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5,828 results for “Traits”
Fig. 4 in Floristic traits and biogeographic characterization of the Gennargentu massif (Sardinia)
Fig. 4. – Life forms percentages of the endemic flora of Gennargentu. H = hemicryptophytes; C = chamaephytes; G = geophytes; NP = nanophanerophytes; P = phanerophytes; T = therophytes.
Fig. 2. – Species prediction for a grid cell. A in Geographical patterns of woody plants' functional traits in Burkina Faso
Fig. 2. – Species prediction for a grid cell. A. Average of maximal plant size; B. Percentage of spinescent species; C. Percentage of species containing latex; D. Percentage of species with compound leaves.
Fig. 1. – Study area. A in Geographical patterns of woody plants' functional traits in Burkina Faso
Fig. 1. – Study area. A. Species richness of the 129 woody plants, distribution records and position of Burkina Faso within Africa; B. Names of landscape elements and cities mentioned in the results.
Data from: Global plant-frugivore trait matching is shaped by climate and biogeographic history
<p>Species interactions are influenced by the trait structure of local multi-trophic communities. However, it remains unclear whether mutualistic interactions, in particular, can drive trait patterns at the global scale, where climatic constraints and biogeographic processes gain importance. Here we evaluate global relationships between traits of frugivorous birds and palms (<em>Arecaceae</em>), and how these relationships are affected, directly or indirectly, by assemblage richness, climate and biogeographic history. We leverage a new and expanded gape size dataset for nearly all avian frugivores, and find a positive relationship between gape size and fruit size, that is, trait matching, which is influenced indirectly by palm richness and climate. We also uncover a latitudinal gradient in trait matching strength, which increases towards the tropics and varies among zoogeographic realms. Taken together, our results suggest trophic interactions have consistent influences on trait structure, but that abiotic, biogeographic and richness effects also play important, though sometimes indirect, roles in shaping the functional biogeography of mutualisms.</p>
Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits
<p>Supporting data associated with the manuscript "Mapping of promoter usage QTL using RNA-seq data reveals their contributions to complex traits". These include:</p> <ul> <li><strong>StringTie2.438.GEUVADIS.gtf.gz</strong> - Assembled transcriptome using RNA-seq from 438 samples from the GEUVADIS project by StringTie2</li> <li><strong>puQTL_nominal_output.txt.gz</strong> - puQTL results from QTLtools nominal pass</li> <li><strong>puQTL_conditional_output.txt.gz</strong> - puQTL results from QTLtools conditional pass</li> <li><strong>eQTL_nominal_output.txt.gz</strong> - eQTL results from QTLtools nominal pass</li> <li><strong>eQTL_conditional_output.txt.gz</strong> - eQTL results from QTLtools conditional pass</li> </ul> <p>See <a href="https://qtltools.github.io/qtltools/">https://qtltools.github.io/qtltools/</a> for column descriptions of QTL mapping results.</p>
FIGURE 3 Morphological trait sampling for all bird families. AVONET contains 718,662 in AVONET: morphological, ecological and geographical data for all birds
FIGURE 3 Morphological trait sampling for all bird families. AVONET contains 718,662 individual trait measurements, all of which are used to calculate species averages. However, sampling per species varies across families depending on taxonomy. Upper phylogram shows sampling under BirdLife International (11,009 species in 243 families). Families where sampling completeness is below 75% indicated by lighter shading. Most families with lower sampling are species poor (numbers in black circles show species richness). Lower panels show that sampling improves under more conservative taxonomic treatments of eBird (10,661 species in 249 families) and BirdTree (9993 species in 194 families). Coloured bars indicate the proportion of species in each family measured to different levels of completeness. 'Complete set' means a full set of all 9 core morphological traits (not necessarily from the same individual). 'Individuals' means any individual bird with one or more traits measured
On the genetic architecture of rapidly adapting and convergent life history traits in guppies
