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Abstract Many plant species can exhibit remarkable variation in leaf characteristics, depending on their abiotic and biotic environment. Environmental changes therefore have the potential to alter leaf traits, which in turn scale up to influence ecosystem processes including net primary productivity, susceptibility to fire, and palatability to herbivores. It is not well understood how consistent trait–environment relationships are among species, across sites and over time. This presents a fundamental challenge for functional ecology, because no study can measure all relevant species in all places at all times. Thus, understanding the limits of transferability is critical. We collected leaf trait measurements on 13 species of grass (family: Poaceae) across 11 sites and five years (n = 3091 individuals). Sites were arrayed along a spatial precipitation gradient in coastal northern California (annual precipitation of 590–1350 mm) with substantial interannual precipitation variability (from 60% below the 30‐year average to 100% above average). Temporal and spatial linear relationships between precipitation and specific leaf area (SLA) appear at first idiosyncratic, with each species sometimes displaying positive and sometimes negative responses. However, this variation arises from sampling different portions of an underlying hump‐shaped relationship, which was shared across most species. This hump‐shaped relationship was driven primarily by changes in leaf tissue density. These results suggest the potential for transferability among species, as well as between space and time, as long as the gradients are sufficiently long to capture the nonlinear response. Future work could explore the physiological basis of the nonlinear SLA response, including the possibility that distinct physiological mechanisms are operating at the two extremes of the gradient. 

Abstract Grass species (family Poaceae) are globally distributed, adapted to a wide range of climates and express a diversity of functional strategies. We explored the functional strategies of grass species using the competitor, stress tolerator, ruderal (CSR) system and asked how a species’ strategy relates to its functional traits, climatic distribution and propensity to become naturalized outside its native range. We used a global set of trait data for grass species to classify functional strategies according to the CSR system based on leaf traits. Differences in strategies in relation to lifespan (annual or perennial), photosynthetic type (C3 or C4), or naturalisation (native or introduced) were investigated. In addition, correlations with traits not included in the CSR classification were analyzed, and a model was fitted to predict a species’ average mean annual temperature and annual precipitation across its range as a function of CSR scores. Values for competitiveness were higher in C4 species than in C3 species, values for stress tolerance were higher in perennials than in annuals, and introduced species had more pronounced competitive-ruderal strategies than native species. Relationships between the CSR classification, based on leaf traits, and other functional traits were analyzed. Competitiveness was positively correlated with height, while ruderality was correlated with specific root length, indicating that both above- and belowground traits underlying leaf and root economics contribute to realized CSR strategies. Further, relationships between climate and CSR classification showed that species with competitive strategies were more common in warm climates and at high precipitation, whereas species with stress tolerance strategies were more common in cold climates and at low precipitation. The findings presented here demonstrate that CSR classification of functional strategies based on leaf traits matches expectations for the adaptations of grass species that underlie lifespan, photosynthetic type, naturalization and climate. 

Plant traits are important for understanding community assembly and ecosystem processes, yet our understanding of intraspecific trait variation (ITV) is limited. This gap in our knowledge is partially because collecting trait data across a species' entire range is impractical, let alone across the ranges of multiple species within a plant family. Using machine learning techniques to predict spatial ITV is an attractive and cost‐effective alternative to sampling across a species range, although this has not been applied beyond regional scales. We compiled a trait database of over 1000 grass species (family: Poaceae), encompassing six key functional traits: specific leaf area (SLA), leaf dry matter content (LDMC), plant height, leaf area, leaf nitrogen (Nmass) and leaf phosphorus content (Pmass). Using a random forest machine learning approach, we predicted local trait values within species' ranges considering climate, soil type, phylogeny, lifespan, and photosynthetic pathway as influential factors. An iterative random forest modeling technique incorporated correlations between traits, resulting in improved model performance (observed versus predicted R range of 0.72–0.91). Our models also highlight the importance of climate in predicting trait variation. For a subset of species (n = 860), we projected trait predictions across their known distribution, informed by expert maps from Royal Botanic Gardens, Kew, to create global maps of ITV for grasses. Such maps have the potential to inform conservation efforts and predictions of grazing and fire dynamics in grasslands worldwide. Overall, our research demonstrates the value and ecological applications of predicting plant traits. 

Environmental conditions are dynamic, and plants respond to those dynamics on multiple time scales. Disequilibrium occurs when a response occurs more slowly than the driving environmental changes. We review evidence regarding disequilibrium in plant distributions, including their responses to paleoclimate changes, recent climate change and new species introductions. There is strong evidence that plant species distributions are often in some disequilibrium with their environmental conditions.This disequilibrium poses a challenge when projecting future species distributions using species distribution models (SDMs). Classically, SDMs assume that the set of species occurrences is an unbiased sample of the suitable environmental conditions. However, a species in disequilibrium with the environment may have higher‐than‐expected occurrence probabilities (e.g. due to extinction debts) or lower‐than‐expected occurrence probabilities (e.g. due to dispersal limitation) in different areas. If unaccounted for, this will lead to biased estimates of the environmental suitability.We review methods for avoiding such biases in SDMs, ranging from simple thinning of the occurrence dataset to complex dynamic and process‐based models. Such models require large data inputs, natural history knowledge and technical expertise, so implementing them can be challenging. Despite this, we advocate for their increased use, since process‐based models provide the best potential to account for biases in model training data and to then represent the dynamics of species occupancy as ranges shift.Synthesis. Occurrence records for a species are often in disequilibrium with climate. SDMs trained on such data will produce biased estimates of a species' niche unless this disequilibrium is addressed in the modelling. A range of tools, spanning a wide gradient of complexity and realism, can resolve this bias. 

Plant traits are useful for predicting how species may respond to environmental change and/or influence ecosystem properties. Understanding the extent to which traits vary within species and across climatic gradients is particularly important for understanding how species may respond to climate change. We explored whether climate drives spatial patterns of intraspecific trait variation for three traits (specific leaf area (SLA), plant height, and leaf nitrogen content (Nmass)) across 122 grass species (family: Poaceae) with a combined distribution across six continents. We tested the hypothesis that the sensitivity (i.e. slope) of intraspecific trait responses to climate across space would be related to the species' typical form and function (e.g. leaf economics, stature and lifespan). We observed both positive and negative intraspecific trait responses to climate with the distribution of slope coefficients across species straddling zero for precipitation, temperature and climate seasonality. As hypothesized, variation in slope coefficients across species was partially explained by leaf economics and lifespan. For example, acquisitive species with nitrogen-rich leaves grew taller and produced leaves with higher SLA in warmer regions compared to species with low Nmass. Compared to perennials, annual grasses invested in leaves with higher SLA yet decreased height and Nmass in regions with high precipitation seasonality (PS). Thus, while the influence of climate on trait expression may at first appear idiosyncratic, variation in trait–climate slope coefficients is at least partially explained by the species' typical form and function. Overall, our results suggest that a species' mean location along one axis of trait variation (e.g. leaf economics) could influence how traits along a separate axis of variation (e.g. plant size) respond to spatial variation in climate.