Integrating correlation between traits improves spatial predictions of plant functional composition

Functional trait composition is increasingly recognized as key to better understand and predict community responses to environmental gradients. Predictive approaches traditionally model the weighted mean trait values of communities (CWMs) as a function of environmental gradients. However, most approaches treat traits as independent regardless of known tradeoffs between them, which could lead to spurious predictions. To address this issue, we suggest jointly modeling a suit of functional traits along environmental gradients while accounting for relationships between traits. We use generalized additive mixed effect models to predict the functional composition of alpine grasslands in the Guisane Valley (France). We demonstrate that, compared to traditional approaches, joint trait models explain considerable amounts of variation in CWMs, yield less uncertainty in trait CWM predictions and provide more realistic spatial projections when extrapolating to novel environmental conditions. Modeling traits and their co‐variation jointly is an alternative and superior approach to predicting traits independently. Additionally, compared to a ‘predict first, assemble later’ approach that estimates trait CWMs post hoc based on stacked species distribution models, our ‘assemble first, predict later’ approach directly models trait‐responses along environmental gradients, and does not require data and models on species’ distributions, but only mean functional trait values per community plot. This highlights the great potential of joint trait modeling approaches in large‐scale mapping applications, such as spatial projections of the functional composition of vegetation and associated ecosystem services as a response to contemporary global change.     

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.     

Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes

There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.     

Leveraging plant hydraulics to yield predictive and dynamic plant leaf allocation in vegetation models with climate change

Plant functional traits provide a link in process‐based vegetation models between plant‐level physiology and ecosystem‐level responses. Recent advances in physiological understanding and computational efficiency have allowed for the incorporation of plant hydraulic processes in large‐scale vegetation models. However, a more mechanistic representation of water limitation that determines ecosystem responses to plant water stress necessitates a re‐evaluation of trait‐based constraints for plant carbon allocation, particularly allocation to leaf area. In this review, we examine model representations of plant allocation to leaves, which is often empirically set by plant functional type‐specific allometric relationships. We analyze the evolution of the representation of leaf allocation in models of different scales and complexities. We show the impacts of leaf allocation strategy on plant carbon uptake in the context of recent advancements in modeling hydraulic processes. Finally, we posit that deriving allometry from first principles using mechanistic hydraulic processes is possible and should become standard practice, rather than using prescribed allometries. The representation of allocation as an emergent property of scarce resource constraints is likely to be critical to representing how global change processes impact future ecosystem dynamics and carbon fluxes and may reduce the number of poorly constrained parameters in vegetation models.     

Processes of community assembly in an environmentally heterogeneous,high biodiversity region

Despite decades of study, the relative importance of niche‐based versus neutral processes in community assembly remains largely ambiguous. Recent work suggests niche‐based processes are more easily detectable at coarser spatial scales, while neutrality dominates at finer scales. Analyses of functional traits with multi‐year multi‐site biodiversity inventories may provide deeper insights into assembly processes and the effects of spatial scale. We examined associations between community composition, species functional traits, and environmental conditions for plant communities in the Kouga‐Baviaanskloof region, an area within South Africa's Cape Floristic Region (CFR) containing high α and β diversity. This region contains strong climatic gradients and topographic heterogeneity, and is comprised of distinct vegetation classes with varying fire histories, making it an ideal location to assess the role of niche‐based environmental filtering on community composition by examining how traits vary with environment. We combined functional trait measurements for over 300 species with observations from vegetation surveys carried out in 1991/1992 and repeated in 2011/2012. We applied redundancy analysis, quantile regression, and null model tests to examine trends in species turnover and functional traits along environmental gradients in space and through time. Functional trait values were weakly associated with most spatial environmental gradients and only showed trends with respect to vegetation class and time since fire. However, survey plots showed greater compositional and functional stability through time than expected based on null models. Taken together, we found clear evidence for functional distinctions between vegetation classes, suggesting strong environmental filtering at this scale, most likely driven by fire dynamics. In contrast, there was little evidence of filtering effects along environmental gradients within vegetation classes, suggesting that assembly processes are largely neutral at this scale, likely the result of very high functional redundancy among species in the regional species pool.     

Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model

Functional diversity is critical for ecosystem dynamics, stability and productivity. However, dynamic global vegetation models (DGVMs) which are increasingly used to simulate ecosystem functions under global change, condense functional diversity to plant functional types (PFTs) with constant parameters. Here, we develop an individual‐ and trait‐based version of the DGVM LPJmL (Lund‐Potsdam‐Jena managed Land) called LPJmL‐ flexible individual traits (LPJmL‐FIT) with flexible individual traits) which we apply to generate plant trait maps for the Amazon basin. LPJmL‐FIT incorporates empirical ranges of five traits of tropical trees extracted from the TRY global plant trait database, namely specific leaf area (SLA), leaf longevity (LL), leaf nitrogen content (N_area), the maximum carboxylation rate of Rubisco per leaf area (), and wood density (WD). To scale the individual growth performance of trees, the leaf traits are linked by trade‐offs based on the leaf economics spectrum, whereas wood density is linked to tree mortality. No preselection of growth strategies is taking place, because individuals with unique trait combinations are uniformly distributed at tree establishment. We validate the modeled trait distributions by empirical trait data and the modeled biomass by a remote sensing product along a climatic gradient. Including trait variability and trade‐offs successfully predicts natural trait distributions and achieves a more realistic representation of functional diversity at the local to regional scale. As sites of high climatic variability, the fringes of the Amazon promote trait divergence and the coexistence of multiple tree growth strategies, while lower plant trait diversity is found in the species‐rich center of the region with relatively low climatic variability. LPJmL‐FIT enables to test hypotheses on the effects of functional biodiversity on ecosystem functioning and to apply the DGVM to current challenges in ecosystem management from local to global scales, that is, deforestation and climate change effects.     

植物功能性状研究进展

植物功能性状是指植物体具有的与其定植、存活、生长和死亡紧密相关的一系列核心植物属性,且这些属性能够显著影响生态系统功能,并能够反映植被对环境变化的响应.越来越多的研究表明,相比大多数基于植物分类和数量的研究,植物功能性状在种群、群落和生态系统尺度上,都已成为解决重要生态学问题的可靠途径.本文回顾了植物功能性状研究的发展历程,总结了近10年来基于植物功能性状研究的前沿科学问题,包括功能性状的全球分布格局和内在关联,沿环境梯度的变化规律,功能多样性的定义及应用,与群落物种共存机制和群落动态变化的关系,与系统发育的关系,对生态系统功能的影响以及对各类干扰的影响和响应.尽管功能性状研究已经延伸到生态学领域的各个方面,有力推动了各个前沿科学问题的研究发展,仍然有很多值得关注和着重研究的方向.本文也对未来基于植物功能性状的研究,从性状测量和选取、研究方法以及研究方向上提出了展望,并指出,在当前全球气候变化背景下,功能性状也可应用于指导生物多样性保护和生态系统管理政策的制定.     

Potential trajectories of old‐growth Neotropical forest functional composition under climate change

Quantifying relationships between plant functional traits and abiotic gradients is valuable for evaluating potential responses of forest communities to climate change. However, the trajectories of change expected to occur in tropical forest functional characteristics as a function of future climate variation are largely unknown. We modeled community level trait values of Costa Rican rain forests as a function of current and future climate, and quantified potential changes in functional composition. We calculated per‐plot community weighted mean (CWM) trait values for leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC), leaf nitrogen (N) and phosphorus (P) content, and wood basic specific gravity (WSG), for tree and palm species in 127 0.25 ha plots. We modeled the response of CWM traits to current temperature and precipitation gradients using generalized additive modeling. We then predicted and mapped CWM traits values under current and future climate, and quantified potential changes under a global warming scenario (RCP8.5, year 2050). We calculated the area within the multi trait functional space occupied by forest plots under both current and future climate, and determined potential changes in functional space occupied by forest plots. Overall, precipitation predicted CWM traits better than temperature. Models indicated increases in CWM SLA, N and P, and a decrease in CWM LDMC under climate change. Lowland forest communities converged on a single direction of change towards more acquisitive CWM trait values, indicating a change in forest functional composition resulting from a changed climate. Functional space occupied by forest plots was reduced by 50% under the future climate. Functional composition changes may have further effects on forests ecosystem services. Assessing functional trait spatial‐gradients can help bridge the gap between species‐based biogeography and biogeochemical approaches to strengthen biodiversity and ecosystem services conservation efforts.     

Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants

Predicting ecosystem responses to global change is a major challenge in ecology. A critical step in that challenge is to understand how changing environmental conditions influence processes across levels of ecological organization. While direct scaling from individual to ecosystem dynamics can lead to robust and mechanistic predictions, new approaches are needed to appropriately translate questions through the community level. Species invasion, loss, and turnover all necessitate this scaling through community processes, but predicting how such changes may influence ecosystem function is notoriously difficult. We suggest that community‐level dynamics can be incorporated into scaling predictions using a trait‐based response–effect framework that differentiates the community response to environmental change (predicted by response traits) and the effect of that change on ecosystem processes (predicted by effect traits). We develop a response‐and‐effect functional framework, concentrating on how the relationships among species' response, effect, and abundance can lead to general predictions concerning the magnitude and direction of the influence of environmental change on function. We then detail several key research directions needed to better scale the effects of environmental change through the community level. These include (1) effect and response trait characterization, (2) linkages between response‐and‐effect traits, (3) the importance of species interactions on trait expression, and (4) incorporation of feedbacks across multiple temporal scales. Increasing rates of extinction and invasion that are modifying communities worldwide make such a research agenda imperative.     

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.     

Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model

Earth system models demonstrate large uncertainty in projected changes in terrestrial carbon budgets. The lack of inclusion of adaptive responses of vegetation communities to the environment has been suggested to hamper the ability of modeled vegetation to adequately respond to environmental change. In this study, variation in functional responses of vegetation has been added to an earth system model (ESM) based on ecological principles. The restriction of viable mean trait values of vegetation communities by the environment, called ‘habitat filtering’, is an important ecological assembly rule and allows for determination of global scale trait–environment relationships. These relationships were applied to model trait variation for different plant functional types (PFTs). For three leaf traits (specific leaf area, maximum carboxylation rate at 25 °C, and maximum electron transport rate at 25 °C), relationships with multiple environmental drivers, such as precipitation, temperature, radiation, and CO₂, were determined for the PFTs within the Max Planck Institute ESM. With these relationships, spatiotemporal variation in these formerly fixed traits in PFTs was modeled in global change projections (IPCC RCP8.5 scenario). Inclusion of this environment‐driven trait variation resulted in a strong reduction of the global carbon sink by at least 33% (2.1 Pg C yr^?1) from the 2nd quarter of the 21st century onward compared to the default model with fixed traits. In addition, the mid‐ and high latitudes became a stronger carbon sink and the tropics a stronger carbon source, caused by trait‐induced differences in productivity and relative respirational costs. These results point toward a reduction of the global carbon sink when including a more realistic representation of functional vegetation responses, implying more carbon will stay airborne, which could fuel further climate change.     

生态系统性状对宏生态研究的启示与挑战

功能性状在器官-物种-种群-群落-生态系统水平都具有其特定的适应或功能优化的意义,但目前对功能性状的测定和研究大都局限于器官或物种水平。然而,当前高速发展的宏生态新研究技术和方法(如遥感观测、通量观测、模型模拟)的研究对象都是在生态系统或区域尺度上,如何将传统功能性状与其相连结并服务于生态环境问题和全球变化问题是科学界的一大难题。为了解决传统性状与宏生态研究"尺度不统一"和"量纲不统一"的难题,研究人员最新发展了"生态系统性状(Ecosystem traits, ESTs)"概念体系,并从"理念-数据源-推导方法-应用"等多角度为后续研究提供了可借鉴案例。生态系统性状将传统性状研究从器官水平拓展到了群落和生态系统水平,以单位土地面积为基础构建了传统性状与宏生态研究(或地学研究)的桥梁,开启了性状研究从"器官到群落"、从"经典理论验证到宏观应用"的美好愿景,为多学科交叉提供了新思路。然而,它在方法学和数据源等方面还存在诸多问题与挑战;在此,我们呼吁相关专家从研究方法、概念体系和应用实践上赋予"生态系统性状"更强大的生命力,尤其从动物群落性状和微生物群落性状等角度。本文在深入解读先前生态性状概念体系、理论意义和潜在挑战的基础上,结合最新进展进行了补充,希望通过广泛讨论,完善生态系统性状概念体系,逐步形成"以性状为基础的生态系统生态学"新研究框架,切实推动宏生态研究和区域生态环境问题的解决。     

