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163 results for “protists”
Protist Dispersal Detection: University of Michigan Biological Station, July 2024
This dataset contains the results of a field dispersal array assembled in Gates Bog, Pellston, Michigan. The data were collected by a graduate student, and consist of measurements of protist presence or absence in 1mL fluid samples taken from pitcher plants and centrifuge tubes in the array. The dataset contains both initial protist detection from the fluid samples, as well as detection after a 24 hour incubation period. The dataset also contains the positions of each plant and tube used for sample collection and their distances from the established source population at the center of the array. We used the purple pitcher plant, Sarracenia purpurea, as a model system to explore questions of specialist protist dispersal. Newly opened pitchers are sterile, providing virgin habitat open to community assembly of highly specialized protist species (Peterson 2008). The placement of a known community of protists at the center of an uncolonized array of habitat patches allows us to identify both sources and destinations of dispersing microbes in the array. The purpose of this study is to measure dispersal rates for a subset of pitcher plant protist species.
Data from: Is a community state reachable, and why?, and Coexistence and collapse: an experimental investigation of the persistent communities of a protist species pool
<p>Deterministic models have difficulties to take into account stochasticity during community assembly. As a tool to circumvent this problem, we present a qualitative discreteevent model, where consequences of interspecific interactions are described as rules. This model provides a map of all possible future dynamics for a given system, which allows to exhaustively describe the possible pathways during an assembly process. Such a description does not rely on species traits details and is insensitive to stochastic effects. This allows to show that subsets of species are sometimes impossible to reach starting from larger sets of species, and therefore to question the reachability of community states during the system’s dynamics. Applying the model to an experimental dataset studying the collapse of protist communities, we obtain a very good theory-experiment agreement. We finally discuss what the notion of reachability can bring to community assembly.</p>
Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila
<p>Abiotic stress is a major force of selection that organisms are constantly facing. While the evolutionary effects of various stressors have been broadly studied, it is only more recently that the relevance of interactions between evolution and underlying ecological conditions, that is, eco-evolutionary feedbacks, have been highlighted. Here, we experimentally investigated how populations adapt to pH-stress under high population densities. Using the protist species <em>Tetrahymena thermophila</em>, we studied how four different genotypes evolved in response to stressfully low pH conditions and high population densities. We found that genotypes underwent evolutionary changes, some shifting up and others shifting down their intrinsic rates of increase (<em>r<sub>0</sub></em>). Overall, evolution at low pH led to the convergence of <em>r<sub>0</sub></em> and intraspecific competitive ability (<em>α</em>) across the four genotypes. Given the strong correlation between <em>r<sub>0</sub></em> and <em>α</em>, we argue that this convergence was a consequence of selection for increased density-dependent fitness at low pH under the experienced high density conditions. Increased density-dependent fitness was either attained through increase in <em>r<sub>0</sub></em> , or decrease of <em>α</em>, depending on the genetic background. In conclusion, we show that demography can influence the direction of evolution under abiotic stress.</p> <p> </p>
Fig. 2A–H in Dirty Tricks in the Plankton: Diversity and Role of Marine Parasitic Protists
Fig. 2A–H. Protistan parasites of marine zooplankton. A – the dinoflagellate Haplozoon inerme (bottom left) parasitizing Appendicularia sicula (after Cachon 1964); B, C – hyperparasitic Amobophrya grassi in Oodinium poucheti, an ectoparasites on Oikopleura (after Cachon 1964); B – several early-stage parasites inside the host; C – macrospore (left) and dividing microspores (right) of A. grassi; D, E – the syndinean dinoflagellate Syndinium bogerti in the acantharian Amphilonche sp. (after Hollande and Enjumet 1955); D – multinuclear parasites inside the host; E – relased parasite macrospore (left) and microspore (right); F–H – Syndinium- like parasite in the copepod Clausocalanus sp. from the NW Mediterranean Sea; F, G – dinospores originating from the infected host in H. Scale bars: 10 µm; H – recently diseased host filled with live dinospores. Scale bar: 100 µm.
