth 1994; Poorter &Bongers 2006; Poorter et al. 2010), for different congenersacross local landscapes (Cavender-Bares, Kitajima & Bazzaz2004; Givnish, Montgomery & Goldstein 2004; Savage &Cavender-Bares 2012), and among populations within aspecies across landscapes (Oleksyn et al. 1998), (ii) that the

Fig. 1. Illustration of traits, resources and linkages across scales. Relationships between organ traits shown in the expanded box; relationshipsacross scales shown otherwise. Hypotheses (H1–H9) are shown; when in parenthesis, it suggests an indirect pathway. Note, although not shown,attributes at organism, community and ecosystem scales can be considered traits and represent the integration of organs at individual scale or thecommunity-weighted mean and variance in the value of any given trait at the scale of community, ecosystem or landscape.

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276 P. B. Reich

predicted species trait trade-offs that occur in one site are pre-dictably similar among other sites locally, regionally or glob-ally (Reich, Walters & Ellsworth 1997; Wright et al. 2004),and (iii) that the aggregate traits of communities and ecosys-tems drive productivity and biogeochemical cycling (Garnieret al. 2004; Cornwell et al. 2008; Ollinger et al. 2008;Laughlin 2011; Reich 2012; Reich et al. 2012). Although Ido not make a formal analysis of the scale dependency ofeconomic spectrum theory, this review does address how itapplies at a variety of hierarchical scales.The focus on resource economics taken in this review sets

it apart from prior work on plant traits and strategies. Theapproach taken herein is like the leaf-height-seed strategyscheme of Westoby (2006) in focusing on the traits them-selves. It is narrower in the range of traits focused on (as itignores height and seeds), but fuller in its exploration of traitsdirectly involved in resource economics. Moreover, the func-tional trait approach adopted herein is radical in its simplicitybecause, as the leaf-height-seed strategy scheme of Westoby(2006), it considers traits themselves as the elements of plantstrategy, rather than using traits to help assign species to apriori (and difficult to define) conceptual strategies, as theC-S-R scheme of Grime (1979). By using traits as the centralelements, defined and quantifiable metrics can be related toother quantifiable metrics such as growth, abundance and dis-tribution. Nonetheless, many of the mechanisms invokedalong the C-S axis of the Grime triangle and the ‘leaf’ com-ponent of the Westoby LHS scheme are paralleled by findingsfrom economics trait spectra studies (Grime 1979; Reich,Walters & Ellsworth 1997; Wright et al. 2004; Westoby2006), despite different ways of describing and labelling thosestrategies.

CONCEPTUAL PREMISE

The premise of this piece is (i) that traits are central to coor-dinated trade-offs between resource acquisition and/or processrates on the one hand (i.e. productivity) and resource conser-vation (i.e. persistence) on the other that help determinewhere on a growth vs. survival trade-off, any taxon is locatedfor a given set of conditions (e.g. Lambers & Poorter 1992;Wright et al. 2004, 2010; Kobe 1999), and (ii) that there issufficient variation in time and space within (and across) com-munities, habitats and ecosystems (e.g. Wright et al. 2004;Liu et al. 2010) that every position along those trade-offsurfaces represents potentially successful strategies. This premiseis consistent with evidence and theory that species trade-offthe ability to be effective exploitative resource competitors,that is, to grow quickly and thereby usurp relatively moreresources when these are abundant, with the ability to avoidmortality under low-resource conditions (Grime 1979; Tilman1982, 1987; Kobe et al. 1995; Pacala et al. 1996; Walters &Reich 1996; Kobe 1999; Aerts & Chapin 2000; Russo et al.2005; Poorter & Bongers 2006; Dybzinski & Tilman 2007;Craine 2009; Kursar et al. 2009; Wright et al. 2010). More-over, in many cases, species good at avoiding mortality atlow resource supply also further reduce their supply under

such conditions (Tilman & Wedin 1991; Reich et al. 2003).Herein, I build on prior plant trait economics research (e.g.Wright et al. 2004; Chave et al. 2009; Freschet et al. 2010,2013; Mommer & Weemstra 2012) and ask whether trait rela-tions and associated trade-offs are consonant among organtypes and the ecologically most important plant resources, thatis, whether a plant economics spectrum exists for all threemajor resources – carbon, water and nutrients – built on thefoundations and linkages of leaf, stem and root economicsspectra.My overarching hypothesis is that the ubiquity of strong

selection along trait and life-history trade-off axes, in tandemwith biophysical constraints (Reich et al. 1999), results inconvergence for any taxon on a uniformly fast, medium orslow strategy (i.e. having high, medium or low rates, respec-tively, of resource acquisition and processing) for all organsand all resources. I posit (i) that being fast at any (leaf, stemor root) organ level at acquiring or using C, nutrients or waterrequires being fast for the other resources at the same organlevel, and (ii) being fast for all resources at any one organlevel (e.g. the leaf level) requires being fast for all resourcesat the other organ levels (e.g. stem and root levels). Thus,despite different traits being of central importance in terms ofselection under different conditions (e.g. among differentresource and disturbance regimes) (Diaz, Cabido & Casanoves1998), the coordination among traits, organs and resourcesresults in fast or slow plants in different systems still converg-ing on a fast or slow, respective, ecological strategy. Forexample, having low respiration may matter most in low lightwhen C is most limiting, having low nutrient requirementsmay matter most when nutrients are limiting, and havingdrought tolerance may matter most in arid environments. Yet,in all cases, plants with the slow strategy will have low respi-ration, low nutrient concentrations, denser tissues and a lessercapacity to move and lose water. In other words, because ofthe small number of coupled resources of plant economics, afast or slow strategy requires similar sets of leaf, root andstem traits regardless of whether the main limiting factor islight, N, P, water or temperature. In essence, the nature ofplant integration sets the ‘fast–slow’ template that serves asthe backbone for a more nuanced ‘low nutrient’ or ‘low light’or ‘low water’ strategy. This over-arching hypothesis is builton five specific ideas, as follows.First, selection is a key driver: being fast at acquiring and

processing carbon, water or nutrients in leaves, stems or rootsis advantageous only when acquiring and processing of allresources is fast for all organ systems, because otherwiseplants will possess excess capacity which is costly and waste-ful. Secondly, biophysics is a key constraint: being fast atprocessing of carbon, water and nutrients for leaves, stems orroots is possible only when processing of other resources isfast for all organ systems, because fast acquisition andprocessing of carbon requires fast acquisition and processingof water and nutrients, and fast acquisition and processing ofwater and nutrients requires fast acquisition and processingof carbon. As evolution and biophysics both set trade-offconstraints (Reich et al. 1999; K€orner 2013) and drive multi-


Global plant economics trait spectrum 277

ple resource acquisition to be coupled and linked amongorgan systems, these generally constrain plants to being gen-erally fast or slow (or in-between) across all resources andorgan systems.Thirdly, having fast traits is advantageous in high-resource

environments, but due to excess costs, is disadvantageous inlow-resource and other low growth capacity (e.g. very cold)environments (Grime 1977, 1979). For light, nutrients andwater, the ‘productive strategy’ implemented by rapidresource acquisition involves high use of resources (C, nutri-ents, and water) to rapidly acquire C (and as well nutrientsand water). A plant must spend considerable resources (i.e. tobuild, deploy and use acquisitive canopy and root systems) toobtain more resources than its neighbours, and this strategycan be successful only when that investment is scaled to theavailable resources such that the return on investment is highenough to offset the costs of such resource investment. Inother words, over-investing (relative to resource supply) inexpensive resource acquisition machinery is not a viable strat-egy, and fast plants suffer from this problem when resourcesare scarce. Slow traits in contrast are advantageous in low-resource settings because resource conservation enhances sur-vival, but being slow is itself a disadvantage at any point inspace or time where resources are abundant, if others are fas-ter. The slow return strategy involves considerable savings,such as reduced respiratory and turnover C loss in shade,reduced water loss in semi-arid conditions or reduced forag-ing costs and nutrient turnover costs in infertile conditions(Grime 1965; Reich, Walters & Ellsworth 1992; Craine2009). Such savings allow plants to either tolerate low-resource conditions, eventually draw down resources to levelscompetitors cannot tolerate, or both (Tilman & Wedin 1991;Baltzer & Thomas 2007a; Dybzinski et al. 2011). Theseadvantages and disadvantages result in a trade-off in perfor-mance (sensu ‘performance traits’, Violle et al. 2007) at highvs. low resource supply, as it is impossible to be equallysuccessful at all resource supply levels.Fourthly, spatial and temporal variation in resource supply

and microenvironment are sufficient locally (within a stand)that taxa located all along the fast–slow trait axis are suc-cessful. Finally, the mean and variance of community-scaletraits (‘effect’ traits; Lavorel & Garnier 2002) determinewhether ecosystem-scale elemental fluxes and associatedcoupled plant-soil system processes are fast or slow. Theseeffects might in some cases also exert selective pressure onindividuals. For example, perhaps shade-tolerant species areselected to cast deep shade as canopy dominants, acidophilicspecies to acidify soils and low N-tolerant strong N competitorsto drive down the available pool below levels other species cantolerate.

CENTRAL QUESTIONS

Given the above context, the goal of this paper is to exploretrait variation between organs and across key resources, andthe impact of this variation on individuals, community assem-bly processes and ecosystem-scale function. To do this, I

focus on three central questions relevant to whether the LESidea (Reich, Walters & Ellsworth 1997; Wright et al. 2004)can be broadly extended to water – the third key resource –

stems, roots and entire plants and to individual, communityand ecosystem scales. The focus is largely on cross-speciescontrasts. Figure 1 diagrams the hypothesized relationshipsbetween these traits, properties and processes; throughout thispaper, I will refer to these hypotheses using shorthand (e.g.H1 for Hypothesis 1, etc).

1. Do economic trait spectra exist for stems and roots, aswell as leaves (H1)? If so, do these apply to water as wellas to carbon and nutrients? Do functional trait spectra forstems and roots mirror those for leaves (H1A to H1L)?Do trait spectra reflect evolutionary history, microenviron-ment and resource supply (H8–9)?

2. Are economic traits correlated with performance measuresand if so, consistent with hypothesized trait-basedmechanisms (H2)? Does trait variation help explain differ-ences between taxa in growth and survival, as well as thegrowth–survival trade-off, and thus the distribution ofspecies and the assembly of communities across light,water, nutrient and thermal gradients (H3)?

3. Does the aggregation of traits at the community and eco-system scales help explain system-scale to biome-scalefunctioning, and feedbacks to biogeochemical processes(H4–7)? Would consideration of traits, and their incorpo-ration into model logic, improve ecosystem- to global-scale models of vegetation change and C, water andnutrient cycling?

To assess the above questions, I review literature relevantto the hypotheses in Fig. 1. In doing so, I focus largely ontraits with direct relevance to terrestrial resource economics(light, C, nutrients and water) and ignore other importanttraits. As a result, many traits relevant to reproduction (seedsize, pollination, dispersal, germination, clonality, sproutingability), disturbance (e.g. fire, wind, snowload, flooding), bio-tic interactions (e.g. herbivory, disease) and design (e.g. adultheight, allometry, architecture) are given short shrift in thisreview, as are plasticity, plant size and colonization capacity.These omissions are in large part to keep this piece to a man-ageable length and focus. Moreover, despite their ecologicalimportance, prior work suggests these other attributes largelyadd other additional layers, or axes, of complexity relative toindividual performance, rather than undermining the centraltrends visited in this review.This review covers territory addressed previously (e.g.

