Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
62
datasets available to search
ShareScore release 0.9.0
Dataset results
62 results for “trait network”
Simplification of plant trait networks among communities along a climatic aridity gradient
Open the record for dataset details and reuse information.
Linking trait network parameters with plant growth across light gradients and seasons
Open the record for dataset details and reuse information.
Supplementary material for: Accounting for within-species variation in continuous trait evolution on a phylogenetic network
Open the record for dataset details and reuse information.
Mapping trait versus species turnover reveals spatiotemporal variation in functional redundancy and network robustness in a plant‐pollinator community
Open the record for dataset details and reuse information.
The influence of biogeographical and evolutionary histories on morphological trait-matching and resource specialization in mutualistic hummingbird-plant networks
Open the record for dataset details and reuse information.
Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
<p>Plant immunity is often negatively impacted by heat stress. However, the underlying molecular mechanisms remain poorly characterized. Based on a genome-wide association mapping approach, this study aims to identify in <em>Arabidopsis thaliana</em> the genetic bases of robust resistance mechanisms to the devastating pathogen<em> Ralstonia solanacearum</em> under heat stress. A local mapping population was phenotyped against the <em>R. solanacearum</em> GMI1000 strain at 27 and 30 °C. To obtain a precise description of the genetic architecture underlying natural variation of quantitative disease resistance (QDR), we applied a genome-wide local score analysis. Alongside an extensive genetic variation found in this local population at both temperatures, we observed a playful dynamics of quantitative trait loci along the infection stages. In addition, a complex genetic network of interacting loci could be detected at 30 °C. As a first step to investigate the underlying molecular mechanisms, the atypical meiotic cyclin <em>SOLO DANCERS</em> gene was validated by a reverse genetic approach as involved in QDR to <em>R. solanacearum </em>at 30 °C. In the context of climate change, the complex genetic architecture underlying QDR under heat stress in a local mapping population revealed candidate genes with diverse molecular functions.</p>
Data from: Incorporating alternative interaction modes, forbidden links and trait-based mechanisms increases the minimum trait dimensionality of ecological networks
<ol> <li>Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, i.e. their 'minimum dimensionality'. Existing methods for estimating minimum dimensionality often lack three features associated with increased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated 'forbidden links' and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a 'minimum mechanistic dimensionality' measure, accounting for these three features.</li> <li>The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey species via a typical predation mode, in each step of the predation sequence the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.</li> <li>We applied our method to 658 published empirical ecological networks including primary consumption, predator–prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), 'forbidden interactions' as trait-mediated interaction outcomes (92%), or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.</li> <li>Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes, or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics.</li> </ol>
Linking species-level network metrics to flower traits and plant fitness
1. Theoretical models indicate that the structure of plant-pollinator networks has important implications for species' reproduction and survival. However, despite the growing information on the mechanisms underlying such structure, we are still far from being able to predict the functional consequences of species' structural positions in such networks. From the plants' perspective, species position and roles in pollination networks might be related to traits describing flower attractiveness, availability, and dependence on pollinators. In turn, both, network metrics and species traits might influence plant fitness. 2. During two field seasons, we collected data of the 23 most abundant plant species from a rich coastal community in order to evaluate the association between population and floral traits (floral abundance at the population level and flowers/individual, flower shape and size, flowering length, nectar volume, pollinator dependence), species-level network metrics (linkage level, specialization –d'–, weighted closeness centrality, network roles related to modularity) and plant fitness (seeds/flower, seed weight). 3. Flowering length, flower size, flower abundance and pollinator dependence were positively related to increased generalization, as measured with different indices. More abundant species and those with larger flowers showed higher linkage levels (i.e. higher number of pollinator species), whereas longer flowering periods were negatively related to d' and positively related to closeness centrality and important roles in the network. Likewise, plants more dependent on pollinators occupied more central positions in the network. Furthermore, species' centrality in the networks was significantly associated with plant fitness. Specifically, central species in the network produced more and heavier seeds than the others. However, other plant traits, such as flower size and pollinator dependence had additional direct effects on seed production. 4. Synthesis. Our study highlights how population and floral traits define the positions and roles of species structuring the pollination communities. Moreover, the relationships between network metrics and plant reproduction indicate, for the first time, the functional implications of these structural positions at the inter-specific level of community assembly.
