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103 results for “Trait composition”
Zooplankton community composition and trait data for Green Lakes Valley, 2009 - ongoing.
Starting in 2012 zooplankton sampling at Green Lake 4 was included in the long term monitoring data set at Niwot Ridge. Immediately after the ice has completely melted from the lakes, zooplankton samples are taken once a week for six consecutive weeks at the deepest portion of the lake from an inflatable raft. Zooplankton were sampled at the deepest location of each lake by pulling a conical net (Wisconsin net) vertically through the water column (i.e., vertical tow sample). For each zooplankton sample obtained, adult organisms were identified to species, or lowest taxonomic level (Chydoridae sp. and Bosminidae sp.). Larvae of cladocerans were counted together as neonates; calanoid and cyclopoid copepodites were counted together as nauplii. Individual body lengths of the first 50 -100 (when possible) individuals of each taxon were recorded using a calibrated eyepiece micrometer and means reported.
Morpho-anatomical traits explain the effects of bacterial-feeding nematodes on soil bacterial community composition and plant growth and nutrition
<p>Soil Bacterial populations</p> <p>V3-V4, of the 16S rRNA gene using the primers 341F CCTAYGGGRBGCASCAG and 806R GGACTACNNGGGTATCTAAT.</p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes
<p>The datasets in this repository include plant community surveys, hyperspectral reflectance data at the leaf and canopy level, leaf trait data, and soil chemistry data collected at five sites along an elevation gradient of 2400m-3500m in the Chilean Andes (33°S, 70°W). The purpose of this study was to evaluate the environmental drivers of community assembly processes along the elevation gradient.</p>
Selection on convergent functional traits drives compositional divergence in a tallgrass prairie restoration experiment
<p>1. Plant biodiversity is often partitioned into taxonomic diversity (species composition and abundance), phylogenetic diversity (breadth of evolutionary lineages) and functional diversity (resource‐use strategies or physical traits). Evaluating the effects and interplay of these dimensions can provide insights into how assembly processes drive compositional changes in plant communities. However, teasing apart the effects of different biodiversity dimensions is challenging in observational studies or retrospective analyses.</p> <p>2. To evaluate how plant phylogenetic and trait history shape community establishment and turnover in restoration of a species‐rich North American tallgrass prairie, we conducted an experiment with 127 species planted in assemblages representing three levels of phylogenetic diversity (PD) and two of functional trait diversity (FD), holding starting species richness (SR) fixed. We tested whether PD and FD of planted assemblages predicted species diversity, compositional turnover and selection on functional traits.</p> <p>3. Rank order of initial functional and phylogenetic diversity levels was maintained throughout the experiment, but neither diversity measure correlated positively with species richness by the end of the experiment. Phylogenetic and taxonomic beta diversity increased among all treatments. This increase in compositional beta diversity was associated with directional selection on phylogenetically dispersed functional traits. A set of functional traits associated with competitiveness in tallgrass prairies predicted species' cover for all survey years: stem dry matter content, leaf dry matter content, vegetative height and rhizomatous growth. Although all plots collectively converged on a similar suite of functional traits, functional beta diversity increased among high‐FD plots.</p> <p>4. <em>Synthesis</em>. Neither higher functional nor phylogenetic diversity maintained higher species richness (SR) over time in our study. Although SR was not maintained, higher levels of PD and FD were. Both types of diversity shaped the rate at which plots changed in composition over time, with high diversity treatment plots increasing in beta diversity. Selection for traits convergent across the tree of life drove phylogenetic and compositional divergence among plots. While optimization of site‐specific functional traits may be most important for maintaining higher SR, our work implies that planting higher initial PD and FD may make grassland restorations more adaptable to site conditions that may be difficult to predict.</p>
Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
<p>Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data in Northern America and Europe for the period 1988 – 2016.</p> <p>We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of all studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization.</p> <p>We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.</p>
Data from: Composition of a chemical signalling trait varies with phylogeny and precipitation across an Australian lizard radiation
<p>The environment presents challenges to the transmission and detection of animal signalling systems, resulting in selective pressures that can drive signal divergence among populations in disparate environments. For chemical signals, climate is a potentially important selective force because factors such as temperature and moisture influence the persistence and detection of chemicals. We investigated an Australian lizard radiation (<em>Heteronotia</em>) to explore relationships between a sexually dimorphic chemical signalling trait (epidermal pore secretions) and two key climate variables: temperature and precipitation. We reconstructed the phylogeny of <em>Heteronotia</em> with exon capture phylogenomics, estimated phylogenetic signal in among-lineage chemical variation, and assessed how chemical composition relates to temperature and precipitation using multivariate phylogenetic regressions. High estimates of phylogenetic signal indicate that the composition of epidermal pore secretions varies among lineages in a manner consistent with Brownian motion; although there are deviations to this, with stark divergences coinciding with two phylogenetic splits. Accounting for phylogenetic non-independence, we found that among-lineage chemical variation is associated with geographic variation in precipitation but not temperature. This contrasts somewhat with previous lizard studies, which have generally found an association between temperature and chemical composition. Our results suggest that geographic variation in precipitation can affect the evolution of chemical signalling traits, possibly influencing patterns of divergence among lineages and species.</p> <p> </p>
Fig. 1 in The invasive white ginger lily (Hedichium coronarium) simplifies the trait composition of an insect assemblage in the littoral zone of a Savanna reservoir
Fig. 1. Location and characterization of plant composition banks of Fazzari reservoir in the Brazilian Savanna (Cerrado Biome, Brazil).