Grime et al. 1997; Chapin 1980; Lavorel & Garnier 2002;K€orner 2003; Diaz et al. 2004; Westoby 2006; Westoby &Wright 2006; Craine 2009), but differs from these in the inte-grated examination of leaf, stem and root traits relevant towater, carbon and nutrient economics, across hierarchicalscales and processes, and in the degree to which it touches onplant hydraulics, phosphorus as a limiting element, and scal-ing from tissue to ecosystem and beyond. Thus, hopefully,this review can be a useful complement to the many earlierworks it builds upon.


278 P. B. Reich

Trait–trait variation

The discovery of coordinated variation in the longevity, mor-phology [e.g. specific leaf area (SLA)], chemistry (e.g. [N],[P]) and metabolism (e.g. photosynthetic capacity) of leaves(e.g. Reich, Walters & Ellsworth 1992; Reich et al. 1999;Wright et al. 2004) helps explain species strategies, commu-nity assembly and ecosystem structure and function (e.g.Garnier et al. 2004; Wright et al. 2004; Poorter & Bongers2006; Cornwell & Ackerly 2010; Kattge et al. 2011; Reich2012; K€orner 2013). Recently, trait ecology has embracedother traits and processes, such as leaf hydraulics, wood den-sity, fine-root demography, senesced leaf decomposition andmany others (see references below). In the following sections,I focus on what has been learned about co-variation in leafeconomic traits, in other leaf traits and in stem and root traits,and about their variation across local- to macro-scaleenvironmental gradients.

ORIGINAL LEAF ECONOMIC SPECTRUM TRAITS

The LES (Table 1) quantified by Reich, Walters and Ells-worth (1992, 1997), Reich et al. (1999) and Wright et al.(2004) has as its core concept the productivity-persistencetrade-off and contrasts inexpensive short-lived leaves withrapid return on C and nutrient (N, P) investment with costlylong-lived leaves with slow returns on investment (H1). How-ever, these examples represent the end-members of the spec-trum, and LES studies have identified that a range ofsuccessful strategies exist in every community/ecosystem(H2–3) and that expected leaf trait differences between broadenvironmental gradients were more modest than originallyhypothesized (H8), because of the diversity of successfulstrategies at local scales (Wright et al. 2004, 2005).A recent re-examination (Osnas et al. 2013) of the LES data

in Wright et al. (2004) supported the fundamental role in leafeconomics for the SLA vs. leaf life span trade-off and formal-ized the treatment of covariance of SLA with mass- and

area-based expressions of leaf traits. Osnas et al. (2013) notedthat the bi-variate relationship of mass-based photosynthesis(Amass) vs. mass-based N (Nmass = [N]) includes co-variationwith SLA, because SLA varies positively with Nmass and thusthe Amass vs. Nmass relation ‘includes’ effects on Amass due toSLA. Because Nmass and SLA are positively related and bothindependently influence Amass positively (Reich, Ellsworth &Walters 1998), the Amass vs. Nmass relationship is steeper than(and over-estimates) the part of that relation that is due strictlyto N (Osnas et al. 2013). For similar reasons, the area-basedAmax vs. N (Aarea vs. Narea) relationship underestimates theinfluence of N on Amax because SLA and Narea are negativelyrelated (hence at rising Narea, the ‘low SLA’ effect drags downAarea). A normalization-independent Amax vs. N relationshipremoves the SLA part of the effect (Osnas et al. 2013). Thisdoes not erase the fact that leaves with either higher Nmass orhigher SLA do have higher Amass; nor that because of this, plusco-variance of Nmass and SLA, leaves with higher Nmass dohave higher Amass. Instead, normalization-independent relationsaccount for such covariances and highlight their hidden rolewhen interpreting solely mass- or area-based bi-variaterelations.Additionally, when traits measured on a leaf area basis

that are uncorrelated with SLA are converted to expres-sion on a mass basis by multiplying by SLA (e.g.Amass = Aarea 9 SLA), strong positive correlations are gener-ated with SLA and with other mass-normalized traits (Osnaset al. 2013). This shows that mass-based correlations could intheory arise from a random selection of area-based traits.However, there is little evidence to suggest that in nature,traits of co-occurring taxa are just random assemblages (andinstead perhaps mass-based traits are selected to roughlyequalize productivity per unit leaf area as well as return onunit mass investment, e.g. Falster et al. 2012). Additionally,Sack et al. (2013) suggest that while such linkages may beconsistent with random mathematics, they still reflect physi-cally based mechanistic processes relevant to trait integrationand plant function. For example, all else being equal, thicker

Table 1. List of abbreviations

Abbreviation Definition Units

AM Arbuscular mycorrhizas NAAarea Light-saturated photosynthetic capacity (at ambient CO2) per unit leaf area lmol m�2 s�1

Amass Light-saturated photosynthetic capacity (at ambient CO2) per unit leaf mass nmol g�1 s�1

Amax Light-saturated photosynthetic capacity NAECM Ectomycorrhizas NAGPP Gross primary productivity g C m�2 year�1

Kleaf Leaf hydraulic conductance mmol m�2 s�1 MPa�1

kstem Stem hydraulic conductivity variousLAI Leaf area index Leaf area per unit ground surface areaLES Leaf economics spectrum NALMA Leaf dry mass per unit area g cm�2

MAP Mean annual precipitation mmMAT Mean annual temperature °CRGR Relative growth rate g g�1 day�1

SLA Specific leaf area m�2 g�1

SRL Specific root length m�1 g�1



cell walls will decrease SLA and decrease Amass. The positiverelationship of Amass with SLA is not trivial in meaning andwill imply, for example, that low-SLA leaves will have lowerreturn per mass investment per time (Westoby, Wright &Reich 2013).Leaf diffusive conductance was considered a key trait in

the leaf economic spectrum in some early LES papers (e.g.Reich, Walters & Ellsworth 1992; Reich et al. 1999; Wright,Reich & Westoby 2003), but received scant attention in oth-ers (Reich, Walters & Ellsworth 1997; Wright et al. 2004),perhaps to some extent because Amax and leaf diffusive con-ductance scale similarly among species. However, the rela-tionship of photosynthesis to leaf diffusive conductance is ofcourse by no means constant, and perhaps some economictrait research overemphasized the role of carboxylation capac-ity (associated with leaf [N]) and overlooked that of stomatallimitation (associated with stomatal conductance). Recentpapers by Medlyn et al. (2011) and Prentice et al. (2011,2014) are notable in showing how the close coupling of waterand carbon flux and the importance of efficient (and perhapsoptimal) use of both resources regulates water loss and carbongain and allows accurate predictions of conductance as afunction of environmental conditions (temperature, vapourpressure, aridity, [CO2]). These studies advance both ourfundamental understanding of the links and trade-offs betweenwater and carbon exchange, and the applicability ofharnessing leaf physiology in a more sophisticated way inecosystem- to global-scale models.The LES incorporates physiology, ecology and evolution

and can be viewed from each of these perspectives (e.g.Reich et al. 1999; Shipley, Vile & Garnier 2006; Donovanet al. 2011). Donovan et al. (2011) identified both abundantgenetic variation for the LES traits and genetic correlationsthat are orthogonal to the main axes of LES trait co-variation. From this, they concluded that genetic constraintsdo not limit the LES trait combinations that can arise, andthus, natural selection is probably the most important factorinfluencing the evolution of the LES. Further exploration ofthese questions is presented elsewhere in this Special Feature,for instance, using Helianthus as a model system (Donovanet al. 2014) or evaluating the contributions of different evo-lutionary lineages to modern-day functional trait diversity(Cornwell et al. 2014).

Despite the primacy of SLA (along with leaf life span) ingenerating the leaf economic spectrum and its area- andmass-based expression, the focus on SLA has been questionedby a number of authors. For example, SLA has shortcomingsas a measure of structure, given that it combines thicknessand density, and may not effectively capture variation inimportant leaf mechanical or physical properties that arerelated to leaf life span, plant–herbivore interactions, litterdecomposition and nutrient cycling (Garnier et al. 2004; Fort-unel et al. 2009; Hodgson et al. 2011). However, based onanalyses of almost 2000 species, Hodgson et al. (2011) con-cluded that SLA often discriminates between communitiesbetter than leaf dry matter content (dry leaf mass/water-saturated fresh leaf mass) because SLA is influenced by bothshade and soil fertility, whereas leaf dry matter content lar-gely reflects soil fertility and thus is a better predictor of soilfertility per se. Moreover, based on data for 2819 speciesfrom 90 sites world-wide, Onoda et al. (2011) noted thatthree components of mechanical resistance (work to shear,force to punch and force-to-tear) were all linearly correlatedwith 1/SLA, tissue density and lamina thickness, and surpris-ingly, much better correlated with the first than the latter two.This suggests that SLA better conveys meaningful informa-tion about leaf mechanical properties than either of its compo-nents. Although understanding the ecological roles of leafdensity, thickness and dry matter content is important, neitherHodgson et al. (2011) nor Onoda et al. (2011) suggest thatany of these is more informative than SLA as a generalmeasure of leaf structure.

LEAF TRAITS BEYOND THE ORIGINAL LES

A recent focus on leaf hydraulics bridges stem hydraulics andleaf economics. Leaf hydraulics should be important to bothwater and C economics, and their coupling, given the largefraction of total plant hydraulic resistance to water flow thatoccurs at the leaf level, and the fact that leaf gas exchangeshould be related to leaf xylem hydraulic traits due to theserial positioning of xylem and stomata in the flow path ofwater through the plant (H1) (Brodribb et al. 2005; Brodribb,Feild & Jordan 2007).In studies of disparate species (widely varied phylogeny,

leaf structure, leaf life span, phylogeny, geography), Brodribb

Fig. 2. Relationships of leaf diffusive conductance (n = 58, r2 = 0.79) and photosynthetic capacity (n = 43, r2 = 0.93) to mean leaf hydraulicconductance (Kleaf), illustrating hypothesis H1A. Best fit relationship and 95% confidence intervals shown. Data for tropical and temperate angio-sperms and gymnosperm trees, ferns and club mosses, redrawn from Brodribb et al. (2005) and Brodribb, Feild & Jordan (2007).


