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18 results for “multidimensional trait”
Data from: Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi
<p>Premise of the study While many studies have measured the aboveground responses of plants to mycorrhizal fungi at a single time point, little is known about how plants respond belowground or across time to mycorrhizal symbiosis. By measuring belowground responses as well as growth over time in many plant species, we create a more complete picture of how mycorrhizal fungi benefit their hosts. Methods We grew 26 prairie plant species with and without mycorrhizal fungi and measured fourteen functional traits measuring above and belowground tissue quality and quantity responses and changes in resource allocation. We used function-value trait (FVT) modeling to characterize changes in species growth rate when colonized. Key results While aboveground biomass responses were positive, the response of traits belowground were much more variable. Changes in aboveground biomass accounted for 60.8% of the variation in mycorrhizal responses, supporting the use of aboveground biomass response as the primary response trait. Responses belowground were not associated with aboveground responses and accounted for 18.3% of the variation. Growth responses over time were highly variable across species. Interestingly, none of the measured responses were phylogenetically conserved. Conclusions Mycorrhizal fungi increase plant growth in most scenarios, but the effects of these fungi belowground and across time are more complicated. This study highlights how differences in plant allocation priorities might affect how they utilize the benefits from mycorrhizal fungi. Identifying and characterizing these differences is a key step to understanding the effects of mycorrhizal mutualisms on whole plant physiology. </p>
Light limitation increases multidimensional trait evenness in phytoplankton populations
<p>This archive includes the final table used for the analyses and a readme file with some explanations.</p> <p>Our dataset consists of phytoplankton cell density and trait diversity estimates measured using scanning flow cytometry under different light intensity regimes.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
<p>Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal underlying abiotic and biotic drivers of community dynamics. Here we present a model-based predictive framework to quantify multidimensional trait distributions of communities (community trait niches), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait niches and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
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Data from: Measuring leaf and root functional traits uncovers multidimensionality of plant responses to arbuscular mycorrhizal fungi
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Precipitation, rather than temperature drives coordination of multidimensional root traits with ectomycorrhizal fungi in alpine coniferous forests
<ol> <li><span>The interactions between roots and mycorrhizal fungi are critical for our understanding of the multidimensional root economics space. Our knowledge on their relationships comes mainly from arbuscular mycorrhizal (AM) plants, and less is known about how roots are coordinated with ectomycorrhizal (ECM) fungal communities, especially in ECM-dominated alpine forests that are highly sensitive to climate change worldwide.</span></li> <li> <span>Here, we investigated the coordination between roots and ECM fungi and their drivers by measuring </span><span>multiple</span><span> root traits, ECM fungal </span><span>composition and environmental factors of 47 coniferous populations across the alpine coniferous forests </span><span>on the Tibetan Plateau.</span> </li> <li> <span>Our results reveal two independent fine-root trait dimensions, i.e., root foraging dimension and root uptake dimension, which are represented by</span> <span>root diameter-specific root length, root tissue density-root N concentration. Importantly, the hyphal exploration-type-based ECM foraging correlated significantly with both root foraging and root uptake dimension. Further, in the low-temperature plateau, it is precipitation-induced changes in soil moisture, soil nutrients and pH that drive the proportion of </span><span>longer-</span><span>distance hyphal exploration types to increase with </span><span>higher root </span><span>foraging </span><span>by higher </span><span>specific root length, and </span><span>to </span><span>decrease with </span><span>higher </span><span>uptake </span><span>by higher </span><span>root N concentration.</span> </li> <li> <span><em>Synthesis</em>.</span><span> The coordination of multidimensional root traits with ECM fungi differs greatly from the well-recognized pattern in AM plants that mycorrhizal fungi connect predominantly with root foraging and that roots and mycorrhizal fungi are temperature sensitive. These findings provide a new insight for our holistic understanding of how roots and mycorrhizal fungi vary collaboratively and hence driving plant community assembly and responses to the changing climate.</span> </li> </ol>
Inter- and intraspecific trait variation shape multidimensional trait overlap between two plant invaders and the invaded communities
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Data from: Different phylogenetic and environmental controls of first-order root morphological and nutrient traits: evidence of multidimensional root traits
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Precipitation, rather than temperature drives coordination of multidimensional root traits with ectomycorrhizal fungi in alpine coniferous forests
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Data from: Climate and soil nutrients differentially drive multidimensional fine root traits in ectomycorrhizal‐dominated alpine coniferous forests
<ol> <li><span><span>Fine root traits vary greatly with environmental changes, but the understanding of root-trait variation and its drivers is limited over broad geographical scales, especially for ectomycorrhizal (ECM)-dominated conifers in alpine forests. Herein, the covariation patterns of and environmental controls for fine root traits among ECM-dominated conifers were examined to test whether and how climate and soil nutrients differentially affect fine root trait variations.</span></span></li> <li><span><span>Eight traits of first- and second-order roots were measured, i.e., root diameter (RD), specific root length (SRL), branching intensity (BRI), root tissue density (RTD), mycorrhizal colonization rate (MCR), and concentrations of carbon (C), nitrogen (N) and phosphorus (P), across 76 alpine coniferous populations on the eastern Tibetan Plateau, China.