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938 results for “Community: structure”
Fig. 3 in Community Structure and Undescribed Species Diversity in Non-Pollinating Fig Wasps Associated with the Strangler Fig Ficus petiolaris
Fig. 3. Maximum-clade credibility tree for Idarnes mtDNA sequences from wasps associated with Ficus petiolaris. Four distinct clades (LO1, SO1, LO2, SO2) each with a posterior probability of 1.0 were recovered.There is little information in the data as to how these four clades are related. Posterior probabilities ≥0.50 are presented.Taxon names are composed of clade name, locality number, and internal lab numbers. In addition, LO1 sequences show phylogeographic structure, with sequences primarily clustering based on if sampled from Baja California (BC) or Sonora (S). One sequence (denoted with black box) is an exception, where the wasp was sampled from Sonora (locale 12; see Fig. 1) yet clusters with Baja California sequences.
Effects of tidal influence on the structure and function of prokaryotic communities in the sediments of a pristine Brazilian mangrove
<p>Mangrove forests are ecosystems that constitute a large portion of the world's coastline and span tidal zones below, between, and above the waterline, while the ecosystem as a whole is defined by the health of these tidal microhabitats. However, we are only beginning to understand tidal zone microbial biodiversity and the role of these microbiomes in nutrient cycling. While extensive research has characterized microbiomes in pristine versus anthropogenically impacted mangroves these have, largely, overlooked differences in tidal microhabitats (sublittoral, intertidal, and supralittoral). Unfortunately, the small number of studies that have sought to characterize mangrove tidal zones have occurred in impacted biomes, making interpretation of the results difficult. Here, we characterized prokaryotic populations and their involvement in nutrient cycling across the tidal zones of a pristine mangrove within a Brazilian Environmental Protection Area of the Atlantic Forest. We hypothesized that the tidal zones in pristine mangroves are distinct microhabitats, which we defined as distinct regions that present spatial variations in the water regime and other environmental factors, and as such, these are composed of different prokaryotic communities with distinct functional profiles. Samples were collected in triplicate from zones below, between, and above the tidal waterline. Using 16S rRNA gene amplicon sequencing, we found distinct prokaryotic communities with significantly diverse nutrient cycling functions, as well as specific taxa with varying contribution to functional abundances between zones. Where previous research from anthropogenically impacted mangroves found the intertidal zone to have high prokaryotic diversity and functionally enriched in nitrogen cycling, we find that the intertidal zone from pristine mangroves have the lowest diversity and no functional enrichment, relative to the other tidal zones. The main bacterial phyla in all samples were Firmicutes, Proteobacteria and Chloroflexi while the main archaeal phyla were Crenarchaeota and Thaumarchaeota. Our results differ slightly from other studies where Proteobacteria is the main phyla in mangrove sediments and Firmicutes make up for only a small percentage of the communities. Salinity and organic matter were the most relevant environmental factors influencing these communities. Bacillaceae was the most abundant family at each tidal zone and showed potential to drive a large proportion of the cycling of carbon, nitrogen, phosphorus and sulfur. Our findings suggest that some aspects of mangrove tidal zonation may be compromised by human activity, especially in the intertidal zone.</p>
Figure 4 in Inter-oceanic comparison of planktonic copepod ecology (vertical distribution, abundance, community structure, population structure and body size) between the Okhotsk Sea and Oyashio region in autumn
Figure 4. Copepod species composition (centre) and copepodid stage structures of the dominant species (left: Oyashio region, right: Okhotsk Sea). All data are integrated means of a 0– 500 m water column based on the IONESS samples in the Oyashio region (St. 19) and Okhotsk Sea (St. OK24) from October to November 1996. Error bars for the copepodid stage indicate standard deviations of each daily duplicate.
Figure 3 in Inter-oceanic comparison of planktonic copepod ecology (vertical distribution, abundance, community structure, population structure and body size) between the Okhotsk Sea and Oyashio region in autumn
Figure 3. Vertical distribution of zooplankton biovolume in the Oyashio region (upper panels) and Okhotsk Sea (lower panels) from September to December in 1996–1998. Note that the biovolume axes are not the same between panels. Tc: thermocline.