280 P. B. Reich

et al. (2005) and Brodribb, Feild & Jordan (2007) found thatleaf hydraulic conductance (Kleaf) was positively related tostomatal conductance and photosynthetic capacity (H1), indi-cating coordination of leaf-level liquid and vapour conduc-tances and fluxes (Fig. 2). Mechanistically, higher Kleaf

enables higher stomatal conductance, which in turn allowshigher photosynthesis. The wider vessels of angiospermsenable higher Kleaf than do the narrower tracheids of conifersand ferns (Brodribb et al. 2005), although considerableheterogeneity occurs within each group.Brodribb, Feild and Jordan (2007) also advanced our under-

standing of the links between the structure of the leaf vein sys-tem, water transport and photosynthetic capacity. Theysuggested that the hydraulic and coupled photosynthetic per-formance of a leaf should be related to the length of mesophylltissue to be traversed as water moves from a vein ending tothe stomatal site of evaporation. This follows from the knowl-edge that hydraulic resistance to water flow is much higherpassing through leaf mesophyll than through vein xylem; as aresult, the distance water must flow through the mesophyllbefore evaporating (a function of the positioning of leaf minorveins) should regulate the leaf’s hydraulic transport efficiency.They produced evidence that strongly supports the hypothe-

sis that the length of the hydraulic pathway through the meso-phyll regulates Kleaf and thus indirectly Amax. Among a broadset of angiosperms, photosynthetic capacity is stronglycorrelated with vein density (also called vein length per area,

Sack et al. 2013) and even more so with the proximity ofveins to the evaporative surfaces of the leaf (as measured bythe mean maximum mesophyll path length) (Brodribb, Feild& Jordan 2007). The influence of vein positioning over leafwater and carbon flux rates was similar across a range of spe-cies that differed dramatically in their evolutionary historyand ecology. Leaf vein length per leaf area is analogous toSLA or specific root length (SRL) in the sense of being ameasure of resource flux capacity (Sack et al. 2013) and thusalso contributes to ‘fast–slow’ contrasts through its influenceon leaf carbon and water fluxes. Although the total length ofthe transpiration pathway in trees can exceed 100 m, traitsrelevant to the last few tens of microns of that path play akey role in regulating the photosynthetic and hydraulic perfor-mance of the individual plant.Thus, the links between leaf hydraulics and gas exchange

appear to be strong, consistent with theoretical modellinglinking the two (Katul, Leuning & Oren 2003). Moreover, theevolution of leaf vein systems that could support high waterflux rates and other ‘fast return’ LES traits (Boyce et al.2009; Brodribb & Feild 2010; Feild et al. 2011; Sack &Scoffoni 2013) may have been crucial to the evolution andsuccessful spread of the angiosperms. The work of Brodribband colleagues along with more recent work on this theme(Sack & Scoffoni 2013; Sack et al. 2013) provides strongsupport for the close coupling of leaf traits that regulatecarbon and water flux (supporting H1A).

Fig. 3. Relationships between leaf photosyn-thetic rate per unit area, maximum leaf-specific stem hydraulic conductivity (kstem),minimum daily leaf water potential (Ψmin)and wood density for 20 lowland tropicalforest canopy tree species in Panama,illustrating H1A, H1C, H1G, H1L. Redrawnfrom data in Santiago et al. (2004). Valuesare species means. Best fit relationship and95% confidence intervals shown.



STEM TRAITS

The past decade has shown growth in understanding of stemtraits and their importance. Chave et al. (2009) proposed awood economics spectrum, based on the relationships ofwood density (n = 8412 taxa) with mechanical and hydraulicproperties. They posited that high wood density would beassociated with a ‘slower’ potential to move water but withstronger and more flexible mechanical properties and greaterprotection from drought stress. The hypothesis was supportedin several respects. Wood density is correlated with mechani-cal traits such as strength and bendability (Chave et al. 2009)and with performance (see below). Evidence demonstrates aconsiderable degree of coordination of stem hydraulic proper-ties with wood density (Sobrado 1986; Meinzer et al. 2008a,b; Chave et al. 2009; Poorter et al. 2010; Russo et al. 2010;Zanne et al. 2010; Markesteijn et al. 2011). For example, instudies of tropical wet and dry forest trees, high wood densitywas associated with low stem hydraulic conductivity (kstem),whether expressed on a sapwood or leaf area basis (Figs 3and 4).

Additionally, a variety of stem hydraulic traits are correlatedwith each other and differ between species with the three radi-cally different wood types: coniferous, diffuse-porous andring-porous (Sperry, Meinzer & McCulloh 2008). The packingfunction describes a strong trade-off and upper limit to the rela-tionship between conduit frequency (the number per xylemarea) and conduit diameter, which holds within and across spe-cies with differing xylem anatomy (Sperry, Meinzer & McCul-loh 2008; McCulloh et al. 2010; Zanne et al. 2010). Largelyas a result of this trade-off, species with strikingly differentxylem anatomy have similar scaling of leaf area and stemhydraulic conductivity with stem diameter of a branch or trunksegment (McCulloh et al. 2010), consistent with ideas embed-ded in metabolic scaling theory (e.g. Enquist et al. 2007;Savage et al. 2010; Sperry et al. 2012). Although they can belogically considered as plant traits, several other stem hydraulicmeasures (e.g. water potential at loss of 50% of conductivity)are discussed in the latter section on performance.To date, differences between taxa in stem metabolism

involving C fluxes have been addressed largely independently

Fig. 4. Relationships between vulnerability to cavitation (the water potential at 50% loss of conductivity, P50) and leaf-area-specific and sap-wood-area-specific stem hydraulic conductivity (kstem) and wood density, for tropical dry forest trees in Bolivia. Redrawn from data in Mark-esteijn et al. (2011).

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10 Fig. 5. Mass-based dark respiration (nmol

g�1 s�1) in relation to tissue nitrogenconcentration (mmol g�1). Both areexpressed on a logarithmic (base10) basis.Data are shown for different plant groups andfor all plant groups pooled, with organ typeslabelled with different colours. Allrelationships, P < 0.0001; R2 ranged from0.53 to 0.80. Note to improve visibility ofeach panel, scales are not identical amongpanels. However, the axis ratios are the same;hence, slopes may be compared betweenpanels. Illustrates Hypotheses H1B, H1D,H1F. From Reich et al. (2008).


282 P. B. Reich

of studies of water flow. Stem dark respiration varies in rela-tion to area : mass proportions and tissue [N] similarly as inleaves. For example (Fig. 5), stem dark respiration was corre-lated with stem N similarly in different plant groups (Reichet al. 2008) and both were, not surprisingly, lower in largerstems with a much lower surface area to mass ratio (andlikely greater fraction of metabolically inactive wood).The studies described above support the idea of an inte-

grated stem economics spectrum for coupled water, C and Nrelations. There is close coupling of stem hydraulic, leafhydraulic and leaf C flux dynamics (Brodribb et al. 2005;Brodribb, Feild & Jordan 2007; Meinzer et al. 2008a,b); leafand canopy C and N dynamics (Wright et al. 2004; Ollingeret al. 2008; Reich 2012); stem hydraulic conductivity withleaf area (McCulloh et al. 2010); and leaf area with C fluxes(Reich 2012; Stark et al. 2012). Together these strongly sup-port the idea that stem traits associated with C, N and waterdynamics represent a unified stem economics spectrum (H1)that is also linked with broader C, N and water scaling pro-cesses at a range of hierarchical (organ, individual, stand)scales.

F INE-ROOT TRAITS

Several studies have assessed whether the LES is paralleledby a similar root economic spectrum (Eissenstat & Yanai1997; Pregitzer et al. 1998, 2002; Reich et al. 1998a,b;Craine et al. 2005; Tjoelker et al. 2005; Withington et al.2006; Freschet et al. 2010; McCormack et al. 2012). Thesestudies have almost entirely focused on fine roots, which isalso the case in this review. Variation in below-ground planttraits remains poorly quantified compared with leaf traits;

hence, any conclusions are still rather more preliminary thanfinal. Moreover, variation in root dimensionality and size(branching order, diameter, etc) complicates both the verydefinition of what a fine root is, as well as operational rootcensusing (Guo et al. 2008).Candidate traits for a root economic spectrum include root

[N], root respiration, root longevity, SRL (length of root perunit mass), root diameter and root architecture. SRL, some-times considered analogous to SLA as an indicator of uptakepotential per g investment, has been correlated with rootdiameter, dry matter content, high levels of branching andlow tissue density (e.g. Craine et al. 2005; Comas & Eissen-stat 2009; Freschet et al. 2010; Holdaway et al. 2011). Thissuite of morphological and structural traits has been correlated(i) with root [N] in some cases (Reich et al. 1998a,b; Craineet al. 2005; Tjoelker et al. 2005; McCormack et al. 2012),but not others (Pregitzer et al. 2002; Withington et al. 2006;Comas & Eissenstat 2009; McCormack et al. 2012), and (ii)often with root respiration (Reich et al. 1998b; Tjoelker et al.2005; Makita et al. 2012). In turn, root [N] has been shownto correlate with root respiration (Reich et al. 1998b, 2003,2008; Tjoelker et al. 2005; Chen et al. 2010) (Fig. 5); andboth root [N] and root respiration have been shown to corre-late with root lifespan (Tjoelker et al. 2005; Withington et al.2006; McCormack et al. 2012). Moreover, data from temper-ate grasslands, woodlands and forests show a significanttrade-off (Fig. 6) between root life span and root [N] (H1).Although not as uniformly or strongly coordinated as theLES, on balance the evidence indicates that a root economicsspectrum exists and represents a ‘fast–slow’ trade-off andassociated strategy axis.There is rising interest in considering mycorrhizal status as

an additional root trait (Fig. 1). Brundrett (2002) concludedthat increasing mycorrhizal dependence should be considereda ‘slow’ strategy and be associated with lower SRL, less rootbranching and longer root life span. The occurrence, andoften dominance, of ectomycorrhizal plants in infertile ecosys-tems has also led to suggestions that they comprise a low-nutrient (slow) trait syndrome (Read 1991; Cornelissen et al.2001). To address these ideas, Koele et al. (2012) identified19 evolutionary clades of ectomycorrhizal plants and used adata set comprising 11 466 samples across c. 3000 species totest whether there were consistent shifts in leaf nutrients withthe evolution of ectomycorrhiza. There were significant differ-ences in foliar [P] but not [N] between ectomycorrhizal andnon-ectomycorrhizal species when not considering phylogeny.However, there was no evidence of consistent differences in[P] or [N] between ectomycorrhizal clades and their nearestnon-ectomycorrhizal relatives. Thus, the hypothesis that ECMspecies are characterized by different leaf nutrient status wasnot supported. However, ectomycorrhizal species may be bet-ter able to acquire and use organic nutrients (Read, Leake &Perez-Moreno 2004; Wurzburger & Hendrick 2009; Orwinet al. 2011; Phillips, Midgley & Brozstek 2013), which couldhelp explain their success in ecosystems with low nutrientavailability. Implications for biogeochemical processes areaddressed below.

Fig. 6. Relationship of tissue%nitrogen vs. life span (days) for fineroots (black circles) and leaves (open grey circles). Leaf data fromWright et al. (2004). Root data compiled from Reich et al. (2001);Tjoelker et al. (2005); Withington et al. (2006), McCormack et al.(2012). The relationships are significant for leaves (P < 0.0001,r = �0.65, n = 706) and for roots (P < 0.0001, r = �0.46, n = 68).The reduced major axis relations shown for both separately (roots,thicker black dashed line; leaves, thinner grey dashed line); the slopesare not significantly different, but at any given life span, roots onaverage have lower% nitrogen than leaves. Illustrates H1B, H1F.



