Habitat properties and plant traits interact as drivers of non‐native plant species’ seed production at the local scale

To answer the long‐standing question if we can predict plant invader success based on characteristics of the environment (invasibility) or the invasive species (invasiveness), or the combination of both, there is a need for detailed observational studies in which habitat properties, non‐native plant traits, and the resulting invader success are locally measured. In this study, we assess the interaction of gradients in the environmental and trait space on non‐native species fitness, expressed as seed production, for a set of 10 invasive and noninvasive non‐native species along a wide range of invaded sites in Flanders. In our multidimensional approach, most of the single environmental gradients (temperature, light availability, native plant species diversity, and soil fertility) and sets of non‐native plant traits (plant size, photosynthesis, and foliar chemical attributes) related positively with invader seed production. Yet correlation with seed production was much stronger when several environmental gradients were assessed in interaction, and even more so when we combined plant traits and habitat properties. The latter increased explanatory power of the models on average by 25% for invasive and by 7% for noninvasive species. Additionally, we report a 70‐fold higher seed production in invasive than in noninvasive species and fundamentally different correlations of seed production with plant traits and habitat properties in noninvasive versus invasive species. We conclude that locally measured traits and properties deserve much more attention than they currently get in invasion literature and thus encourage further studies combining this level of detail with the generality of a multiregion and multispecies approach across different stages of invasion.     

Towards a functional basis for predicting vegetation patterns; incorporating plant traits in habitat distribution models

Reliably predicting vegetation distribution requires habitat distribution models (HDMs) that are ecologically sound. Current correlative HDMs are increasingly criticized because they lack sufficient functional basis. To include functional information into these models, we integrated two concepts from community ecology into a new type of HDM. We incorporated: 1) species selection by their traits in which only those species that pass the environmental filter can be part of the community (assembly theory); 2) that the occurrence probability of a community is determined by the extent to which the community mean traits fit the required traits as set by the environment. In this paper, our trait‐based HDM is presented and its predictive capacity explored. Our approach consists of two steps. In step 1, four plant traits (stem‐specific density and indicator values for nutrients, moisture and acidity) are predicted from four dominant environmental drivers (disturbance, nutrient supply, moisture supply and acidity) using regression. In step 2, these traits are used to predict the occurrence probability of 13 vegetation types, covering the majority of vegetation types across the Netherlands. The model was validated by comparison to the observed vegetation type for 263 plots in the Netherlands. Model performance was within the range of conventional HDMs and decreased with increasing uncertainty in the environment‐trait relationships and with an increasing number of vegetation types. This study shows that including functionality into HDMs is not necessarily at the cost of model performance, while it has several conceptual advantages among including an increased insight in the functional characteristics of the vegetation and sources of unpredictability in community assembly. As such it is a promising first step towards more functional HDMs. Further development of a trait‐based HDM hinges on replacing indicator values by truly functional traits and the translation of these relationships into mechanistic relationships.     

Predicting individual plant performance in grasslands

Plant functional traits are widely used to predict community productivity. However, they are rarely used to predict individual plant performance in grasslands. To assess the relative importance of traits compared to environment, we planted seedlings of 20 common grassland species as phytometers into existing grassland communities varying in land‐use intensity. After 1 year, we dug out the plants and assessed root, leaf, and aboveground biomass, to measure plant performance. Furthermore, we determined the functional traits of the phytometers and of all plants growing in their local neighborhood. Neighborhood impacts were analyzed by calculating community‐weighted means (CWM) and functional diversity (FD) of every measured trait. We used model selection to identify the most important predictors of individual plant performance, which included phytometer traits, environmental conditions (climate, soil conditions, and land‐use intensity), as well as CWM and FD of the local neighborhood. Using variance partitioning, we found that most variation in individual plant performance was explained by the traits of the individual phytometer plant, ranging between 19.30% and 44.73% for leaf and aboveground dry mass, respectively. Similarly, in a linear mixed effects model across all species, performance was best predicted by phytometer traits. Among all environmental variables, only including land‐use intensity improved model quality. The models were also improved by functional characteristics of the local neighborhood, such as CWM of leaf dry matter content, root calcium concentration, and root mass per volume as well as FD of leaf potassium and root magnesium concentration and shoot dry matter content. However, their relative effect sizes were much lower than those of the phytometer traits. Our study clearly showed that under realistic field conditions, the performance of an individual plant can be predicted satisfyingly by its functional traits, presumably because traits also capture most of environmental and neighborhood conditions.     

