This article is modified from Perez-Harguindeguy et al (2013). The “New handbook for standardised measurement of plant functional traits worldwide” is a product of and is hosted by Nucleo Diversus (with additional Spanish translation). For more on this and on its context as part of the entire trait handbook visit its primary site Nucleo DiverSus at http://www.nucleodiversus.org/?lang=en
This summary presents guidelines for selecting species and individuals within species for trait measurement, as well as general considerations of the necessary number of replicates. In addition, suggested numbers of replicates for all traits are given in Table 1 (below).
Selection of species
Study objectives will always determine which species are selected for trait measurement. For species-level analyses of trait variation, and for identifying general strategies or syndromes of resource use, or trade-offs at the local, regional or global scale (e.g. Reich et al. 1997; Westoby et al. 1998; Díaz et al. 2004; Wright et al. 2004; Gubsch et al. 2011), species or populations from a broad range of environments and phylogenetic groups should be selected. For questions about evolution, the choice of species may be based on the inclusion of representatives of different enough phylogenetic groups, or on other phylogenetically relevant criteria (such as being members of particular clades), with little consideration about their abundance in situ. In contrast, when trying to understand how environmental variables shape vegetation characteristics, or how vegetation characteristics affect local flows of matter and energy (e.g. primary and secondary production, carbon, water and mineral nutrient cycling), the main criterion for species selection should be local abundance. In those cases, species should be selected that collectively make up for ~80% of cumulative relative abundance, following Garnier et al. (2004) and Pakeman and Quested (2007) (see specifics for abundance measurements below). Exceptions may be made if this criterion would imply measurements for an impracticably large numbers of species, e.g. communities with unusually high species richness per unit area, especially combined with a very high evenness. Examples are tropical rainforests and fynbos vegetation, in which well over 100 species per plot may be needed to reach the 80% biomass threshold.
In forests and other predominantly woody vegetation, the most abundant species of the understorey may also be included (e.g. when the research question relates to the whole-community or ecosystem level), even if their biomass is much lower than that of the overstorey woody species. In predominantly herbaceous communities, species contribution to a particular community may vary with time during a growing season. As a first step, we suggest that the relative abundance and the traits should be measured at the time of peak standing biomass of the community. This does not always apply to reproductive structures, which obviously have to be measured when they are present and fully developed, which sometimes does not coincide with the time of maximum vegetative growth.
For comparing sites or for monitoring trends in ecosystem-level properties across environmental conditions (e.g. pollution, or different regional climate or fertility levels), indicator species can be selected on the basis of the sensitivity of their trait values to the environmental factor of interest, and their importance locally and regionally, as well as for the ease with which they can be found and identified in the field (independent of their relative abundance) (Ansquer et al. 2009; De Bello et al. 2011). In this sense, it may be useful to distinguish -variable’ traits from more -stable’ traits (Garnier et al. 2007). Although most traits show some variation within species along environmental gradients, or in response to specific environmental changes, the intraspecific variation of so-called -stable traits’ is low compared with their interspecific variation. The reverse is the case for so-called -variable traits’, which implies that they should preferably be measured in more than one site or condition across the habitat range (Garnier et al. 2007). By contrast, -stable traits’ can be measured for any representative population from the entire gradient. Traits known to often be -variable’ include vegetative and reproductive plant height, mineral nutrient concentration in leaves, onset of flowering, branching architecture and spinescence. Traits that are relatively -stable’ include categorical traits, such as life form, clonality, dispersal and pollination modes, and to a lesser degree photosynthetic type (C3 or C4). Some quantitative traits such as leaf and stem dry matter content, or leaf toughness can be -stable’ along certain gradients, e.g. of nutrients or disturbance, but not along others, e.g. a light gradient (cf. Poorter et al. 2009). Species may therefore vary in which quantitative traits are stable across given gradients, so tests should be made before a trait is taken to be stable for a given species (Albert et al. 2010, 2012; Hulshof and Swenson 2010; Messier et al. 2010; Moreira et al. 2012).
Table 1 gives a rough indication of the within-species variability (coefficients of variation; i.e. standard deviation divided by the mean, hereafter CV) for some of the quantitative traits described in the present handbook, along with the more frequently used units and the range of values that can be expected. Table 1 summarises field data collected in several studies for a wide range of species coming from different environments. Because of the low number of replicates generally used, each of the individual estimates bears an uncertainty (and CV will likely increase as scale increases); however, by looking at the range of CVs calculated across a wide range of species, a reasonable estimate of the typical within-species-at-a-site variability can be obtained. We, therefore, present in Table 1 the 20th and 80th percentiles of the CV distribution.
