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42 results for “Bat diet”
DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats
<p>Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.</p>
Fig. 1 in Is the Egyptian fruit-bat Rousettus aegyptiacus a pest in Israel? An analysis of the bat's diet and implications for its conservation
Fig. 1. Monthly mean number of droppings (with standard error bars) of Rousettus aegyptiacus in Nachash and Rakefet caves during 1994 and 1995. Samples collected from three 1 m2 sheets during 48 h each month.
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>
Pond bat capture and diet data from the Netherlands
<p>The goal of this study was to describe the spatial segregation and diet of Pond bats (<em>Myotis dasycneme</em>). A wide range of water-bound habitats throughout the Netherlands was sampled, including marshes, lakes, rivers, wetlands and waterways. For all the locations water depth and soil type were determined. Life animals were captured during 471 nights using a mist net. No Pond bats were captured during 134 of those nights. Water depth and soil type was based on top 10 vector maps (www.pdok.nl/geo-services) with information about both variables. Each captured individual was placed in a separate cotton holding bag until it was weighted, sexed, and the reproductive status and age were assessed by observation of external characteristics. Before dissecting, the dry weight of each faecal pellet was measured with an electronic scale. All samples were dissected under a Carl Zeiss Discovery V20 stereomicroscope. All identifiable fragments were photographed and stored for later use. </p> <p>Genetic analysis: The pellets were ground to a fine powder in liquid nitrogen with a mortar and pestle using the protocol of the commercial Qiagen QIAamp DNA Stool Mini Kit in a special Ancient DNA facility dedicated to work with samples with degraded DNA and following established protocols to avoid contamination such as the inclusion of extraction blanks. Subsequently, aliquots of each extraction were further purified using Promega PCR purification columns. Amplifications of the ~313 bp long mitochondrial COI mini-barcoding marker were performed using forward primer ZBJ-ArtF1c 5’-AGATATTGGAACWTTATATTTTATTTTTGG-3’ and reverse primer ZBJ-ArtR2c 5’- WACTAATCAATTWCCAAATCCTCC-3’. The ~157 bp long mitochondrial 16S barcoding marker was amplified using the forward primer P7_FO-16S 5’- RGACGAGAAGACCCTATARA-3’ and P7_R0-16S 5’-ACGCTGTTATCCCTAARGTA-3’. Primers were labelled for DNA metabarcoding with IonExpress labels. The PCR was carried out in 30 microliter reactions containing 0.20 µl Qiagen taq 5u/µl, 3 µl 10x Qiagen buffer, 2 µl 2,5mM dNTP’s, 0,5 µl 10 µM forward primer, 0,5 µl 10 µM reverse primer, 1,5 µl 25mM MgCl<sub>2, </sub>0,5 µl 10 mg/ml BSA, 19.80 µl MiliQ and 2 µl template. Amplifications were performed using the following PCR programme: 5 min denaturation at 95°C followed by 40 cycles of 20 seconds denaturation at 95°C, 20 seconds annealing at 50°C and 1-minute elongation at 72°C. Final elongation was conducted at 72°C for 7 minutes on a C1000 Biorad PCR machine. Primer dimer and other contaminants were removed by using 0.9x Ampure XP beads (Agencourt) to which the PCR products were bound. The beads were washed with 150 microliter 70% EtOH twice and resuspended in 20 microliter TE buffer. Cleaned PCR products were quantified using an Agilent 2100 Bioanalyzer DNA High sensitivity chip. An equimolar pool was prepared of the amplicon libraries at the highest possible concentration. This equimolar pool was diluted according to the calculated template dilution factor to target 10-30% of all positive Ion Sphere Particles. Template preparation and enrichment was carried out with the Ion One Touch 200 Template kit with use of the Ion One Touch System, according to the manufacturer's protocol. The quality control of the Ion One Touch 200 Ion Sphere Particles was done with the Ion Sphere Quality Control kit using a Life Qubit 2.0. The enriched Ion Spheres were prepared for sequencing on a Personal Genome Machine (PGM) with the Ion PGM 200 Sequencing kit as described in the protocol and deposited on an Ion-314 chip (520 cycles per run) in three consecutive sequencing runs. Reads obtained from Ion Torrent sequencing were automatically sorted into separate sequence files based on the MID labels by the Ion Torrent software. The reads were further processed with PRINSEQ (version 0.20.3) with the following settings: a minimum read length of 100 bp, trimming to 140 bp, minimum mean quality of Q24 per read, additional trimming of '3 end bases with a Q lower than 24 and removal of full duplicate sequences. Filtered reads were clustered into Operational Taxonomic Units (OTUs) defined by a sequence similarity of at least 97% using CD-HIT-EST. Singletons were omitted. For each cluster the representative sequences were BLASTed with the NCBI-blast+ software package (version 2.2.28+) against either the NCBI GenBank nucleotide database or a custom database containing all Arthropod sequences located on the Barcode of Life Database. BLAST hits were filtered according to the following criteria: minimum hit length of a 100 bp, minimum hit similarity of 97% and a maximum e-value of 0.05. Reference databases of Dutch species such as http://www.nederlandsesoorten.nl/ were used to check if a species had been recorded for the Netherlands. All species not (yet) known for The Netherlands were reduced to genus level or to the family level if the genus is also unknown to occur.