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6,771 results for “freshwater”
Using marsh organs to test seed recruitment in tidal freshwater marshes
<p><strong>Premise:</strong> Seed recruitment niches along estuarine elevation gradients are seldom experimentally field-tested under tidal regimes of the Pacific Northwest of North America. Addressing this knowledge gap is important to better understand estuary restoration and plant community response to sea level rise.</p> <p><strong>Methods:</strong> Germination was tested in marsh organ mesocosms across an elevation gradient (0.5–1.7 m above mean sea level). Seeds were sown on sterile peat moss, and the tops of pipes were secured with horticultural "frost cloth" to ensure no experimental seeds were washed out and no new seeds were introduced. The trials tested artificial and overwinter chilling regimes, as well as the presence and/or absence of a near-neighbor transplant.</p> <p><strong>Results:</strong> <em>Carex</em> <em>lyngbyei</em> had significant elevation-driven germination after overwinter and artificial chilling. <em>Schoenoplectus tabernaemontani</em> had near-significant germination across elevation after overwinter chilling, and germination in the absence of competition was significantly greater than with a near-neighbor transplant.</p> <p><strong>Discussion:</strong> <em>Carex lyngbyei</em> had the highest germination rate at higher elevations, which suggests restricted seed recruitment potential, and required clonal expansion to extend into lower marsh elevations. Identifying species-specific recruitment niches provides insight for restoration opportunities or invasive species monitoring, as well as for estuary migration under sea level rise.</p>
Environmental nucleic acids: a field-based comparison for monitoring freshwater habitats using eDNA and eRNA
<p>Nucleic acids released by organisms and isolated from environmental substrates are increasingly being used for molecular biomonitoring. While environmental DNA (eDNA) has received attention recently, the potential of environmental RNA as a biomonitoring tool remains less explored. Several recent studies using paired DNA and RNA metabarcoding of bulk samples suggest that RNA might better reflect "metabolically active" parts of the community. However, such studies mainly capture organismal eDNA and eRNA. For larger eukaryotes, isolation of extra-organismal RNA will be important, but viability needs to be examined in a field-based setting. In this study we evaluate (a) whether extra-organismal eRNA release from macroeukaryotes can be detected given its supposedly rapid degradation, and (b) if the same field collection methods for eDNA can be applied to eRNA. We collected eDNA and eRNA from water in lakes where fish community composition is well documented, enabling a comparison between the two nucleic acids in two different seasons with monitoring using conventional methods. We found that eRNA is released from macroeukaryotes and can be filtered from water and metabarcoded in a similar manner as eDNA to reliably provide species composition information. eRNA had a small but significantly greater true positive rate than eDNA, indicating that it correctly detects more species known to exist in the lakes. Given relatively small differences between the two molecules in describing fish community composition, we conclude that if eRNA provides significant advantages in terms of lability, it is a strong candidate to add to the suite of molecular monitoring tools.</p>
Differential reproductive plasticity under thermal variability in a freshwater fish (Danio rerio)
<p>Human-driven increases in global mean temperatures are associated with concomitant increases in thermal variability. Yet, few studies have explored the impacts of thermal variability on fitness-related traits, limiting our ability to predict how organisms will respond to dynamic thermal changes. Among the myriad organismal responses to thermal variability, one of the most proximate to fitness – and, thus, a population's ability to persist - is reproduction. Here, we examine how a model freshwater fish (Danio rerio) responds to diel thermal fluctuations that span the species' viable developmental range of temperatures. We specifically investigate reproductive performance metrics including spawning success, fecundity, egg provisioning, and sperm concentration. Notably, we apply thermal variability treatments during two ontogenetic timepoints to disentangle the relative effects of developmental plasticity and reversible acclimation. We found evidence of direct, negative effects of thermal variability during later ontogenetic stages on reproductive performance metrics. We also found complex interactive effects of early and late-life exposure to thermal variability, with evidence of beneficial acclimation of spawning success and modification of the relationship between fecundity and egg provisioning. Our findings illuminate the plastic life-history modifications that fish may undergo as their thermal environments become increasingly variable.</p>
Eddy-freshwater interaction in Bay of Bengal
<p>The model simulation was performed using the Regional Ocean Modeling System (ROMS) in the Bay of the Bengal at 3 Km to study the mechanism behind the eddy-freshwater interaction during the post-monsoon period, i.e., from October to November of 2015. It is a part of the doctoral work by Nihar Paul at the Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, India, done in 2022.</p>
Fig. 8 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 8. Presence of i56 sequence varieties in Spongilla lacustris (Linnaeus, 1759) at different collecting sites. Black lines on the map represent watersheds between the catchment areas and cross hairs mark the actual sampling site. Seven sequence varieties were found, designated z-242T, z-242G, y-290G, y-290A, y-24G, w-377G and w-377T. Pie charts indicate fraction of the varieties at a site, and circle area corresponds to number of specimens sequenced from the site.