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a ‘Holy Grail’ in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community‐ and ecosystem‐level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait‐based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta‐analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.     

Stress from cold and drought as drivers of functional trait spectra in North American angiosperm tree assemblages

Understanding how environmental change alters the composition of plant assemblages, and how this in turn affects ecosystem functioning is a major challenge in the face of global climate change. Assuming that values of plant traits express species adaptations to the environment, the trait‐based approach is a promising way to achieve this goal. Nevertheless, how functional traits are related to species’ environmental tolerances and how trait spectra respond to broad‐scale environmental gradients remains largely unexplored. Here, we identify the main trait spectra for US angiosperm trees by testing hypotheses for the relationships between functional traits and species’ environmental tolerances to environmental stresses, as well as quantifying the environmental drivers of assemblage means and variances of these traits. We analyzed >74,000 community assemblages from the US Forest Inventory and Analysis using 12 functional traits, five traits expressing species’ environmental tolerances and 10 environmental variables. Results indicated that leaf traits, dispersal traits, and traits related to stem hydraulics were related to cold or drought tolerance, and their assemblage means were best explained by minimum temperatures. Assemblage means of traits related to shade tolerance (tree growth rate, leaf phosphorus content, and bark thickness) were best explained by aridity index. Surprisingly, aridity index, rather than minimum temperature, was the best predictors of assemblage variances of most traits, although these relationships were variable and weak overall. We conclude that temperature is likely to be the most important driver of functional community structure of North American angiosperm trees by selecting for optimum strategies along the cold and drought stress trade‐off. In turn, water availability primarily affects traits related to shade tolerance through its effect on forest canopy structure and vegetation openness.     

植物性状研究的机遇与挑战:从器官到群落

植物性状(Plant trait)或植物功能性状(Plant functional trait)通常是指植物对外界环境长期适应与进化后所表现出的可量度、且与生产力优化或环境适应等密切相关的属性。近几十年来,植物性状研究在性状-生产力、性状-养分、性状间相互关系、性状-群落结构维持等方面取得了卓越成就。然而,由于大多数性状调查都是以植物群落内优势种或亚优势种为对象,使其在探讨群落尺度的性状-功能关系、性状数据如何用于改进或优化模型、性状数据如何与遥感连接等问题时,存在空间尺度和量纲不匹配的极大挑战。为了破解上述难题,亟需发展新的、基于单位土地面积的群落性状(Community trait)概念体系、数据源和计算方法等,推动植物性状数据与快速发展的宏观生态学新技术(遥感、模型和通量观测等)相结合,既拓展了植物性状研究范畴,又可推动其更好地服务于区域生态环境问题的解决。所定义的群落性状(如叶片氮含量、磷含量、比叶面积、气孔密度、叶绿素含量等),是在充分考虑群落内所有物种的性状实测数据,再结合比叶面积、生物量异速生长方程和群落结构数据等,推导而成的基于单位土地面积的群落性状。受测试方法的影响,传统的直接算术平均法或相对生物量加权平均法所获得的群落水平的植物性状(如叶片氮含量g/kg或%),虽然可以有效地探讨群落结构维持机制,由于无法实现对群落性状在量纲上向单位土地面积转换,使它很难与模型和遥感数据相匹配。基于单位土地面积的群落性状,可在空间尺度匹配(或量纲匹配)的前提下实现个体水平测定的植物性状数据与生态模型和遥感观测相联系,更好地探讨区域尺度下自然生态系统结构和功能的关系及其对全球变化的响应与适应。同时,它也可更好地建立群落水平的性状-功能的定量关系(非物种水平),为更好地探讨自然群落结构维持机制和生产力优化机制提供了新思路。     