Climate and plant trait strategies determine tree carbon allocation to leaves and mediate future forest productivity

Forest leaf area has enormous leverage on the carbon cycle because it mediates both forest productivity and resilience to climate extremes. Despite widespread evidence that trees are capable of adjusting to changes in environment across both space and time through modifying carbon allocation to leaves, many vegetation models use fixed carbon allocation schemes independent of environment, which introduces large uncertainties into predictions of future forest responses to atmospheric CO₂ fertilization and anthropogenic climate change. Here, we develop an optimization‐based model, whereby tree carbon allocation to leaves is an emergent property of environment and plant hydraulic traits. Using a combination of meta‐analysis, observational datasets, and model predictions, we find strong evidence that optimal hydraulic–carbon coupling explains observed patterns in leaf allocation across large environmental and CO₂ concentration gradients. Furthermore, testing the sensitivity of leaf allocation strategy to a diversity in hydraulic and economic spectrum physiological traits, we show that plant hydraulic traits in particular have an enormous impact on the global change response of forest leaf area. Our results provide a rigorous theoretical underpinning for improving carbon cycle predictions through advancing model predictions of leaf area, and underscore that tree‐level carbon allocation to leaves should be derived from first principles using mechanistic plant hydraulic processes in the next generation of vegetation models.     

Going beyond limitations of plant functional types when predicting global ecosystem–atmosphere fluxes: exploring the merits of traits‐based approaches

Aim Despite their importance for predicting fluxes to and from terrestrial ecosystems, dynamic global vegetation models have insufficient realism because of their use of plant functional types (PFTs) with constant attributes. Based on recent advances in community ecology, we explore the merits of a traits‐based vegetation model to deal with current shortcomings. Location Global. Methods A research review of current concepts and information, providing a new perspective, supported by quantitative analysis of a global traits database. Results Continuous and process‐based trait–environment relations are central to a traits‐based approach and allow us to directly calculate fluxes based on functional characteristics. By quantifying community assembly concepts, it is possible to predict trait values from environmental drivers, although these relations are still imperfect. Through the quantification of these relations, effects of adaptation and species replacement upon environmental changes are implicitly accounted for. Such functional links also allow direct calculation of fluxes, including those related to feedbacks through the nitrogen and water cycle. Finally, a traits‐based model allows the prediction of new trait combinations and no‐analogue ecosystem functions projected to arise in the near future, which is not feasible in current vegetation models. A separate calculation of ecosystem fluxes and PFT occurrences in traits‐based models allows for flexible vegetation classifications. Main conclusions Given the advantages described above, we argue that traits‐based modelling deserves consideration (although it will not be easy) if one is to aim for better climate projections.     

Quantifying trait selection driving community assembly: a test in herbaceous plant communities under contrasted land use regimes

Plant traits are particularly important in determining plant community structure. However, how can one identify which traits are the most important in driving community assembly? Here we propose a method 1) to quantify the direction and strength of trait selection during community assembly and 2) to obtain parsimonious lists of traits that can predict species relative abundances in plant communities. We tested our method using floristic data from 32 plots experiencing different treatments (fertilisation and grazing) in southern France. Twelve functional traits were measured on 68 species. We determined the direction and strength of selection on these 12 traits using a metric derived from a maximum entropy model (i.e. lambda). We then determined our parsimonious list of traits using a backward selection of traits based on these lambda values (for all treatments and in each treatment separately). We finally compared our method to two other methods: one based on iterative RLQ and the other based on an entropy‐based forward selection of traits. We found major differences in the direction and strength of selection across the 12 traits and treatments. From the 12 traits, plant vegetative and reproductive heights, leaf dry matter content leaf nitrogen content, specific leaf area, and leaf phosphorus content were particularly important for predicting species relative abundances when considering all treatments together. Our method yielded results similar to those produced by the entropy‐based approach but differed from those produced by the iterative RLQ, whose selected traits could not significantly predict species relative abundances. Together these results suggest that the assembly of these communities is primarily driven by a small number of key functional traits. We argue that our method provides an objective way of determining a parsimonious list of traits that together accurately predict community structure and which, despite its complementarities with entropy‐based method, offers significant advantages.     