</p> <p> </p> <p>Description of the data files:</p> <p> </p> <p><strong>pelletsMicroscopy.csv</strong></p> <p>Details of individual faecal pellets with prey remains analysed using microscopic analysis.</p> <p>ID: pellet ID</p> <p>sex: sex of the caught Pond bat individual</p> <p>age: age class of the bat</p> <p>date: capture night</p> <p>province: province in which the bat was captured</p> <p>x and y: spatial coordinates within the Netherlands</p> <p>waterDepth: water depth in meters at the capture location</p> <p>pelletWeight: weight of the pellet in grams</p> <p>year: capture year</p> <p>dayOfYear: day of the year, since January 1</p> <p>period: I: end of hibernation till May 20, II May 21- June 29, III: June 30- July 30 and IV: July 31 till hibernation</p> <p>meanPreyWeight: average weight of prey in mg</p> <p>peat: whether the capture site was located in peatland or not</p> <p>propPupae: the proportion of pupae of chironomids: the number of pupae divided by the total number of organisms of all species in a pellet</p> <p>evenness: Pielou’s evenness of the abundance of prey items in a pellet</p> <p>shannon: Shannon index of the diversity of prey items in a pellet</p> <p>saFemale and saMale: sexual activity status of females and males at the time of capture</p> <p>temp: mean temperature (in 0.1 degrees Celsius) during the first two hours after sunset during the capture night</p> <p>wind: mean wind speed (in 0.1 m/s) during the first two hours after sunset during the capture night</p> <p>nPupae: number of Chironomidae pupae found in a pellet</p> <p>nPrey: total number of prey items found in a pellet</p> <p> </p> <p><strong>pelletsMetabarcoding.csv</strong></p> <p>Details of individual faecal pellets analysed using metabarcoding. </p> <p>ID: pellet ID</p> <p>sex: sex of the caught Pond bat individual</p> <p>reproductiveState: reproductive status of the bat</p> <p>age: age class of the bat</p> <p>mature: sexually mature (1) or not (0)</p> <p>date: capture night</p> <p>peat: whether the capture site was located in peatland or not</p> <p>province: province in which the bat was captured</p> <p>x and y: spatial coordinates within the Netherlands</p> <p>waterDepth: water depth in meters at the capture location</p> <p>pelletWeight: weight of the pellet in grams</p> <p> </p> <p><strong>preyWeight.csv</strong></p> <p>Mean weight (milligram) of taxonomic and developmental prey groups, including information on length (mm) and width (mm) of each group.</p> <p> </p> <p><strong>orderPellet.csv</strong></p> <p>Number of pellets in which at least 1 prey of a certain taxon is found, separately per sex of the bat and per analysis method (microscopy or metabarcoding). Total number of analyzed pellets was 365 for females – microscopy, 170 for males – microscopy, 95 for females – metabarcoding, and 65 for males – metabarcoding. </p> <p> </p> <p><strong>taxaCountsMicroscopy.csv</strong></p> <p>Number of prey individuals per taxonomic group found by using morphological analyses (microscopy) of faecal pellets of Pond bats. For each group the number of observations are given separately for male and female bats.</p> <p> </p> <p><strong>taxaCountsMetabarcoding.csv</strong></p> <p>Number of prey individuals per taxonomic group found by using metabarcoding of faecal pellets of Pond bats. For each group the number of observations are given for both male and female bats.</p>
Images 2-3 in First record of Lesser Yellow House Bat Scotophilus kuhlii Leach, 1821 from Secunderabad, Andhra Pradesh, India with a note on its diet
Images 2-3. Lesser Yellow House Bat
Image 1. Lesser Yellow House Bat Scotophilus kuhlii Leach, 1821 in First record of Lesser Yellow House Bat Scotophilus kuhlii Leach, 1821 from Secunderabad, Andhra Pradesh, India with a note on its diet
Image 1. Lesser Yellow House Bat Scotophilus kuhlii Leach, 1821 from Secunderabad, Andhra Pradesh
Reliable phylogenetic regressions for multivariate comparative data: illustration with the MANOVA and application to the effect of diet on mandible morphology in Phyllostomid bats
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Bat-flower trait matching: Extreme phenotypic specialization affects diet preferences but not diet breadth
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Bird species in the diet of the birdlike noctule bat, Nyctalus aviator, in Japan.