Data from: Species traits and abundances predict metrics of plant–pollinator network structure, but not pairwise interactions
Plant–pollinator mutualistic networks represent the ecological context of foraging (for pollinators) and reproduction (for plants and some pollinators). Plant–pollinator visitation networks exhibit highly conserved structural properties across diverse habitats and species assemblages. The most successful hypotheses to explain these network properties are the neutrality and biological constraints hypotheses, which posit that species interaction frequencies can be explained by species relative abundances, and trait mismatches between potential mutualists respectively. However, previous network analyses emphasize the prediction of metrics of qualitative network structure, which may not represent stringent tests of these hypotheses. Using a newly documented temporally explicit alpine plant–pollinator visitation network, we show that metrics of both qualitative and quantitative network structure are easy to predict, even by models that predict the identity or frequency of species interactions poorly. A variety of phenological and morphological constraints as well as neutral interactions successfully predicted all network metrics tested, without accurately predicting species observed interactions. Species phenology alone was the best predictor of observed interaction frequencies. However, all models were poor predictors of species pairwise interaction frequencies, suggesting that other aspects of species biology not generally considered in network studies, such as reproduction for dipterans, play an important role in shaping plant–pollinator visitation network structure at this site. Future progress in explaining the structure and dynamics of mutualistic networks will require new approaches that emphasize accurate prediction of species pairwise interactions rather than network metrics, and better reflect the biology underlying species interactions.
Data from: Quantifying species contributions to ecosystem processes: a global assessment of functional trait and phylogenetic metrics across avian seed-dispersal networks
Quantifying the role of biodiversity in ecosystems not only requires understanding the links between species and the ecological functions and services they provide, but also how these factors relate to measurable indices, such as functional traits and phylogenetic diversity. However, these relationships remain poorly understood, especially for heterotrophic organisms within complex ecological networks. Here, we assemble data on avian traits across a global sample of mutualistic plant–frugivore networks to critically assess how the functional roles of frugivores are associated with their intrinsic traits, as well as their evolutionary and functional distinctiveness. We find strong evidence for niche complementarity, with phenotypically and phylogenetically distinct birds interacting with more unique sets of plants. However, interaction strengths—the number of plant species dependent on a frugivore—were unrelated to evolutionary or functional distinctiveness, largely because distinct frugivores tend to be locally rare, and thus have fewer connections across the network. Instead, interaction strengths were better predicted by intrinsic traits, including body size, gape width and dietary specialization. Our analysis provides general support for the use of traits in quantifying species ecological functions, but also highlights the need to go beyond simple metrics of functional or phylogenetic diversity to consider the multiple pathways through which traits may determine ecological processes.
Data from: Influence of the honeybee and trait similarity on the effect of a non-native plant on pollination and network rewiring
Introduced entomophilous non-native plants usually become well integrated into the diet of generalist pollinators. This integration can affect the entire recipient plant–pollinator network. Effects vary from facilitative to competitive, and understanding the factors that govern such variability is one of the fundamental goals in invasion ecology. Species traits determine the linking patterns between plant and pollinator species. Therefore, trait similarity among plants or among pollinators might modulate how they affect each other. We conducted a flower removal experiment to investigate the effects of the non-native entomophilous legume Hedysarum coronarium on the pollination patterns of a Mediterranean shrubland plant–pollinator network. Specifically, we explored whether effects were influenced by similarity with the resident plant species in flower morphology (papilionate vs. non-papilionate), and whether effects on the pollinator community were influenced by similarity in functional group with its main visitor species (bees vs. non-bees). In addition, we explored whether Hedysarum had an effect on the identity of interactions. For this purpose, we calculated the interaction rewiring, that is the number of plant–pollinator interactions that were gained or lost after invasion. Hedysarum was well integrated into the diet of 15 generalist pollinators having the honeybee as its main visitor species. Such integration did not affect visitation rates, normalized degree (i.e. proportion of pollinators they are visited by) nor niche overlap (i.e. proportion of plant species they share pollinators with) of plants, irrespective of their flower morphology. Only the proportion of honeybee visits to resident plants decreased with invasion. On the other hand, Hedysarum reduced visitation rates and niche overlap of pollinators, mainly those of bee species. Finally, we observed that changes in the foraging behaviour of the honeybee were positively associated with the interaction rewiring involving the rest (92 taxa) of pollinators. In conclusion, pollinators show a plastic use of floral resources, responding to the presence of non-native plants. When the non-native attracts highly competitive pollinators such as the honeybee, plasticity is especially significant in pollinators that are functionally close to that competitive pollinator. The result is an interaction rewiring, probably due to pollinators avoiding competition with the honeybee. Though this plasticity might not quantitatively affect the pollination of plants, consequences on their reproduction and the functioning of the network can derive from the interaction rewiring.
