Author: Gya R1, Wilfahrt P2
Reviewer: Halbritter AH1, Vandvik V1
Trait-based ecology has seen a steady rise in recent decades by helping explain patterns of how organisms affect and mediate ecosystem processes and functioning (Violle et al., 2007; de Bello et al., 2010). Functional traits are morphological, physiological, or phenological features measurable at the individual level that link an individual’s performance to its biotic and abiotic environment (Webb et al., 2010). Plant functional traits are particularly well-studied and traits such as relative growth rate, leaf stoichiometry, and photosynthetic rate can yield mechanistic insights into demographics, species interactions, and ecosystem processes (Wright et al., 2004; Pérez-Harguindeguy et al., 2013; Díaz et al., 2016). Incorporating traits into climate-change studies can greatly increase our ability to understand how plants and other organisms mediate changes to both the physical and biotic environment.
Pérez-Harguindeguy et al. (2013) compiled an extensive overview with protocols for measuring plant traits and we do not replicate that effort here. Rather, we discuss how traits should be incorporated into climate-change studies, highlight traits that are most likely to be useful (Table 4.16.1), and direct readers to the handbook by Pérez-Harguindeguy et al. (2013) for instructions on how to measure them. Although we focus on climate-change studies in this protocol, many of the traits mentioned in this section are also relevant for other global-change studies on topics such as land-use change (Garnier et al., 2006), invasive plants (Drenovsky et al., 2012), and disturbance (Mouillot et al., 2013). While the trait handbook gives a broad coverage, there are potentially important suites of traits that are not covered here, for example floral traits important for pollination (see Hegland & Totland, 2005; Pellissier et al., 2010). and plant modularity (Klimešová et al., 2019). We also encourage readers to consult the stress physiology section, which provides protocols for additional growth- and stress response-related plant traits that are highly relevant to global-change studies (see Chapter S5). We also provide a short overview of trait ecology and databases for other organisms than plants (see below).
Traits can be broadly partitioned into two groups: response traits that relate to how community structure and diversity are affected by environmental factors and effect traits that drive changes in ecosystem functioning (Lavorel & Garnier, 2002; Suding et al., 2008; Funk et al., 2017). Often traits can fall into either category, with the focus of the study defining how they are used. Response traits facilitate predictions of how communities will change with climate change and can be studied both through extant communities along climatic gradients (Guittar et al., 2016) and as species turnover from climate-change experiments (Hudson et al., 2011). Effect traits facilitate an improved understanding of the underlying processes of ecosystems properties such as carbon and water dynamics (Pappas et al., 2016). Combined, trait-based approaches can lead to important insights of the causes and consequences of changing plant communities in response to climate-change and other global-change factors.
A second important partition to consider for trait-based studies is the relative importance of inter- and intra-specific trait variability. A global meta-analysis of plant communities revealed that, on average, intraspecific variation accounted for 25% of the variation within communities, but this ranged from 2% to 67% for different traits (Siefert et al., 2015). Generally, the relative importance of intraspecific variability decreases as the geographic scale of study increases (Albert et al., 2011): the magnitude of contribution is also habitat specific, with intraspecific trait variability being relatively greater in species-poor and colder habitats (Siefert et al., 2015). Thus, we recommend strongly that researchers collect trait data from within their own study area, although this relaxes for studies focused on broad-scale geographic variation or traits that are known to be relatively non-plastic (e.g. wood density). For robust inference, it is recommended to sample as many species as possible, although the most abundant species should be prioritised and, as a rule of thumb, an acceptable coverage is achieved by sampling the species representing 80% or more of the relative abundance at the plot scale (Pakeman & Quested, 2007). Typically, at least five individuals per species per sampling unit (experimental plot, site, or species, depending on the research question and trait data resolution of the particular study) should be sampled (see Appendix 1 of Pérez-Harguindeguy et al., 2013).
