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21 results for “insectivorous diet”
Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird
<p>The diet of an individual animal is subject to change over time, both in response to short-term food fluctuations and over longer time scales as an individual ages and meets different challenges over its life cycle. A metabarcoding approach was used to elucidate the diet of different life stages of a migratory songbird, the Eurasian reed warbler (<em>Acrocephalus scirpaceus</em>) over the 2017 summer breeding season in Somerset, UK. The faeces of adult, juvenile and nestling warblers were screened for invertebrate DNA, enabling the identification of prey species. Dietary analysis was coupled with monitoring of Diptera in the field using yellow sticky traps. Seasonal changes in warbler diet were subtle whereas age class had a greater influence on overall diet composition. Age classes showed high dietary overlap, but significant dietary differences were mediated through the selection of prey; i) from different taxonomic groups, ii) with different habitat origins (aquatic versus terrestrial) and iii) of different average approximate sizes. Our results highlight the value of metabarcoding data for enhancing ecological studies of insectivores in dynamic environments. </p>
Impacts of rainforest degradation on the diets of the insectivorous bats of Sabah
<b>Description: </b><p>The work was carried out within Sabah, at the SAFE project, Danum Valley and Maliau basin. Bats were captured by deploying 6 harp traps per night, during field seasons taking place in 2015, 2016 and 2017. Bat guano samples were collected by placing individual bats into cloth bags, and then releasing them after 12 hours. Any guano in the bottom of the bag was then transferred into 95% ethanol and stored at -20. DNA was extracted from the faecal samples using a Qiagen Stool Mini kit, and then amplified using the ZBJ-ArtF1c ZBJ-ArtR2c primers, and sequencing the DNA on an Illumina MiSeq.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/182"><b>Impacts of rainforest degradation on the diets of the insectivorous bats of Sabah</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, NE/K016407/1)</li><li>Royal Society (Standard grant, RG130793)</li><li>Bat Conservation International (Standard grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.4 (46))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3247465">here</a></p><p><b>Files: </b>This dataset consists of 3 files: Bat_SAFE_data_metadata.xlsx, interaction_network.csv, sequences_95.fasta</p><p><b>Bat_SAFE_data_metadata.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>data</b> (described in worksheet Data)</p><p>Description: measurements collected</p><p>Number of fields: 21</p><p>Number of data rows: 3292</p><p>Fields: </p><ul><li><b>TrapName</b>: The trap ID which the bats were captured in (Field type: ID)</li><li><b>Lat</b>: Latitude of trap (Field type: Latitude)</li><li><b>Long</b>: Longitude of trap (Field type: Longitude)</li><li><b>Elevation</b>: Elevation of trap (Field type: Numeric)</li><li><b>Bat_no</b>: Bat ID (Field type: ID)</li><li><b>Date</b>: Date of capture (Field type: Date)</li><li><b>Faeces_no1</b>: Tube number used to store faecal sample. Pairs up with column names of interaction matrix (Field type: ID)</li><li><b>Faeces_no2</b>: Number of any additional faeces (Field type: ID)</li><li><b>Biopsy_Dave</b>: Tube used to store wing biopsy (Field type: ID)</li><li><b>Block</b>: If sampling occurred within the SAFE landscape, this is the block it occurred within (Field type: ID)</li><li><b>Fragment</b>: If sampling occurred within the SAFE landscape, this is the fragment size it occurred within (Field type: ID)</li><li><b>Site</b>: The site within Sabah sampling occurred at (Field type: ID)</li><li><b>Species</b>: The bat species ID (Field type: Taxa)</li><li><b>Sex</b>: Male or Female (Field type: Categorical trait)</li><li><b>Age</b>: Was the bat an adult or juvenile (Field type: Categorical trait)</li><li><b>Forearm</b>: The forearm length of the bat (Field type: Numeric trait)</li><li><b>Weight</b>: The weight of the bat (Field type: Numeric Trait)</li><li><b>Reproductive_condition</b>: If a female bat, if the bat was Non-Reproductive, PRegnant, LActating or Post-Lactating (Field type: Categorical trait)</li><li><b>Parasite</b>: Tube used to store any ectoparasites obtained (Field type: ID)</li><li><b>Time</b>: If the bat was captured in evening or morning (Field type: Categorical)</li><li><b>Tag</b>: Band ID, if used (Field type: ID)</li></ul></li></ol><p><b>interaction_network.csv</b></p><p>Description: A network of operational taxonomic units found within the guano of bats captured in Sabah. The column names refer to the bat guano id, as found in the columns 'Faeces_no1' and 'Faeces_no2' in the fieldwork data, and the rownames refer to the OTU of the prey, which is paired to the names of the OTUs in the fasta file.</p><p><b>sequences_95.fasta</b></p><p>Description: A fasta file of prey OTUs found in bat guano, generated using 95% similarity for clustering. The sequence names correspond with the rownames of the file interaction_network.csv</p><p><b>Date range: </b>2015-02-16 to 2017-07-21</p><p><b>Latitudinal extent: </b>4.5000 to 5.0933</p><p><b>Longitudinal extent: </b>116.7500 to 117.8380</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Chiroptera<br> -  -  -  - Emballonuridae<br> -  -  -  -  - <i>Emballonura</i><br> -  -  -  -  -  - <i>Emballonura alecto</i><br> -  -  -  -  -  - <i>Emballonura monticola</i><br> -  -  -  - Hipposideridae<br> -  -  -  -  - <i>Hipposideros</i><br> -  -  -  -  -  - <i>Hipposideros ater</i><br> -  -  -  -  -  - <i>Hipposideros bicolor</i><br> -  -  -  -  -  - <i>Hipposideros cervinus</i><br> -  -  -  -  -  - <i>Hipposideros diadema</i><br> -  -  -  -  -  - <i>Hipposideros doriae</i><br> -  -  -  -  -  - <i>Hipposideros dyacorum</i><br> -  -  -  -  -  - <i>Hipposideros galeritus</i><br> -  -  -  -  -  - <i>Hipposideros ridleyi</i><br> -  -  -  - Megadermatidae<br> -  -  -  -  - <i>Megaderma</i><br> -  -  -  -  -  - <i>Megaderma spasma</i><br> -  -  -  - Nycteridae<br> -  -  -  -  - <i>Nycteris</i><br> -  -  -  -  -  - <i>Nycteris tragata</i><br> -  -  -  - Pteropodidae<br> -  -  -  -  - <i>Balionycteris</i><br> -  -  -  -  -  - <i>Balionycteris maculata</i><br> -  -  -  -  - <i>Macroglossus</i><br> -  -  -  -  -  - <i>Macroglossus minimus</i><br> -  -  -  -  - <i>Megaerops</i><br> -  -  -  -  -  - <i>Megaerops wetmorei</i><br> -  -  -  - Rhinolophidae<br> -  -  -  -  - <i>Rhinolophus</i><br> -  -  -  -  -  - <i>Rhinolophus acuminatus</i><br> -  -  -  -  -  - <i>Rhinolophus affinis</i><br> -  -  -  -  -  - <i>Rhinolophus borneensis</i><br> -  -  -  -  -  - <i>Rhinolophus creaghi</i><br> -  -  -  -  -  - <i>Rhinolophus luctus</i><br> -  -  -  -  -  - <i>Rhinolophus sedulus</i><br> -  -  -  -  -  - <i>Rhinolophus trifoliatus</i><br> -  -  -  - Vespertilionidae<br> -  -  -  -  - <i>Harpiocephalus</i><br> -  -  -  -  -  - <i>Harpiocephalus harpia</i><br> -  -  -  -  - <i>Hesperoptenus</i><br> -  -  -  -  -  - <i>Hesperoptenus blanfordi</i><br> -  -  -  -  - <i>Kerivoula</i><br> -  -  -  -  -  - <i>Kerivoula hardwickii</i><br> -  -  -  -  -  - <i>Kerivoula intermedia</i><br> -  -  -  -  -  - <i>Kerivoula lenis</i><br> -  -  -  -  -  - <i>Kerivoula minuta</i><br> -  -  -  -  -  - <i>Kerivoula papillosa</i><br> -  -  -  -  -  - <i>Kerivoula pellucida</i><br> -  -  -  -  -  - <i>Kerivoula whiteheadi</i><br> -  -  -  -  - <i>Murina</i><br> -  -  -  -  -  - <i>Murina aenea</i><br> -  -  -  -  -  - <i>Murina cyclotis</i><br> -  -  -  -  -  - <i>Murina rozendaali</i><br> -  -  -  -  -  - <i>Murina suilla</i><br> -  -  -  -  - <i>Myotis</i><br> -  -  -  -  -  - <i>Myotis muricola</i><br> -  -  -  -  -  - <i>Myotis ridleyi</i><br> -  -  -  -  - <i>Phoniscus</i><br> -  -  -  -  -  - <i>Phoniscus atrox</i><br> -  -  -  -  - <i>Pipistrellus</i><br> -  -  -  -  -  - <i>Pipistrellus javanicus</i><br> -  -  -  -  -  - <i>Pipistrellus tenuis</i><br> -  -  -  -  - <i>Scotophilus</i><br> -  -  -  -  -  - <i>Scotophilus kuhlii</i><br></div><p></p>
In silico and empirical evaluation of twelve metabarcoding primer sets for insectivorous diet analyses
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Insectivore diet and abundance determine the contribution of bird species to services and disservices in an agricultural ecosystem
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Seasonal and ontological variation in diet and age-related differences in prey choice, by an insectivorous songbird
