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56 results for “bioindicators”
Data from: Butterflies are not a robust bioindicator for assessing pollinator communities, but floral resources offer a promising way forward
<p>Monitoring pollinators is crucial for the evaluation of biodiversity and potential pollination services. Yet, efficiently monitoring multiple taxa over large areas can be costly. An alternative approach is using simple species bioindicators that represent the entire pollinator community. One of the requirements of a good bioindicator is that it can be easily identified to lower taxonomic levels and be sensitive to changes in habitat. This is the case for butterflies, a taxon for which many countries have a country-wide long-term monitoring scheme. We tested whether butterfly diversity can be used to predict diversity of bees and hoverflies both spatially and temporally. We surveyed 42 transects of the Dutch Butterfly Monitoring Scheme in 2020, to record species richness and abundance of butterflies, bees and hoverflies. We also recorded flower area and richness in the pollinator transects. To test whether pollinators with similar functional traits are more closely correlated than the entire pollinator community, we categorized bee and butterfly species according to their diet breadth (polyphagous vs. non-polyphagous), nitrogen-affinity (nitrophobous vs. nitrophilous larval resources) and body size. We used the same methods to test for temporal correlations over seven years for one site in Spain. Butterfly richness was not spatially correlated with bee richness (Pearson's r = 0.13), nor were the two taxa temporally correlated (Pearson's r = 0.02). Interestingly, hoverfly richness was spatially correlated with butterfly richness (Pearson's r = 0.43) and with bee richness (Pearson's r = 0.36) in the Netherlands and, hence, hoverflies might be slightly more suitable as a bioindicator of pollinator diversity in this area. Abundance of all three taxa showed no significant inter-correlation, except for correlations between diet specialist bees and butterflies (Pearson's r = 0.39). Importantly, all three taxa were strongly correlated with flower richness, but they varied in their preferences for host plant families. This is in line with 75% of the plant-pollinator studies finding significant positive relations. For monitoring schemes to be effective in informing better pollinator conservation, they should expand to include bees and hoverflies as well as simple indicators of habitat quality such as floral resources.</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.
<p>Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.:</p> <p><br> - Trait-based distances calculated for each pair of taxa;</p> <p>- CWM, CWM(LN) values, and their difference (CWMDIFF)</p> <p>Refer to the published articles for further details.</p>
Interactions between land use, taxonomic group and aspects and levels of diversity in a Brazilian savanna: implications for the use of bioindicators
<p>The study was carried out in the Triângulo Mineiro region of Minas Gerais state, covering the municipalities of Uberlândia, Monte Alegre, and Nova Ponte, in south-eastern Brazil. We conducted the study in five habitat types, comprising two natural habitats (savanna and semideciduous forest), and three anthropogenic land-uses: cattle pastures (planted with introduced Urochloa grasses), soy fields (where sampling took place when plants were at the vegetative phase) and plantations of Eucalyptus trees (≥ 6 yrs old). Ants and beetles were sampled at the same 40 sites (8 replicates per land use), and birds at 30 sites (6 replicates per land use), only some of which were the same as for ants and beetles. </p> <p>Ants that forage on ground and dung beetles were sampled using pitfall traps. Sampling took place in November and December (early wet season) 2017. In each site, eight traps were installed with traps located at the corners of a 100×100 m square, and at the mid-points of the sides of the square, keeping a minimum distance of 50 m between any two traps. All traps were at least 75 m distant from the edge of the respective land use. Traps were plastic containers (19 cm diam, 11 cm height) filled with 150 ml of a saline solution and detergent. Each trap had a wire hoop suspended over it to accommodate a small (4 cm diam, 4 cm height) plastic container for holding a dung bait. We used a 20 cm diameter plastic cover supported by three sticks to protect traps from rain. Traps were baited with ~40 g of a mixture of pig dung and human faeces (4:1 proportion) and left in the field for 48-hrs.