4.16.1 What and how to measure?
Gold standard
We recommend sampling traits in situ, and, in the case of experiments, within each of the experimental treatments and controls (i.e. at the plot scale), with at least five measurements per species per site/plot. For community-focused research, measure the most abundant species that together represent 80% or more of the total community abundance per plot (Pakeman & Quested, 2007). Abundance may be determined by cover, biomass, or other metrics appropriate for the study (see protocol 4.8 Plant community composition and 2.1.1 Aboveground plant biomass). If you are specifically interested in certain species, in the rare species, and/or in biodiversity issues, you may need higher numbers of species and/or all plots or treatments where a particular species is present.
For protocols on how to measure these traits, see Pérez-Harguindeguy et al. (2013). Traits that are especially relevant for climate-change research are presented in Table 4.16.1.
Bronze standard
When traits cannot be collected in situ for all treatments, species trait data can be collected from site-level sampling, or they can be complied from other sources. Several open-source trait databases exist that provide functional traits for a large number of plant species (TRY – Kattge et al., 2011; BIEN – Enquist et al., 2016; TTT – Bjorkman et al., 2018). When using traits from such databases, it is highly recommended to select trait values from individuals sampled under as similar as possible conditions, habitat(s), and climate(s) to the current study site (Cordlandwehr et al., 2013). When traits are collected in situ, it is highly recommended to add the trait data to these global repositories to aid further empiricial studies, meta-analyses, and ecological modelling (Gallagher et al., 2019).
Interpretation
The most common metric used in trait-based studies is the community weighted mean. This combines the relative abundance of species with their trait value and provides a central tendency of a community-trait score (Funk et al., 2017). De Bello et al. (2011) provide methods for decomposing the variance contributions of inter- v. intra-specific variation to community weighted means. However, focusing solely on single-trait means may miss important trends in the data and additional moments such as variance, skewness, or kurtosis may be used to infer processes such as stability or the relative strengths of environmental filters v. biotic interactions (Enquist et al., 2015). Additionally, a variety of tools have emerged to calculate multivariate trait indices such as functional richness and evenness, which can lend greater support to inferences on the processes structuring communities (Villéger et al., 2008).
Where to start
Enquist et al. (2015), Funk et al. (2017), Pérez-Harguindeguy et al. (2013), Violle et al. (2012)
4.16.2 Special cases, emerging issues, and challenges
Currently, existing trait protocols and databases for plants do not include cryptogams. Nonetheless, there have been advances in developing trait protocols for cryptogams such as bryophytes and lichens (Cornelissen et al., 2007; St. Martin & Mallik, 2017) and soil crusts (Mallen-Cooper & Eldridge, 2016). While no large database has been developed, there have been renewed calls for the integration of cryptogram trait ecology into plant-based trait studies (Deane‐Coe & Stanton, 2017; St. Martin & Mallik, 2017).
Trait-based ecology has proliferated in many non-plant taxa as well, offering substantial value to climate-change studies. However, given the increased trait specialisation across and within the other organismal domains, we do not cover any other taxa in detail here. Considerations of response and effect traits and inter- and intra-specific variability also apply to non-plant taxa. The availability of trait databases is highly variable across guilds of species and include the PanTHERIA database for mammals (Jones et al., 2009), an amniote database for birds, reptiles, and mammals (Myhrvold et al., 2015), the GlobalAnts database that includes both abundance and trait data linked to local assemblages (Parr et al., 2017), and the FUNGuild database which has begun classifying fungal operational taxonomic units identified by high throughput sequencing into functional guilds (Nguyen et al., 2016). This is not a comprehensive list of trait databases (many databases exist for aquatic organisms), and regional scientific societies often curate their own such databases for a variety of organisms.
In line with the goal to make climate change experiments more compatible, data more available, and science more transparent, we encourage the same mentality with newly collected trait data. One opportunity is through the Open Traits Network (Gallagher et al., 2019), which fosters an international alliance of researchers and institutions working towards open data and workflows to improve the way we work with functional traits.