Fig. 1 in Problematic Biases in the Availability of Molecular Markers in Protists: The Example of the Dinoflagellates
Fig. 1. Number of species of the most speciose dinoflagellate genera (> 11 species per genus). The empty bars represented the number of described species based on Gómez (2012a). The black bars represent the number of species with, at least, one nucleotide sequence available in DDBJ/EMBL/GenBank in January 2013.
Fig. 1A in Dirty Tricks in the Plankton: Diversity and Role of Marine Parasitic Protists
Fig. 1A–-E. Protistan parasites of marine phytoplankton. A – Amoeba biddulphiae in the diatom Odontella sinensis. Left: recently attached parasite cell. Center: parasitic amoeba inside the host. Rigth: almost empty diatom frustule with protoplasm transformed into 10 amoebae (after Zuelzer 1927); B, C – the stramenopile fungi Lagenisma coscinodisci in the diatom Coscinodiscus sp.; B – host cell protoplasm transformed into parasite hyphae; C – expulsion of parasite swarmer cells. Courtesy of Gerhard Drebes, Plankton*Net Data Provider at the Alfred Wegener Institute for Polar and Marine, http://planktonnet. awi.de; D – Parvilucifera sp. sporangium in a deceased dinoflagellate, Tripos macroceros, from the North Sea; E – Amoebophrya sp. in the dinoflagellate Tripos fusus from the North Sea. Arrows show extreme points of parasite. All scale bars: 50 µm.
Fig. 2 in The Challenges of Incorporating Realistic Simulations of Marine Protists in Biogeochemically Based Mathematical Models
Fig. 2. The mechanistic phytoplankton model of Flynn (2001) that represents multi nutrient uptake and utilisation of N – nitrate; A – ammonium; F – bioavailable iron; P – phosphate; S – silicate; and the interaction with light (PFD). Major flows in and out of state variables (boxes) are depicted by solid arrows, with the major feedback processes depicted by dashed arrows. C – carbon biomass; Cell – cell density; NC – N C-quota; ChlC – chlorophyll C-quota, FC – iron C-quota; IPC – inorganic P C-quota, OPC – organic P C-quota; Scell – silicon cell-quota (reproduced with permission).
Fig. 2 in Living Together in the Plankton: A Survey of Marine Protist Symbioses
Fig. 2. Transmission electron microscopic ultrathin section images of symbionts in some open ocean protists. a – free-living open ocean amoeba with two kinds of intracytoplasmic bacteroids (arrows): round to oval within double membranes, and curved dense rod within a single-membrane vacuole; b – dinoflagellate symbionts as found in planktonic foraminiferans and radiolarians; c – putative prymnesiid symbiont from a radiolarian; d – prasinomonad symbiont from a large spongiose skeletal radiolarian. Figs. b–d: N – nucleus, V – vacuole, arrows – light absorbing plastids (adapted from Anderson 1983). All scale bars: 2 µm.
Fig. 1 in Living Together in the Plankton: A Survey of Marine Protist Symbioses
Fig. 1. Light microscopic images of living symbiont-bearing open ocean protists. a – radiolarian showing the central capsule (Cp) containing the nucleus and surrounding cytoplasm, and external to it, long-tapered, radiating pseudopodia known as axopodia (Ax) appearing as a bright halo, including numerous golden-hued algal symbionts on the axopodia (arrows). Scale bar: 500 µm; b – planktonic foraminiferan bearing a calcitic shell (Sh) and peripherally radiating calcite spines that are covered by pseudopodial cytoplasm bearing scattered algal symbionts (arrows). The small greenish, rounded shell chamber contains dense clusters of symbionts within the intrashell cytoplasm. Scale bar: 100 µm; c – composite image of a portion of a colonial radiolarian with numerous central capsules containing dinoflagellate symbionts (arrows) in the peripheral cytoplasm of the central capsules. The entire colony is enclosed within a optically clear spherical gelatinous sheath. This portion of the colony is illuminated from the lower right-hand side. Scale bar: 500 µm; d – the dinoflagellate Noctiluca scintillans (green form) with numerous living prasinomonad algal symbionts (Pedimonas noctilucae), appearing as clumps of green particles, scattered throughout the cytoplasm. Scale bar: 200 µm.