RELATIONSHIPS AMONG LEAF, STEM AND ROOT

TRAITS

Various lines of evidence (summarized above) supporthypothesized axes (H1) of multiple trait variation for leaves,stems and roots, each viewed independently (e.g. Wrightet al. 2004; Craine et al. 2005; Chave et al. 2009; Baralotoet al. 2010). This leads to the question of whether leaf, stemand root trait syndromes are coordinated – representing asingle axis of variation – or whether these are largely inde-pendent. Strong integration of traits of all three tissue typeswould be predicted if parallel ‘productive vs. persistent’ tissuestrategies are advantageous at the whole-plant scale. Resultsfor a subarctic flora suggest that there is some degree of coor-dination of root, stem and leaf traits, supporting the idea of awhole-plant-based strategy (Freschet et al. 2010).In a broad survey of leaf and wood tissues from 668 Neo-

tropical tree species, Baraloto et al. (2010) confirmed theexistence of leaf and wood trait spectra, but found trade-offsin leaf economics and stem economics spectra to be indepen-dent. The sets of traits examined in Baraloto et al. (2010)was quite limited (to wood density and water content, andbark thickness), so perhaps the results would differ in a morecomprehensive contrast. For example, Brodribb & Feild(2000), Santiago et al. (2004), and Campanello, Gatti andGoldstein (2008) found strong coupling of kstem with leafphotosynthetic capacity across species and light environments,echoing the coupling of hydraulic and leaf economic traits atthe leaf level (Brodribb et al. 2005; Brodribb, Feild & Jordan2007) mentioned earlier. Additionally, across a set of Nothaf-agus species, leaf and stem hydraulic conductivity were cou-pled, and both were inversely correlated with wood density(Bucci et al. 2012) and in a subarctic flora stem and leaf,chemical and structural traits were strongly correlated (Fres-chet et al. 2010). Other evidence for coupling of leaf andstem traits comes from Choat, Sack & Holbrook (2007),Meinzer et al. (2008a,b), Blackman, Brodribb & Jordan(2010), and Markesteijn et al. (2011). They observed a coor-dination of wood density, stem hydraulic conductivity, leafgas exchange rates and leaf water potential (e.g. Figs 3 and4). Trees with denser wood had lower kstem, more negativeleaf water potential at minimum daily (Ψmin), at the turgorloss point (ΨTLP) and at the point of vulnerability to cavita-tion, and lower rates of photosynthesis and transpiration.Savage and Cavender-Bares (2012) found similar trade-offs ina study of co-occurring willows and poplars, with specieswith denser wood having more negative ΨTLP. Not everydetailed study found close association of stem and leaf traits,though; Ackerly (2004) found independent stem and leaf traitsyndromes in a study of 20 co-occurring chaparral shrubs.However, the preponderance of studies above suggest thatcoupling of stem and leaf traits is likely more common thansuggested by the Baraloto et al. (2010) analysis, and a meta-analysis of a comprehensively broad set of leaf and stem traitsshould be informative.Similarly, root traits likely mirror stem and leaf traits to

some degree. Positive correlation between species for pairs

of traits (e.g. leaf vs. root [N]; SLA vs. SRL; leaf vs. rootlife span; leaf vs. root respiration) has been observed insome studies, but not all (e.g. Craine & Lee 2003; Craineet al. 2005; Tjoelker et al. 2005; Kerkhoff et al. 2006; Wi-thington et al. 2006; Freschet et al. 2010; Liu et al. 2010).Additionally, in a survey of > 2500 measurements from 287species, Reich et al. (2008) observed consistent mass-baseddark respiration–nitrogen scaling for roots, stems and leavesexamined separately. This was true for life-forms (woody,herbaceous plants) and phylogenetic groups (angiosperms,gymnosperms) examined separately or pooled (Fig. 5). Noconsistent differences in the slopes of the log–log scalingrelations were observed between organs or between plantgroups. Similarly, a new compilation that compares life spanvs. [N] correlations for roots (n = 68) with those of leaves(n = 706) shows similar relations for leaves and roots(Fig. 6). The slope of tissue [N]–longevity did not differ sig-nificantly for leaves and roots, but at any common life span,leaves have higher [N] than roots. These results indicate thatdifferent organs have similar respiration [N] and longevity[N] relationships.Other studies that measured leaf, stem or whole-plant

traits as well as root traits also support the notion of traitcoordination between organs. For example, McCormacket al. (2012) found shorter root life span and higher root [N]in faster-growing trees with lower wood density (all fasttraits), and Comas and Eissenstat (2004) found faster-grow-ing taxa to have smaller root diameter and high SRL thancongeners. As higher root [N] is also associated with shortroot life span (Fig. 6) (Craine et al. 2002; Withington et al.2006) and with high root respiration (Fig. 5) and high plantgrowth rates (Reich et al. 1998a,b, 2003; Tjoelker et al.2005; Reich et al. 2008), it seems likely that fine-root chem-istry, metabolism and duration are related to stem, leaf andplant traits as well. As with stems (Meinzer et al. 2010), thegreater relative variation in size, order, age and architecturalposition of fine roots (Pregitzer et al. 2002; Guo et al.2008) that is included in the measurements available in theliterature (as compared to leaves, for which a narrow andstandardized ontogenetic stage has been adopted) likely con-tributes to the greater uncertainty about the details of a rooteconomics spectrum.If the above findings are broadly applicable (and evidence

suggests they are), they support the existence of a plant eco-nomics spectrum that applies to water as well as C and nutri-ents and that integrates across the leaf, stem and root systems.This spectrum suggests that plants at the ‘fast’ end of the pro-ductivity-persistence trade-off have high growth potentialbecause they have high capacity to move water and to acquireand use nutrients and light to fix C, but build flimsy, dispos-able tissues (whether root, stem, or leaf) and are less tolerantof low resources (whether water, nutrients or light). In con-trast, taxa with ‘slow’ traits are better protected from high Closses (low respiration, low leaf turnover rates) and droughtstress (e.g. greater capacity to withstand low water potentialwithout loss of turgor or hydraulic conductivity).


284 P. B. Reich

Traits, biogeography and individualperformance

TRAITS AND CLIMATE GRADIENTS

Given evidence of coupled economic traits for leaves, root,stems and whole plants, do these vary along large-scale envi-ronmental gradients (H8)? Interspecific relationships betweenplant C and nutrient economic traits on the one hand and tem-perature or precipitation gradients on the other have beenidentified, but explain surprisingly little of the total variances(Wright et al. 2004, 2005; ter Steege et al., 2006; Ordo~nezet al. 2009).For example, mean annual precipitation (MAP) and mean

annual temperature (MAT) explained < 1% and 10% of glo-bal interspecific variation in SLA (Wright et al. 2004). Thecombination of four simple, widely available climate metrics(MAT, MAP, mean vapour pressure deficit and solar irradi-ance) explained only 5–20% of the overall interspecific varia-tion in 5 LES traits (SLA, leaf life span, Amass, [N], [P]) at175 sites (Reich, Wright & Lusk 2007). Similarly, only 1%to 13% of variance in three common measures of leafmechanical strength could be explained by MAT or MAP(Onoda et al. 2011). Hydraulic margin of safety did not differconsistently across major global moisture gradients (Choatet al. 2012). Climate explains only a small fraction of traitvariance in part because species with a variety of economicstrategies are successful in communities and ecosystems allalong these environmental gradients, reflecting high levels oflocal resource and microenvironmental niche diversity, as wellas different alternative plant designs to make a living in simi-lar microenvironments (Reich, Walters & Ellsworth 1997;Kobe 1999; Grime 2001; Wright et al. 2004; Ackerly &Cornwell 2007). Indeed, 38–67% of interspecific variation indark respiration, leaf life span, Amass and [N] occurredbetween coexisting species within sites (Wright et al. 2004),

41–72% of variance in mechanical properties occurred withinsites (Onoda et al. 2011), and 75–85% of SRL, root [N] androot N per length occurred within communities (Liu et al.2010). Thus, the majority of the total variance of economicfunctional traits at organ scales is not explained by broad-scale climatic influences (see also Cornwell et al. 2008; Fres-chet et al. 2010). However, community-weighted mean traitsmay be better explained by climate. For example, climateexplained from 38% to 55% of variance in a range of com-munity-weighted mean traits including wood density, SRL,SLA and leaf [N] and P, across 19 sites spanning a 12 °Celevational range in MAT (Laughlin et al. 2011).Moreover, although across all taxa, leaf life span is very

poorly related to climate (see above), leaf life span of ever-green species decreases with MAT, whereas that of deciduousspecies increases (Wright et al. 2005; van Ommen Kloekeet al. 2012). The explanation for both is likely related to thelength of the favourable portion of the season – for deciduousspecies that become leafless for a time each year, that periodis as expected, closely related to the length of the unfavourableseason (Kikuzawa et al. 2013). Why species that are evergreenshould have longer leaf life span at lower MAT is not as obvi-ous. One explanation, supported by optimization modelling, isthat evergreen species need to increase the longevity of theirfoliage to optimize C gain (and offset the construction costs)as the favourable season becomes a shorter fraction of the year(Kikuzawa et al. 2013). The global pattern of increasing leaflife span in evergreen species with shorter, colder growing sea-sons is also observed within widely distributed boreal ever-green species (Reich et al. 2014) (Fig. 7). Intraspecific needleleaf life span increases by as much as 50–125% from thesouthern to northern regions of the boreal forest. The divergentimpacts of growing season length on leaf life span of ever-green and deciduous species likely influence other LES traits,given their strong linkages with leaf life span.Global data illustrate correlations of leaf size and shape

with climate that are considerably weaker for species meansthan for site means (Peppe et al. 2011; Royer et al. 2012),because species within a site vary considerably in leaf sizeand shape. Several assessments report that leaf size is not partof the LES (Ackerly & Reich 1999; Ackerly et al. 2002), andit is generally unclear what role leaf size and shape play inresource economics, despite decades of study (Nicotra et al.2011).In summary, economic traits are often weakly correlated

(but see below for P) with climate at the species level (H8).Climate appears to exert some control on the average leaf(shape and economic) characteristics (hence stronger relationsusing site than species means), but many positions along leafproductivity-persistence trade-off axes are viable strategies inmost communities and ecosystems.

THE UNIQUE BIOGEOGRAPHY OF PLANT

PHOSPHORUS?

Perhaps the strongest biogeographical gradient for any eco-nomic plant trait exists for tissue [P] (and as a result,

Fig. 7. Intraspecific needle life span (mean among individuals at eachsite) in relation to mean annual temperature (MAT, °C) for five borealconifers at between 19 and 78 sites (52 on average) across naturalgradients in Eurasia (Pinus sylvestris) or North America (all others).Species include (from longest to shortest needle life span at lowMAT) Picea mariana (open circles), Picea glauca (stars), Abiesbalsamea (closed circles), P. sylvestris (squares) and Pinus banksiana(triangles). Relations significant (P < 0.01) for all species, meanR2 = 0.46. Illustrates H8. Data from Reich et al. (2014).