Table 4.16.1 Selected traits from Pérez-Harguindeguy et al. (2013) that may be of particular value for climate-change research with their relevance as response or effect traits, and links to relevant internal protocols. Other traits not listed here may still be situationally informative. Note that Pérez-Harguindeguy et al. (2013) does not cover the full range of traits.
| Trait name | Description | Relevance to climate change studies | Relevant protocols |
| Whole plant | |||
| Life history and maximum plant lifespan | Classification (annual, biennial, perennial) or quantification of plant life span | Effect: Distinguishing between dominant life-history categories, e.g. perennial or annual, informs carbon and nutrient cycling and expected rate of species turnover. Decreases or increases in life span affect these rates | 4.3 Plant demography |
| Plant height | Maximum vegetative height of free-standing, mature individual | Response: Indicates position in vertical light gradient, competitiveness for light capture, growth potential
Effect: Used in allometric equations for estimating biomass |
2.1.1 Aboveground plant biomass |
| Spinescence | Quantifies type, size, and density of spines, prickles, and thorns | Response: Indicator of vertebrate pressure on plants | 4.15 Vertebrate herbivory |
| Leaf area:sapwood area ratio | Capacity for water transport and mechanical strength | Response: Balance between transpiration and stem water supply
Effect: Indicates potential for transpiration |
3.7 Sap flux |
| Root-mass fraction | Proportion of plant dry mass found in roots | Response: Indicates plant strategy for belowground foraging. Increase may indicate nutrient-poor soils, BUT can also occur in nutrient-rich sites where competition is high | 2.1.2 Belowground plant biomass |
| Relative growth rate and components | Increase in relative size of plant across a defined time interval. Can be separated into leaf, stem, and root mass components | Response: Increases or decreases to vital rates may indicate shifts in competitive dominance. Separation into components indicates trade-offs, e.g. between aboveground and belowground allocation strategy
Effect: Growth rate determines rate of carbon sequestration and nutrient cycling |
2.1.1 Aboveground plant biomass
4.3 Plant demography |
| Water-flux traits | Plant stature on hydrological fluxes external to plant (e.g. free throughfall, retention followed by evaporation, stemflow) | Effect: Impacts hydrologic cycle of system | 3.8 Ecosystem water stress |
| Leaf | |||
| Specific leaf area (SLA) | Leaf area (fresh) divided by dry mass (LMA, leaf mass per area, is simply inverse of SLA).
Part of leaf economic spectrum |
Response: Higher values indicate resource-acquisitive strategies; lower values indicate resource-conservatism
Effect: When leaves are collected in a known area, the dry mass multiplied by total SLA gives the leaf area index (LAI), a useful parameter in modelling productivity and water stress |
2.1.1 Aboveground plant biomass
3.8 Ecosystem water stress See Breda (2003) for more information on measuring LAI |
| Leaf dry-matter content | Leaf dry weight divided by water saturated fresh weight | Response: Negatively correlated to relative growth rates and resource capture and usage. Similar to SLA, but independent of leaf size
Effect: Negatively correlated to litter decomposition rates |
|
| pH of green leaves or leaf litter | pH of green or senesced leaf tissue (generally yield the same values) | Response: Positively related to palatability and digestibility to herbivores
Effect: Persists in leaf litter, affecting decomposition rates |
|
| Leaf N and P concentration | Total amount of N or P per unit of leaf dry mass | Response: Positively correlated with growth rates and nutritional quality for consumers
Effect: Single or co-limitation may limit primary production |
2.1.6 Foliar stoichiometry and resorption protocol |
| Light-saturated photosynthesis | Carbon dioxide assimilation with full light | Response: Positively correlated to resource acquisition capacity
Effect: Relates to biomass accumulation and carbon sequestration |
2.1.3 Leaf-scale photosynthesis |
| Leaf dark respiration | Measure of basal metabolism and rough correlate to night-time respiratory carbon flux | Response: Sensitive to rises in temperature, related to resource acquisition-conservatism
Effect: Determines net primary production, which is the difference between photosynthesis and respiration |
2.1.4 Plant respiration |