Fig. 3. A in Changing Views of Arctic Protists (Marine Microbial Eukaryotes) in a Changing Arctic
Fig. 3. A – Whole eukaryotic microbial community bootstrap-supported UPGMA hierarchical clustering tree based on Bray-Curtis ss-diversity metrics. Metazoa were excluded of this analysis; OTU definition as 98% similarity. Analysis were carried out in Qiime as in Kuczynski et al. 2002; samples were subsampled 100 times selecting 2533 sequences (75% of the smallest subsample). B – Only rare eukaryotic community bootstrap-supported UPGMA hierarchical clustering tree based on Bray-Curtis ss-diversity metrics. Metazoa and abundant OTUs (> 0.1%) were excluded of this analysis; OTU definition as 98% similarity. Analysis were carried out in Qiime as in Kuczynski et al. 2002; samples were subsampled 100 times selecting 125 sequences (75% of the smallest subsample).
Fig. 1 in Changing Views of Arctic Protists (Marine Microbial Eukaryotes) in a Changing Arctic
Fig. 1. Polar projections of Arctic Ocean, indicating the three regions outside of the Beaufort Sea, used as an example of community clustering in this review.
Fig. 3 in Helping Protists to Find Their Place in a Big Data World
Fig. 3. Selected component of Catalogue of Life (URL 17), showing that the Family Cyrtolophosidae is classified in three locations (with variant spellings).
Fig 2 in Helping Protists to Find Their Place in a Big Data World
Fig 2. Reconciliation of alternative names for the same taxon (an invasive diatom species). The diagram shows three classes of 'names': scientific names, vernacular names, and surrogates or strings that act in the same way as names (sequence data in this example). Gomphonema vulgare and Echinella geminata were applied independently to the same species and are heterotypic synonyms. The reconciliation group includes the homotypic synonyms (Echinella geminata and Didymosphenia geminata), and the lexical variants of all names. Reconciliation groups allow computer-based queries initiated with one name to be answered with information associated with all names.
Fig. 1 in Helping Protists to Find Their Place in a Big Data World
Fig. 1. Modular' model for the infrastructure of a big data world. a – Within a module, nodes obtain content from one or more sources, normalize, enrich, and deliver it to end users. Annotation systems allow users to advise the source and nodes as to the quality of content. b – Nodes interconnect in anarchic ways that allow for evolution and expanding functionality.
Fig. 5 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 5. Variation in the mean species richness (A) and density (B) of testate amoebae recorded among the different sampling points on the São Francisco stream in Acre state, northern Brazil.
Fig. 4 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 4. The most abundant species of testate amoeba recorded in the present study: (A) Netzelia corona, (B) Arcella vulgaris, (C) Arcella brasiliensis, (D) Arcella discoide, (E) Centropyxis aculeata. Examples of the species of testate amoeba recorded in the state of Acre for the first time: (F) Difflugia distenda, (G) Difflugia sinuata, (H) Arcella gandalfi.
Fig. 2 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 2. Phytophysiognomy of sampling points in the São Francisco stream in the state of Acre, Brazil according to the degree of urbanization.
Fig. 1 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 1. Location of sampling points on the São Francisco stream in the municipalities of Rio Branco and Bujari, in Acre state, northern Brazil.
Fig. 6 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 6. Biplot of the first two axes of the Redundancy Analysis of the scores of the streams according to the abiotic variables (TC = Thermotolerant Coliforms; EC = Electrical Conductivity, DO = Oxygen; pH = Hydrogen potential; TotP = Total Phosphorus; Temp = Temperature; Turb = Turbidity; NH 4 = Ammonia; NO 2 = Nitrate), the legend explains the symbols corresponding to the family names.
Fig. 3 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 3. Plot of the results of the Principal Components Analysis (PCA) of the environmental variables of the São Francisco stream in Rio Branco e Bujari, Acre (Brazil): TC = Thermotolerant Coliforms; EC = Electrical Conductivity; pH = Hydrogen potential; DO = Oxygen; TotP = Total Phosphorus; Phos = Phosphate; Temp = Temperature; Transp = Transparency; Turb = Turbidity; FR = Flow Rate; Dep = Depth; NH 4 = Ammonia; NO 3 = Nitrite; NO 2 = Nitrate.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.