How species abundance should be measured to determine the species making up 80% of cumulative abundance (e.g. whether to lay out transects, select points or quadrats at random or systematically, or to follow a different method) is beyond the scope of the present handbook and is extensively covered in plant-ecology and vegetation-science textbooks. However, it should be noted that different methods are relevant to different ecological questions and associated traits (Lavorel et al. 2008; see also Baraloto et al. 2010b, specifically for tropical forest). Taxon-free approaches that do not require species identification offer an alternative to estimates of relative abundance, and effectively capture the contribution of more abundant species. These include measuring traits regardless of species identity, along a transect (-trait-transect’ method, Gaucherand and Lavorel 2007), or for individuals rooted nearest to random sampling points, as long as the canopy structure is quite simple (-trait-random’ method – Lavorel et al. 2008). Methods of taxon-free sampling have also been applied to tropical forests, being, in this case, strongly based on the frequency or basal area of individual trees (Baraloto et al. 2010b). Trait values obtained through these methods can differ from those obtained using the standard approach of selecting robust, -healthy-looking’ plants for trait measurement.
Selection of individuals within a species
For robust comparisons across species, traits should be generally measured on reproductively mature, healthy-looking individuals, unless specific goals suggest otherwise. To avoid interaction with the light environment, which may strongly depend on neighbouring vegetation, often plants located in well lit environments, preferably totally unshaded, should be selected. This is particularly important for some leaf traits (see Specific leaf area ). This criterion creates sampling problems for true shade species found, e.g. in the understorey of closed forests, or very close to the ground in multilayered grasslands. Leaves of these species could be collected from the least shady places in which they still look healthy and not discoloured (see Specific leaf area). Plants severely affected by herbivores or pathogens should be excluded. If feasible, for consistency among sets of measurements, use the same individual to measure as many different traits as possible.
Defining -individuals’ reliably may be difficult for clonal species (see Clonality, bud banks and below-ground storage organs), so the fundamental unit on which measurements are taken should be the ramet, defined here as a recognisably separately rooted, above-ground shoot. This choice is both pragmatic and ecologically sound, because genets are often difficult to identify in the field and, in any case, the ramet is likely to be the unit of most interest for most functional, trait-related questions (however, be aware that sampling of neighbouring ramets may not provide biologically independent replicates for species-level statistics). Individuals for measurement should be selected at random from the population of appropriate plants, or by using a systematic transect or quadrat method.
Trait values are often used comparatively, to classify species into different functional groups or to analyse variation across species within or between ecosystems or geographical regions. This type of research almost inevitably implies a conflict between scale and precision; given constraints of time and labour, the greater the number of species covered, the fewer replicate measurements can be made for each species. The number of individuals (replicates) selected for measurement should depend on the natural within-species variability in the trait of interest (see above for a discussion on within-species variability), as well as on the number or range of species to be sampled. Table 1 shows the minimum and preferred number of replicates for different traits, mainly based on common practice. The most appropriate sample size depends on the purpose and scope of the study. Ideally, researchers should check within-species CV at their site before deciding this. In broad-scale interspecific studies, one may sample relatively few plants of any given species, whereas when the study concerns just a small number of species or a modest local gradient, one may need to sample more heavily within each species. It is highly recommended to quantify the relative contributions of intra- v. interspecific variation. A formal analysis of statistical power based on an assumed or known variance among individuals, compared with that among species means, can be used. Commonly used statistical packages generally include routines for power analysis, as well as for variance component analyses (used to partition variance among different levels, e.g. species v. individuals). Other more powerful techniques can also be used, such as mixed models (Albert et al. 2010; Messier et al. 2010; Moreira et al. 2012).
Table 1: Summary of plant traits. The range of values corresponds to those generally reported for field-grown plants. Ranges of values are based on the literature and the authors’ datasets and do not always necessarily correspond to the widest ranges that exist in nature or are theoretically possible. Recommended sample size indicates the minimum and preferred number of individuals to be sampled, so as to obtain an appropriate indication of the values for the trait of interest; when only one value is given, it corresponds to the number of individuals (=replicates); when two values are given, the first one corresponds to the number of individuals and the second one to the number of organs to be measured per individual. Note that one replicate can be compounded from several individuals (for smaller species), whereas one individual cannot be used for different replicates. The expected coefficient of variation (CV) range gives the 20th and the 80th percentile of the CV (=s.d. scaled to the mean) as observed in a number of datasets obtained for a range of field plants for different biomes.
