Manipulating community assemblages to achieve functional targets is a key component of restoring degraded ecosystems. The response‐and‐effect trait framework provides a conceptual foundation for ...translating restoration goals into functional trait targets, but a quantitative framework has been lacking for translating trait targets into assemblages of species that practitioners can actually manipulate. This study describes new trait‐based models that can be used to generate ranges of species abundances to test theories about which traits, which trait values and which species assemblages are most effective for achieving functional outcomes. These models are generalisable, flexible tools that can be widely applied across many terrestrial ecosystems. Examples illustrate how the framework generates assemblages of indigenous species to (1) achieve desired community responses by applying the theories of environmental filtering, limiting similarity and competitive hierarchies, or (2) achieve desired effects on ecosystem functions by applying the theories of mass ratios and niche complementarity. Experimental applications of this framework will advance our understanding of how to set functional trait targets to achieve the desired restoration goals. A trait‐based framework provides restoration ecology with a robust scaffold on which to apply fundamental ecological theory to maintain resilient and functioning ecosystems in a rapidly changing world.
Summary
Plants are multifaceted organisms that have evolved numerous solutions to the problem of establishing, growing and reproducing with limited resources. The intrinsic dimensionality of plant ...traits is the minimum number of independent axes of variation that adequately describes the functional variation among plants and is therefore a fundamental quantity in comparative plant ecology. Given the large number of functional traits that are measured on plants, the dimensionality of plant form and function is potentially vast.
A variety of linear and nonlinear methods were used to estimate the intrinsic dimensionality of three large trait data sets. The results of these analyses indicate that while the dimensionality of plant traits is generally larger than we have admitted in the past, it does not exceed six in the most comprehensive data set.
The dimensionality of plant form and function is a blessing, not a curse. The higher the intrinsic dimension of traits in an analysis, the more easily our models will be able to accurately discriminate species in trait space and therefore be able to predict species distributions and abundances. Recent analyses indicate that the ability to predict community composition increases rapidly with additional traits, but reaches a plateau after four to eight traits.
Synthesis. There appears to be a tractable upper limit to the dimensionality of plant traits. To optimize research efficiency for advancing our understanding of trait‐based community assembly, ecologists should minimize the number of traits while maximizing the number of dimensions, because including multiple correlated traits does not yield dividends and including more than eight traits leads to diminishing returns. It is recommended to measure traits from multiple organs whenever possible, especially leaf, stem, root and flowering traits, given their consistent performance in explaining community assembly across different ecosystems.
There appears to be a tractable upper limit to the dimensionality of plant traits. To optimize research efficiency for advancing our understanding of trait‐based community assembly, ecologists should minimise the number of traits while maximising the number of dimensions, because including multiple correlated traits does not yield dividends and including more than eight traits leads to diminishing returns. It is recommended to measure traits from multiple organs whenever possible, especially leaf, stem, root, and flowering traits, given their consistent performance in explaining community assembly across different ecosystems.
•Integrating fitness into community ecology will improve trait-based predictions.•Dynamic adaptive landscapes link phenotypes to fitness across environments.•Fitness is a function of multidimensional ...phenotype–environment interactions.•Intraspecific trait covariation constrains environmental niche breadth.
Phenotypic traits influence species distributions, but ecology lacks established links between multidimensional phenotypes and fitness for predicting species responses to environmental change. The common focus on single traits rather than multiple trait combinations limits our understanding of their adaptive value, and intraspecific trait covariation has been neglected in ecology despite its importance in evolutionary theory and its likely impact on species distributions. Here, we extend the adaptive landscape framework to ecological sorting of multidimensional phenotypes across environments and discuss how two analytical approaches can be used to quantify fitness as a function of the interaction between the phenotype and the environment. We encourage ecologists to consider how phenotypic integration will constrain species responses to environmental change.
1. The internal cycling of nitrogen (N) is a critical process in terrestrial ecosystems. Nitrification occurs when soil microbes convert ammonium to nitrate. Nitrification is known to be regulated by ...abiotic soil properties, but less is known about how plant communities influence this important ecosystem function. 2. Two contrasting hypotheses propose mechanisms for how communities influence ecosystem processes. The ‘mass ratio hypothesis’ proposes that dominant species control ecosystem processes, e.g. communities dominated by plants with high leaf N content may increase rates of N cycling. The ‘diversity hypothesis’ proposes that community diversity controls ecosystem processes, e.g. diverse communities may increase rates of N cycling by providing a more consistent supply of organic N as variable litter qualities break down at different rates. Each hypothesis was simultaneously evaluated using structural equation modelling in the context of a ponderosa pine forest. 3. The first principle component extracted from a species–trait matrix captured interspecific covariation in leaf traits such as specific leaf area and leaf N content. This ‘leaf economics spectrum’was scaled to the community‐level by calculating community‐weighted mean leaf trait axis scores. Quadratic entropy was used as the index of understorey functional diversity to capture variation in functional traits within the community. 4. Structural equation modelling results suggest that soil factors, such as pH, temperature, C:N mass ratio and total N were the dominant factors exerting direct control over nitrification potential. Understorey leaf traits were positively associated with nitrification potential, suggesting that high‐quality litter inputs increase rates of nitrification. The negative indirect effects of pine abundance on nitrification mediated through soil properties were much stronger than the direct effect of understorey leaf traits. 5. Synthesis. The leaf economics spectrum is an important gradient of functional trait variation that has consequences for internal N cycling. Nitrification potential was more strongly linked to dominant leaf traits than to functional diversity, thereby lending more support to the mass ratio hypothesis. However, functional diversity still explained some of the observed variation in biomass production. Leaf traits can be used to understand how internal N cycling rates will be affected by changes in vegetation structure.