<p>The genetic basis of traits shapes and constrains how adaptation proceeds in nature; rapid adaptation can be facilitated by polygenic traits, which subsequently provide multiple, redundant, genetic routes to adaptive phenotypes, reducing re-use of the same genes (genetic convergence). Guppy life history traits evolve rapidly and convergently among natural high- (HP) and low-predation (LP) environments in northern Trinidad. This system has been studied extensively at the phenotypic level, but little is known about the underlying genetic architecture. Here, we use an F2 QTL design to examine the genetic basis of seven (five female, two male) guppy life history phenotypes to assess whether the genetic architecture of these traits reflects theoretical predictions. We use RAD-sequencing data (16,539 SNPs) from 370 male and 267 female F2 individuals. We perform linkage mapping, estimates of genome-wide and per-chromosome heritability (multi-locus associations), and QTL ma pping (single-locus associations). Our results are consistent with architectures of many-loci of small effect for male age and size at maturity and female interbrood period. Male trait associations are clustered on specific chromosomes, but female interbrood period exhibits a weak genome-wide signal suggesting a potentially highly polygenic component. Offspring weight and female size at maturity are also associated with a single significant QTL each. These results suggest rapid phenotypic evolution of guppies may be facilitated by polygenic trait architectures, but these could fuel redundancy and limit gene re-use across populations, in agreement with an absence of strong signatures of genetic convergence from recent population genomic analyses of wild HP-LP guppies.</p>
Assessing the vulnerability of plant functional trait strategies to climate change
<p><strong><span>Aim: </span></strong><span>Our ability to understand how species may respond to changing climate conditions is hampered by a lack of high-quality data on the adaptive capacity of species. Plant functional traits are linked to many aspects of species life history and adaptation to environment, with different combinations of trait values reflecting alternate strategies for adapting to varied conditions. If the realised climate limits of species can be partially explained by plant functional trait combinations, then a new approach of using trait combinations to predict the expected climate limits of species trait combinations may offer considerable benefits. </span></p> <p><strong><span>Location:</span></strong><span> Australia.</span></p> <p><span><strong>Time period:</strong> </span><span>Current and future. </span></p> <p><strong><span>Methods:</span></strong><span><strong> </strong>Using trait data for leaf size, seed mass and plant height for 6,747 Australian native species from 27 plant families, we model the expected climate limits of trait combinations and use future climate scenarios to estimate climate change impacts based on plant functional trait strategies. </span></p> <p><strong><span>Results:</span></strong><span><strong> </strong>Functional trait combinations were a significant predictor of species climate niche metrics with potentially meaningful relationships with two rainfall variables (R<sup>2</sup> = 0.36 & 0.45) and three temperature variables (R<sup>2</sup> = 0.21, 0.28, 0.30). Using this method, the proportion of species exposed to conditions across their range that are beyond the expected climate limits of their trait strategies will increase under climate change. </span></p> <p><strong><span>Main conclusions:</span></strong><span><strong> </strong>Our new approach, called Trait Strategy Vulnerability, includes three new metrics. For example, the Climate Change Vulnerability (CCV) metric identified a small but important proportion of species (4.3%) that will on average be exposed to conditions beyond their expected limits for summer temperature in the future. These potentially vulnerable species could be high priority targets for deeper assessment of adaptive capacity at the genomic or physiological level. Our methods can be applied to any suite of co-occurring plants globally.</span></p>
Data and code from: Geomorphological processes shape plant community traits in the Arctic
<p><strong>Aim</strong></p> <p>Geomorphological processes profoundly affect plant establishment and distributions, but their influence on functional traits is insufficiently understood. Here, we unveil trait-geomorphology relationships in Arctic plant communities.</p> <p> </p> <p><strong>Location</strong></p> <p>High-Arctic Svalbard, low-Arctic Greenland, and sub-Arctic Fennoscandia.</p> <p> </p> <p><strong>Time period</strong></p> <p>2011-2018</p> <p> </p> <p><strong>Major taxa studied</strong></p> <p>Vascular plants</p> <p> </p> <p><strong>Methods</strong></p> <p>We collected field-quantified data on vegetation, geomorphological processes, microclimate, and soil properties from 5280 plots and 200 species across the three Arctic regions. We combined these data with database trait records to relate local plant community trait composition to dominant geomorphological processes of the Arctic, namely cryoturbation, deflation, fluvial processes, and solifluction. We investigated the relationship between plant functional traits and geomorphological processes using hierarchical generalised additive modelling.