Fig. 3 in The invasive white ginger lily (Hedichium coronarium) simplifies the trait composition of an insect assemblage in the littoral zone of a Savanna reservoir
Fig. 3. The average values of CWM-trait values of aquatic insect assemblages in invaded and non-invaded banks by white ginger lily of a reservoir in Brazilian Savanna (Cerrado Biome). A – CWM-FFG, B – CWM-feed, C – CWM-habit, D – CWM body length. CWM, Community level Weight-Mean.
Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes
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Data from: Composition of a chemical signalling trait varies with phylogeny and precipitation across an Australian lizard radiation
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Landscape composition and life‐history traits influence bat movement and space use: Analysis of 30 years of published telemetry data
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Selection on convergent functional traits drives compositional divergence in a tallgrass prairie restoration experiment
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Species composition and plant functional traits on Hog and Metompkin Islands, VA 2016-2017
Physical characteristics (soil salinity, elevation, distance from shoreline) and biological parameters (species composition, cover, biomass, and functional traits) were measured for plants on Hog Island (Northampton Co. VA) and Metompkin Island (Accomack County, VA). Physical parameters were measured during 2016. Plant biomass, species composition, percent cover, and functional traits were measured in 2017. Data Table 1: Environmental and geographic data on Hog and Metompkin Islands, VA 2016 Data Table 2: Species composition and aboveground plant functional traits on Hog and Metompkin Islands, VA 2017 Data Table 3: Vegetative biomass on Hog and Metompkin Islands, VA 2017 Data Table 4: Plot-level root functional traits on Hog and Metompkin Islands, VA 2017 Data Table 5: Latitude-longitude coordinates of plots on Hog and Metompkin Islands, VA 2016-2017
Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models
1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.
Raw data and Matlab code for: Convergence in carnivorous pitcher plants reveals a mechanism for composite trait evolution
<p>Composite traits involve multiple components that, only when combined, gain a new synergistic function. Thus, how they evolve remains a puzzle. We combined field experiments, microscopy, chemical analyses and laser Doppler vibrometry with comparative phylogenetic analyses to show that two carnivorous <em>Nepenthes</em> pitcher plant species convergently evolved identical adaptations in three distinct traits to acquire a new, composite trapping mechanism. Comparative analyses suggest that this new trait arose convergently via 'spontaneous coincidence' of the required trait combination, rather than directional selection in the component traits. Our results indicate a plausible mechanism for composite trait evolution and highlight the importance of stochastic phenotypic variation as a facilitator of evolutionary novelty.</p>
Stem decomposition of temperate tree species is determined by stem traits and fungal community composition during early stem decay
<p>Dead trees are vital structural elements in forests playing key roles in the carbon and nutrient cycle. Stem traits and fungal community composition are both important drivers of stem decay, and thereby affect ecosystem functioning, but their relative importance for stem decomposition over time remains unclear.</p> <p>To address this issue, we used a common garden decomposition experiment in a Dutch larch forest hosting fresh logs from 13 common temperate tree species. In total 25 fresh wood and bark traits were measured as indicators of wood accessibility for decomposers, nutritional quality, and chemical or physical defense mechanisms. After one and four years of decay, we assessed the richness and composition of wood-inhabiting fungi using amplicon sequencing and determined the proportional wood density loss.</p> <p>Average proportional wood density loss for the first year was 18.5%, with further decomposition occurring at a rate of 4.3% yr<sup>-1</sup> for the subsequent three years across tree species. Proportional wood density loss varied widely across tree species in the first year (8.7-24.8% yr<sup>-1</sup>) and subsequent years (0-11.3% yr<sup>-</sup><sup>1</sup>). The variation was directly driven by initial wood traits during the first decay year, then later directly driven by bark traits and fungal community composition. Moreover, bark traits affected the composition of wood-inhabiting fungi and thereby indirectly affected decomposition rates. Specifically, traits promoting resource acquisition of the living tree, such as wide conduits that increase accessibility and high nutrient concentration, increased initial wood decomposition rates. Fungal community composition, but not fungal richness explained differences in wood decomposition after four years of exposure in the field, where fungal communities dominated by brown-rot and white-rot Basidiomycetes were linked to higher wood decomposition rate.