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Data from: DNA metabarcoding reveals rangewide variation in aquatic diet of a riparian avian insectivore, the Prothonotary warbler
<p>Riparian avian insectivores not only depend on terrestrial insect prey but also benefit from the inclusion of aquatic prey during critical life history periods. Diets identified herein show that Prothonotary Warbler (<em>Protonotaria citrea</em>) nestlings were provisioned with aquatic prey throughout the breeding season across their range, but with variation in prey frequency of occurrence and taxonomy. Anthropogenic activity and climate change may impact the trophic link especially between aquatic and riparian habitats by altering the presence, abundance, and timing of prey availability. Thus, we used DNA metabarcoding of fecal samples to quantify the frequency of occurrence of nestling diet items at nine sites across their breeding range that differed in expected aquatic prey consumption. We analyzed spatial and temporal differences in the occurrence and multivariate diet assemblages of each prey source. Lepidoptera was the predominant terrestrial prey occurring in diets across space and time, whereas emergent aquatic insects and freshwater mollusks in aquatic diet exhibited greater variation. The frequency of emergent aquatic prey occurrence in nestling diets ranged from 61-100% across sites and was greater for early-season nestlings. The seasonal decrease in aquatic prey consumption indicates a potential temporal shift in the nutritional landscape from aquatic to terrestrial prey sources and a possible nutritional phenological mismatch for early nestlings as climate change advances the timing of insect emergence. Our findings also suggest that Prothonotary Warblers respond to environmental variability by consuming alternative prey and argue for future research investigating the extent to which shifting diets have nutritional consequences for riparian nestlings.</p>
Data from: DNA metabarcoding reveals rangewide variation in aquatic diet of a riparian avian insectivore, the Prothonotary warbler
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Data from: Determining diet from faeces: selection of metabarcoding primers for the insectivore Pyrenean desman (Galemys pyrenaicus)
Molecular techniques allow non-invasive dietary studies from faeces, providing an invaluable tool to unveil ecological requirements of endangered or elusive species. They contribute to progress on important issues such as genomics, population genetics, dietary studies or reproductive analyses, essential knowledge for conservation biology. Nevertheless, these techniques require general methods to be tailored to the specific research objectives, as well as to substrate- and species-specific constraints. In this pilot study we test a range of available primers to optimise diet analysis from metabarcoding of faeces of a generalist aquatic insectivore, the endangered Pyrenean desman (Galemys pyrenaicus, É. Geoffroy Saint-Hilaire, 1811, Talpidae), as a step to improve the knowledge of the conservation biology of this species. Twenty-four faeces were collected in the field, DNA was extracted from them, and fragments of the standard barcode region (COI) were PCR amplified by using five primer sets (Brandon-Mong, Gillet, Leray, Meusnier and Zeale). PCR outputs were sequenced on the Illumina MiSeq platform, sequences were processed, clustered into OTUs (Operational Taxonomic Units) using UPARSE algorithm and BLASTed against the NCBI database. Although all primer sets successfully amplified their target fragments, they differed considerably in the amounts of sequence reads, rough OTUs, and taxonomically assigned OTUs. Primer sets consistently identified a few abundant prey taxa, probably representing the staple food of the Pyrenean desman. However, they differed in the less common prey groups. Overall, the combination of Gillet and Zeale primer sets were most cost-effective to identify the widest taxonomic range of prey as well as the desman itself, which could be further improved stepwise by adding sequentially the outputs of Leray, Brandon-Mong and Meusnier primers. These results are relevant for the conservation biology of this endangered species as they allow a better characterization of its food and habitat requirements.
