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85 results for “Aquatic community”
Data from: Leaf litter diversity and structure of microbial decomposer communities modulate litter decomposition in aquatic systems
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Data from: Effects of management on aquatic tree-hole communities in temperate forests are mediated by detritus amount and water chemistry
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Data from: Forest management intensity affects aquatic communities in artificial tree holes
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Data from: Communities at the extreme: aquatic food webs in desert landscapes
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Data from: Quantifying the importance of functional traits for primary production in aquatic plant communities
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Environmental DNA as a non-invasive alternative for surveying aquatic communities in tank bromeliads
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Data supplementing the article "Aquatic biofilms as passive environmental DNA samplers: application to benthic macroinvertebrate communities in rivers" - raw MiSeq data inventories
<p>These data supplement the article “Aquatic biofilms as passive environmental DNA samplers: application to benthic macroinvertebrate communities in rivers” Sinziana F. Rivera, Valentin Vasselon, Nathalie Mary, Olivier Monnier, Fréderic Rimet & Agnès Bouchez submitted to “Molecular Ecology Resources” journal.</p> <p>The directory is composed of: “38_samples_fastq:files”: contains raw demultiplexed fastq files (R1. fastq and R2. fastq) for each of the 38 samples used in this study to produce OTUs and taxonomic inventories.</p> <p>“Samples id.xlsx”: contains the samples ID of the fastq files</p> <p>“Inventories.xlsx”: contains single and multi-habitat morphological inventories as well as molecular inventories resulting from the study</p>
Figure 6. A in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 6. A schematic cross-section of the study wetland (Mallín Crespo) contrasting the condition of the three studied ponds (P1, P2 and P3) during hydrological phases: isolation and connected periods. Distances between ponds, the weather station and sheep are not to scale. Volume (m3) is indicated below each pond. Environment variables are: water temperature (WT), precipitation (PP), pH, specific conductivity (C), dissolved oxygen (DO), total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Invertebrate attributes are: taxa richness (R) density (D), biomass (B) and dominant functional feeding groups (FFG). Dominant taxa in terms of density and frequency are listed over each pond. Bold letters are used for taxa that are also dominants in biomass. For both periods first and second dominant FFG are represented. P, predators; CG, collector–gatherers; and CF, collector–filterers.
Figure 4 in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 4. Seasonal patterns of functional feeding groups (FFG), (A) by density (103 individuals m−3) and (B) by biomass [g DM m−3] at three ponds (May 2008 to April 2009) of Mallín Crespo wetland (Argentina). Sh, shredders; Sc, scrapers; P, predators; CG, collector–gatherers; CF, collector–filterers; P–H, piercers herbivores.
Figure 2 in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 2. Seasonal variation of particulate organic matter (POM, dashed lines) and aquatic plant coverage (solid line) at three ponds on a Patagonian steppe wetland (Argentina) during the study period (May 2008 to April 2009). Categories of aquatic plant coverage explained in methodology. Livestock stocking period is indicated in the figure (black bar).
Figure 1 in Temporal dynamics of invertebrate and aquatic plant communities at three intermittent ponds in livestock grazed Patagonian wetlands
Figure 1. (A) Location of the sampling sites (P1, P2 and P3) at Mallín Crespo (Chubut Province, Patagonia, Argentina) during connected (June–December) and hydrologically isolated (January–May) periods. The three ponds are in the same scale. (B) Daily rain (dashed line) and mean daily air temperature (solid line), from May 2008 to April 2009. (C–E) Physicochemical variables sampled monthly and once per pond. Dashed line (D), indicates unavailable data.