</p> <p>Birds were surveyed using 20-min point counts in the rainy season (November 2017 to March 2018). At each site, five sampling points were established, 200 m distant from each other. All surveys started at sunrise (about 6 a.m.), and all species seen or heard from each point were recorded. Each sampling site was re-surveyed in the following dry season (April to October to 2018); however, for logistic reasons we were unable to re-survey the plantation sites. </p> <p>Ant and dung beetle species were identified to species or morphospecies by comparison with named species in the Zoological Collection at the Federal University of Uberlândia (UFU) or with specialist assistance from Fernando Vaz de Mello, respectively. Vouchers of all species have been deposited at UFU´s Zoological Collection. Birds were identified directly in the field and species names follow the checklist produced by the Brazilian Ornithological Records Committee.</p> <p>We classified species functionally based on primary diet, foraging location and/or behaviour, and body size, as these traits are known to be sensitive to habitat modifications and of importance for the ecosystem services provided by ants, birds, and dung beetles.</p> <p>Ant species were classified according to their diet as predators, fungivores, nectarivores or omnivores, and according to their main foraging location as arboreal, epigeal (aboveground) or hypogeal (in soil and litter), based on information provided by Brown (2000) and Silvestre et al. (2003). Species were further classified into four body size categories based on our measurements of body length (Weber´s length; Brown, 1953) of 1-5 ant workers per species: 1 (< 0.75 mm), 2 (0.75-1.74 mm), 3 (1.75-3 mm), and 4 (> 3 mm).</p> <p>Dung beetles were classified as coprophagous, necrophagous, frugivore, generalist or predator, according to the type of food resource each species is most often attracted to. This classification was based on over 30 years of field experience throughout Brazil by one of the authors of this study (FVM), who used multiple types of baits (e.g., carcasses, fruits, faeces) to attract and collect dung beetles, and/or on literature information. Although information about the “attractiveness” of different types of baits to dung beetles (used here as a proxy for primary diet) was not obtained directly in the sites of the present study, it is importat to note that we are not aware of any evidence of geographic or habitat variation in bait preference among tropical species of dung beetles. Dung beetles were also classified according to their foraging behaviour as: telecoprid (species that make a dung ball and roll it away for burial), paracoprid (species that store dung in tunnels dug immediately below the dung source), or endocoprid (species living within or immediately below the dung, without moving it). For this, we used the database of the Zoological Collection of the Federal University of Mato Grosso (UFMT). Whenever sample sizes allowed, 30 individuals from each species were weighed for determination of body mass (following Almeida et al., 2011), and species were classified according to the following ordinal scale: 1 (< 10 mg); 2 (10-99 mg); 3 (100-300 mg); and 4 (>300 mg).</p> <p>Each bird species was classified according to its primary diet as frugivores granivore, insectivore, nectarivore, carnivore, detritivore, or omnivore, and according to the main foraging location as ground, understory/shrubby vegetation, or tree canopy, based on the Wilman et al. (2014) database and our own field experience. Using these same sources, we obtained information on mean body weights of each species and assigned them to one of five size categories: 1- (<15 g); 2 (15-39 g); 3 (40-199 g); 4 (200-599 g); and 5 (> 600 g).</p>
Fig. 2 in Does the Mean Individual Biomass (MIB) of carabids as a bioindicator of forest succession follow a logistic function? - Examples from Western German beech and Polish Scots pine forests
Fig. 2. Logistic regression curve – Relationship between age of the Polish Scots pine stands (years) and mean individual biomass of carabids (mg)
Fig. 1 in Does the Mean Individual Biomass (MIB) of carabids as a bioindicator of forest succession follow a logistic function? - Examples from Western German beech and Polish Scots pine forests
Fig. 1. Logistic regression curve – Relationship between age of the Western German beech stands (years) and mean individual biomass of carabids (mg)
Figure 3 in Histological biomarkers and biometric data on trahira Hoplias malabaricus (Pisces, Characiformes, Erythrinidae): a bioindicator species in the Mearim river, Brazilian Amazon
Figure 3. Values of Bernet et al. (1999) index and Poleksic and Mitrovic-Tutundzic (1994) (HAI), in the dry and rainy seasons.