Data for: Microclimate structures communities, predation and herbivory in the High Arctic
<p> </p> <p>In a warming world, changes in climate may result in species-level responses as well as changes in community structure through knock-on effects on ecological interactions such as predation and herbivory. Yet, the links between these responses at different levels are still inadequately understood. Assessing how microclimatic conditions affect each of them at local scales provides information essential for understanding the consequences of macroclimatic changes projected in the future. </p> <p>Focusing on the rapidly changing High Arctic, we examine how a community based on a common resource species (avens, <i>Dryas spp</i>.), a specialist insect herbivore (<i>Sympistis zetterstedtii</i>), and natural enemies of lepidopteran herbivores (parasitoids) varies along a multidimensional microclimatic gradient. We ask (1) how parasitoid community composition varies with local abiotic conditions, (2) how the community-level response of parasitoids is linked to species-specific traits (koino- or idiobiont life cycle strategy and phenology) and (3) whether the effects of varying abiotic conditions extend to interaction outcomes (parasitism rates on the focal herbivore and realized herbivory rates). </p> <p>We recorded the local communities of parasitoids, herbivory rates on <i>Dryas</i> flowers and parasitism rates in <i>Sympistis</i> larvae at 20 sites along a mountain slope. For linking community-level responses to microclimatic conditions with parasitoid traits, we used joint species distribution modelling. We then assessed whether the same abiotic variables also affect parasitism and herbivory rates, by applying generalized linear and additive mixed models.</p> <p>We find that parasitism strategy and phenology explain local variation in parasitoid community structure. Parasitoids with a koinobiont strategy preferred high-elevation sites with higher summer temperatures or sites with earlier snowmelt and lower humidity. Species of earlier phenology occurred with higher incidence at sites with cooler summer temperatures or later snowmelt. Microclimatic effects also extend to parasitism and herbivory, with an increase in the parasitism rates of the main herbivore <i>S. zetterstedtii</i> with higher temperature and lower humidity, and a matching increase in herbivory rates. </p> <p>Our results show that microclimatic variation is a strong driver of local community structure, species interactions and interaction outcomes in Arctic ecosystems. In view of ongoing climate change, these results predict that macroclimatic changes will profoundly affect arctic communities. </p> <p> </p>
Shifts in Phytoplankton Community Structure Across Oceanic Boundaries
<p>The two datasets "EnvironmentalData.csv" and "PSD_TransitionZone.csv" provide information about the environmental conditions and the distribution of phytoplankton populations in the North Pacific Ocean. </p> <p>Specifically, the "EnvironmentalData.csv" dataset contains measurements of various environmental parameters, including salinity, temperature, nutrient concentrations, and light availability. These measurements were used to characterize the physical and chemical conditions of the different regions sampled during the study, including the North Pacific Subtropical Gyre (NPSG) and the surrounding regions. The dataset include:</p> <ul> <li><strong>cruise</strong>: The identifier for the research cruise.</li> <li><strong>lat</strong>: Latitude.</li> <li><strong>lon</strong>: Longitude.</li> <li><strong>date</strong>: Date and time of the observation.</li> <li><strong>salinity</strong>: Salinity of the water (PSU).</li> <li><strong>temp</strong>: Water temperature (ºC).</li> <li><strong>par</strong>: Photosynthetically Active Radiation (µmol photons m-2 s-1).</li> <li><strong>SiO4</strong>: Silicate concentration (µM).</li> <li><strong>NO3_NO2</strong>: Nitrate and nitrite concentration (µM).</li> <li><strong>PO4</strong>: Phosphate concentration (µM).</li> <li><strong>MLD</strong>: Mixed layer depth (m).</li> <li><strong>light</strong>: Daily averaged PAR (µmol photons m-2 s-1).</li> </ul> <p>The "PSD_TransitionZone.csv" dataset contains information about the abundance, size, and biomass of different phytoplankton populations, including Prochlorococcus, Synechococcus, picoeukaryotes, and nanoeukaryotes. These data were collected using <a href="https://seaflow.netlify.app/">SeaFlow - a custom-built flow cytometer</a>, and were used to investigate how the distribution and composition of phytoplankton communities change in relation to environmental gradients. The dataset include:</p> <ul> <li><strong>cruise</strong>: The identifier for the research cruise.</li> <li><strong>date</strong>: Date and time of the observation.</li> <li><strong>pop</strong>: Population type.</li> <li><strong>lat</strong>: Latitude.</li> <li><strong>lon</strong>: Longitude.</li> <li><strong>n_per_uL</strong>: Number of cells per microliter (10^6 cells µL-1).</li> <li><strong>c_per_uL</strong>: Carbon per microliter (pgC µL-1).</li> <li><strong>qc</strong>: Carbon quotas (pgC/cell).</li> <li><strong>diam</strong>: Diameter (µm).</li> </ul> <p>The R script "analysis.R" perfoms the analysis of the environmental and phytoplankton data in the North Pacific Ocean. The script performs the following steps:</p> <ol> <li><strong>Data Preprocessing</strong>: Cleans and prepares the data for analysis, including handling missing values and converting date/time formats.</li> <li><strong>Diel Trend Extraction</strong>: Extracts diel (day-night) trends from the phytoplankton data using a custom function that decomposes time series data into seasonal, trend, and residual components.</li> <li><strong>Data Merging</strong>: Merges the environmental and phytoplankton data into a single dataset for analysis.</li> <li><strong>Growth Rate Calculation</strong>: Calculates the cellular growth rate of different phytoplankton populations based on changes in their carbon quotas during daylight hours.</li> <li><strong>North Pacific Subtropical Gyre (NPSG) Boundary Definition</strong>: Defines the boundaries of the NPSG based on changes in salinity along the cruise tracks.</li> <li><strong>Binning and Summarization</strong>: Bins the data over distance from the NPSG boundaries and calculates mean and standard deviation for various parameters within each bin.</li> <li><strong>Figure Generation</strong>: Generates several figures, including: <ul> <li>A map showing the cruise tracks and the location of the NPSG.</li> <li>Plots showing the change in environmental parameters (salinity, temperature, nutrients) across the NPSG boundaries.</li> <li>Plots showing the change in phytoplankton biomass, abundance, and growth rate across the NPSG boundaries.</li> <li>A correlation plot showing the relationship between phytoplankton growth, biomass, and environmental parameters.</li> </ul> </li> </ol>
Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"
<p>This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".</p><p>The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. </p><p>The code folder contains the code to generate the results, and to generate the figures of the manuscript. </p>
III PhasAGE International Conference - PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins - Lecture
<p>The III PhasAGE International Conference "Multiscale understanding of protein aggregation and biomolecular condensates in aging and disease" brought together members of the PhasAGE consortium as well as outstanding international speakers from multidisciplinary fields dedicated to unraveling the intricacies of protein aggregation and biomolecular condensates in the context of aging and disease. For details on the conference program please see https://phasage.eu/iii-phasage-international-conference/. </p>
Data from: The role of fish predators and their foraging traits in shaping zooplankton community structure
<p><span>Differentiation of foraging traits among predator populations may help explain observed variation in the structure of prey communities. However, few studies have investigated the phenotypic effects of predators on their prey in natural communities. Here, we use a comparative analysis of 78 Greenlandic lakes to examine how foraging trait variation among threespine stickleback populations can help explain variation in zooplankton community composition among lakes. We find that landscape-scale variation in zooplankton composition was jointly explained by lake properties, such as size and water chemistry, and the presence and absence of both stickleback and arctic char. </span><span>Additional variation in zooplankton community structure can be explained by stickleback jaw protrusion, a trait with known utility for foraging on zooplankton, but only in lakes where stickleback co-occur with arctic char. Overall, our results illustrate how trait variation of consumers, alongside other ecosystem properties, can influence the composition of prey communities in nature.</span></p>
FIG. 2 in Nematode community structure of forest woodlots. I. Relationships based on similarity coefficients of nematode species
FIG. 2. Dendrogram of forest sites in Tippecanoe County, Ind., based on similarity indices of nematode species.
FIG. 1 in Nematode community structure of forest woodlots. I. Relationships based on similarity coefficients of nematode species
FIG. 1. Influence of the number of soil cores taken at Tippecanoe County, Ind., at site P on the number of nematode species recovered.
Figure 2 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 2. Variation of multiple factors (temperature, salinity, oxygen (mg.L-1), and pH) in each collection station over the sampled months of 2010 and 2011.
Figure 1 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 1. Map of the coast of the state of Rio de Janeiro pointing out the 8 sampling stations of the Araruama lagoon.
Figure 4 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 4. Temporal correlations between larvae of Cirripedia and Acartia tonsa (A) and between Acartia tonsa and temperature (B).
Figure 3 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 3. Relationship between temperature, salinity, and pH and their effect on the abundance of Cirripedia larvae over the months.
Figure 5 in Tracking of spatial changes in the structure of the zooplankton community according to multiple abiotic factors along a hypersaline lagoon
Figure 5. Variation of zooplankton density in each collection station, variations in the index of Shannon-Weaver which measures the Diversity (H) and the Pielou's uniformity which measures the Equitability (J) over the sampled months.
Figure 3 in Diversity and community structure of oribatid mites (Acari: Oribatida) in the dominant habitats of Machakhela National Park (Georgia, Caucasus)
Figure 3. The results of the cluster analysis of the different forest habitats in Machakhela National Park.
Figure 1 in Diversity and community structure of oribatid mites (Acari: Oribatida) in the dominant habitats of Machakhela National Park (Georgia, Caucasus)
Figure 1. Distribution of the sites of the sample collection of the oribatid mite diversity in the Machakhela National Park.
Plant silicon content as a proxy for understanding plant community properties and ecosystem structure
<p>Main dataset from the paper entitled "Plant silicon content as a proxy for understanding plant community properties and ecosystem structure".</p>
Figure 6 in Evaluation of vertical and horizontal changes in community structure of zooplankton in a deep dam lake
Figure 6. CCA triplots for zooplankton abundance and environmental variables (variables are represented by arrows. Species are depicted by points; the numbers indicate sampling stations). Abbreviations: K.coch: K. cochlearis; K.quad: K. quadrata; K.trop: K. tropica; K.long: K. longispina; P.vulg: P. vulgaris; P.doli: P. dolichoptera; S.oblo: S. oblonga; A.prio: A. priodonta; A.brig: A. brightwelli; L.pate: L. patella; L.rhom: L. rhomboides; L.quad: L. quadridentata; Habro.: Habrotrocha sp.; F.long: F. longiseta; D.cucu: D. cucullata; D.long: D. longispina; B.long: B. longirostris; C.spha: C. sphaericus; C.rect: C. rectangula; Cyc sp: Cyclops sp.; Naup: nauplius.
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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.