Aphid genotypes vary in their response to the presence of fungal endosymbionts in host plants

Genetic variation for fitness‐relevant traits may be maintained in natural populations by fitness differences that depend on environmental conditions. For herbivores, plant quality and variation in chemical plant defences can maintain genetic variation in performance. Apart from plant secondary compounds, symbiosis between plants and endosymbiotic fungi (endophytes) can produce herbivore‐toxic compounds. We show that there is significant variation among aphid genotypes in response to endophytes by comparing life‐history traits of 37 clones of the bird cherry‐oat aphid Rhopalosiphum padi feeding on endophyte‐free and endophyte‐infected tall fescue Lolium arundinaceum. Clonal variation for life‐history traits was large, and most clones performed better on endophyte‐free plants. However, the clones differed in the relative performance across the two environments, resulting in significant genotype × environment interactions for all reproductive traits. These findings suggest that natural variation in prevalence of endophyte infection can contribute to the maintenance of genetic diversity in aphid populations.     

Genetic analysis and characterization of a new maize association mapping panel for quantitative trait loci dissection

Association mapping based on the linkage disequilibrium provides a promising tool to identify genes responsible for quantitative variations underlying complex traits. Presented here is a maize association mapping panel consisting of 155 inbred lines with mainly temperate germplasm, which was phenotyped for 34 traits and genotyped using 82 SSRs and 1,536 SNPs. Abundant phenotypic and genetic diversities were observed within the panel based on the phenotypic and genotypic analysis. A model-based analysis using 82 SSRs assigned all inbred lines to two groups with eight subgroups. The relative kinship matrix was calculated using 884 SNPs with minor allele frequency ≥20% indicating that no or weak relationships were identified for most individual pairs. Three traits (total tocopherol content in maize kernel, plant height and kernel length) and 1,414 SNPs with missing data <20% were used to evaluate the performance of four models for association mapping analysis. For all traits, the model controlling relative kinship (K) performed better than the model controlling population structure (Q), and similarly to the model controlling both population structure and relative kinship (Q + K) in this panel. Our results suggest this maize panel can be used for association mapping analysis targeting multiple agronomic and quality traits with optimal association model.     

Using functional traits to predict species growth trajectories,and cross‐validation to evaluate these models for ecological prediction

Modeling plant growth using functional traits is important for understanding the mechanisms that underpin growth and for predicting new situations. We use three data sets on plant height over time and two validation methods—in‐sample model fit and leave‐one‐species‐out cross‐validation—to evaluate non‐linear growth model predictive performance based on functional traits. In‐sample measures of model fit differed substantially from out‐of‐sample model predictive performance; the best fitting models were rarely the best predictive models. Careful selection of predictor variables reduced the bias in parameter estimates, and there was no single best model across our three data sets. Testing and comparing multiple model forms is important. We developed an R package with a formula interface for straightforward fitting and validation of hierarchical, non‐linear growth models. Our intent is to encourage thorough testing of multiple growth model forms and an increased emphasis on assessing model fit relative to a model's purpose.     

Are traits measured on pot grown plants representative of those in natural communities?

