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35 results for “Parasite conservation”
Herbivore dung and parasite counts, Ol Pejeta Conservancy and Mpala Research Centre, Kenya (2015-2018)
Data package contains two datasets of dung surveys, one dataset of parasite egg measurements, and two camera trap datasets collected from Mpala Research Centre and Ol Pejeta Conservancy, Laikipia County, Kenya from November 2015-September 2018. Datasets are provided as part of the publication `Watering sources aggregate parasites with increasing effects in more arid conditions`. Source data files for figures in the manuscript are also provided here.
Figure 3 in Host conservation through their parasites: molecular surveillance of vector-borne microorganisms in bats using ectoparasitic bat flies
Figure 3. Comparison of detected microorganism prevalence (prevalence of infection) between bats and bat flies. Different bars represent hosts (black), all bat flies (dark grey), and consensus fly results, meaning that at least one infected fly individual was present on the host (light grey).
Figure 2 in Host conservation through their parasites: molecular surveillance of vector-borne microorganisms in bats using ectoparasitic bat flies
Figure 2. Prevalence of Bartonella spp., Polychromophilus spp., and Trypanosoma spp. infection in nycteribiid flies collected from 28 bats, which carried between 2 and 7 flies. Black: all flies are infected, dark grey: all flies are non-infected, light grey: both infected and non-infected flies occurred on the same host.
Figure 1 in Host conservation through their parasites: molecular surveillance of vector-borne microorganisms in bats using ectoparasitic bat flies
Figure 1. Number of detected vector-borne microorganisms in bats (A) and bat flies (B). Black colour corresponds to Miniopterus natalensis (A), and Nycteribia schmidlii scotti (B), whereas grey shows Miniopterus schreibersii (A) and Nycteribia schmidlii (B).
Fig. 3 in Cluster Analysis of Non-conserved Proteins of Trypanosoma cruzi Reference Strains Displays Parity between these Groupings (Peptidemes) and the Consensually Accepted Parasite Lineages
Fig. 3. Phenogram of the peptidemes (P) of eight Trypanosoma cruzi reference strains obtained using the SM coefficient and the UPGMA clustering algorithm, based on data from non-conserved proteins, as seen in SDS-PAGE analysis. The major peptidemes are indicated as mP 1 and mP 2. Their subgroups are identified on the right (P II, P VI, P I), and were numbered following their respective genetic types (TcII, TcVI, TcI), as currently used.
Fig. 1 in Cluster Analysis of Non-conserved Proteins of Trypanosoma cruzi Reference Strains Displays Parity between these Groupings (Peptidemes) and the Consensually Accepted Parasite Lineages
Fig. 1. Total protein profiles of eight Trypanosoma cruzi reference strains separated in 10% SDS-PAGE at 250 V, 25 mA, 90 min, and stained by Coomassie brilliant blue. The position of some conserved proteins is indicated on the right. M: molecular mass markers. (kDa) are indicated on the left.
Fig. 2 in Cluster Analysis of Non-conserved Proteins of Trypanosoma cruzi Reference Strains Displays Parity between these Groupings (Peptidemes) and the Consensually Accepted Parasite Lineages
Fig. 2. Diagrammatic representation of the twenty-two protein bands not shared by all Trypanosoma cruzi reference strains (nonconserved proteins), as visualized in SDS-PAGE. These bands were coded and analyzed by numerical taxonomy procedures. At the top is indicated the number of the major groups they belong, as identified by different approaches. The bands that were exclusive of one or more strains were highlighted with rectangles. M: molecular mass markers. (kDa) are indicated on the left.
Fig. 1. A in Neglected wild life: Parasitic biodiversity as a conservation target
Fig. 1. A conceptual framework for parasite conservation. Practitioners should establish conservation priorities (here we show the prioritization categories in Gómez et al., 2012) and design conservation strategies using a combination of interrelated approaches.
Fig. 5 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 5. Phylogenetic trees based on 28S (left) and cox1 (right) sequences of Strongyloides eggs. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference.
Fig. 2 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 2. Egg morphotypes found in the faeces of Mexican primates. A) Trypanoxyuris sp., B) Controrchis biliophilus, arrow pointing to the two eyespot remnants; C) trematode, diagnosed as C. biliophilus by molecular data; D) unidentified ancylostomatid; E) Strongyloides sp.; F) unidentified ascarid. Scale bar is equal to 15 Mm.
Fig. 4 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 4. Phylogenetic tree based on 28S sequences of Controrchis biliophilus. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference. Host species are indicated within parenthesis.
Fig. 1 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 1. Surveyed sites for parasites in Mexican primates. Dots indicate sampling sites, black: Alouatta palliata; white: A. pigra; and grey: Atetes geoffroyi. Polygons indicate the primate distribution range in Mexico, diagonal lines: A. palliata; dashes: A. pigra; and grey: A. geoffroyi.
Fig. 3 in Helminth parasites of howler and spider monkeys in Mexico: Insights into molecular diagnostic methods and their importance for zoonotic diseases and host conservation
Fig. 3. Phylogenetic trees based on 28S (left) and cox1 (right) sequences of Trypanoxyuris sp. Sequences obtained from the eggs are bold type and indicated with an *. Numbers at the nodes represent posterior probabilities from Bayesian inference.
Fig. 1 in Review on parasites of wild and captive giant pandas (Ailuropoda melanoleuca): Diversity, disease and conservation impact
Fig. 1. Distribution of wild giant pandas in six mountain regions (Qinling, Minshan, Qionglai, Liangshan, Daxiangling and Xiaoxiangling) in three Provinces (Gansu, Shaanxi, and Sichuan) of China. Adapted from Wang et al. (2018).
Fig. 1 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 1. Map illustrating localities where the five cyprinid hosts were collected in the Cape Fold ecoregion in the Western Cape, South Africa.
Fig. 7 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 7. Rarefaction/extrapolation curve estimating the diversity of parasites as a function of sampling effort for three of the five hosts collected in the OlifantsDoorn River System, Western Cape Province, South Africa. Shaded area represents the 95% confidence interval obtained using the bootstrap method based on 100 repetitions. Created using iNEXT Online (Chao et al., 2016).
Fig. 4 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 4. Pseudobarbus calidus (Barnard, 1938) (max. length: 125 mm) (A). Sclerites of Paradiplozoon sp. from the gills (B). Acanthocephala from the body cavity, whole specimen (C) and hooks on proboscis (top left insert). Larval Contracaecum sp. from the body cavity, anterior (D) and posterior (E) ends, lateral view. Scale bars: 100 μm (B, C, D, E).
Fig. 1 in Conservation of parasites: A primer
Fig. 1. Decision tree for conservation management of threatened parasite species. See text for potential management actions. For other examples of decision trees, used in a more restricted fashion to assess parasite conservation status or to determine which parasites should be the focus of conservation actions, see Moir et al. (2011); Dougherty et al. (2016); Kwak (2018).
Fig. 6 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 6. Sedercypris erubescens (Skelton, 1974) (max. length: 120 mm) (A). Larval Contracaecum sp. from the body cavity, anterior (B) and posterior (C) ends, lateral view. Scale bars: 100 μm (B, C).
Fig. 5 in Working towards a conservation plan for fish parasites: Cyprinid parasites from the south African cape fold freshwater ecoregion as a case study
Fig. 5. Pseudobarbus phlegethon (Barnard, 1938) (max. length: 65 mm) (A); Acanthogyrus sp. found from the body cavity (B). Scale bar: 500 μm.
ScienceDex guides
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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.