</p> <p> </p> <p><strong>Results</strong></p> <p>Our results demonstrate that community-level traits are related to geomorphological processes, with cryoturbation most strongly influencing both structural and leaf economic traits. These results were consistent across regions, suggesting a coherent biome-level trait response to geomorphological processes.</p> <p> </p> <p><strong>Main conclusions</strong></p> <p>The results indicate that geomorphological processes shape plant community traits in the Arctic. We provide empirical evidence for the existence of generalisable relationships between plant functional traits and geomorphological processes. The results indicate that the relationships are consistent across these three distinct tundra regions and that geomorphological processes should be considered in future investigations of functional traits.</p> <p> </p> <p>Kemppinen, Niittynen, Happonen, le Roux, Aalto, Hjort, Maliniemi, Karjalainen, Rautakoski & Luoto. Provisionally accepted. Geomorphological processes shape plant community traits in the Arctic. Global Ecology and Biogeography.</p> <p>These are the data and code from Kemppinen et al. (Provisionally accepted).</p>
Extended Data Figure 7 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 7 | Global distribution of viral and host species richness for primates (order Primates). a, Observed total viral richness (for n = 71 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 73);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3e); g, global host species richness for Primates (n = 400); h, host species richness for Primates in our database (n = 98);i, primate species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in primates. Red/pink colours in panel i highlight areas with poor viral surveillance in primate species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 4 | Global distribution of viral and host species richness for wild carnivores (order Carnivora). a, Observed total viral richness (for n = 55 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 55); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3b); g, global host species richness for Carnivora (n = 276);h, host species richness for Carnivora in our database (n = 79); i, species of the order Carnivora with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in carnivores. Red/pink colours in panel i highlight areas with poor viral surveillance in carnivore species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 3 | Global distribution of viral and host species richness for all wild mammals. a, Observed total viral richness (for n = 576 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 584);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3a); g, global mammal species richness (n = 5,290); h, mammal richness for species in our database (n = 753);i, mammal species with no described viruses in the literature. Warmer colours (larger values) in panels c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in mammals. Red/pink colours in panel i highlight areas with poor viral surveillance in mammal species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Figure 4 | Traits that predict zoonotic potential of a virus. a, Box plot of maximum phylogenetic host breadth per virus (PHB, see methods) for each of 586 mammalian viruses, aggregated by 28 viral families. Individual points represent viral species, colour-coded by zoonotic status. Box plots coloured and sorted by the proportion of zoonoses in each viral family. b–d, Partial effect plots for the best-fit GAM to predict the zoonotic potential of a virus. b, Maximum PHB. Viruses that infect a phylogenetically broader range of hosts are more likely to be zoonotic. c, Research effort (log, number of PubMed citations per viral species). d, Whether or not a virus replicates in the cytoplasm or is vector-borne. Viral genome length and whether or not a virus is enveloped improved the overall predictive power but were non-significant and are not shown (see Extended Data Table 1).
Extended Data Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 2 | Heat map of observed total viral richness by mammalian order and viral family. Dataset includes 754 mammalian species and 586 unique ICTV recognized viral species. Heat map aggregated by rows and columns to group taxa with similar levels of observed viral richness.
Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Figure 3 | Global distribution of the predicted number of 'missing zoonoses' by order. Warmer colours highlight areas predicted to be of greatest value for discovering novel zoonotic viruses. a, All wild mammals (n = 584 spp. included in the best-fit model). b, Carnivores (order Carnivora, n = 55).c, Even-toed ungulates (order Cetartiodactyla, n = 70). d, Bats (order Chiroptera, n = 157).e, Primates (order Primates, n = 73). f, Rodents (order Rodentia, n = 183). Hatched regions represent areas where model predictions deviate systematically for the assemblage of species in that grid cell (approximately 18 km × 18 km, see Methods). Animal silhouettes from PhyloPic.