Question: The quantification of functional traits in natural communities can be difficult (e.g. root traits, RGR). Can functional traits measured on pot grown plants be reliably applied to natural communities? Alternatively, can below‐ground plant traits be predicted from above‐ground traits? Location: Southeastern Australia. Methods: We compared 17 shoot, root and whole‐plant morphological traits measured on 14 plant species in a native grassland community to those measured under two different pot conditions: unfertilised and fertilised. Results: The majority of trait values for pot grown plants differed to plants in the field, however, species ranking remained consistent for most leaf traits between the field and the two pot growing conditions. In contrast, species ranking was not consistent for most whole plant traits when comparing field plants to fertilised pot grown plants, providing a caution against the tendency to grow plants in controlled conditions at ‘optimal’ (high) resource levels. Moderate to strong correlations were found between below‐ground and above‐ground plant traits, including between root dry matter content and leaf dry matter content, and between specific root area and specific leaf area. Conclusions: The utility of pot grown plants to quantify traits for field plants is highly dependent on the selection of the growing conditions in the controlled environment. The consistency we observed between above‐ground and below‐ground trait strategies suggests that below‐ground traits may be predictable based on above‐ground traits, reducing the need to quantify root traits on cultured plants.     

Growth‐form and spatiality driving the functional difference of native and alien aquatic plants in Europe

Trait‐based approaches are widely used in community ecology and invasion biology to unravel underlying mechanisms of vegetation dynamics. Although fundamental trade‐offs between specific traits and invasibility are well described among terrestrial plants, little is known about their role and function in aquatic plant species. In this study, we examine the functional differences of aquatic alien and native plants stating that alien and native species differ in selected leaf traits. Our investigation is based on 60 taxa (21 alien and 39 native) collected from 22 freshwater units of Hungarian and Italian lowlands and highlands. Linear mixed models were used to investigate the effects of nativeness on four fundamental traits (leaf area, leaf dry matter content, specific leaf area, and leaf nitrogen content), while the influence of growth‐form, altitude, and site were employed simultaneously. We found significantly higher values of leaf areas and significantly lower values of specific leaf areas for alien species if growth‐form was included in the model as an additional predictor.We showed that the trait‐based approach of autochthony can apply to aquatic environments similar to terrestrial ones, and leaf traits have relevance in explaining aquatic plant ecology whether traits are combined with growth‐forms as a fixed factor. Our results confirm the importance of traits related to competitive ability in the process of aquatic plant invasions. Alien aquatic plants can be characterized as species producing soft leaves faster. We argue that the functional traits of alien aquatic plants are strongly growth‐form dependent. Using the trait‐based approach, we found reliable characteristics of aquatic plants related to species invasions, which might be used, for example, in conservation management.     

Breeding new varieties for controlled environments

Agricultural production in controlled environments is increasingly feasible, and may play an important role in providing nutrition and choice to growing urban centres. New technologies in lighting, ventilation, robotics and irrigation are just a few of the innovations that enable production of high‐value specialty crops outside of a traditional field setting. However, despite all of the advances in the hardware within the plant factory operation, innovation of the most complex machine has been neglected – the plant itself. Indoor agricultural operations typically rely on legacy varieties, plants selected and bred for field conditions. In the field, phenotypic stability is paramount, as production must be consistent in an unpredictable and changing environment. However, the controlled environment affords focus on different breeding priorities as environmental flux, pests, pathogens and post‐harvest quality are less formidable barriers to production. On the contrary, breeding for controlled environments shifts the focus to a completely different set of plant traits, such as rapid growth, performance in low light environments and active manipulation of plant stature. Instead of breeding for phenotypic stability, plants may be bred to maximise genetic plasticity, allowing specific traits to be presented as a function of the quality of the ambient light spectrum. In this scenario plant varieties may be grown with optimal size, supporting a focus on consumer traits like flavour or accumulation of health‐related compounds. Gene editing may be a central technology in the production of designer plants for controlled environments. This review considers the opportunity for breeding for controlled environments, with a focus on a revision of priorities for controlled‐environment breeders.     

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.     