|Plant trait||Preferred unit||Range of values||Recommended no. of replicates||CV range (%)|
|Spine length : leaf length||Unitless||0-30||5||10||–|
|Branching architecture||No. of ramifications per branch||0 – >100||5||10||–|
|Leaf to sapwood area||Unitless||100-103||5||10||–|
|Root-mass fraction||Unitless||0.15-0.40 (for seedlings, 0.10)||5||10||–|
|Relative growth rate||mg g-1 day-1||2-300||10||20|
|Shoot flammability||Unitless||0 – ~3||5||10|
|Water retention on plant surface||g m-2||0-500||10||20|
|Leaf wettability||Degrees (contact angle)||0-180||10||20|
|Droplet retention ability||degrees (slope angle)||0-90||10||20|
|Specific leaf area||m2 kg-1 (mm2 mg-1)A||<1-300||5, 5||10, 4||8-16|
|Area of a leaf||mm2||1 – >206||5, 5||10, 4||17-36|
|Leaf dry-matter content||mg g-1||50-700||5, 5||10, 4||4-10|
|pH of green leaves or leaf litter||Unitless||3.5-6.5||5, 5||10, 4||1-6|
|Nitrogen concentration||mg g-1||5-70||5, 5||10, 4||8-19|
|Phosphorus concentration||mg g-1||0.2-5||5, 5||10, 4||10-28|
|Physical strength of leaves|
|Force to tear||N mm-1||0.17-40||5, 5||10, 4||14-29|
|Work to shear||J m-1||0.02-0.5||5, 5||10, 4||14-29|
|Force to punch||N mm-1||0.03-1.6||5, 5||10, 4||14-29|
|Leaf lifespan and duration of green foliage|
|(a) Leaf lifespan||Month||0.5-200||5, 40||10, 160||11-39|
|(b) Duration of green foliage||Month||1-12||5||10||–|
|Vein density||mm mm-2||0.5-25||5||10|
|Light-saturated photosynthetic rate||μmol m-2 s-1||2-30||5||10|
|Leaf dark respiration||μmol m-2 s-1||0.4-4||5||10|
|Electrolyte leakage||%||2-100||5, 5||10, 4||9-26|
|Leaf water potential||MPa||-7-0||5, 5||5, 10||11-33|
|Leaf palatability||%Leaf area consumed||0-100||10||20|
|Litter decomposabilityC||%Mass loss||0-100||10||20||7-14|
|Stem-specific density||mg mm-3 (kg l-1)A||0.1-1.3||5||10||5-9|
|Twig dry-matter content||mg g-1||150-850||5||10||2-8|
|Bark thickness||mm||0.1 – >30||5||10|
|Stem-specific xylem hydraulic conductivity (KS)||kg m-1 s-1 MPa-1||1 (gymnosperms) to 200 (tropical lianas)|
|Leaf-area-specific xylem hydraulic conductivity (KL)||kg m-1 s-1 MPa-1||6 x 10-5 (gymnosperms) to 1×10-2 (tropical lianas)|
|Vulnerability to embolism ( 50)||MPa||0.25-14||5||10||20-45|
|Specific root length||m g-1||3-350||5, 10||10, 10||15-24|
|Density of exploration||mm mm-3||10-4-1|
|Nutrient uptake strategy||Categorical||–||5||10||–|
|Dispersule size and shape|
|Size (mass)||mg (g)A||5||10|
|Dispersal potential||Dispersules dispersed/dispersules produced||–||10||20||–|
A – Alternative, preferred, units in parantheses.
B – Considering only photosynthetic tissue; total leaf thickness can be >40 mm in some succulent plants.
C – Replicate numbers correspond to the number of individual plants (replicates) from which to collect leaf litter; number of leaves in each sample will depend on its weight and the size of the litterbag.
Albert CH, Thuiller W, Yoccoz NG, Douzet R, Aubert S, Lavorel S (2010) A multi-trait approach reveals the structure and the relative importance of intra- vs.interspecific variability in plant traits. Functional Ecology 24, 1192-1201 doi:101111/j1365-2435201001727x
Ansquer P, Duru M, Theau J-P, Cruz P (2009) Convergence in plant traits between species within grassland communities simplifies their monitoring. Ecological Indicators 9, 1020-1029 doi:101016/jecolind200812002
Baraloto C, Paine CET, Patiño S, Bonal D, Hérault B, Chave J (2010) Functional trait variation and sampling strategies in species-rich plant communities. Functional Ecology 24, 208-216 doi:101111/j1365-2435200901600x
De Bello F, Lavorel S, Albert CH, Thuiller W, Grigulis K, Dolezal J, Janecek S, Lêps J (2011) Quantifying the relevance of intraspecific trait variability for functional diversity. Methods in Ecology and Evolution 2, 163-174 doi:101111/j2041-210X201000071x
Díaz S, Hodgson JG, Thompson K, Cabido M, Cornelissen JHC, Jalili A, Montserrat-Martí G, Grime JP, Zarrinkamar F, Asri Y, Band SR, Basconcelo S, Castro-Díez P, Funes G, Hamzehee B, Khoshnevi M, Pérez-Harguindeguy N, Pérez-Rontomé MC, Shirvany FA, Vendramini F, Yazdani S, Abbas-Azimi R, Bogaard A, Boustani S, Charles M, Dehghan M, de Torres-Espuny L, Falczuk V, Guerrero-Campo J, Hynd A, Jones G, Kowsary E, Kazemi-Saeed F, Maestro-Martínez M, Romo-Díez A, Shaw S, Siavash B, Villar-Salvador P, Zak MR(2004) The plant traits that drive ecosystems: evidence from three continents. Journal of Vegetation Science 15, 295-304.
