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FIGURE 2 in Damming in the Madeira River modifies the food spectrum of piscivorous and affects their resource partitioning
FIGURE 2 | The percent volume of prey consumed by four piscivorous fishes in Madeira River. A. Acestrorhynchus falcirostris, B. Acestrorhynchus heterolepis, C. Hydrolycus scomberoides, and D. Rhaphiodon vulpinus. Blue bars correspond to the pre-damming period and red bars to the post-damming period. Items are Aces = Acestrorhynchidae, Cyno = Cynodontidae, Char = Characidae, Curi = Curimatidae, Ster = Sternopygidae, Hemi = Hemiodontidae, Lori = Loricariidae, Auch = Auchenipteridae, Dora = Doradidae, Pime = Pimelodidae, Inse = Insects, Vege = Vegetable.
FIGURE 1 in Damming in the Madeira River modifies the food spectrum of piscivorous and affects their resource partitioning
FIGURE 1 | Study area in the Madeira River portion. Black points representing fixed sampling sites for both pre-and post-damming phases. Triangles represent the sampling sites located in the reservoir area in the post-damming phase. Arrows indicate the river flow.
FIGURE 7 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 7 | Relationships between body size (standard length, mm) and total Hg of fishes commonly used for consumption. Top panel (non-mined) contains three species that were common at mined sites (bottom panel). Relationships were estimated for five species that are commonly found and consumed, and for what a class structure was observed. Symbols represent individual fish. Clear diamonds = frugivores [1 sp.: Myloplus asterias (n = 19)], dark triangles = carnivores [1 sp.: Serrasalmus eigenmanni (n = 14)], green circles = piscivores [(3 spp.: S. rhombeus (n = 24), Ageneiosus ucayalensis (n = 2), Hoplias malabaricus (n = 1)].
FIGURE 6 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 6 | Mean Hg concentrations (dry weight) in sediments and instream biota separated by trophic groups (producers and consumers) between mined and non-mined sites in the Mazaruni River, Guyana. Species were grouped together based on their trophic guild. Polynomial trendline: y = 0.05x2 - 0.22x + 0.28, coefficient of determination R² = 0.98.
FIGURE 2 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 2 | Locations of sampling sites in the middle Mazaruni River drainage in A. Guyana within B. South America. Sampling sites included location in the C. Main channel of the Mazaruni River (mined) and three main tributaries including Eping River (non-mined), Puterang and Kurupung rivers (mined).
FIGURE 5 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 5 | Biomagnification of Hg in the Mazaruni River, Guyana, represented as the log total Hg concentrations plotted against nitrogen (δ15N) isotope ratios. Species were grouped together based on their trophic guild. Each symbol represents the average of all species within each trophic guild. The dashed horizontal line indicates the reference limit of 0.5 µg/g by WHO guideline for Hg in fish consumed by humans.
FIGURE 1 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 1 | Conceptual model of how Hg enters riverine food webs of the Mazaruni River, Guyana. First, artisanal gold mining operations installed in the river use a suction dredge to reach gold contained in bottom sediments. These mining operations use mercury for gold amalgamation of which the majority is lost to the atmosphere or river. Once in the river, Hg can be methylated by microbes into the toxic Methylmercury (MeHg) and be assimilated rapidly by aquatic biota. Hg concentration bioaccumulates and biomagnifies in the trophic food chain. Fishes at higher trophic levels [piscivores and carnivores (e.g., piranhas, aimaras, and catfishes)] are important items in the diet of local communities and can become a direct source of Hg uptake via direct consumption.
FIGURE 4 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 4 | Mean nitrogen (δ15N) and carbon (δ13C) isotope ratios of fish species collected from mined (right panel) and non-mined (left panel) sites in the Mazaruni River, Guyana. Species were grouped together based on their trophic guild. Each symbol represents the average of all species within each trophic guild. Numbers represent the basal resources collected at surveyed sites (1 = bryophyte, 2 = benthic algae, 3 = aquatic macrophytes). The abbreviations for the fish trophic guilds: Algivore/ Detritivore (Alg/Det), Insectivore (Insec), Herbivore (Herb), Omnivore (Omni), Carnivore (Carn), Piscivore (Pisc), as well as one shrimp (Macrobrachium).