Figure 2 in Histological biomarkers and biometric data on trahira Hoplias malabaricus (Pisces, Characiformes, Erythrinidae): a bioindicator species in the Mearim river, Brazilian Amazon
Figure 2. Histological lesions in H. malabaricus. (A) Normal gill tissue; (B) aneurysm (arrow); (C) epithelial displacement (arrow); and (D) congestion (arrow).
Figure 1 in Histological biomarkers and biometric data on trahira Hoplias malabaricus (Pisces, Characiformes, Erythrinidae): a bioindicator species in the Mearim river, Brazilian Amazon
Figure 1. Location of the Mearim River stretches in the Baixada Maranhense Environmental Protection Area: Engenho Grande village (A1) and Curral da Igreja village (A2).
Fig. 1 in Mussels (Perna perna) as bioindicator of environmental contamination by Cryptosporidium species with zoonotic potential
Fig. 1. Map of the studied area in the municipality of Mangaratiba, Rio de Janeiro State, Brazil. Red marker A — Collection site A; Red marker B — Collection site B; Green marker — The river known as "Rio do Saco" which leads to the ocean at collection site B. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).
Fig. 6 in Morpho-histological characterization of immature of the bioindicator midge Chironomus sancticaroli Strixino and Strixino (Diptera, Chironomidae)
Fig. 6. Micrographs of nervous system structures of immature of Chironomus sancticaroli. (A) Longitudinal section showing the brain cortical and neuropile; (B) longitudinal section of a ganglion in the nerve cord and a connective sheaf formed by axons; (C) longitudinal section showing the brain, thoracic ganglia and the first abdominal ventral nerve cord (numbers); (D) cross-section of the brain; (E) longitudinal section of the cephalic region of the larva, in detail is the frontal ganglion, anterior to the brain. 1st: first thoracic gangliom, 2nd: second thoracic gangliom; 3rd: third thoracic gangliom, I–III: thoracic segments; 4th: first abdominal gangliom; br: brain; cl: cortical layer, cn: connective; dv: diverticulum; fg: frontal gangliom; g: gangliom, ne: neuropile; nl: neural lamella, oe: esophagus; sg: salivary gland, tr: trophoblastes. Stain: Harris hematoxylin and eosin. Scale bar = 20 µm.
Fig. 3 in Morpho-histological characterization of immature of the bioindicator midge Chironomus sancticaroli Strixino and Strixino (Diptera, Chironomidae)
Fig. 3. Micrographs of the midgut of immature Chironomus sancticaroli. (A) Cross-section of the midgut region I; (B) longitudinal section of the midgut region I; (C) cells of the epithelium of the midgut region I, showing the little brush border area (arrow) and apical and basal eosinophilia of the cell (arrowhead); (D) cross-section of the midgut region II; (E) Cross-section of the region III of the midgut; (F) Brush border (arrow) and peritrophic matrix (arrowhead) in region II of the midgut and (G) Brush border (arrow) and cells in the process of secretion (arrowhead) in region III of midgut. cae: gastric caeca; ep: gut epithelia; fd: food; lu: lumen. Stain: Harris hematoxylin and eosin. Scale bar = 20 µm.
Fig. 4 in Morpho-histological characterization of immature of the bioindicator midge Chironomus sancticaroli Strixino and Strixino (Diptera, Chironomidae)
Fig. 4. Micrographs of hindgut immature of Chironomus sancticaroli. (A) cross-section between the transitional epithelium of the midgut and hindgut; (B) longitudinal section of the transition region between the mid and hindgut, showing the proctodeal valve (arrow); (C) in detail, epithelium of the proctodeal valve; (D) cross-section of ileum showing extensive muscle layer and the longitudinal folds formed by the epithelium (arrow); (E) longitudinal section of the colon and rectum; (F) cross-section of the epithelium of the colon and rectum demonstrating basal eosinophilia of the cell (arrowhead). p: epithelia; fd: food; lu: lumen; ml: muscle layer; mlp: Malpighian tubule, pm: perithrofic membrane; vep: valve epithelia. Stain: Harris hematoxylin and eosin. Scale bar = 20 µm.