N : P ratio). From 19 to 58% of the global variation of N : Pratio in green and senesced foliage and in four fine-root sizeclasses is related to MAT (H8) (McGroddy, Daufresne &Hedin 2004; Reich & Oleksyn 2004; Kerkhoff et al. 2005;Yuan, Chen & Reich 2011). This is likely due to a combina-tion of biogeographical patterns of temperature and its season-ality, soil substrate type and age, erosion and occlusion (forP) and nutrient leaching (for N) (Reich & Oleksyn 2004;Lambers et al. 2008; Ordo~nez et al. 2009; Vitousek et al.2010; Turner & Condron 2013).Soil substrate age gradients may be particularly notable at

regional scales (Vitousek, Turner & Kitayama 1995; Lamberset al. 2008, 2013), especially along soil chronosequenceswhich serve as powerful model systems (e.g. Vitousek,Turner & Kitayama 1995; Richardson et al. 2004; Lalibert�eet al. 2012). Along the Franz Josef chronosequence in NewZealand, community-level average traits (both abundanceweighted and presence/absence) for root tissue density,branching architecture, [P], [N] and N : P were all correlatedwith soil age (Holdaway et al. 2011). The older soils, charac-terized by low P availability (Richardson et al. 2004), wereinhabited by plant communities comprising species withhigher root and leaf tissue density, low [N], low [P], highN : P and low Aarea (Turnbull et al. 2005; Whitehead et al.2005; Holdaway et al. 2011) – all traits correlated with the‘slow return’ economic strategy. Similar contrasts of coupledtissue chemistry and metabolism are seen in tropical forestsvarying in both N and P availability (Reich, Walters & Ells-worth 1994; Reich, Oleksyn & Wright 2009; Domingues

et al. 2010). This view of the importance of soil P to planteconomics is consistent with broad positive relations of leaf Pto soil P (Ordo~nez et al. 2009) and root P to soil P (Yuan,Chen & Reich 2011) as well as with studies showing lowphotosynthetic gain per unit leaf N in species inhabitinglow-P sites (Reich, Walters & Ellsworth 1994; Reich,Oleksyn & Wright 2009; Domingues et al. 2010).Clearly, whether the gradients are local, regional or conti-

nental, soil P gradients associated with soil substrate type orage are influential with respect to economic plant traits. Ineven more extremely P-impoverished (or strongly P-sorbed)soils, a broader syndrome of root traits exists (Lambers et al.2008, 2013), with the increased abundance of species withcarboxylate-releasing cluster roots or their equivalent thatmore efficiently scavenge P from soils than do mycorrhizalassociates. Plants in such landscapes, especially in south-western Australia, are characterized by very low leaf phos-phorus (P) concentrations, very high N : P ratios and verylow SLA: fittingly, extremely ‘slow’ traits for an extremelylow-resource environment (Lambers et al. 2013).

Traits and performance

The evidence above shows that coordinated multiple traitspectra exist for leaves, stems and roots considered separately(H1), that these organ-specific spectra are to some degreecoordinated (H1), and that there is some relationship of thesespectra to large gradients in climate and soils (H8). But dotrait spectra reflect performance outcomes (Figs 8 and 9) in

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Fig. 8. Top panel (a) is the growth–survival trade-off for saplings expressed as the 95th percentile relative growth rate (RGR) (within species)vs. the survival rate over 5 years (% per 5 years) of the slowest growing 25% of individuals, for 103 tree species on Barro Colorado Island, Pan-ama. Dotted line is regression line between the two; shaded area shows 95% confidence interval. Redrawn from Wright et al. (2010). Bottompanels show relationship between wood density and RGR (log-transformed, b), and mortality rate (log-transformed, c), for saplings in two tropicalforest sites (Barro Colorado Island, Panama, white circles; and Pasoh, Malaysia, black circles). Correlations were significant (P < 0.001), and thecorrelation coefficients ranged from between r2 = 0.13 and 0.19. From Chave et al. (2009).


286 P. B. Reich

ecologically relevant settings (H2)? Moreover, what does traitcoordination across organs and resources mean for a trait-based approach to performance, competition and communityassembly/function? Because a single trait for a single organlikely represents a reasonable surrogate for a diversity of traits(of that organ, and of the other organs and the whole organ-ism), a trait-based approach has a much greater chance ofbeing of use. Clearly, as any single trait is only partiallycorrelated with other traits, the greater the number of traitsavailable and the greater relevance to the system and questionat hand, the more powerful will be a trait-based analysis ormodel. Below I outline evidence about the role that traits playin individual performance and thus community assemblyprocesses.

L IGHT

The most abundant data linking traits to species performanceand community assembly involve forests and resource variabil-ity, in particular light. There is evidence at the plant scale for aproductivity-persistence trade-off; species with high maximumgrowth rate have higher mortality and are found in higher light(e.g. Kitajima 1994; Kobe et al. 1995; Walters & Reich 1996;Poorter & Bongers 2006; Wright et al. 2010) (Fig. 8). There isalso considerable evidence that key traits of species in anygiven community vary significantly in relation to the typicallight environment inhabited by each species and that such dif-ferences help explain performance differences that lead tothose differing distributions (e.g. Grime 1965; Loach 1967;Kitajima 1994; Reich et al. 1995; Walters & Reich 1999; Lusk& Reich 2000; Poorter & Bongers 2006; Sterck, Poorter &Schieving 2006; Baltzer & Thomas 2007a,b; Poorter et al.2008; Kitajima & Poorter 2010; Poorter et al. 2010; Wrightet al. 2010; Lusk et al. 2011; Lusk & Jorgensen 2013)(Figs 10–12).Species with ‘fast’ traits grow best and dominate in higher

resource conditions, with ‘slow’ species surviving best (lead-ing to eventual dominance) when resources are scarce and

conservation of resources results in better growth and/or sur-vival at low light (Figs 8–12). Across a gradient from higherto lower light requirements, species possess leaf and stem tis-sues that are longer-lived, tougher, denser and have lower[N], [P], Amax and dark respiration and lower hydraulicconductances (Reich et al. 1995, Walters & Reich 1999; Lusk& Reich 2000; Poorter & Bongers 2006; Meinzer et al.2008a,b; Chave et al. 2009; Kitajima & Poorter 2010; Poorteret al. 2010) (Figs 10–12). Such species also have a lowerwhole-plant light compensation point (Fig. 11) (Baltzer &Thomas 2007a,b; Lusk & Jorgensen 2013). Such trait–environment relations also occur between closely relatedspecies. Givnish, Montgomery and Goldstein (2004) showedthat the SLA, Amax, leaf respiration and light compensationpoint of 11 lobeliad species that occupy a wide range of lightregimes in Hawaii all were positively coupled with the aver-age photon flux density of their native habitats (Fig. 12), inaccord with leaf economic theory.Comparisons of traits and performance in natural field

settings may be complicated though by differences in lightmicrohabitats where species occur. Such differences maymake it difficult to discern to what extent differences in traitsand/or performance between species (e.g. Fig. 10) are due tospecies intrinsic differences or to influences of the differencesin light microhabitat on traits and/or performance. Accountingfor such light microhabitat differences can therefore be valu-able in testing traits purported to be influential to perfor-mance. For example, by comparing 11 species across a widerange of light microhabitats in native forest, Lusk and Reich(2000) found that species differences in light habitat affinitieswere related to differences in respiration rate at a standardizedlight microhabitat. Species absent from more shaded micro-sites have higher respiration rates in a standardized light envi-ronment (Fig. 12). These results are consistent with manystudies of plants in controlled experiments that found shade-tolerant species to have lower respiration rates and thus lowercarbon losses than intolerants (H8, H2) (Loach 1967; Reichet al. 1998b).

Fig. 9. Relative abundance at high N availability in relation to performance at low N availability (left) and photosynthetic capacity (right). Leftpanel is the high N vs. low N availability performance trade-off for the most abundant species in temperate grasslands in a long-term experimentin Minnesota, USA (Tilman 1987). Data shown, averaged from 1991 to 2010, relative abundance per species (% of total above-ground biomass)for plants at high N (in the second highest N supply rate; 17 g N m�2 year�1) vs. an index of low N performance. The index is the shift in rela-tive abundance measured comparing lowest N availability to the highest (relative abundance in ambient soil divided by relative abundance athighest N supply rate (27 g N m�2 year�1). Right panel shows relative abundance at second highest N supply in relation to the maximum light-saturated photosynthetic rate of the species growing in ambient soil. Data for relative abundances from Isbell et al. (2013) and for photosyntheticrates from Tjoelker et al. (2005).



Moreover, dark respiration is not only a component ofshade tolerance, it may be among the most important traitsconferring tolerance. A long-running debate asks whethershade tolerance is primarily a function of traits maximizingnet C gain and growth in low light, or of traits minimizing Closses. Several recent papers suggest that conservation ofenergy at the leaf and plant scale (i.e. low respiratory losses,slow tissue turnover; slow traits par excellence) is moreimportant to success in deep shade than maximizingefficiency of C gain at low light (Baltzer & Thomas 2007a,b;Lusk et al. 2011).

NUTRIENTS

As expected, studies of strongly nutrient-limited systemsprovide the best examples of trait-based nutrient economicstrategies, with slow traits associated with infertile

conditions (K€orner 2003; Craine 2009; Holdaway et al.2011). Although data contrasting growth with mortality arescarce across nutrient gradients, species’ differential successacross a long-term N availability gradient supports thenotion of ‘fast vs. slow’ trait-based trade-offs that enablesuccess at high resource supply or low, but not both(Fig. 9). Species with low tissue nutrient concentrations andhigh tissue longevity, which excel at tolerating lowresources and/or suppressing resource supply to neighbours,are successful under low nutrient supply (Tilman 1987; Til-man & Wedin 1991; Aerts & Chapin 2000; Craine et al.2002; Craine 2009; Holdaway et al. 2011; Mason et al.2012).The role of traits in relation to nutrient economy has been

examined jointly with light in forested systems (e.g. Russoet al. 2005; Baltzer & Thomas 2007a,b; Holste, Kobe &Vriesendorp 2011). The pronounced growth-mortality trade-

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Fig. 10. Relationships between survival rate,height growth rate, leaf life span and anindex of light habitats of juveniles (CEjuv) of53 rain forest tree species in Boliva.Regression lines, coefficients of determinationand significance levels are given.***P < 0.0001. Note the log–log scale. FromPoorter & Bongers (2006).


288 P. B. Reich

off (e.g. Fig. 8) that is a feature of forest taxa across lightavailability gradients (Kobe et al. 1995; Wright et al. 2010)is altered by soil nutrient supply, with mortality at a givengrowth rate being higher in less fertile soils, especially for thespecies with the ‘fastest’ resource strategies (Russo et al.2005). The simplest explanation for this is that ‘fast’ traits are

more costly in the face of any kind of resource shortfall.Challenges of measuring root traits and plant performance inrelation to resource gradients in situ in the field, plus thepotential multiple elemental limitations and complex role ofmycorrhizal associations, continue to result in a still ratherunderdeveloped collective understanding of relations of

Fig. 11. Relationships between 24-h period whole-plant light compensation point and sapling traits minimum instantaneous leaf-level light com-pensation point (LLCP), mass-based dark respiration rate, and leaf life span; as well as the relations between LLCP and dark respiration, and darkrespiration and leaf life span. Each data point corresponds to the average values for a single species. Dotted line and associated solid lines repre-sent best fits and 95% confidence intervals for the relationships. Redrawn from data in Baltzer and Thomas (2007a).