FIGURE 3 in The cost of gold: Mercury contamination of fishes in a Neotropical river food web
FIGURE 3 | Sampling locations on the middle Mazaruni River: A. Main channel of the upper Eping River a small, black water tributary of the Mazaruni River. This river appeared more pristine and less impacted by gold mining activities; B. Sandy, shallow habitat sampled in middle channel of the upper Eping River; C. A gold dredge in the middle of the Kurupung River, on the right side is evidence of soil removal that resulted from mining activities; D. Deforestation (to establish mining stations) along the Kurupung River upstream, left bank from the confluence with the Mazaruni River; E-F. Main channel of the Mazaruni River at Olive Creek. At this location, the main channel of Mazaruni River is highly impacted by gold mining activities and gold dredges and 'tailing' beaches are commonly observed; G-H. The main channel of the lower Puterang River before the confluence with the Mazaruni River. At these locations, the Puterang River carries down heavily silted sediments that are spilled into the Mazaruni River.
Figure 2 in Food habits of indian crested porcupine (Hystrix indica) (Kerr 1792), in district Bagh, Azad Jammu and Kashmir
Figure 2. Percent relative frequency of different parts of plants recovered from the fecal samples collected from the study area.
Figure 3 in Food habits of indian crested porcupine (Hystrix indica) (Kerr 1792), in district Bagh, Azad Jammu and Kashmir
Figure 3. Seasonal variation in the percentage of different parts of plant recovered from fecal samples in the study area.
Formulas for calculating food self-sufficiency ratios in previous studies and author comments
<p><span>The<strong> food self-sufficiency ratio of each country</strong> is obtained by dividing the amount of food production by the amount of food supply, but this is not the only way to calculate the food self-sufficiency ratio. Before starting the calculation, issues such as the food to be calculated, the unit of measurement of food and its data source, and the calculation method for primary and secondary products must be resolved. We summarized how major previous studies have addressed this issue.<strong> The author of this document selected 20 studies that contain specific descriptions from previous studies on this issue</strong>. The author then added comments to all previous studies. The main comment was that although <strong>the formula for calculating the food self-sufficiency ratio of each country is incomplete</strong>, there is a large amount of valid previous research that contains many suggestions.</span></p>
Framework for the Supply-side Food self-sufficiency ratio (SSFSSR)
<p><span>The SSFSSR is<span> <strong> </strong></span><strong>a method of calculating Food self-sufficiency Ratio (FSSR) by considering food from the point it enters the supply chain after harvest until it is consumed.</strong> It thus incorporates the stages of post-harvest, storage, import, and export. Most food in these stages is in the form of primary products, with some being processed into secondary products, such as livestock products, edible oils, sugar products, and juices, which undergo compositional changes. The SSFSSR automatically accounts for the quantitative loss of food due to spoilage, waste, rodent damage, and attrition during processing after harvest. <strong>The conventional methods of the FSSR excludes the calories of primary products used as intermediate inputs, such as livestock feed and raw materials for edible oils, as well as losses</strong>, spoilage, waste, rodent damage, and attrition during processing from the post-harvest stage until human consumption.</span></p>
Figure 1 in The common wall lizard Podarcis muralis (Reptilia: Lacertidae) shows diverse food preferences and intraspecific differences: a study case from Bulgaria
Figure 1. Percentage share of the OTUs according to: number of faecal samples of P. muralis in which the OTU was found (Fr.); number of specimens registered in the faecal samples (N); number of specimens, collected by pit-fall traps (Tr.).
Figure 3 in The common wall lizard Podarcis muralis (Reptilia: Lacertidae) shows diverse food preferences and intraspecific differences: a study case from Bulgaria
Figure 3. Percentage share of the categories of evasiveness (E1, E2, and E3) and hardness (H1, H2, and H3) according to the number of categorized prey items from the faecal samples of P. muralis (Ad. = adults; Imm. = immatures; M = males; F = females; Tot. = the entire sample).
Figure 2 in The common wall lizard Podarcis muralis (Reptilia: Lacertidae) shows diverse food preferences and intraspecific differences: a study case from Bulgaria
Figure 2. Diversity profiles of the diet of P. muralis based on the faecal samples from the study sites (Ad. = adults; Imm. = immatures; M = males; F = females).