Data from: Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies
Interspecific differences in traits can alter the relative niche use of species within the same environment. Bats provide an excellent model to study niche use because they have a wide variety of behavioural, acoustic and morphological traits that may lead to multi-species, functional groups. Predatory bats have been classified by their foraging location (edge, clutter, open space), ability to aerial hawk and/or substrate glean prey and echolocation call design and flexibility, all of which may dictate their diet. For example, high frequency, broadband calls do not travel far but offer high object resolution while high intensity, low frequency calls travel further but provide lower resolution. Because these behaviours can be flexible four behavioural categories have been proposed: (1) gleaning, (2) behaviourally flexible (gleaning and hawking), (3) clutter tolerant hawking, and (4) open space hawking. Recent studies of diet in bats use molecular tools to identify prey but mainly focus on one or two species in isolation and few studies provide evidence for substantial differences in prey use despite the many behavioural, acoustic and morphological differences. Here we analyse the diet of 17 sympatric species in the Chihuahuan desert and test the hypothesis that peak echolocation frequency and behavioural categories are linked to differences in diet. We find no significant correlation between dietary richness and echolocation frequency (though it spanned close to 100kHz across species). However, our data suggest that behaviourally flexible bats that use gleaning and aerial hawking have the broadest diets and are the most differentiated from clutter-tolerant aerial hawking species.
Data from: DNA metabarcoding reveals the broad and flexible diet of a declining aerial insectivore
Aerial insectivores are highly mobile predators that feed on diverse prey items that have highly variable distributions. As such, investigating the diet, prey selection, and prey availability of aerial insectivores can be challenging. In this study, we used an integrated DNA barcoding method to investigate the diet and food supply of Barn Swallows, an aerial insectivore whose North American population has declined over the past 40 years. We tested the hypotheses that Barn Swallows are generalist insectivores when provisioning their young and select prey based on size. We predicted that the diets of nestlings would contain a range of insect taxa but would be biased towards large prey items and that the diet of nestlings would change as prey availability changed. We collected insects using Malaise traps at ten breeding sites and identified specimens using standard DNA barcoding. The sequences from these insect specimens were used to create a custom reference database of prey species and their relative sizes for our study area. We identified insect prey items from nestling fecal samples by using high-throughput DNA sequencing and comparing the sequences to our custom reference database. Barn Swallows fed nestlings prey items from 130 families representing 13 orders but showed selection for larger prey items that were predominantly from seven dipteran families. Nestling diet varied both within and between breeding seasons as well as between breeding sites. This dietary flexibility suggests that Barn Swallows are able to adjust their provisioning to changing prey availability on the breeding grounds when feeding their nestlings. Our study demonstrates the utility of custom reference databases for linking the abundance and size of insect prey in the habitat with prey consumed when employing molecular methods for dietary analysis.
List of agricultural pests and disease vectors detected in the diet of insectivorous bats in northern Madagascar
<p>This table is part of the PhD thesis of Carme Tuneu-Corral, entitled '<strong>Bats and rice: promoting Integrated Pest Management to enhance biodiversity conservation</strong>'. It is the <span>Table A4.3</span> of the supplementary material of the Chapter 5 '<em>Beyond borders: evaluating the role of protected areas in promoting bat-mediated pest suppression in rural areas of northern Madagascar</em>', and shows the list of agricultural pests (known and potential) and disease vectors detected in the diet of insectivorous bats, BOLD ID percentage (similarity), and information on the type of crops attacked or disease transmitted by them in Madagascar and/or continental Africa.</p> <p>Methodology:</p> <p><span>To evaluate whether bats were consuming agricultural pests or disease vectors, we only considered prey identified to species level. Using published scientific literature, we classified each arthropod species in one of the following categories: ‘non-pest prey’, ‘known human-disease vector’ (species confirmed as human-disease vector in Madagascar), ‘known livestock-disease vector’ (species confirmed as livestock-disease vector in Madagascar), ‘potential human-disease vector’ (species not confirmed as human-disease vector in Madagascar, but considered as such in continental Africa), ‘potential livestock-disease vector’ (species not confirmed as livestock-disease vector in Madagascar, but considered as such in continental Africa), ‘known agricultural pest’ (species confirmed as agricultural pest in Madagascar), ‘potential agricultural pest’ (species not confirmed as agricultural pest in Madagascar, but considered as such in continental Africa).</span></p> <p> </p>
Figure 1 in Diet of tropical insectivorous birds in lowland Malaysian rainforest
Figure 1. Map of Krau Wildlife Reserve, Pahang, Peninsular Malaysia. The reserve is represented by light grey, forest areas surrounding the reserve by dark grey, and non-forest areas by white. Map adapted from Zakaria et al. (2014).