Garnier E, Cortez J, Billès G, Navas M-L, Roumet C, Debussche M, Laurent G, Blanchard A, Aubry D, Bellmann A, Neill C, Toussaint J-P (2004) Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630-2637 doi:101890/03-0799
Garnier E, Lavorel S, Ansquer P, Castro H, Cruz P, Dolezal J, Eriksson O, Fortunel C, Freitas H, Golodets C, Grigulis K, Jouany C, Kazakou E, Kigel J, Kleyer M, Lehsten V, Lepš J, Meier T, Pakeman R, Papadimitriou M, Papanastasis VP, Quested H, Quétier F, Robson M, Roumet C, Rusch G, Skarpe C, Sternberg M, Theau J-P, Vile D, Zarovali MP (2007) Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: a standardized methodology and lessons from an application to 11 European sites. Annals of Botany 99, 967-985 doi:101093/aob/mcl215
Gaucherand S, Lavorel S (2007) New method for rapid assessment of the functional composition of herbaceous plant communities. Austral Ecology 32, 927-936 doi:101111/j1442-9993200701781x
Gubsch M, Buchmann N, Schmid B, Schulze ED, Lipowsky A, Roscher C (2011) Differential effects of plant diversity on functional trait variation of grass species. Annals of Botany 107, 157-169 doi:101093/aob/mcq220
Hulshof CM, Swenson NG (2010) Variation in leaf functional trait values within and across individuals and species: an example from a Costa Rican dry forest. Functional Ecology 24, 217-223 doi:101111/j1365-2435200901614x
Lavorel S, Grigulis K, McIntyre S, Williams NSG, Garden D, Dorrough SB, Quétier F, Thébault A, Bonis A (2008) Assessing functional diversity in the field – methodology matters! Functional Ecology 22, 134-147
Messier J, McGill BJ, Lechowicz MJ (2010) How do traits vary across ecological scales A case for trait-based ecology. Ecology Letters 13, 838-848 doi:101111/j1461-0248201001476x
Moreira B, Tavsanoglu Ç, Pausas JG (2012) Local versus regional intraspecific variability in regeneration traits. Oecologia 168, 671-677 doi:101007/s00442-011-2127-5
Pakeman RJ, Quested HM (2007) Sampling plant functional traits: what proportion of the species need to be measured Applied Vegetation Science 10, 91-96 doi:101111/j1654-109X2007tb00507x
Poorter H, Niinemets Ü, Poorter L, Wright IJ, Villar R (2009) Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytologist 182, 565-588 doi:101111/j1469-8137200902830x
Reich PB, Walters MB, Ellsworth DS (1997) From tropics to tundra: global convergence in plant functioning. Proceedings of the National Academy of Sciences, USA 94, 13730-13734 doi:101073/pnas942513730
Westoby M, Cunningham SA, Fonseca C, Overton J, Wright IJ (1998) Phylogeny and variation in light capture area deployed per unit investment in leaves: designs for selecting study species with a view to generalizing. In Variation in growth rate and productivity of higher plants. (Eds H Lambers, H Poorter H, MMI Van Vuuren) pp. 539-566 (Backhuys Publishers: Leiden, The Netherlands)
Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F, Cavender-Bares J, Chapin T, Cornelissen JHC, Diemer M, Flexas J, Garnier E, Groom PK, Gulias J, Hikosaka K, Lamont BB, Lee T, Lee W, Lusk C, Midgley JJ, NavasML, Niinemets U, Oleksyn J, Osada N, Poorter H, Poot P, Prior L, Pyankov VI, Roumet C, Thomas SC, Tjoelker MG, Veneklaas EJ, Villar R (2004) The worldwide leaf economics spectrum. Nature 428, 821-827 doi:101038/nature02403