The distribution of seaweed forms and foundational assumptions in seaweed biology.

Seaweeds are the most phylogenetically diverse group of multicellular organisms and rank foremost among marine keystone species. Due to their taxonomic diversity and functional importance, previous studies have classified seaweeds into functional groups based on qualitative or semi-quantitative traits, such as seaweed form, anatomy, and thickness. Despite the widespread use of seaweed functional groups from basic marine ecology to coastal monitoring, it is not known how accurate such morphology-based proposals are in grouping seaweeds by their form. To address this uncertainty at the foundations of seaweed biology, we surveyed and gathered all available data on seaweed forms using PRISMA protocols. We used the surface area to volume ratio (SA:V), a quantitative and universal measure of seaweed form, to assess the distribution and diversity of seaweed morphology across 99 species from three phyla. We show that seaweed surface area to volume ratio values span 3.64 orders of magnitude and follow a continuous and exponential distribution, without any significant gaps or clusters. We also tested current functional group schemes based on morphology and anatomy and showed that only 30% to 38% of their groups showed any significant pairwise differences in morphology. Our results challenge the basis of the current functional group approach in seaweed biology and suggest that a trait-based framework based on quantitative and continuous measures of seaweed form could provide a simpler and more accurate alternative to functionally assess seaweed ecology and physiology, as well as its implications for coastal ecosystem management.

ZooTraits: An R shiny app for exploring animal trait data for ecological and evolutionary research.

Animal trait data are scattered across several datasets, making it challenging to compile and compare trait information across different groups. For plants, the TRY database has been an unwavering success for those ecologists interested in addressing how plant traits influence a wide variety of processes and patterns, but the same is not true for most animal taxonomic groups. Here, we introduce ZooTraits, a Shiny app designed to help users explore and obtain animal trait data for research in ecology and evolution. ZooTraits was developed to tackle the challenge of finding in a single site information of multiple trait datasets and facilitating access to traits by providing an easy-to-use, open-source platform. This app combines datasets centralized in the Open Trait Network, raw data from the AnimalTraits database, and trait information for animals compiled by Gonçalves-Souza et al. (2023, Ecology and Evolution 13, e10016). Importantly, the ZooTraits app can be accessed freely and provides a user-friendly interface through three functionalities that will allow users to easily visualize, compare, download, and upload trait data across the animal tree of life-ExploreTrait, FeedTrait, and GetTrait. By using ExploreTrait and GetTrait, users can explore, compare, and extract 3954 trait records from 23,394 species centralized in the Open Traits Network, and trait data for ~2000 species from the AnimalTraits database. The app summarizes trait information for numerous taxonomic groups within the Animal Kingdom, encompassing data from diverse aquatic and terrestrial ecosystems and various geographic regions worldwide. Moreover, ZooTraits enables researchers to upload trait information, serving as a hub for a continually expanding global trait database. By promoting the centralization of trait datasets and offering a platform for data sharing, ZooTraits is facilitating advancements in trait-based ecological and evolutionary studies. We hope that other trait databases will evolve to mirror the approach we have outlined here.

Impacts of climate timescale on the stability of trait-environment relationships.

Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait-environment model predictions if timescale is not accounted for. Here, the timescale dependence of trait-environment relationships is investigated by regressing in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales. We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub-optimal climate timescales reduces the accuracy of the resulting trait-environment relationships. This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long-term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait-environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.

Habitat traits and predation interact to drive abundance and body size patterns in associated fauna.