Architectural and growth traits differ in effects on performance of clonal plants: an analysis using a field-parameterized simulation model

Individual traits are often assumed to be linked in a straightforward manner to plant performance and processes such as population growth, competition and community dynamics. However, because no trait functions in isolation in an organism, the effect of any one trait is likely to be at least somewhat contingent on other trait values. Thus, to the extent that the suite of trait values differs among species, the magnitude and even direction of correlation between values of any particular trait and performance is likely to differ among species. Working with a group of clonal plant species, we assessed the degree of this contingency and therefore the extent to which the assumption of simple and general linkages between traits and performance is valid. To do this, we parameterized a highly calibrated, spatially explicit, individual‐based model of clonal plant population dynamics and then manipulated one trait at a time in the context of realistic values of other traits for each species. The model includes traits describing growth, resource allocation, response to competition, as well as architectural traits that determine spatial spread. The model was parameterized from a short‐term (3 month) experiment and then validated with a separate, longer term (two year) experiment for six clonal wetland sedges, Carex lasiocarpa, Carex sterilis, Carex stricta, Cladium mariscoides, Scirpus acutus and Scirpus americanus. These plants all co‐occur in fens in southeastern Michigan and represent a spectrum of clonal growth forms from strong clumpers to runners with long rhizomes. Varying growth, allocation and competition traits produced the largest and most uniform responses in population growth among species, while variation in architectural traits produced responses that were smaller and more variable among species. This is likely due to the fact that growth and competition traits directly affect mean ramet size and number of ramets, which are direct components of population biomass. In contrast, architectural and allocation traits determine spatial distribution of biomass; in the long run, this also affects population size, but its net effect is more likely to be mediated by other traits. Such differences in how traits affect plant performance are likely to have implications for interspecific interactions and community structure, as well as on the interpretation and usefulness of single trait optimality models.     

Predictability of stream insect distributions is dependent on niche position,but not on biological traits or taxonomic relatedness of species

Species distributions can be analysed under two perspectives: the niche‐based approach, which focuses on species–environment relationships; and the dispersal‐based approach, which focuses on metapopulation dynamics. The degree to which each of these two components affect species distributions may depend on habitat fragmentation, species traits and phylogenetic constraints. We analysed the distributions of 36 stream insect species across 60 stream sites in three drainage basins at high latitudes in Finland. We used binomial generalised linear models (GLMs) in which the predictor variables were environmental factors (E models), within‐basin spatial variables as defined by Moran's eigenvector maps (M models), among‐basin variability (B models), or a combination of the three (E + M + B models) sets of variables. Based on a comparative analysis, model performance was evaluated across all the species using Gaussian GLMs whereby the deviance accounted for by binomial GLMs was fitted on selected explanatory variables: niche position, niche breadth, site occupancy, biological traits and taxonomic relatedness. For each type of model, a reduced Gaussian GLM was eventually obtained after variable selection (Bayesian information criterion). We found that niche position was the only variable selected in all reduced models, implying that marginal species were better predicted than non‐marginal species. The influence of niche position was strongest in models based on environmental variables (E models) or a combination of all types of variables (E + M + B models), and weakest in spatial autocorrelation models (M models). This suggests that species–environment relationships prevail over dispersal processes in determining stream insect distributions at a regional scale. Our findings have clear implications for biodiversity conservation strategies, and they also emphasise the benefits of considering both the niche‐based and dispersal‐based approaches in species distribution modelling studies.     

Correlation of drought traits and the predictability of osmotic potential at full leaf turgor in vegetation from New Zealand

Scientists do not know precisely how severe will be the impact of climate change on species. Evidence suggests that for some species, their future distributions might be jeopardized by local extinctions and drought‐induced tree mortality. Thus, we require models capable of estimating drought tolerance across many species. We can approach this goal by assessing functional traits. The trait osmotic potential at full turgor, π_O, is potentially a good drought indicator; however, few studies address its importance as a drought‐tolerance predictor and it is difficult to measure in the field with accuracy. In this work, we aim to answer the questions: which drought traits correlate with π_O?; do morpho‐anatomical traits correlate with π_O?; and which trees and shrubs are more (or less) vulnerable to drought? To achieve this aim, we assessed physiological and morpho‐anatomical traits for 14 native species from New Zealand forests. We included leaf‐ and wood‐related traits, π_O, water potential and stomatal conductance. We examined how these traits correlate with π_O and sought to generate models to predict π_O as a function of other traits. We tested 33 different models and evaluated them using Akaike's information criterion. Unfortunately, none of the morpho‐anatomical traits correlated well with π_O. Instead, water potential correlated most strongly with π_O. None of the models using only morpho‐anatomical traits produced plausible results. The model with the best predictive performance incorporated the effects of both morpho‐anatomical and physiological traits: water potential and wood saturated water content. Of the species analysed, and based on their π_O response, Lophozonia menziesii was considered the most vulnerable to drought stress, whereas Plagianthus regius was the least vulnerable. Our findings imply that it is potentially valuable to keep exploring the use of π_O as a drought indicator and that the effort required to measure some physiological traits, such as water potential, may be essential to consider plant drought responses and to predict π_O.     