Fig. 12. Left panel; leaf dark respiration per unit leaf area for individuals growing in average light environments for 11 species of Hawaiianlobeliads (closed circles) and for 11 angiosperm and gymnosperm tree species in Minnesota (open circles), in relation to their average in situ dailyphoton flux density. Sampled leaves differ in light conditions experienced. Slopes and intercepts did not differ between the two groups so a singleregression line (P < 0.001, R2 = 0.83, with 95% confidence interval) is shown. Right panel, leaf dark respiration for the Minnesota species forplants grown at a standardized light (20% canopy openness), in relation to the minimum%canopy openness, as measured by the 95th percentiledarkest individuals encountered. Respiration at 20% of canopy openness was determined from the parameters of log–log regressions of dark respi-ration rate vs. % canopy openness for each species individually. Thus, for this comparison, sampled leaves did not differ in light conditions expe-rienced. A nonlinear regression fit (P < 0.001, R2 = 0.76, with 95% confidence interval) is shown. Data for Hawaiian lobeliads from Givnish,Montgomery and Goldstein (2004) and for Minnesota tree species from Lusk and Reich (2000).



below-ground traits to species strategies and nutrient econom-ics, especially compared with what is known for leaf traits inrelation to light and water.

WATER

There is ample evidence that the slow traits strategy is associ-ated with drought tolerance (H1–2). At local scales, speciesrankings in daily minimum leaf water potential (Ψmin) mirrorrankings in bulk leaf osmotic potential and the water potentialat the turgor loss point (ΨTLP) (Meinzer et al. 2008a,b). Spe-cies differences in both Ψmin (Santiago et al. 2004; Meinzeret al. 2008a,b) and the water potential at loss of 50% ofhydraulic conductance (Ψ50) (Maherali, Pockman & Jackson2004; Blackman, Brodribb & Jordan 2010) also tend to trackin parallel with each other and with water availability (H8).Ψmin and Ψ50 both vary inversely with leaf- and sapwood-specific kstem, vessel diameter and/or wood density (e.g. Santi-ago et al. 2004; Markesteijn et al. 2011) (Figs 3–5), butvariably (e.g. Meinzer et al. 2010) and not always strongly

(Blackman, Brodribb & Jordan 2010). Broad-scale speciesand community differences in ΨTLP and Ψ50 correlated withdifferences in broad-scale water availability (H8) (Figs 13 and14) (Bartlett, Scoffoni & Sack 2012; Choat et al. 2012),although there is considerable variation between specieswithin any given climate zone. Species with lower ΨTLP alsohave low Ψ50 (Cavender-Bares, Kitajima & Bazzaz 2004;Choat, Sack & Holbrook 2007; Meinzer et al. 2008a,b;Blackman, Brodribb & Jordan 2010). Campanello, Gatti andGoldstein (2008) also showed that species with greater leaf-and sapwood-specific kstem had higher photosynthesis and fas-ter growth potential. The negative relationships seen oftenbetween wood density and both growth and mortality rate(Chave et al. 2009; Wright et al. 2010; Poorter et al. 2010)(but not always, Russo et al. 2010) are consistent with watereconomics as well, given the tendency of high wood densityspecies to have low hydraulic conductance, low Ψ50 and lowΨTLP.Collectively, these findings indicate that species that move

and store water well have capacity to achieve higher C fluxand growth rates, advantageous when conditions are good,but face greater mortality risk and are more vulnerable, interms of their Ψ50 and ΨTLP. In contrast, species that copewell with drought tend to grow slowly, move and use lesswater and have dense tissues, all slow traits.The difference between Ψmin and Ψ50 is a measure of the

‘safety margin’ for a plant in a given environment (Meinzeret al. 2009) and is thus another indicator of a plant’shydraulic strategy. A comprehensive synthesis (Choat et al.2012) reported that, despite large differences in droughtoccurrence between forested biomes, angiosperms on averageoperate with narrow safety margins that, surprisingly, did notdiffer across enormous water availability gradients. In otherwords, the safety margin is ‘standardized’ to the water poten-tials typically experienced in any given region. Thus, angio-sperm trees on average risk xylem failure during (locally)anomalously low rainfall in a manner that is largely indepen-dent of rainfall region and biome, suggesting a global con-vergence in the vulnerability of trees to drought (Choat et al.2012). However, although species with the greatest embolismresistance were more common in drier climates, in every cli-

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Fig. 13. Global data for pressure-volume parameters (osmotic potential at full turgor, left and at the turgor loss point, right), with mean � stan-dard error across biome categories, with inset plots of biome category means against the Priestly-Taylor coefficient of annual moisture availability(a). Biome categories: semi-desert, Mediterranean-type vegetation/dry temperate woodland, tropical dry and wet forest, temperate forest angio-sperm and conifer, coastal vegetation, mangrove and crop herb. Data within biomes were separated into herb (H) vs. woody (W), or evergreen(E) vs. deciduous (D) when significantly different. p0 and ptlp showed separation of moist and dry biomes (dark and light bars respectively) andcorrelated with a across biomes (both r2 = 0.81, P = 0.03–0.006). From Bartlett, Scoffoni and Sack (2012).

Fig. 14. Embolism resistance (w50) in relation to precipitation of thedriest quarter for 384 angiosperm (open circles and dashed line) and96 gymnosperm species (closed circle and solid line) from multiplesites across the globe. w50 is the water potential at loss of 50% ofhydraulic conductance. The absolute value of the natural logarithm ofw50 was significantly linearly related (P < 0.0001) to precipitationof the driest quarter for both groups, with decreasing resistance toembolism corresponding to increasing rainfall (R2 = 0.15 in bothgroups). From data in Choat et al. (2012).


290 P. B. Reich

mate regime (and especially the dry ones), species exist withvery wide ranges of both Ψ50 (Fig. 14) and safety margin(which I take as evidence that a range of strategies are suc-cessful). Moreover, although the Choat et al.’s (2012) syn-thesis found that the margin of safety varied little withprecipitation gradients between angiosperms, within twowell-studied systems (one in Tasmania, the other in Bolivia),species with the slow strategy did have greater margins ofsafety (Blackman, Brodribb & Jordan 2010; Markesteijnet al. 2011) as did gymnosperms in increasingly arid biomes(Choat et al. 2012). Whether safety margin is a trait that ispart of a local or regional ‘fast–slow’ trade-off thus remainsan open question.Hydraulic traits also help explain species distributions

(H2–3). Engelbrecht et al. (2007) showed that for 48 woodyspecies at 122 sites spanning a rainfall gradient in Panama,niche differentiation with respect to soil water availabilitydetermined local- and regional-scale distributions of trees.Complementary studies suggest that traits explain these pat-terns. Kursar et al. (2009) reported that species differences intolerance to low leaf water status were related to both theirdrought performance in the field and with their distributionacross a gradient of water availability gradient (Fig. 15).Species that had lower stem hydraulic conductance wilted anddied at lower (more negative) water potentials, had higher rel-ative survival in droughted conditions and inhabited drier hab-itats. These results point to a causal link between hydraulictraits and performance in an ecological context. Studies else-where provide similar links of traits and performance. Forexample, for Tasmanian rain forest species, Ψ50 correspondedclosely with an index of habitat distribution (the percentile ofmean annual rainfall across each of the Tasmanian species’geographical distribution) (Blackman, Brodribb & Jordan2012) (Fig. 15).The importance of hydraulic traits is not limited strictly to

marked precipitation gradients. Savage and Cavender-Bares(2012) found that for a group of co-occurring willows, a setof traits that included ΨTLP and wood density varied in paral-

lel with species abundances along a local (topographic) mois-ture gradient. Moreover, the weighted community meansparalleled the species habitat affinities, suggesting that specieswith traits well matched to specific locations along the mois-ture gradient dominated those locations, supporting the ideathat narrow niche breadth allowed coexistence and nichepartitioning, consistent with a trait-based niche theory, butinconsistent with a neutral explanation.There is also evidence of a trade-off between both growth

rate and maximum leaf gas exchange with embolism riskassociated with freezing tolerance for co-occurring Mediterra-nean oaks in France (Cavender-Bares et al. 2005), co-occur-ring evergreen angiosperms in Australia (Choat et al. 2011)and closely related North American willows and poplars(Savage & Cavender-Bares 2013). Cavender-Bares et al.2005; Poorter et al. (2010), and Choat et al. (2011) foundthat shade tolerant, ‘dry habitat’ specialists and cold-tolerantspecies were all characterized by high wood and vessel den-sity and small vessels, traits associated with slow growth andhigh water stress tolerance. This suggests that traits associatedwith highvs. low-resource strategies may be similar for light,nutrients and water.

Traits and processes at community scales

Trait-based approaches offer compelling, if incomplete,frameworks to examine biotic interactions and community-scale traits (H3), and species-level differences in ecologicaltraits play a key role in much of coexistence theory (Ackerly& Cornwell 2007; Ackerly & Cornwell 2009; Suding &Goldstein 2008). Trait-based approaches and models there-fore offer promise regarding community assembly processes(e.g. Shipley, Vile & Garnier 2006; Suding et al. 2008;Dybzinski et al. 2011; Falster et al. 2011; Laughlin et al.2012). For example, Adler et al. (2014) demonstrated thattraits could explain variation in life history (includingfitness) measures for more than 200 species from severalterrestrial ecosystems.

Fig. 15. Left panel. The relationship between species tolerance of low leaf water status and their drought performance (Dp) in the forest understo-rey in Panama. Dp was defined as survival in non-irrigated conditions as a percentage of survival in irrigated conditions. Tolerance of low leafwater potential was assessed as the leaf water potential of severely wilted plants (SWw). From Kursar et al. (2009). Right panel. The relationshipbetween leaf vulnerability to cavitation (P50leaf) and the 5th percentile of mean annual rainfall across each of 18 Tasmanian species’ geographicaldistribution. Solid symbols represent montane rain forest species, while open symbols represent dry sclerophyll species. A hyperbolic curve wasfitted through all the Tasmanian species data based on a theoretical intercept at �22 MPa. Closely related species pairs are denoted by enlargedsymbols and connected by solid regression lines. A significant phylogenetically independent relationship was recorded between leaf vulnerabilityand climate (t-test; P < 0.01). From Blackman, Brodribb and Jordan (2012).