Habitat-forming organisms provide three-dimensional structure that supports abundant and diverse communities. Variation in the morphological traits of habitat formers will therefore likely influence how they facilitate associated communities, either via food and habitat provisioning, or by altering predator-prey interactions. These mechanisms, however, are typically studied in isolation, and thus, we know little of how they interact to affect associated communities. In response to this, we used naturally occurring morphological variability in the alga Sargassum vestitum to create habitat units of distinct morphotypes to test whether variation in the morphological traits (frond size and thallus size) of S. vestitum or the interaction between these traits affects their value as habitat for associated communities in the presence and absence of predation. We found morphological traits did not interact, instead having independent effects on epifauna that were negligible in the absence of predation. However, when predators were present, habitat units with large fronds were found to host significantly lower epifaunal abundances than other morphotypes, suggesting that large frond alga provided low-value refuge from predators. The presence of predators also influenced the size structure of epifaunal communities from habitat units of differing frond size, suggesting that the refuge value of S. vestitum was also related to epifauna body size. This suggests that habitat formers may chiefly structure associated communities by mediating size-selective predation, and not through habitat provisioning. Furthermore, these results also highlight that habitat traits cannot be considered in isolation, for their interaction with biotic processes can have significant implications for associated communities.

Frugivore-mediated seed dispersal in fragmented landscapes: Compositional and functional turnover from forest to matrix.

Seed dispersal by frugivores is a fundamental function for plant community dynamics in fragmented landscapes, where forest remnants are typically embedded in a matrix of anthropogenic habitats. Frugivores can mediate both connectivity among forest remnants and plant colonization of the matrix. However, it remains poorly understood how frugivore communities change from forest to matrix due to the loss or replacement of species with traits that are less advantageous in open habitats and whether such changes ultimately influence the composition and traits of dispersed plants via species interactions. Here, we close this gap by using a unique dataset of seed-dispersal networks that were sampled in forest patches and adjacent matrix habitats of seven fragmented landscapes across Europe. We found a similar diversity of frugivores, plants, and interactions contributing to seed dispersal in forest and matrix, but a high turnover (replacement) in all these components. The turnover of dispersed seeds was smaller than that of frugivore communities because different frugivore species provided complementary seed dispersal in forest and matrix. Importantly, the turnover involved functional changes toward larger and more mobile frugivores in the matrix, which dispersed taller, larger-seeded plants with later fruiting periods. Our study provides a trait-based understanding of frugivore-mediated seed dispersal through fragmented landscapes, uncovering nonrandom shifts that can have cascading consequences for the composition of regenerating plant communities. Our findings also highlight the importance of forest remnants and frugivore faunas for ecosystem resilience, demonstrating a high potential for passive forest restoration of unmanaged lands in the matrix.

Within-individual leaf trait variation increases with phenotypic integration in a subtropical tree diversity experiment.

Covariation of plant functional traits, that is, phenotypic integration, might constrain their variability. This was observed for inter- and intraspecific variation, but there is no evidence of a relationship between phenotypic integration and the functional variation within single plants (within-individual trait variation; WTV), which could be key to understand the extent of WTV in contexts like plant-plant interactions. We studied the relationship between WTV and phenotypic integration in c. 500 trees of 21 species in planted forest patches varying in species richness in subtropical China. Using visible and near-infrared spectroscopy (Vis-NIRS), we measured nine leaf morphological and chemical traits. For each tree, we assessed metrics of single and multitrait variation to assess WTV, and we used plant trait network properties based on trait correlations to quantify phenotypic integration. Against expectations, strong phenotypic integration within a tree led to greater variation across leaves. Not only this was true for single traits, but also the dispersion in a tree's multitrait hypervolume was positively associated with tree's phenotypic integration. Surprisingly, we only detected weak influence of the surrounding tree-species diversity on these relationships. Our study suggests that integrated phenotypes allow the variability of leaf phenotypes within the organism and supports that phenotypic integration prevents maladaptive variation.

Community form, function and phylogenetic diversity respond differently across microhabitat and recovery gradients.