CHOWNET: An Image Dataset of Nigerian Food
<p>CHOWNET-V1 is a high-quality dataset consisting of 118 human-annotated food images, specifically curated for multi-label classification, food object detection, and food captioning tasks. The dataset includes 99 unique labels, serving as a valuable resource for a range of computer vision challenges within the food domain.</p> <p><br>Github Link: <a href="https://github.com/AISaturdaysLagos/chownet">https://github.com/AISaturdaysLagos/chownet</a></p> <p>Data Annotation for CHOWNET-V1 was led by: <a href="https://www.linkedin.com/in/tejumadeafonja/">Tejumade Afonja</a> and <a href="https://www.linkedin.com/in/george-igwegbe/">George Igwegbe</a></p> <p>This dataset was contributed by the AI Saturdays Lagos community in 2018.</p> <p> </p> <blockquote> <p>The dataset structure is described in About.txt</p> </blockquote>
Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos / Practical Transcriptomics: Differential gene expression applied to food production
<p>Data for the eLearning tutorial Practical Transcriptomics: Differential gene expression applied to food production</p> <p>Datos para el tutorial eLearning Práctica de transcriptómica: expresión diferencial de genes aplicado a la producción de alimentos</p>
Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA
<p>Data and code necessary to replicate the findings from the manuscript titled "Hydrology and trophic flexibility structure alpine stream food webs in the Teton Range, Wyoming, USA".</p> <p><span>Abstract:</span><strong><span> </span></strong><span>Understanding biotic interactions and how they vary across habitats is important for assessing the vulnerability of communities to climate change. Receding glaciers in high mountain areas can lead to the hydrologic homogenization of streams and reduce habitat heterogeneity, which are predicted to drive declines in regional diversity and imperil endemic species. However, little is known about food web structure in alpine stream habitats, particularly among streams fed by different hydrologic sources (e.g., glaciers or snowfields). We used gut content and stable isotope analyses to characterize food web structure of alpine macroinvertebrate communities in streams fed by glaciers, subterranean ice, and seasonal snowpack in the Teton Range, Wyoming, USA. Specifically, we sought to: (1) assess community resource use among streams fed by different hydrologic sources; (2) explore how variability in resource use relates to feeding strategies; and (3) identify which environmental variables influenced resource use within communities. Average taxa diet differed among all hydrologic sources, and food webs in subterranean ice-fed streams were largely supported by the gold alga <em>Hydrurus</em>. This finding bolsters a hypothesis that streams fed by subterranean ice may provide key habitat for cold-water species under climate change by maintaining a longer growing season for this high-quality food resource. While a range of environmental variables associated with hydrologic source (e.g., stream temperature) were related to diet composition, hydrologic source categories explained the most variation in diet composition models. Less variable diets within versus among streams suggests high trophic flexibility, which was further supported by high levels of omnivory. This inherent trophic flexibility may bolster alpine stream communities against future changes in resource availability as the mountain cryosphere fades. Ultimately, our results expand understanding of the habitat requirements for imperiled alpine taxa while empowering predictions of their vulnerability under climate change.</span></p>
Supplementary data and code for "Predicting anthropogenic food supplementation from individual tracking data"
<p><span>This repository contains data and R-Scripts to reproduce the results presented in the paper “</span><span>Predicting anthropogenic food supplementation from individual tracking data</span><span>” published in Ibis. Please note that some data (basemap of Switzerland, forests, buildings) are publicly available from </span><span><a href="https://map.geo.admin.ch/"><span>https://map.geo.admin.ch/</span></a></span><span> and are therefore not provided here. The abstract of the article is as follows: </span><span>Many wildlife species consume food or refuse provided by humans. To understand the effect of anthropogenic food subsidies on wildlife populations, we first need to quantify where and when individuals can access such food sources. The red kite <em>Milvus milvus</em> is an opportunistic raptor species and uses both inadvertent and deliberate food subsidies provided by citizens. Here we present a new approach using GPS-tracking data to predict where anthropogenic food subsidies likely occur. We tracked 497 individuals with solar-powered GPS transmitters over an average of 3.2 (range 1 – 9) breeding seasons in Switzerland, and combined these data with locations of 125 known feeding sites obtained through interviews. We used two sequential random forest models, at both individual movement and population levels, to predict where anthropogenic food subsidies were attended by red kites. The first model classified locations that were frequently and regularly revisited, and successfully predicted 85% of locations that were within 50 m of an externally validated feeding site. These predicted locations were aggregated in 500 m grid cells to calculate the proportion of individuals and locations associated with predicted food subsidy. A second model related the presence of known food subsidies to the aggregated predictions. In our study area, 80% of known anthropogenic food provision locations could be correctly identified using red kite tracking data, but data sparsity beyond the core range of tracked individuals limits predictions of anthropogenic food subsidies at larger geographic scales. Nonetheless, biologging data can identify ephemeral food sources, and facilitate an assessment of the importance of anthropogenic food subsidies on the fitness of individuals in tracked populations.</span></p>
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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)
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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.