A variety of models, experiments and observations haveshown how trait differences between species indicative ofcapacity to pre-empt resources and reduce availability to com-petitors can explain the outcomes of competition between twoor many species at a time (Tilman 1987; Tilman & Wedin1991; Dybzinski & Tilman 2007; Dybzinski et al. 2011;Falster et al. 2011). For example, species with fast traits (e.g.Fig. 9, right panel) dominate at high N availability and usurpresources leading to reduced species diversity via competitiveexclusion (Clark & Tilman 2008), but those with long rootlife span (Reich et al. 2001) and capacity to draw down soilN concentrations (Tilman & Wedin 1991) dominate at low Navailability. Similar gradients in species abundances and traits(i.e. the ‘fast–slow’ contrast) in relation to soil P gradientssupport the links between traits, resources and biodiversity.For example, at low P supply in western Australia, plantswith ‘slow’ traits dominate and fail to competitively excludeone another, leading to high diversity (Lambers et al. 2010,2013), similar to low N situations in temperate grasslands(Clark & Tilman 2008). Several trait-based competition mod-els have successfully modelled outcomes in multiple resource-limited conditions, a considerable achievement given our stillnascent ability to understand multiple limitations and traitstrategies (Sterck, Poorter & Schieving 2006; Farrior et al.2013).Trait-based biotic interactions can also be positive (facilita-

tive); in fact the same traits can have positive and negativeimpacts on neighbours depending on conditions during a sin-gle season (Armas & Pugnaire 2005). Traits that lead to nega-tive interactions through resource competition can also havepositive (facilitative) impacts via amelioration of the microcli-mate under harsh abiotic conditions (Callaway & Walker1997; Brooker et al. 2008; Valladares et al. 2008). Althoughlikely more common in extreme environments, facilitationmay be common, if overlooked, in more temperate and mesicenvironments (e.g. Montgomery, Palik & Reich 2010).Increased recognition that biotic interactions are a net effectof the positive and negative interactions has inspired the startof incorporating facilitation into trait-based community assem-bly theory (Sch€ob, Butterfield & Pugnaire 2012).Phylogenetically oriented studies of co-occurring organisms

have provided new tools and insight into the integrated explo-ration of community assembly, niche evolution and patternsof (taxonomic, trait and phylogenetic) diversity (Cavender-Bares, Ackerly & Kozak 2012). Despite abundant evidence ofadaptive radiation of coordinated plant economic traits withinlineages (e.g. Ackerly & Reich 1999; Cavender-Bares,Kitajima & Bazzaz 2004; Givnish, Montgomery & Goldstein2004; Choat, Sack & Holbrook 2007; Savage & Cavender-Bares 2012), closely related taxa are often more alike in termsof their traits than would occur randomly, because of sharedhistory (H9) (Cavender-Bares et al. 2009). Thus, convergentevolution of trait spectra across lineages (e.g. Ackerly &Reich 1999) and niche conservatism within lineages are notincompatible. This suggests that phylogenetic informationcannot substitute for trait data, but that together both strandsof data likely can explain more about ecological and evolu-

tionary processes than either alone. For instance, both special-ization within lineages and differences between lineages cancontribute to extant trait patterns among taxa (Comas &Eissenstat 2009).The tendency of related species to share form and function

influences their broad habitat affinities as well as their localniche space occupancy and provides tools to help examinethe filtering of functional traits into local-scale species assem-blages and identify the ecological mechanisms governingcommunity assembly processes (Cavender-Bares et al. 2009).Clade-based studies of the evolutionary and trait history oflineages can also help evaluate the role of traits in evolution-ary diversification. For example, Savage and Cavender-Bares(2012) identified patterns of trait and phylogenetic structureof willow communities across a local hydrological gradient,providing evidence that the evolution of trait differenceshelped related species differentiate between niches even at afine spatial scale and likely contributed to that diversification.Kraft, Valencia and Ackerly (2008) and Kraft and Ackerly

(2010) combined phylogenetic and trait data to examineniche-based vs. neutral theories of community assembly andcoexistence in a species-rich tropical forest (H2–3, H9). Theyfound that co-occurring trees are often less ecologically simi-lar than niche-free (neutral) theory would predict, indicatingthat strategy differentiation between species contributes to themaintenance of high tropical forest diversity. They found evi-dence that some combination of trait-based strategy differenti-ation and/or enemy-mediated density dependence regulatesspecies occurrence patterns at small scales (5–20 m), and spe-cies were not distributed randomly with respect to traits.However, the effect size of traits was modest; thus, the trait-based processes at play were weak, the statistical power ofthe tests was low, and/or the traits available were not the bestproxies for detailed functional traits or trait combinations thatinfluence fitness and thus population dynamics. The func-tional traits available to Kraft and Ackerly (2010) were wooddensity, SLA and leaf N; missing were any leaf metabolic(Kleaf, A, dark respiration) traits, or any root trait data at all.This is a common issue with tests of the importance of traits.If three or four traits explain a modest fraction of the variancein distribution (e.g. Kraft & Ackerly 2010) or in growth andsurvival (e.g. Wright et al. 2010), but these are not the traitsone would choose if trait data was unconstrained, the powerof the available data is not necessarily a good test of thepower of a well-developed trait-based approach.Evidence at community scale for simultaneous clustering

and overdispersion in functional traits (Cavender-Bares, Kitaj-ima & Bazzaz 2004; Swenson & Enquist 2009; Cavender-Bares & Reich 2012) indicates both the role of multiple pro-cesses and the signature of spatial scale on those processes.Swenson and colleagues have pioneered the consolidation oftrait and inventory databases to examine how continental orglobal pools of functional trait diversity are filtered into regio-nal-scale assemblages (e.g. Swenson & Enquist 2009; Swen-son & Weiser 2010; Swenson et al. 2012a,b). Scaling traitsup to the continental-scale enabled Swenson and Weiser(2010) to determine that the average trait values of temperate


292 P. B. Reich

forest communities (in the eastern U.S.) were generally corre-lated with important climate metrics. In aggregate, these stud-ies suggest that making full use of the promise of jointphylogeny–trait approaches to studies of community functionand assembly is simultaneously a major challenge and oppor-tunity.

Traits and processes at ecosystem scale andbeyond

What are the consequences of leaf, stem and root spectrawhen aggregated to the system scale? I focus on two aspectshere: first, the direct implications of plant traits for resourceacquisition at the ecosystem scale (H4–5), and secondly, theirindirect implications promulgated through impacts onbelow-ground community and biogeochemical processes (H6)(similar to ‘effects traits’, Lavorel & Garnier 2002).

IMPACT OF ECONOMIC TRAIT SPECTRA ON

ECOSYSTEM PROCESSES AND CYCLES

The aggregated traits of co-occurring plants (e.g. canopies orroot systems) regulate the uptake of all major resources (light,C, nutrients, water). Here, I consider the impact of both theaverage traits of a community [i.e. mass ratio hypothesis orcommunity-weighted mean traits (Grime 1998)] and of traitheterogeneity. A straightforward hypothesis is that the sizeand chemistry of a canopy influence the ability to interceptlight and the capacity to use that light to drive photosynthesis,and in parallel, the magnitude of water fluxes needed to sup-port canopy photosynthesis. This hypothesis is supported bydata from studies across multiple grassland (Garnier et al.

2004) and forest stands (Reich 2012) showing productivitycorrelates with community-weighted mean traits. For instance,ecosystem-scale maximum instantaneous canopy photosyn-thetic rate and annual net primary production are both jointfunctions of average leaf area index (LAI) and canopy [N](H4) (Fig. 16, Reich 2012). This system-scale response mir-rors the leaf-scale response (H1), whereby for a given SLA,leaves of higher [N] have greater instantaneous Amax, and fora given [N], those with higher SLA (which intercept morelight per gram foliage) also have greater Amax. Thus, eco-nomic traits that lead to greater light harvesting and greatphotosynthetic potential lead to greater C uptake at leaf andstand scales, from second to year. Moreover, the average can-opy [N] is influenced by trait-based competitive interactionsthat influence the abundance-weighted [N] (H2–4) (Dybzinskiet al. 2013). Thus, traits of species and biotic interactionsbetween species together regulate the scaling up of resourceeconomics from leaf to ecosystem.The evolution in angiosperms of the fast traits that enable

high leaf hydraulic and diffusive conductance, and thus highrates of transpiration and photosynthesis, also profoundlyaltered regional C, nutrient and water cycles (Boyce et al.2009; Feild et al. 2011). The recycling of transpired water isan important source of rainfall, especially in the tropics, andclimate modelling suggests that the tropics would be hotter,drier and more seasonal in the absence of the angiosperms (asexplained http://www.youtube.com/watch?v=Y3OWgb0Bv-Alink), and the overall area of tropical rain forest woulddecline substantially (Boyce et al. 2009, 2010). Thus, the riseto ecological dominance of high transpiration angiosperms inthe tropics not only likely altered regional climate but createdconditions that increased the spatial extent of their dominance,

(a)

(d)

(b)

(c)

Fig. 16. Relationships, at different scales, ofproductivity to leaf area and nitrogenconcentration in temperate forests. Except forpanel ‘c’, the measures are equivalent toabundance-weighted, community mean traits.Relationships shown: (a) above-ground NPPper year in relation to leaf area index (LAI)and canopy% nitrogen (closed circles for 128stands in Minnesota and Wisconsin, opencircles for 18 stands in New Hampshire). (b)ANPP per day in relation to LAI and canopy[N] (data for 128 Minnesota and Wisconsinforests). (c) Instantaneous leaf-scale netphotosynthetic capacity in relation to specificleaf area and leaf [N] for 296 tree speciesworld-wide. (d) Relationship of maximuminstantaneous ecosystem photosynthetic rateto LAI and canopy [N] for 33 forests. FromReich (2012).



with consequential effects on C, nutrient cycling and fireregimes (Feild et al. 2011). Given the close coupling of car-bon uptake and water loss (see earlier in the review), it islikely that trait means at community scale generally have par-allel impacts on water cycling as on carbon cycling. However,the relationship of traits to water cycling has received muchless attention than relationships of traits to carbon and nutrientcycling – this represents a major opportunity for future work.A logical first connection between plant economic traits

and below-ground processes is through litter decomposition,that is, a test for afterlife effects. Considerable evidenceshows strong impacts of a variety of litter traits associatedwith the LES on decomposition (e.g. Cornelissen 1996;Cornelissen & Thompson 1997; Santiago 2007; Hobbie2008). In a synthesis of data for 818 species in 66 litter

decomposition experiments across six continents, Cornwellet al. (2008) showed that the magnitude of species-drivendifferences was larger than climate-driven variation. More-over, litter decomposition was faster for species with highergreen leaf [N] and SLA, or higher litter [N] and lower lignin.In a separate meta-study, the N-release patterns of decompos-ing litter strongly influence litter N mineralization and arejointly regulated by the initial chemical composition of the lit-ter and the stoichiometric requirements of the decomposers(Manzoni et al. 2008). Freschet et al. 2013 (Fig. 17) found astriking correspondence between decomposition of leaves, finestems and fine roots across > 100 species from 13 ecosys-tems, driven both by shared traits and by climate variation.Several studies show that plant traits influence soil micro-

bial communities at a range of scales, with likely conse-quences for biogeochemical cycling. de Vries et al. (2012)identified a combination of abiotic and biotic predictors oflandscape-scale soil microbial community composition. Inaddition to the usual suspects – climatic factors and soilchemical and physical properties – community-weighted planttraits also explained variation in soil microbial communitycomposition. Based on studies of leaf, fine-stem and fine-rootlitter decomposition rates in terrestrial, riparian and freshwatersystems, Freschet, Aerts and Cornelissen (2012) found thatplant traits regulate litter decomposition through both directlitter effects and indirect effects (mediated by regulation ofheterotroph community composition). In a study of temperategrasslands, Grigulis et al. (2013) found coupled control byplant and microbial functional traits of biomass productionand soil C and N cycling. Plant species with the ‘fast’ strat-egy (high SLA, high Nmass) were linked to fast cycling andlow retention of C and N. Given that decomposition rates arecontrolled jointly by environment (temperature, moisture) andtissue quality (Cornwell et al. 2008; Manzoni et al. 2008;Freschet, Aerts & Cornelissen 2012), continuous quantitativetraits provide an avenue towards mechanistic modelling ofdecomposition.Several studies have linked mycorrhizal status with biogeo-

chemical processes at local and biogeographical scales(Cornelissen et al. 2001). As ectomycorrhizal association (andespecially for ericoids) per se has been considered a func-tional trait, linked to the slow strategy, this is relevant to thetheme of this review. Assuming ectomycorrhizal species arebetter able to acquire and use organic nutrients (Read, Leake& Perez-Moreno 2004, Wurzburger & Hendrick 2009; Orwinet al. 2011; Phillips, Midgley & Brozstek 2013), their organicnutrient uptake should slow soil C cycling through changes inthe form and quantity of C exudates and detritis (H4, H6).Organic nutrient uptake may result in high soil C in ectomy-corrhizal and especially ericoid-dominated habitats (Read &Perez-Moreno 2003; Read, Leake & Perez-Moreno 2004;Wurzburger & Hendrick 2009). The role of mycorrhizas inacquiring organic N and influencing C and N cycling as aresult may be more widespread however. Hodge and Fitter(2010) and Whiteside et al. 2012 similarly found AM fungito be able to acquire organic N in both controlled settingsand in a boreal forest, respectively.