</span></span></li> <li><span><span>Our results showed that variations of the fine root traits fell into two major dimensions: the first dimension (32.39% of the total variance) was mainly represented by RD and SRL, potentially conveying a tradeoff between root lifespan and efficiency of resource foraging; the second dimension (23.70% of the variance) represented coordinated variation for root nutrients (i.e., N and P) and RTD, which depicts the conservation-acquisition tradeoff in resource uptake, i.e., root economic spectrum (RES). Variations in RD and SRL were mainly driven by climatic variables, characterized by a significant increase in RD and a decrease in SRL with increasing mean annual precipitation. In contrast, variations in fine root nutrients (i.e., N and P) and RTD were primarily driven by soil fertility, showing a significant increase in root N and P concentrations but a decrease in RTD with increasing soil resource levels.</span></span></li> <li><span><span><i>Synthesis. </i>Our study clearly shows two distinct dimensions of the variation of fine root traits in ECM-dominated alpine coniferous forests, providing further evidence of the inherent multidimensionality of root traits. Moreover, our findings highlight different roles of climatic and soil variables in driving the variation of fine root traits, potentially leading to the multidimensionality of root traits. This study provides new insights for understanding and predicting shifts in plant belowground strategies in climate-sensitive alpine forests worldwide.</span></span></li> </ol>
Multidimensional trait morphology predicts ecology across ant lineages
<p>1. Understanding the link between ecology and morphology is a fundamental goal in biology. Ants are diverse terrestrial organisms, known to exhibit ecologically-driven morphological variation. While relationships between individual traits and ecologies have been identified, multidimensional interactions among traits and their cumulative predictive power remain unknown. Because selective pressures may generate convergent syndromes spanning multiple traits, we applied multivariate analyses across a wide sampling of taxa to assess ecomorphological variation in an integrative context.</p> <p>2. How well does morphology predict ecology? Moreover, are there quantitatively-supported ant ecomorphs? We investigated the links between trait morphology and ecology by assembling a morphometric dataset spanning over 160 species within 110 genera. Because ants occupy a wide range of ecologies, we compiled natural history data on nesting microhabitat, foraging stratum, and functional role into 35 defined niche combinations. This tripartite ecological classification and our morphological dataset were optimized under dimension reduction techniques including Principal Component Analysis, Principal Coordinate Analysis, Linear Discriminant Analysis, and Random Forest supervised machine learning.</p> <p>3. Our results describe ant ecomorphospace as comprising regions of shared, generalized morphology as well as unique phenotypic space associated with specialized ecologies. Dimension reduction and model-based approaches predict ecology with 77-85% accuracy and Random Forest analysis consistently outperforms LDA. While accounting for shared ancestry, we found eye, antennal scape, and leg morphology to be most informative in differentiating among ecologies. We also note some heterogeneity between trait significance in each ecological aspect (nesting niche, foraging niche, functional role). To increase the utility of ecomorphological classification we simplified our 35 observed niche combinations into 10 ecomorph syndromes, which were also predicted by morphology. The predictive power of these machine learning methods underscores the strong role that ecology has in convergently shaping overall body plan morphology across ant lineages. We include a pipeline for predictive ecomorphological modeling using morphometric data, which may be expanded with additional specimen-based and natural history data.</p>
Data from: Individual-level trait diversity concepts and indices to comprehensively describe community change in multidimensional trait space
Global environmental change can influence ecosystem processes directly or through changes in the trait composition of natural communities. Traits are individual-level features of organisms, and theory predicts that diversity in traits should relate to ecosystem processes. Validated indices that account for both intra- and interspecific trait variation in multidimensional trait space are lacking. In this article, we highlight how an individual-level perspective requires new concepts for trait diversity (TD) and we validate a set of measures suitable to study trait richness, evenness and divergence at the individual scale. First, we tested a selection of multivariate indices for trait richness, evenness and divergence from the literature (FRic, FEve, FDis and the Rao coefficient) using simulated and real individual-level data. We compared the observed changes in the tested indices with those predicted from their expected/required behaviour (i.e. increase or decrease under specific manipulation of community trait structure) and found unsatisfactory results only for FRic and FEve, whereas FDis and the Rao coefficient showed the expected changes. Therefore, we propose two novel concepts and related indices for individual-level trait richness (TOP = trait onion peeling) and evenness (TED = trait even distribution). TOP represents the sum of all successive convex hull areas touching all individuals (points) within a multidimensional trait distribution. TED is a measure of how evenly distributed are individuals within the multidimensional trait space. It is calculated comparing the probability distributions of pairwise distances between individuals and between points of a perfectly even reference distribution. We tested TOP and TED on the same simulated and real data as above, and results indicated appropriate behaviour for TOP (trait richness) and TED (trait evenness). By validating TD indices in an individual-level context, this study contributes to the expansion of functional ecology towards individual-level dynamics. Future comprehensive investigations of individual trait differences in natural communities may improve our understanding of the pathways by which environmental changes affect ecosystem functioning through biodiversity change.
Data from: Individual-level trait diversity concepts and indices to comprehensively describe community change in multidimensional trait space
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Data from: Climate and soil nutrients differentially drive multidimensional fine root traits in ectomycorrhizal‐dominated alpine coniferous forests
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Multidimensional trait morphology predicts ecology across ant lineages
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Data from: Multidimensional ecological analyses demonstrate how interactions between functional traits shape fitness and life history strategies
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Data from: Multidimensional analyses of physical performance reveal a size dependent trade-off between suites of traits
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Data from: Diversity in form and function: vertical distribution of soil fauna mediates multidimensional trait variation
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.