Fig. 7 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 7. Networks of i56 varieties in Swedish Spongilla lacustris (Linnaeus, 1759). Colours indicate catchment area. Seven varieties are designated; z-242T, z-242G, t-24G, y-290G, y-290A, w-377G and w-377T, see Table 3. A. PopART integer neighbor-joining network (described in Leigh & Bryant 2015). Pie chart size proportional to the number of specimens found of the variety. Hatches indicate number of substitutions between varieties, sites with indels are excluded; thus, no differences are picked up between z-242T and z-242G. B. SplitsTree neighbor network based on HKY85 distances.
Fig. 6 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 6. Neighbor-joining trees based on HKY85 distances. A. coxI dataset. B. 28S dataset. Sequences with a specimen field number (starting with P052- or P059-) are from the present study, remaining sequences are from GenBank with the accession number in the label.
Fig. 5 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 5. Eunapius fragilis (Leidy, 1851). A. Habitus (P059-170829-4). B. Spiculae, m = megasclere (P059-170830-13), g = gemmulosclere (P059-170902-1). C–D. Estimated spicula size distribution within and between specimens, P059-170802-10 (solid line), P059-170830-2 (dashed) and P059-170830- 4 (dotted). Marks on the x-axis represent spiculae measured. C. Megascleres. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019). D. Gemmuloscleres.
Fig. 3 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 3. Ephydatia fluviatilis (Linnaeus, 1759). A. Habitus (P059-211231-1). B. Spiculae, m = megasclere (P059-170902-2), g = gemmulosclere (P059-170902-1). C. Estimated megasclere size distribution within and between specimens, P059-170902-1 (solid line), P059-170902-2 (dashed), P059-170902-3 (dotted). Marks on the x-axis represent spiculae measured. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019).
Fig. 4 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 4. Ephydatia muelleri (Lieberkühn, 1856). A. Habitus (P059-170829-4). B. Spiculae, m = megascleres (P059-170830-13), g = gemmuloscleres (P059-130828-18). C. Estimated megasclere size distribution within and between specimens, P059-130829-3 (solid line), P059-170830-13 (dashed) and UP-16-1-2 (dotted). Marks on the x-axis represent spiculae measured. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019).
Fig. 1 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 1. Sampling sites and species found in main catchment areas in Sweden. Black lines represent watersheds between catchment areas, from the Swedish Meteorological and Hydrological Institute SVAR database (Westman et al. 2017). Crosses are sites visited but where no sponge was found, dots are sites where sponges were collected. The neighbouring pie icon indicates the species present at that site: blue (top right) is Spongilla lacustris (Linnaeus, 1759), yellow (bottom right) Eunapius fragilis (Leidy, 1851), black (bottom left) Ephydatia fluviatilis (Linnaeus, 1759) and green (top left) Ephydatia muelleri (Lieberkühn, 1856).
Fig. 2 in Addition to Sweden's freshwater sponge fauna and a phylogeographic study of Spongilla lacustris (Spongillida, Porifera) in southern Sweden
Fig. 2. Spongilla lacustris (Linnaeus, 1759). A–B. Habitus. A. Specimen growing under a pontoon (P059-171011-6). B. Specimen growing on anchor chain, with finger–like projections (P059-170802- 48). C–D. Spiculae. C. Megasclere (P059-171008-4). D. Microsclere (P059-171008-1). E–F. Estimated spicula size distribution within and between specimens, P059-130831-1 (solid line), P059-170802- 8 (dashed) and P059-171008-4 (dotted). Marks on the x-axis represent spiculae measured. Brackets correspond to ranges in literature (red = Tendal 1967b; blue = Penney & Racek 1968; black = Evans & Montagnes 2019). E. Megascleres. F. Microscleres.