| C-isotope composition (water-use efficiency) | Analysis of 13C:12C indicates ratio of photosynthesis to transpiration | Response: May indicate inter- and intra-specific shifts in water-use efficiency in plants in response to environmental change
Effect: Traces where carbon is allocated during CO2 uptake |
2.2.3 Soil CO2 (and other trace gas) fluxes |
| Electrolyte leakage (frost sensitivity) | Cell membranes ruptured following frost damage reduces retention of solutes | Response: Sensitivity to frost damage may explain how species sort along thermal gradients or probability of a species to withstand frost events | |
| Leaf water potential | Indicates status of water in leaf by measuring pressure required to induce leaf water loss | Response: Can quantify drought tolerance across individuals, populations, or species
Effect: Can indicate soil water potential when measured pre-dawn. Night-time transpiration or xylem cavitation may disrupt this equilibrium |
3.4 Soil water potential |
| Litter decomposability | Mass loss of leaves or litter contained in litter bags | Response: Influenced by temperature and microbial activity
Effect: Differing rates of decomposition results in different rates of CO2 and nutrient release |
2.2.6 Litter decomposition |
| Stem | |||
| Stem-specific density | Volume of fresh stem biomass divided by dry mass | Response: May indicate changes in water availability, pressure from consumers, resistance to disturbance events
Effect: Critical for carbon storage. Often used in allometric equations to estimate biomass |
2.1.1 Aboveground plant biomass |
| Xylem conductivity | Ability to move water from soil to leaves; measured by rate of water flow per xylem area and per unit gradient of pressure | Response: Low conductivity leads more rapidly to drought-induced leaf damage
Effect: High conductivity increases transpiration rates |
|
| Vulnerability to embolism | When air gets into xylem tissue, it rapidly expands and blocks water flow. Measured by building a xylem conductivity curve | Response: Positively related to mortality risk during drought (i.e. inversely correlated with drought tolerance) | |
| Belowground | |||
| Specific root length | Ratio of root length to root dry mass | Response: Higher values are associated with more rapid nutrient uptake ability but decreased root longevity | 2.1.2 Belowground plant biomass |
| Root-system morphology | Primarily defined by three components: depth, lateral extent, and exploration intensity (fine-root biomass per unit soil volume). These can be further refined into parameters at different soil depths | Response: Indicative of the resource space where plants forage for soil nutrients, and the competitiveness of areas where they do (intensity)
Effect: The distribution of root biomass at different depths is a useful trait for modelling productivity and water stress |
2.1.2 Belowground plant biomass
3.8 Ecosystem water stress |
| Regenerative | |||
| Dispersal syndrome | Categorical trait detailing main vector of dispersal | Response: Turnover at the community level may indicate possible dispersal limitations or explain recent invasions | 4.7 Propagule rain |
| Dispersule size and shape | Dry mass and variance of length, width, and thickness of dispersule (i.e. seed and associated structures) | Response: Dispersule size is related to seed-bank persistence and therefore long-term community recruitment dynamics | 4.6 The soil seed bank (buried seed pool) |
| Seed mass | Dry mass of seed without associated structures (e.g. fruit) | Response: Indicates parental investment per unit offspring. Higher seed mass may confer initial stress tolerance to seedlings, lower seed mass allows more offspring per unit energy investment and often longer individual seed persistence | 4.6 The soil seed bank (buried seed pool) |
4.16.3 References
Theory, significance, and large datasets
Bjorkman et al. (2018), Enquist et al. (2015), Jones et al. (2009), Kattge et al. (2011), Kleyer et al. (2008), Myhrvold et al. (2015), Nguyen et al. (2016), Parr et al. (2017)
More on methods and existing protocols
Klimešová et al (2019), Pérez-Harguindeguy et al. (2013)
All references
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Author: Gya R1, Wilfahrt PA2
Reviewer: Halbritter AH1, Vandvik V1
Affiliations
1 Department of Biological Sciences and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
2 Department of Disturbance Ecology, BayCEER, University of Bayreuth, Bayreuth, Germany