On the identification of the most suitable traits for plant functional trait analyses

Within the past few years plant functional trait analyses have been widely applied to learn more about the processes and patterns of ecosystem development in response to environmental changes. These approaches are based on the assumption that plants with similar ecologically relevant trait attributes respond to environmental changes in comparable ways. Several methods have been described on how to analyse a priori defined trait sets with respect to environment. Irrespective of the statistical methods used to contrast ecosystem responses and environmental conditions, each functional trait approach depends strongly on the initial trait set. In nearly all recent studies on functional trait analysis a test, if a trait is responsible, is applied independently from the core analysis. In the current study we present a method that extracts those traits from a wider set of traits which are optimal for describing the ecosystem response to a given environmental gradient. This was done by the use of iterative three‐table ordination techniques with each possible trait combination. We further concentrated on the effect of the inclusion of too many traits in such analyses. As examples the method was applied to three long term studies on abandoned arable fields. The approach was validated by comparing the results with literature‐knowledge on arable field succession. Although the trait pre‐selection was only based on a statistical procedure, our method was able to identify all relevant processes of ecosystem responses. All three sites show comparable ecosystem responses; the importance of the competitive ability of plants was highlighted. We further demonstrated that the use of too many traits results in an over‐fitting of the trait‐environment model. The presented method of iterative RLQ‐analyses is adequate to identify responding traits to environmental changes: the discovered processes of successional development of abandoned arable fields are consistent with our knowledge from the literature.     

Trait–performance relationships of grassland plant species differ between common garden and field conditions

The way functional traits affect growth of plant species may be highly context‐specific. We asked which combinations of trait values are advantageous under field conditions in managed grasslands as compared to conditions without competition and land‐use. In a two‐year field experiment, we recorded the performance of 93 species transplanted into German grassland communities differing in land‐use intensity and into a common garden, where species grew unaffected by land‐use under favorable conditions regarding soil, water, and space. The plants’ performance was characterized by two independent dimensions (relative growth rates (RGR) of height and leaf length vs. aboveground biomass and survival) that were differently related to the eight focal key traits in our study (leaf dry matter content (LDMC), specific leaf area (SLA), height, leaf anatomy, leaf persistence, leaf distribution, vegetative reproduction, and physical defense). We applied multivariate procrustes analyses to test for the correspondence of the optimal trait–performance relationships between field and common garden conditions. RGRs were species‐specific and species ranks of RGRs in the field, and the common garden were significantly correlated. Different traits explained the performance in the field and the common garden; for example, leaf anatomy traits explained species performance only in the field, whereas plant height was found to be only important in the common garden. The ability to reproduce vegetatively, having leaves that are summer‐persistent and with high leaf dry matter content (LDMC) were traits of major importance under both settings, albeit the magnitude of their influence differed slightly between the field and the common garden experiment. All optimal models included interactions between traits, pointing out the necessity to analyze traits in combination. The differences between field and common garden clearly demonstrate context dependency of trait‐based growth models, which results in limited transferability of favorable trait combinations between different environmental settings.     

Ecophysiological and bioclimatic foundations for a global plant functional classification

Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs? Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole‐plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models. Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm‐temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade‐offs contributes to the diversity of plants along bioclimatic gradients. Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.