Extended Data Figure 6 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 6 | Global distribution of viral and host species richness for bats (order Chiroptera). a, Observed total viral richness (for n = 156 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 157);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3d); g, global host species richness for Chiroptera (n = 1117);h, host species richness for Chiroptera in our database (n = 192);i, species of the order Chiroptera with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in bats. Red/pink colours in panel i highlight areas with poor viral surveillance in bat species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 1 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 1 | Conceptual model of zoonotic spillover, viral richness, and summary of models. a, Conceptual model of zoonotic spillover showing primary risk factors examined, colour-coded according to generalized additive models used. b, Conceptual model of observed, predicted, and actual viral richness in mammals. c, GAMs used in our study to address specific components of a and b, colour-coded by model. Variables listed with 'or' under each GAM covaried and were provided as competing terms in model selection, and those in bold were included in the best-fit model using all host–virus associations. Significant variables from each best-fit GAM are noted with an asterisk. Zoonotic viral spillover first depends on the underlying total viral richness in mammal populations and the ecological, taxonomic, and life-history traits that govern this diversity (GAM 1). Second, host- and virus-specific factors may facilitate viral spillover. We examine the relative importance of host phylogenetic distance to humans, ecological opportunity for contact, or other species-specific life-history and taxonomic traits (GAM 2), and identify viral traits associated with a higher likelihood of an observed virus being zoonotic (GAM 3). We estimate the total and zoonotic viral richness per host species using GAMs 1 and 2, and calculate the missing viruses and missing zoonoses under a scenario of increased research effort (b, Methods). Owing to imperfect surveillance in both humans and wildlife and biases in viral detection, there may be uncertainty in the exact proportion of viruses that are zoonotic (b, light grey), and also between the actual, or true, viral richness (dotted lines) and the predicted maximum viral richness per host (dashed line).
Extended Data Figure 9 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 9 | Order-level phylogenies showing residuals from zoonoses model. a–e, Subtrees from cytochrome b maximum likelihood phylogeny for 558 mammal species (constrained to order-level topology of mammal supertree) for bats (a), carnivores (b), even-toed ungulates (c), rodents (d) and primates (e). Species included have at least one described virus association and available genetic data. Wildlife species names and terminal branches are colour-coded by the residuals (predicted minus observed) from the best-fit GAM to predict the number of zoonotic viruses using all data. Species with residual values between −1 and 1 (black) are accurately predicted within one virus. Warm colours represent species with positive residuals (orange>1 to 3; red>3). Cool colours represent species with negative residuals (green <−1 to−3; blue<−3). Marine mammals, domestic animals, and species with missing data and not included in the best-fit models are shown in grey.
Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Figure 2 | Host traits that predict total viral richness (top row) and proportion of zoonotic viruses (bottom row) per wild mammal species. Partial effect plots show the relative effect of each variable included in the best-fit GAM, given the effect of the other variables. Shaded circles represent partial residuals; shaded areas, 95% confidence intervals around mean partial effect. a–e, Best model for total viral richness includes: a, number of disease-related citations per host species (research effort, log); b, phylogenetic eigenvector regression (PVR) of body mass (log); c, geographic range area of each species (log km2); d, number of sympatric mammal species overlapping with at least 20% area of target species range; and e, mammalian orders. f–i, Best model for proportion of zoonoses includes: f, research effort (log); g, phylogenetic distance from humans (cytochrome b tree constrained to the topology of the mammal supertree28); h, ratio of urban to rural human population within species range; and i, three mammalian orders. Bats are the only order with a significantly larger proportion of zoonotic viruses than would be predicted by the other variables in the all-data model. Three additional mammalian orders, and whether or not a species is hunted, improved the overall predictive power of the best zoonotic virus model but were non-significant and are not shown (see Extended Data Table 1).
Extended Data Figure 8 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 8 | Global distribution of viral and host species richness for rodents (order Rodentia). a, Observed total viral richness (for n = 178 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 183);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3f); g, global host species richness for Rodentia (n = 2206);h, host species richness for Rodentia in our database (n = 221); i, rodent species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in wild rodents. Red/pink colours in panel i highlight areas with poor viral surveillance in rodent species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
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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.