Research Highlight: Hoenle, P. O., Staab, M., Donoso, D. A., Argoti, A., & Blüthgen, N. (2023). Stratification and recovery time jointly shape ant functional reassembly in a neotropical forest. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13896. Space, time and abiotic variation are primary axes across investigations of community ecology and disturbed ecosystems offer tractable systems for assessing their relative impact. While recovering forests can act as isolated case studies in understanding community assembly, it is not well understood how individual microhabitats respond to recovery and ultimately shape community attributes. Hoenle et al. (2023) leverage the ubiquity and microhabitat-specific diversity of ants across a gradient from active agricultural sites to old-growth forest and assess how recovery and stratification together shape communities. The authors find distinct stratification across phylogenetic, functional and trait diversity as forest recovery time increases, while also recovering unique recovery trajectories contingent on trait sampling. While stratified, phylogenetic and functional diversity did not increase along this recovery gradient. Ten out of 13 sampled traits were jointly influenced by both stratification and recovery time. In contrast to intuitive predictions, a majority of trait means converged throughout the recovery period. Results highlight the multifaceted nature of recovery-based community assembly and the capacity of multidimensional sampling to uncover surprising patterns in ecologically diverse lineages.

Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field.

Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.

Towards multivariate functional trait syndromes: Predicting foliar water uptake in trees.

Analysis of functional traits is a cornerstone of ecology, yet individual traits seldom explain useful amounts of variation in species distribution or climatic tolerance, and their functional significance is rarely validated experimentally. Multivariate suites of interacting traits could build an understanding of ecological processes and improve our ability to make sound predictions of species success in our rapidly changing world. We use foliar water uptake capacity as a case study because it is increasingly considered to be a key functional trait in plant ecology due to its importance for stress-tolerance physiology. However, the traits behind the trait, that is, the features of leaves that determine variation in foliar water uptake rates, have not been assembled into a widely applicable framework for uptake prediction. Focusing on trees, we investigated relationships among 25 structural traits, leaf osmotic potential (a source of free energy to draw water into leaves), and foliar water uptake in 10 diverse angiosperm and conifer species. We identified consistent, multitrait "uptake syndromes" for both angiosperm and conifer trees, with differences in key traits revealing suspected differences in the water entry route between these two clades and an evolutionarily significant divergence in the function of homologous structures. A literature review of uptake-associated functional traits, which largely documents similar univariate relationships, provides additional support for our proposed "uptake syndrome." Importantly, more than half of shared traits had opposite-direction influences on the capacity of leaves to absorb water in angiosperms and conifers. Taxonomically targeted multivariate trait syndromes provide a useful tool for trait selection in ecological research, while highlighting the importance of micro-traits and the physiological verification of their function for advancing trait-based ecology.

Multi-trait analysis reveals large interspecific differences for phytoplankton in response to thermal change.

Understanding the responses of multiple traits in phytoplankton, and identifying interspecific variabilities to thermal changes is crucial for predicting the impacts of ocean warming on phytoplankton distributions and community structures in future scenarios. Here, we applied a trait-based approach by examining the patterns in multi-traits variations (eight traits) and interspecific variabilities in five phytoplankton species (two diatoms, three dinoflagellates) in response to a wide range of ecologically relevant temperatures (14-30 °C). Our results show large inter-traits and interspecific variabilities of thermal reaction norms in all of the tested traits. We also found that the interspecific variability exceeded the variations induced by thermal changes. Constrained variations and trade-offs between traits both revealed substantial interspecific differences and shifted as the temperature changed. Our study helps to understand the species-specific response patterns of multiple traits to ocean warming and to investigate the implications of these responses in the context of global change.

Bringing light onto the Raunkiæran shortfall: A comprehensive review of traits used in functional animal ecology.