Data and code for Freshwater corridors in the conterminous US: a coarse-filter approach based on lake-stream networks
<p>This repository contains various datasets used to map and analyze freshwater connectivity (i.e., corridors) in the conterminous US based on networks of lakes, streams and rivers. We considered lake-stream networks as analogous to habitat corridors. Hub lakes are individual lakes that are disproportionately important for maintaining intact networks. We also analyzed the protection status of freshwater connectivity using the US Protected Areas Database v. 2.0. R analysis scripts can also be found in this repository. Much of the data we used came from published or soon-to-be published sources, which are referenced below.</p>
Fig. 7 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 7. Martensina thailandica gen. et sp. nov. A–B, D. Holotype, ♂ (MSU-ZOC.317). C, E. Paratype, ♂ (MSU-ZOC.319). A. T3. B. CR attachment. C. CR. D. Hemipenis. E. Zenker organ. Arrow indicates reticulated structure. Scale bars = 100 μm.
Fig. 5 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 5. Martensina thailandica gen. et sp. nov., holotype, ♂ (MSU-ZOC.317). A. Md-coxa. B. Md-palp. B'. Md-palp, α, β, γ setae. C. Mx1. Scale bars: A–B = 100 μm; C = 50 μm.
Fig. 6 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 6. Martensina thailandica gen. et sp. nov. A–C, E. Holotype, ♂ (MSU-ZOC.317). D. Allotype, ♀ (MSU-ZOC.318). A. Right T1. B. T1, left prehensile palp. C. T1 protopodite. D. T1 endopodite. E. T2. Scale bars: A–B, D–E = 100 μm; C = 50 μm.
Fig. 2 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 2. Martensina thailandica gen. et sp. nov. A–F. Paratype, ♂ (MSU-ZOC.319). G–H. Allotype, ♀ (MSU-ZOC.318). A. LV, internal view. B. RV, internal view. C. LV, internal view, posterior part. D. LV, internal view, anterior part. E. RV, internal view, anterior part. F. RV, internal view, posterior part. G. LV, internal view. H. RV, internal view. Scale bars: A–B, G–H = 200 μm; C–F = 100 μm.
Fig. 4 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 4. Martensina thailandica gen. et sp. nov. A–B. Holotype, ♂ (MSU-ZOC.317). C. Allotype, ♀ (MSU-ZOC.318). A. A1. B. A2. C. A2, last two segments. Scale bars = 100 μm.
Fig. 3 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 3. Martensina thailandica gen. et sp. nov., allotype, ♀ (MSU-ZOC.318). A. LV, internal view, posterior part. B. LV, internal view, anterior part. C. LV, internal view, muscle scars. D. RV, internal view, anterior part. E. RV, internal view, posterior part. Scale bars: A–B, D–E = 100 μm; C = 50 μm.
Fig. 1 in Martensina thailandica gen. et sp. nov. a freshwater ostracod representing a new subfamily of Cyprididae, Martensininae subfam. nov. (Crustacea: Ostracoda) from Thailand
Fig. 1. Martensina thailandica gen. et sp. nov., paratypes. A–E. Males. F–I. Females. A. Cp, dorsal view (MSU-ZOC.321). B. Cp, ventral view (MSU-ZOC.324). C. Cp, left lateral view (MSU-ZOC.322). D. Cp, right lateral view (ditto). E. Cp, left lateral view, valve surface (ditto). F. Cp, left lateral view, valve surface (MSU-ZOC.330). G. Cp, dorsal view (MSU-ZOC.329). H. Cp, left lateral view (MSUZOC.330). I. Cp, right lateral view (ditto). Scale bars: A–D, G–I = 200 μm; E–F = 10 μm.
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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.