Fig. 17. Relationships between decomposition constant k of leaves,fine roots and fine stems across numerous species and studies(redrawn from Freschet et al. 2013). Each point represents one spe-cies. Ellipses show the 95% confidence intervals for the data. Theslope of standardized major axis regressions across all data is shownin the dotted lines.


294 P. B. Reich

Phillips, Midgley and Brozstek (2013) looked further atwhether ECM vs. AM trees differ in other aspects of theirbiogeochemical nutrient economy. As found elsewhere(Cornelissen et al. 2001; Hobbie et al. 2006), decompositionrates were higher for foliage of AM than ECM species. NetN mineralization rates did not differ between AM- and ECM-dominated plots, but the ammonium concentration was greaterin AM-dominated plots and likely promoted greater nitrifica-tion rates there. Phillips, Midgley and Brozstek (2013)hypothesize that trait-integrated biogeochemical syndromesexist in AM and ECM forests due to differences in their‘nutrient economies’ (i.e. the primary forms of nutrients uti-lized by plants and microbes). They suggest that this is a use-ful framework to characterize the biogeochemical attributes ofAM- and ECM-dominated temperate forests.Studies in 32-year-old monoculture experiment with 14

temperate tree species in Poland also examined consequencesof plant economic traits for soil biology and geochemistry(H4, H6). Leaf litter chemical traits were strongly correlatedwith green leaf traits (Reich et al. 2005), but idiosyncraticallycorrelated with root traits (Reich et al. 2005; Withingtonet al. 2006; Hobbie et al. 2010). Differences in litter calciumconcentrations between tree species result from intrinsic dif-ferences in species physiology – the faster-growing specieshad higher tissue [Ca] (Dauer et al. 2007) and caused pro-found changes in soil acidity and fertility (Fig. 18) (Reichet al. 2005). Calcium-rich species had higher soil pH,exchangeable calcium, percentage base saturation and forestfloor turnover rate (Reich et al. 2005; Hobbie et al. 2006)(H4, H6). Species with Ca-rich tissues and high soil pH alsohad greater SOM decomposition and microbial biomass in themineral horizon. However, species drove soil net N minerali-zation and nitrification rates largely via differences in tissue[N] (Hobbie et al. 2007), as has been seen in other ecosys-tems (Fig. 18) (e.g. Reich et al. 2001; Orwin et al. 2010;Laughlin 2011). In one such study, nitrification potential wasmore strongly linked to dominant leaf traits than to functionaldiversity (Laughlin 2011), consistent with the mass ratiohypothesis (Grime 1998).Similar results come from a 7-year, 9-species monoculture

grassland experiment that found support for some parts of the‘fast traits–soil process’ hypothesis (Orwin et al. 2010). Spe-cies with high relative growth rate (RGR) had leaf and litterwith high [N] and low toughness, an elevated bacteria : fungibiomass ratio in soil, high rates of soil N mineralization andconcentrations of extractable inorganic N, and to some extent

higher available phosphorus pools. However, fast above-ground traits did not match fast soil C cycling (soil respirationnor decomposition). The authors concluded that it may bemore complex and difficult to use plant traits to predict pro-cesses that influence soil C cycling than to predict processesthat influence N and P cycling.The links noted above between plant traits and biogeo-

chemical processes (decomposition, net N mineralization) arecrucial for understanding vegetation–soil feedbacks and forimproving models of global C and nutrient cycles. Clearly,plant traits influence soil processes in a manner generally con-sistent with a ‘fast–slow’ framework and can feedback toinfluence performance of competing species (Tilman & Wedin1991; Berendse 1994; Dybzinski & Tilman 2007; McCarthy-Neumann & Kobe 2010a,b). Beyond direct evidence of suchfeedbacks is circumstantial evidence from literally hundredsof studies showing that plants modify soils in ways likely tobenefit themselves and their offspring (e.g. acidiphile speciestend to reduce soil pH and nutrient availability).

ECONOMIC TRAIT -BASED APPROACHES TO

MODELL ING ECOSYSTEMS AND BEYOND

That plant traits influence ecosystem-scale properties and pro-cesses (Lavorel & Grigulis 2012) is a cornerstone of manymechanistically oriented ecosystem models, dynamic globalvegetation models and land surface models (terrestrial bio-sphere models hereafter) (H4–H5). Many traits, most eco-nomic in nature, are included in model algorithms and areused to estimate ecosystem properties (e.g. Brovkin et al.2012) and/or to drive calculated process rates (e.g. leaf lifespan, root life span, leaf, stem or root [N] and/or respirationrates, several photosynthetic traits, hydraulics). In most suchmodels, traits have been parameterized using plant functionaltypes (5–10 or so depending on the model), each of which isassigned a set of traits, usually based on empirical data. Theuse of plant functional types has persisted because of thechallenge of developing continuous mapped trait surfaces.Improving characterization of trait variability in models is anactive area at present; both trait–trait and trait–environmentapproaches may be fruitful (Swenson et al. 2012a,b; vanBodegom et al. 2012). Additionally, consideration of realistictrait values and correlations can uncover problems with pro-cess models. Herein, I describe three examples of attempts totake a continuous trait-based approach to terrestrial biospheremodels.

Fig. 18. Soil net N mineralization rate inrelation to mean litter N across 20 oaksavanna and woodland stands of differing firefrequency in eastern Minnesota, USA (left)and exchangeable soil Ca (0–40 cm mineralsoil horizon) in relation to litter Ca acrossreplicated monocultures of 14 tree species inwestern Poland (right). Redrawn from data ofReich et al. (2001, 2005).



Bonan et al. (2012) attempted to reconcile the problem ofa mis-match between the maximum carboxylation velocity(which strongly correlates with Amax) needed to accuratelypredict gross primary productivity (GPP) with the CommunityLand Model (v4) and the maximum carboxylation velocityderived from a synthesis of empirical observations (Kattgeet al. 2009). A set of exploratory simulations identified previ-ously unrecognized deficiencies in the Community LandModel (version 4) parameterization of the canopy as a sunlitand a shaded ‘big-leaf’. This demonstrates that workingtowards accurate prediction of outcomes (e.g. GPP) simulta-neously with accurate characterization of trait-based vegeta-tion physiology and chemistry can help identify ‘off-setting’model errors that provide the ‘right’ answer in some contextsbut with the wrong parameter values.Another example comes from Reich et al. (2014) who

incorporated biogeographical patterns of evergreen boreal nee-dle longevity and [N] (H8) into a land surface model, Austra-lian Community Atmosphere Biosphere Land Exchange(CABLE), to assess their impacts on C cycling processes(H4–6). Incorporating realistic parameterization of these vari-ables improved predictions of canopy LAI and GPP as com-pared to observations from flux sites. However, this was a‘one step back, two steps forward process’, because at firstthe model performed much more poorly with the new needletrait parameterization. Exploratory simulations identifiedproblems with how needle life span influenced canopy LAI inCABLE. When more realistic biomass distribution algorithmswere also incorporated, CABLE did a better job of predictingLAI and GPP than previously. A third trait-based approachwas taken by Wang et al. (2012) who incorporated multi-leaftrait covariance ([N], LMA, leaf life span) into CABLE. Con-straining trait correlations to those observed for the threeparameters (H1) did not alter the mean but reduced the vari-ance of modelled GPP (H5) by 28% and resulted in fewerextremely high or extremely low (and unlikely) GPP predic-tions. The results suggest that correlations between planttraits, and relationships with environmental drivers, offerpromise as constraints on the estimates of model parametersor predictions by those models.

Conclusions

The works reviewed in this article describe the nature, causesand consequences of functional trait spectra at organ andwhole-plant scales, within and among species, communities,lineages and biomes. It is fair to ask just how much do traits(and in particular those directly involved in resource econom-ics) help us to better understand, quantify and model key eco-logical processes at tissue to organism to ecosystem to globalscales. In essence, do such traits provide enough meaningfulcharacterization, explanation and quantification of the natureof key ecological relationships to be of broad use? Althoughbelow I use the percentage of variance explained as a metric,and some might complain that is just a statistical correlation,it is hoped that the literature synthesized above makes it clearthat there are functional, causal links at work.

As an illustration of whether the proverbial glass is half full,or half empty, I consider the role of traits in explaining thestrong trade-off between growth and survival (Fig. 8) amongtropical tree species (Wright et al. 2010). This narrative repro-duces a set of conversations (while writing the paper) amongco-authors who came at this work with different perspectives.In that study, four traits were available to be tested as potentialpredictors of the growth–survival trade-off. Two were signifi-cant (wood density and LMA) and explained �40% of the var-iance in growth and survival. Is this a useful amount ofexplanation? One co-author initially thought not, because, afterall, 40% is far less than a full explanation. However, the fourtraits available did not include other traits likely equally ormore influential to the processes in question: traits such as leafhydraulic conductance or stem hydraulic conductivity, photo-synthetic capacity, leaf dark respiration rate or leaf nutrientconcentration, or anything about roots. Thus, other authorsthought that if the discipline can move 40% down the path to afull understanding of a process or system using only a fewtraits, and not even those one would choose from a full menuthat suggests considerable potential for a trait-based approach.The weight of evidence reviewed in this document conveys a

similar message. The key traits involved in carbon, nutrient andwater economics vary in coordinated ways both within andamong the leaf, stem and root systems of higher plants (H1).Traits vary with environment (H7–8) and evolutionary history(H9) and influence whole-plant performance (H2), communityassembly and ecosystem/landscape function (H3–5), and soilfeedbacks (H6, H8). Thus, I conclude that a trait-based ecology,and in particular, one based on the plant economic spectrum, hasalready half-filled ecology’s glass, because where the traits ofspecies fall on that spectrum tells us much about their ecologyand that of the community and ecosystem they comprise.

Acknowledgements

I thank two anonymous referees and a large number of colleagues who sharedpapers, data, and ideas with me as I struggled to develop this manuscript. Inparticular, I would like to thank J. Baltzer, T. Brodribb, B. Choat, H. Cornelis-sen, G. Freschet, T. Givnish, T. Kursar, H. Lambers, L. Markesteijn, Meinzer,L.Sack, L. Santiago and L Williams. This work was supported by the U.S.National Science Foundation Long-Term Ecological Research program (DEB-1234162) and the Institute on the Environment, University of Minnesota.

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Received 22 July 2013; accepted 16 December 2013Handling Editor: Hans Cornelissen



SPECIAL FEATURE FORUM THE TREE OF LIFE IN ......Journal of Ecology 2014, 102, 275–301 doi:...

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