Global environmental change effects on ecosystems: the importance of land‐use legacies

One of the major challenges in ecology is to predict how multiple global environmental changes will affect future ecosystem patterns (e.g. plant community composition) and processes (e.g. nutrient cycling). Here, we highlight arguments for the necessary inclusion of land‐use legacies in this endeavour. Alterations in resources and conditions engendered by previous land use, together with influences on plant community processes such as dispersal, selection, drift and speciation, have steered communities and ecosystem functions onto trajectories of change. These trajectories may be modulated by contemporary environmental changes such as climate warming and nitrogen deposition. We performed a literature review which suggests that these potential interactions have rarely been investigated. This crucial oversight is potentially due to an assumption that knowledge of the contemporary state allows accurate projection into the future. Lessons from other complex dynamic systems, and the recent recognition of the importance of previous conditions in explaining contemporary and future ecosystem properties, demand the testing of this assumption. Vegetation resurvey databases across gradients of land use and environmental change, complemented by rigorous experiments, offer a means to test for interactions between land‐use legacies and multiple environmental changes. Implementing these tests in the context of a trait‐based framework will allow biologists to synthesize compositional and functional ecosystem responses. This will further our understanding of the importance of land‐use legacies in determining future ecosystem properties, and soundly inform conservation and restoration management actions.     

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‐based ecology of terrestrial arthropods

In focusing on how organisms' generalizable functional properties (traits) interact mechanistically with environments across spatial scales and levels of biological organization, trait‐based approaches provide a powerful framework for attaining synthesis, generality and prediction. Trait‐based research has considerably improved understanding of the assembly, structure and functioning of plant communities. Further advances in ecology may be achieved by exploring the trait–environment relationships of non‐sessile, heterotrophic organisms such as terrestrial arthropods, which are geographically ubiquitous, ecologically diverse, and often important functional components of ecosystems. Trait‐based studies and trait databases have recently been compiled for groups such as ants, bees, beetles, butterflies, spiders and many others; however, the explicit justification, conceptual framework, and primary‐evidence base for the burgeoning field of ‘terrestrial arthropod trait‐based ecology’ have not been well established. Consequently, there is some confusion over the scope and relevance of this field, as well as a tendency for studies to overlook important assumptions of the trait‐based approach. Here we aim to provide a broad and accessible overview of the trait‐based ecology of terrestrial arthropods. We first define and illustrate foundational concepts in trait‐based ecology with respect to terrestrial arthropods, and justify the application of trait‐based approaches to the study of their ecology. Next, we review studies in community ecology where trait‐based approaches have been used to elucidate how assembly processes for terrestrial arthropod communities are influenced by niche filtering along environmental gradients (e.g. climatic, structural, and land‐use gradients) and by abiotic and biotic disturbances (e.g. fire, floods, and biological invasions). We also review studies in ecosystem ecology where trait‐based approaches have been used to investigate biodiversity–ecosystem function relationships: how the functional diversity of arthropod communities relates to a host of ecosystem functions and services that they mediate, such as decomposition, pollination and predation. We then suggest how future work can address fundamental assumptions and limitations by investigating trait functionality and the effects of intraspecific variation, assessing the potential for sampling methods to bias the traits and trait values observed, and enhancing the quality and consolidation of trait information in databases. A roadmap to guide observational trait‐based studies is also presented. Lastly, we highlight new areas where trait‐based studies on terrestrial arthropods are well positioned to advance ecological understanding and application. These include examining the roles of competitive, non‐competitive and (multi‐)trophic interactions in shaping coexistence, and macro‐scaling trait–environment relationships to explain and predict patterns in biodiversity and ecosystem functions across space and time. We hope this review will spur and guide future applications of the trait‐based framework to advance ecological insights from the most diverse eukaryotic organisms on Earth.