Data from: Trait correlation network analysis identifies biomass allocation traits and stem specific length as hub traits in herbaceous perennial plants
Correlations among plant traits often reflect important trade‐offs or allometric relationships in biological functions like carbon gain, support, water uptake, and reproduction that are associated with different plant organs. Whether trait correlations can be aggregated to "spectra" or "leading dimensions," whether these dimensions are consistent across plant organs, spatial scale, and growth forms are still open questions. To illustrate the current state of knowledge, we constructed a network of published trait correlations associated with the "leaf economics spectrum," "biomass allocation dimension," "seed dimension," and carbon and nitrogen concentrations. This literature‐based network was compared to a network based on a dataset of 23 traits from 2,530 individuals of 126 plant species from 381 plots in Northwest Europe. The observed network comprised more significant correlations than the literature‐based network. Network centrality measures showed that size traits such as the mass of leaf, stem, below‐ground, and reproductive tissues and plant height were the most central traits in the network, confirming the importance of allometric relationships in herbaceous plants. Stem mass and stem‐specific length were "hub" traits correlated with most traits. Environmental selection of hub traits may affect the whole phenotype. In contrast to the literature‐based network, SLA and leaf N were of minor importance. Based on cluster analysis and subsequent PCAs of the resulting trait clusters, we found a "size" module, a "seed" module, two modules representing C and N concentrations in plant organs, and a "partitioning" module representing organ mass fractions. A module representing the plant economics spectrum did not emerge. Synthesis. Although we found support for several trait dimensions, the observed trait network deviated significantly from current knowledge, suggesting that previous studies have overlooked trait coordination at the whole‐plant level. Furthermore, network analysis suggests that stem traits have a stronger regulatory role in herbaceous plants than leaf traits.
Plant species with the trait of continuous flowering do not hold core roles in a Neotropical lowland plant-pollinating insect network
<p>Plant-animal interaction science repeatedly finds that plant species differ by orders of magnitude in the number of interactions they support. The identification of plant species that play key structural roles in plant-animal networks is a global conservation priority, however, in hyperdiverse systems such as tropical forests, empirical datasets are scarce. Plant species with longer reproductive seasons are posited to support more interactions compared to plant species with shorter reproductive seasons but this hypothesis has not been evaluated for plant species with the longest reproductive season possible at the individual plant level, the continuous flowering phenology. Resource predictability is also associated with promoting specialization, and therefore continuous flowering may instead favor specialist interactions. Here we use quantitative pollinating insect-plant networks constructed from countryside habitat of the Tropical Wet forest Life Zone and modularity analysis to test if species that share the trait of continuous flowering hold core roles in mutualistic networks. With a few exceptions, most<span> plant species sampled within our network were assigned to the role of peripheral. All but one network had significantly high modularity scores and each continuous flowering plant species was in a different module. Our work reveals that the continuous flowering plant species differed in some networks in their topological role, and that more evidence was found for the phenology to support specialized subsets of interactions. Our findings suggest that the conservation of Neotropical pollinating insect communities may require planting species from each module rather than identifying and conserving network hubs. </span></p>
Dataset for: Trait-mediated speciation and human-driven extinctions in proboscideans revealed by unsupervised Bayesian neural networks
<p>This repository contains the output of all birth-death neural network simulations and the empirical analyses of Proboscidean diversification. For details see the Readme of the individual directories.</p>
The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms
<p>Data used in the "The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms"</p>
Data from: Trait correlation network analysis identifies biomass allocation traits and stem specific length as hub traits in herbaceous perennial plants
Open the record for dataset details and reuse information.
Data from: Quantifying species contributions to ecosystem processes: a global assessment of functional trait and phylogenetic metrics across avian seed-dispersal networks
Open the record for dataset details and reuse information.
Data from: Influence of the honeybee and trait similarity on the effect of a non-native plant on pollination and network rewiring
Open the record for dataset details and reuse information.
Data from: Species traits and abundances predict metrics of plant–pollinator network structure, but not pairwise interactions
Open the record for dataset details and reuse information.
Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.