Data from: DNA metabarcoding reveals the broad and flexible diet of a declining aerial insectivore
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Data from: Molecular diet analysis finds an insectivorous desert bat community dominated by resource sharing despite diverse echolocation and foraging strategies
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Data from: Determining diet from faeces: selection of metabarcoding primers for the insectivore Pyrenean desman (Galemys pyrenaicus)
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Data from: Diet of the insectivorous bat Pipistrellus nathusii during autumn migration and summer residence
Migration is widespread among vertebrates. Yet bat migration has received little attention and only in the recent decades knowledge of it has been gained. Migration can cause significant changes in behaviour and physiology, due to increasing energy demands and aerodynamic constraints. Dietary shifts, for examples, have been shown to occur in birds before onset of migration. For bats it is not known if a change in diet occurs during migration, although especially breeding season related dietary preference has been documented. It is known that fat-rich diets, and subsequent accumulation of high fat deposits, do increase the flight range of migratory bats. Some bat species can be regarded as long-distance migrants, covering up to 2,000 km on their way between summer and winter roosting areas. Pipistrellus nathusii (Vespertilionidae), a European long-distant migrant, travels each year along the Baltic Sea from north-eastern Europe to hibernate in central and southern Europe. This study presents data on the dietary habits of migrating Pipistrellus nathusii compared with dietary habits during the breeding season. We analysed faecal samples from bats on fall migration caught at the Ornithological Field Station in Pape, Latvia and from samples collected in North-Latvian summer roosts. We applied both morphological identification and molecular methods, as morphological methods also recognize life stages of prey and can contribute frequency data. The diets of bats on migration and breeding bats were similar, with Diptera and Lepidoptera comprising the major prey categories. However certain prey groups could be explained by the different hunting habitats used during migration vs. summer residence.
Figure 2 in Diet of tropical insectivorous birds in lowland Malaysian rainforest
Figure 2. The overall distribution of prey individuals determined in dietary samples of birds.
DNA metabarcoding data characterizing insectivorous diet of purple martins (Progne subis subis) using two COI primer sets (ANML and ZBJ)
<p>DNA metabarcoding is a molecular technique frequently used to characterize diet composition of insectivorous birds. However, results are sensitive to methodological decisions made during sample processing, with primer selection being one of the most critical. The most frequently used DNA metabarcoding primer set for avian insectivores is ZBJ. However, recent studies have found that ZBJ produces significant biases in prey classification that likely influence our understanding of foraging ecology. A new primer set, ANML, has shown promise for characterizing insectivorous bat diets with fewer taxonomic biases than ZBJ, but ANML is not yet widely used to study insectivorous birds. Here, we evaluate the ANML primer set for use in metabarcoding of avian insectivore diets through comparison with the more commonly used ZBJ primer set. Fecal samples were collected from both adult and nestling Purple Martins (<i>Progne subis subis</i>) at two sites in the USA and one site in Canada to maximize variation in diet composition and to determine if primer selection impacts our understanding of diet variation among sites. In total, we detected 71 arthropod prey species, 39 families, and 10 orders. Of these, 40 species were uniquely detected by ANML, whereas only 11 were uniquely detected by ZBJ. We were able to classify 54.8% of exact sequence variants from ANML libraries to species compared to 33.3% from ZBJ libraries. We found that ANML outperformed ZBJ for PCR efficacy, taxonomic coverage, and specificity of classification, but that using both primer sets together produced the most comprehensive characterizations of diet composition. Significant variation in both alpha- and beta-diversity between sites was found using each primer set separately and in combination. To our knowledge, this is the first published metabarcoding study to directly compare avian diet characterizations produced with both ANML and ZBJ primer sets.</p>
Data from: Diet of the insectivorous bat Pipistrellus nathusii during autumn migration and summer residence
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DNA metabarcoding data characterizing insectivorous diet of purple martins (Progne subis subis) using two COI primer sets (ANML and ZBJ)
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.