</p> <p><em>Synthesis.</em> Understanding what drives deadwood decomposition through time is important to understand the dynamics of carbon stocks. Here, using a tailor-made experimental design in a temperate forest setting, we have shown that stem trait variation is key to understanding the roles of these drivers; Initially, wood traits explained decomposition rates while subsequently, bark traits and fungal decomposer composition drove decomposition rates. These findings inform forest management with a view to selecting tree species to promote carbon storage.</p>
Does fortune follow function? Exploring how consumer preferences drive the functional trait composition of the global songbird trade
<p>Defaunation and extinction undermine the resilience and functioning of ecological communities and ecosystems. Relative to other disturbances, overexploitation for the global wildlife trade presents a unique case of trait-based selection, as demand for specific individuals is often tied to unique morphological or aesthetic traits desired by consumers ("market traits"). Because evolutionary history leads to species that share both market and functional traits, we posit that non-random patterns of exploitation will result in non-random loss of functional diversity. <br>We applied a trait-based framework to the global songbird trade for 4616 species, 148 of which are plausibly threatened by the trade. We quantified select market traits, such as unique coloration and song quality, and ecological traits related to body size, diet, and foraging strategy to assess whether the trade disproportionately threatens particular functional groups. We additionally looked for patterns of association between market traits and functional traits to assess whether selection on certain market traits could drive selection on associated functional traits.<br>We found that overexploited birds are a distinct functional subset of the global songbird pool, with the trade disproportionately threatening large bodied, frugivorous, and seed eating birds. Across all songbirds, there were multiple non-random associations between market traits and functional traits, with the strongest associations observed among trade-threatened birds; this was consistent with our theory that consumer-driven selection on market traits could theoretically result in selection on functional traits. However, there was mixed evidence for this hypothesis at a global scale, suggesting that disproportionate threat to functional diversity may be more likely in regions where there is heavy demand for associated market traits. <br><em>Policy Implications:</em> Our results highlight the need for increased focus on the mechanistic drivers of trait-based selection on the consumer side of wildlife trade, and how patterns of overexploitation can systematically affect ecological communities and ecosystem services.</p>
Data on Trait diversity and spider community composition are associated with lower herbivory in young forest plantations
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Data from: Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
<p><span><b>Aim: </b>Animal movement determines home range patterns, which in turn affect individual fitness, population dynamics and ecosystem functioning. Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data.</span></p> <p><span><b>Location</b>: Northern America and Europe.</span></p> <p><span><b>Time period</b>: 1988 – 2016.</span></p> <p><span><b>Major taxa studied</b>: Bats.</span></p> <p><span><b>Methods</b>: We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of the 165 studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization.</span></p> <p><span><b>Results:</b><i> </i>We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.</span></p> <p><span><b>Main conclusions: </b>Promoting bat movements through the landscape surrounding roosts at large spatial scales is crucial for bat conservation. Forest loss and overall landscape homogenization lead temperate bats to fly farther to meet their ecological requirements, by increasing home range sizes and daily foraging distances. Both processes might be more detrimental for smaller, habitat-specialized bats, less able to travel increasingly longer distances to meet their diverse needs.</span></p>
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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.