Trait-based approaches elucidate the mechanisms underlying biodiversity response to, or effects on, the environment. Nevertheless, the Raunkiæran shortfall-the dearth of knowledge on species traits and their functionality-presents a challenge in the application of these approaches. We conducted a systematic review to investigate the trends and gaps in trait-based animal ecology in terms of taxonomic resolution, trait selection, ecosystem type, and geographical region. In addition, we suggest a set of crucial steps to guide trait selection and aid future research to conduct within and cross-taxon comparisons. We identified 1655 articles using virtually all animal groups published from 1999 to 2020. Studies were concentrated in vertebrates, terrestrial habitats, the Palearctic realm, and mostly investigated trophic and habitat dimensions. Additionally, they focused on response traits (79.4%) and largely ignored intraspecific variation (94.6%). Almost 36% of the data sets did not provide the rationale behind the selection of morphological traits. The main limitations of trait-based animal ecology were the use of trait averages and a rare inclusion of intraspecific variability. Nearly one-fifth of the studies based only on response traits conclude that trait diversity impacts ecosystem processes or services without justifying the connection between them or measuring them. We propose a guide for standardizing trait collection that includes the following: (i) determining the type of trait and the mechanism linking the trait to the environment, ecosystem, or the correlation between the environment, trait, and ecosystem, (ii) using a "periodic table of niches" to select the appropriate niche dimension to support a mechanistic trait selection, and (iii) selecting the relevant traits for each retained niche dimension. By addressing these gaps, trait-based animal ecology can become more predictive. This implies that future research will likely focus on collaborating to understand how environmental changes impact animals and their capacity to provide ecosystem services and goods.

Temperature and nutrients drive eco-phenotypic dynamics in a microbial food web.

Anthropogenic increases in temperature and nutrient loads will likely impact food web structure and stability. Although their independent effects have been reasonably well studied, their joint effects-particularly on coupled ecological and phenotypic dynamics-remain poorly understood. Here we experimentally manipulated temperature and nutrient levels in microbial food webs and used time-series analysis to quantify the strength of reciprocal effects between ecological and phenotypic dynamics across trophic levels. We found that (1) joint-often interactive-effects of temperature and nutrients on ecological dynamics are more common at higher trophic levels, (2) temperature and nutrients interact to shift the relative strength of top-down versus bottom-up control, and (3) rapid phenotypic change mediates observed ecological responses to changes in temperature and nutrients. Our results uncover how feedback between ecological and phenotypic dynamics mediate food web responses to environmental change. This suggests important but previously unknown ways that temperature and nutrients might jointly control the rapid eco-phenotypic feedback that determine food web dynamics in a changing world.

Plant Functional Dispersion, Vulnerability and Originality Increase Arthropod Functions from a Protected Mountain Mediterranean Area in Spring.

Plant diversity often contributes to the shape of arthropod communities, which in turn supply important ecosystem services. However, the current biodiversity loss scenario, particularly worrying for arthropods, constitutes a threat for sustainability. From a trait-based ecology approach, our goal was to evaluate the bottom-up relationships to obtain a better understanding of the conservation of the arthropod function within the ecosystem. Specifically, we aim: (i) to describe the plant taxonomic and functional diversity in spring within relevant habitats of a natural protected area from the Mediterranean basin; and (ii) to evaluate the response of the arthropod functional community to plants. Plants and arthropods were sampled and identified, taxonomic and functional indices calculated, and the plant-arthropod relationships analyzed. Generally, oak forests and scrublands showed a higher plant functional diversity while the plant taxonomic richness was higher in grasslands and chestnut orchards. The abundance of arthropod functional groups increased with the plant taxonomic diversity, functional dispersion, vulnerability and originality, suggesting that single traits (e.g., flower shape or color) may be more relevant for the arthropod function. Results indicate the functional vulnerability of seminatural habitats, the relevance of grasslands and chestnut orchards for arthropod functions and pave the way for further studies about plant-arthropod interactions from a trait-based ecology approach.

How to improve scaling from traits to ecosystem processes.

Scaling approaches in ecology assume that traits are the main attributes by which organisms influence ecosystem functioning. However, several recent empirical papers have found only weak links between traits and ecosystem functioning, questioning the usefulness of trait-based ecology (TBE). We argue that these studies often suffer from one or more widespread misconceptions. Specifically, these studies often (i) conflict with the conceptual foundations of TBE, (ii) lack theory- or hypothesis-driven selection and use of traits, (iii) tend to ignore intraspecific variation, and (iv) use experimental or study designs that are not well suited to make strong tests of TBE assumptions. Addressing these aspects could significantly improve our ability to scale from traits to ecosystem functioning.

Habitat configurations shape the trophic and energetic dynamics of reef fishes in a tropical-temperate transition zone: implications under a warming future.