Local and regional drivers influence how aquatic community diversity, resistance and resilience vary in response to drying
<p>Disturbance events govern how the biodiversity of ecological communities varies in both space and time. In freshwater ecosystems, there is evidence that local and regional-scale drivers interact to influence ecological responses to drying disturbances. However, most research provides temporal snapshots at the local scale, whereas few studies encompass a gradient of drying severity spanning multiple years. Using a dataset of rare spatiotemporal extent and detail, we demonstrate how independent and interacting local and regional-scale factors drive shifts in the α and β diversities of communities in dynamic river ecosystems. We examined aquatic invertebrate assemblage responses to hydrological variability (as characterized by monthly observations of instream conditions) at 30 sites over a 12-year period encompassing typical years and two severe drought disturbances. Sites varied in their disturbance regimes and hydrological connectivity at both local (i.e. site-specific) and regional (i.e. river catchment) scales. Whereas α diversity was mainly influenced by local factors including flow permanence and the temporal extent of ponded and dry conditions, both temporal and spatial β diversities also responded to regional-scale metrics such as the spatial extent of flow and hydrological connectivity. We observed stronger local negative responses by taxa with lower capacities to tolerate drying (i.e. resistance) and/or to recover after flow resumes (i.e. resilience), whereas taxa with functional traits promoting resilience made an increasing contribution to spatial β diversity as hydrological connectivity declined. As droughts increase in extent and severity across global regions, our findings highlight the functional basis of taxonomic responses to disturbance and connectivity, and thus advance understanding of how drying disturbances shape biodiversity in river networks. Our identification of the role of regional hydrological factors could inform catchment-scale management strategies that support ecosystem resilience in a context of global change.</p>
Fig. 1 in Review paper The Role of Bacterial-based Protist Communities in Aquatic and Soil Ecosystems and the Carbon Biogeochemical Cycle, with Emphasis on Naked Amoebae
Fig. 1. Protocol for respiration and microbial biomass experiments. Respiration was measured with an infrared CO gas analyzer, glu2 cose (444 µg/g soil) in solution was added as a supplement. Microbial assays for naked amoebae used a culture observation method (COM) for living amoebae routinely employed in our laboratory (e.g. Anderson 2000), glutaraldehyde-fixed portions of the soil sample were examined microscopically for bacteria and heterotrophic nanoflagellates using a fluorescent staining technique (Anderson et al. 2001). For experiments of one-week duration, sampling was done on Days 1, 4, and 8. For two-week duration experiments, sampling was done on Days 1, 8, and 15.
Data from: Identification of habitat-specific biomes of aquatic fungal communities using a comprehensive nearly full-length 18S rRNA dataset enriched with contextual data
Molecular diversity surveys have demonstrated that aquatic fungi are highly diverse, and that they play fundamental ecological roles in aquatic systems. Unfortunately, comparative studies of aquatic fungal communities are few and far between, due to the scarcity of adequate datasets. We combined all publicly available fungal 18S ribosomal RNA (rRNA) gene sequences with new sequence data from a marine fungi culture collection. We further enriched this dataset by adding validated contextual data. Specifically, we included data on the habitat type of the samples assigning fungal taxa to ten different habitat categories. This dataset has been created with the intention to serve as a valuable reference dataset for aquatic fungi including a phylogenetic reference tree. The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.The combined data enabled us to infer fungal community patterns in aquatic systems. Pairwise habitat comparisons showed significant phylogenetic differences, indicating that habitat strongly affects fungal community structure. Fungal taxonomic composition differed considerably even on phylum and class level. Freshwater fungal assemblage was most different from all other habitat types and was dominated by basal fungal lineages. For most communities, phylogenetic signals indicated clustering of sequences suggesting that environmental factors were the main drivers of fungal community structure, rather than species competition. Thus, the diversification process of aquatic fungi must be highly clade specific in some cases.
Fig. 2 in Aquatic insects as the main food resource of fish the community in a Neotropical reservoir
Fig. 2. Alimentary Index (%) of food categories consumed by the fish community of the Nova Avanhandava Reservoir. AI = Aquatic insect; TI = Terrestrial insects; CR = Crustaceans; FI = Fish; MA = Macroinvertebrates; MI = Microcrustaceans; AL = Algae; VM = Vegetal matter; DS = Detritus/sediment; SC = Scales. The acronyms of species are in Table 1.
Fig. 6 in Aquatic invertebrate communities of perennial pans in Mpumalanga, South Africa: a diversity and functional approach
Fig. 6. RDA plot showing the similarity among sites during the different seasons, based on the various community traits (metrics) with physico-chemical variables superimposed. This tri-plot describes 56.2 % of the variation in the data, where 38.6 % is displayed on the first axis and 17.6 % on the second axis. Only metrics of which more than 31 % is explained by the model and the 14 most significant variables are visualised.
Data from: Identification of habitat-specific biomes of aquatic fungal communities using a comprehensive nearly full-length 18S rRNA dataset enriched with contextual data
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Data from: Rapid divergence of predator functional traits affects prey composition in aquatic communities
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Fishing for mammals: landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from lotic ecosystems
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Local and regional drivers influence how aquatic community diversity, resistance and resilience vary in response to drying
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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.