Fig. 7 in Morpho-histological characterization of immature of the bioindicator midge Chironomus sancticaroli Strixino and Strixino (Diptera, Chironomidae)
Fig. 7. Micrographs of glands from the retrocerebral complex of the immature Chironomus sancticaroli. (A) Longitudinal section of the corpora allata, showing the glandular epithelium, demonstrating the cell nucleus (arrowhead); (B) longitudinal section of the prothoracic gland; (C) longitudinal section showing the region of the complex, demonstrating the anterior postcerebral gland and the small group of cells that make up the corpora cardiac; (D) detail of the anterior postcerebral gland, note the granules in their cytoplasm (arrowhead); (E) detail of the small group of cells that make up the corpora cardiaca (arrows). cc: corpora cardiaca; ga: anterior postcerebral gland; ptg: prothoracic gland, tr: trachea. Stain: Harris hematoxylin and eosin. Scale bar = 20 µm.
Figure 1 in Positioning entomopathogenic nematodes for the future viticulture: exploring their use against biotic threats and as bioindicators of soil health
Figure 1. Example of the progression of authorized phytosanitary product usage in Spain against the most important diseases and pests of vineyards during the last decade. The size of each circle is proportional to the total number of phytosanitary authorized against each biotic threat.1
Fig. 2 in Foraminiferal assemblages as palaeoenvironmental bioindicators in Late Jurassic epicontinental platforms: Relation with trophic conditions
Fig. 2. Mean values of the proportions of test type and life habit of the foraminiferal assemblages in the examples studied from Boreal (Inner Moray Firth Basin) (A) and Tethyan (Prebetic) domains (B).
Fig. 1 in Foraminiferal assemblages as palaeoenvironmental bioindicators in Late Jurassic epicontinental platforms: Relation with trophic conditions
Fig. 1. Location of the sections studied Brora, Riogazas−Chorro, and Navalperal (A) with geological sketch of northeastern Scotland (B) and southeastern Spain (C), lithological columns (D) with detailed sample locations (reviewed in black circle and new in white circle), and palaeogeographic reconstruction of the western Tethys during the Callovian–Oxfordian transition (E).
Fig. 5 in Foraminiferal assemblages as palaeoenvironmental bioindicators in Late Jurassic epicontinental platforms: Relation with trophic conditions
Fig. 5. Palaeoecological reconstruction of foraminiferal assemblages from lumpy lithofacies group and marl−limestone rhythmite, and changes in selected palaeoenvironmental features (organic matter content, oxygenation, sedimentation rate, consolidation of substrate and relative distance to shore). Legends of foraminifera and pie−diagrams are in Table 2 and Fig. 3.
Fig. 4 in Foraminiferal assemblages as palaeoenvironmental bioindicators in Late Jurassic epicontinental platforms: Relation with trophic conditions
Fig. 4. Palaeoecological model of foraminiferal assemblages from Brora Brick Clay and Fascally Siltstone members, and changes in selected palaeoenvironmental features (organic matter content, oxygenation, sedimentation rate and relative distance to shore). The model tries to give a rough idea about what was deeper and shallower, but the Brora Brick Clay and Fascally Siltstone are not contemporaneous. Legends of foraminifera and pie−diagrams are in Table 2 and Fig. 3.
Figure 5 in First data on water mite (Acari, Hydrachnidia) assemblages of Point Rosa Marsh, Harrison Township, Michigan, USA, and their use as environmental bioindicators of aquatic health
Figure 5 Frequency of water mite genera collected from habitats surrounding Point Rosa Marsh including Lake St. Clair. Comparable samples were collected on ten collection dates during 2017, 2018 and 2019. Graphs are arranged (left to right, and then by row) in the order of the overall frequency of each genus. Each bar graph shows the number of taxa collected on the six collection dates with bars color-coded to assist in comparing graphs on various dates. Dark blue (B) [Oct. 18 2017], red (A) [Oct. 20 2017 (1)], light green (B) [Oct. 20 2017], dark green (D&C) [Oct. 27 2017], black (A) [Aug. 7 2018], orange (A) [Aug. 21 2018], grey (D&C) [Aug. 31 2018], yellow (A) [Sept. 16 2019], light
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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.