Understanding the extent to which species' traits mediate patterns of community assembly is key to predict the effect of natural and anthropogenic disturbances on ecosystem functioning. Here, we apply a trait-based community assembly framework to understand how four different habitat configurations (kelp forests, Sargassum spp. beds, hard corals, and turfs) shape the trophic and energetic dynamics of reef fish assemblages in a tropical-temperate transition zone. Specifically, we tested (i) the degree of trait divergence and convergence in each habitat, (ii) which traits explained variation in species' abundances, and (iii) differences in standing biomass (kg ha-1), secondary productivity (kg ha-1 day-1) and turnover (% day-1). Fish assemblages in coral and kelp habitats displayed greater evidence of trait convergence, while turf and Sargassum spp. habitats displayed a higher degree of trait divergence, a pattern that was mostly driven by traits related to resource use and thermal affinity. This filtering effect had an imprint on the trophic and energetic dynamics of reef fishes, with turf habitats supporting higher fish biomass and productivity. However, these gains were strongly dependent on trophic guild, with herbivores/detritivores disproportionately contributing to among-habitat differences. Despite these perceived overall gains, turnover was decoupled for fishes that act as conduit of energy to higher trophic levels (i.e. microinvertivores), with coral habitats displaying higher rates of fish biomass replenishment than turf despite their lower productivity. This has important implications for biodiversity conservation and fisheries management, questioning the long-term sustainability of ecological processes and fisheries yields in increasingly altered marine habitats.

Erratum: A perspective on plant phenomics: Coupling deep learning and near-infrared spectroscopy.

[This corrects the article DOI: 10.3389/fpls.2022.836488.].

Testing the association of relative growth rate and adaptation to climate across natural ecotypes of Arabidopsis.

Ecophysiologists have reported a range of relationships, including intrinsic trade-offs across and within species between plant relative growth rate in high resource conditions (RGR) vs adaptation to tolerate cold or arid climates, arising from trait-based mechanisms. Few studies have considered ecotypes within a species, in which the lack of a trade-off would contribute to a wide species range and resilience to climate change. For 15 ecotypes of Arabidopsis thaliana in a common garden we tested for associations between RGR vs adaptation to cold or dry native climates and assessed hypotheses for its mediation by 15 functional traits. Ecotypes native to warmer, drier climates had higher leaf density, leaf mass per area, root mass fraction, nitrogen per leaf area and carbon isotope ratio, and lower osmotic potential at full turgor. Relative growth rate was statistically independent of the climate of the ecotype native range and of individual functional traits. The decoupling of RGR and cold or drought adaptation in Arabidopsis is consistent with multiple stress resistance and avoidance mechanisms for ecotypic climate adaptation and would contribute to the species' wide geographic range and resilience as the climate changes.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.

Chemical phenotype as important and dynamic niche dimension of plants.

Niche theory considering the traits of species and individuals provides a powerful tool to integrate ecology and evolution of species. In plant ecology, morphological and physiological traits are commonly considered as niche dimensions, whereas phytochemical traits are mostly neglected in this context despite their pivotal functions in plant responses to their environment and in mediating interactions. The diversity of plant phytochemicals can thus mediate three key processes: niche choice, conformance and construction. Here, we integrate frameworks from niche theory with chemical ecology and argue that plants use their individual-specific diversity in phytochemicals (chemodiversity) for different niche realization processes. Our concept has important implications for ecosystem processes and stability and increases the predictive ability of chemical ecology.

AVONET: morphological, ecological and geographical data for all birds.

Functional traits offer a rich quantitative framework for developing and testing theories in evolutionary biology, ecology and ecosystem science. However, the potential of functional traits to drive theoretical advances and refine models of global change can only be fully realised when species-level information is complete. Here we present the AVONET dataset containing comprehensive functional trait data for all birds, including six ecological variables, 11 continuous morphological traits, and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database. The AVONET dataset provides the most detailed picture of continuous trait variation for any major radiation of organisms, offering a global template for testing hypotheses and exploring the evolutionary origins, structure and functioning of biodiversity.