Plant strategies are phenotypes shaped by natural selection that enable populations to persist in a given environment. Plant strategy theory is essential for understanding the assembly of plant ...communities, predicting plant responses to climate change, and enhancing the restoration of our degrading biosphere. However, models of plant strategies vary widely and have tended to emphasize either functional traits or life-history traits at the expense of integrating both into a general framework to improve our ecological and evolutionary understanding of plant form and function. Advancing our understanding of plant strategies will require investment in two complementary research agendas that together will unify functional ecology and population ecology. First, we must determine what is phenotypically possible by quantifying the dimensionality of plant traits. This step requires dense taxonomic sampling of traits on species representing the broad diversity of phylogenetic clades, environmental gradients, and geographical regions found across Earth. It is important that we continue to sample traits locally and share data globally to fill biased gaps in trait databases. Second, we must test the power of traits for explaining species distributions, demographic rates, and population growth rates across gradients of resource limitation, disturbance regimes, temperature, vegetation density, and frequencies of other strategies. This step requires thoughtful, theory-driven empiricism. Reciprocal transplant experiments beyond the native range and synthetic demographic modelling are the most powerful methods to determine how trait-by-environment interactions influence fitness. Moving beyond easy-to-measure traits and evaluating the traits that are under the strongest ecological selection within different environmental contexts will improve our understanding of plant adaptations. Plant strategy theory is poised to (i) unpack the multiple dimensions of productivity and disturbance gradients and differentiate adaptations to climate and resource limitation from adaptations to disturbance, (ii) distinguish between the fundamental and realized niches of phenotypes, and (iii) articulate the distinctions and relationships between functional traits and life-history traits.
Trade‐offs maintain diversity and structure communities along environmental gradients. Theory indicates that if covariance among functional traits sets a limit on the number of viable trait ...combinations in a given environment, then communities with strong multidimensional trait constraints should exhibit low species diversity. We tested this prediction in winter annual plant assemblages along an aridity gradient using multilevel structural equation modelling. Univariate and multivariate functional diversity measures were poorly explained by aridity, and were surprisingly poor predictors of community richness. By contrast, the covariance between maximum height and seed mass strengthened along the aridity gradient, and was strongly associated with richness declines. Community richness had a positive effect on local neighbourhood richness, indicating that climate effects on trait covariance indirectly influence diversity at local scales. We present clear empirical evidence that declines in species richness along gradients of environmental stress can be due to increasing constraints on multidimensional phenotypes.
The promise of “trait-based” plant ecology is one of generalized prediction across organizational and spatial scales, independent of taxonomy. This promise is a major reason for the increased ...popularity of this approach. Here, we argue that some important foundational assumptions of trait-based ecology have not received sufficient empirical evaluation. We identify three such assumptions and, where possible, suggest methods of improvement: (i) traits are functional to the degree that they determine individual fitness, (ii) intraspecific variation in functional traits can be largely ignored, and (iii) functional traits show general predictive relationships to measurable environmental gradients.
1. Root, stem and leaf traits are thought to be functionally coordinated to maximize the efficiency of acquiring and using limited resources. However, evidence is mixed for consistent whole-plant ...trait coordination among woody plants, and we lack a clear understanding of the adaptive value of root traits along soil resource gradients. If fine roots are the below-ground analogue to leaves, then low specific root length (SRL) and high tissue density should be common on infertile soil. Here, we test the prediction that root, stem and leaf traits and relative growth rate respond in unison with soil fertility gradients. 2. We measured fine root, stem and leaf traits and relative growth rate on individual seedlings of 66 tree species grown in controlled conditions. Our objectives were (i) to determine whether multiple root traits align with growth rate, leaf and stem traits and with each other and (ii) to quantify the relationships between community-weighted mean root traits and two strong soil fertility gradients that differed in spatial extent and community composition. 3. At the species level, fast growth rates were associated with low root and stem tissue density and high specific leaf area. SRL and root diameter were not clearly related to growth rate and loaded on a separate principal component from the plant economic spectmm. 4. At the community level, growth rate was positively related to soil fertility, and root tissue density (RTD) and branching were negatively related to soil fertility. SRL was negatively related and root diameter was positively related to soil fertility on the large-scale gradient that included ectomycorrhizal angiosperms. 5. Synthesis. Root, stem and leaf tissue traits of tree seedlings are coordinated and influence fitness along soil fertility gradients. RTD responds in unison with above-ground traits to soil fertility gradients; however, root traits are multidimensional because SRL is orthogonal to the plant economic spectrum. In contrast to leaves, trees are not constrained in the way they construct fine roots: plants can construct high or low SRL roots of any tissue density. High RTD is the most consistent below-ground trait that reflects adaptation to infertile soil.
ABSTRACT
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.
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