<p>We investigate the birds in the diet of the birdlike noctule (<i>Nyctalus aviator</i>) through DNA amplification from feather remains found in fecal pellets. Our goal was to confirm that <i>N. aviator</i> preys on nocturnally migratory species, as does its European relative <i>N. lasiopterus</i>, and to gain insights into this hunting strategy (e.g. on the wing vs. from cavity-nest). The diversity and the characteristics of the birds found in the feces remains indicate that in Japan <i>N. aviator</i> employs a similar hunting strategy on songbirds as <i>N. lasiopterus</i> in Iberia. Both noctules are primarily generalist insectivores that prey on 5-25 g body mass songbirds seasonally during their migration and probably hunt them at high altitude. Accordingly, predation on birds seems to be a strategy that appears in distant points across the Palearctic and is not the result of local specialization. We hypothesize that the bird hunting behavior appeared during evolution as a trophic jump from a pre-existing hunting behavior on the many insects that carry out seasonal nocturnal migrations in temperate zones at high-altitude.</p>
Data from: Metabarcoding for the parallel identification of several hundred predators and their preys: application to bat species diet analysis
Assessing diet variability is of main importance to better understand the biology of bats and design conservation strategies. Although the advent of metabarcoding has facilitated such analyses, this approach does not come without challenges. Biases may occur throughout the whole experiment, from fieldwork to biostatistics, resulting in the detection of false negatives, false positives or low taxonomic resolution. We detail a rigorous metabarcoding approach based on a short COI minibarcode and two-step PCR protocol enabling the 'all at once' taxonomic identification of bats and their arthropod preys for several hundreds of samples. Our study includes faecal pellets collected in France from 357 bats representing 16 species, as well as insect mock communities that mimic bat meals of known composition, negative and positive controls. All samples were analysed using three replicates. We compare the efficiency of DNA extraction methods and we evaluate the effectiveness of our protocol using identification success, taxonomic resolution, sensitivity, and amplification biases. Our parallel identification strategy of predators and preys reduces the risk of mis-assigning preys to wrong predators and decreases the number of molecular steps. Controls and replicates enable to filter the data and limit the risk of false positives, hence guaranteeing high confidence results for both prey occurrence and bat species identification. We validate 551 COI variants from arthropod including 18 orders, 117 family, 282 genus and 290 species. Our method therefore provides a rapid, resolutive and cost-effective screening tool for addressing evolutionary ecological issues or developing 'chirosurveillance' and conservation strategies.
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: Eating local: influences of habitat on the diet of little brown bats (Myotis lucifugus)
We employ molecular methods to profile the diet of the little brown bat, Myotis lucifugus, and describe spatial and temporal changes in diet over their maternity season. We identified 61 prey species of insects and 5 species of arachnid. The largest proportion of prey (∼32%) were identified as species of the mass-emerging Ephemeroptera (mayfly) genus Caenis. Bats roosting in agricultural settings had lower dietary richness than those occupying a roost located on a forest fragment in a conservation area. We detected temporal fluctuations in diet over the maternity season. Dipteran (fly) species dominated the diet early in the season, replaced later by species of mayfly. Because our methodology provides species-level identification of prey, we were able to isolate environmental indicator species in the diet and draw conclusions about the location and type of their foraging habitat and the health of these aquatic systems. The species detected suggested that the bats use variable habitats; members of one agricultural roost foraged on insects originating in rivers or streams while those in another agricultural roost and the forest roost fed on insects from pond or lake environments. All source water for prey was of fair to good quality, though no species detected are intolerant of pollution thus the habitat cannot be classified as pristine. Our study outlines a model system to investigate the abiotic and biotic interactions between habitat factors through this simple food chain to the top predator.
Data from: Using DNA metabarcoding for simultaneous inference of common vampire bat diet and population structure
Metabarcoding diet analysis has become a valuable tool in animal ecology; however, co-amplified predator sequences are not generally used for anything other than to validate predator identity. Exemplified by the common vampire bat we demonstrate the use of metabarcoding to infer predator population structure alongside diet assessments. Growing populations of common vampire bats impact human, livestock and wildlife health in Latin America through transmission of pathogens, such as lethal rabies infections. Techniques to determine large scale variation in vampire bat diet and bat population structure would empower locality- and species-specific projections of disease transmission risks. However, previously used methods are not cost-effective and efficient for large scale applications. Using blood meal and faecal samples from common vampire bats from coastal, Andean and Amazonian regions of Peru, we showcase metabarcoding as a scalable tool to assess vampire bat population structure and feeding preferences. Dietary metabarcoding was highly effective, detecting vertebrate prey in 93.2% of the samples. Bats predominantly preyed on domestic animals, but fed on tapirs at one Amazonian site. In addition, we identified arthropods in 9.3% of samples, likely reflecting consumption of ectoparasites. Using the same data, we document mitochondrial geographic population structure in the common vampire bat in Peru. Such simultaneous inference of vampire bat diet and population structure can enable new insights into the interplay between vampire bat ecology and disease transmission risks. Importantly, the methodology can be extrapolated to metabarcoding diet studies of other animals to couple information on diet and population structure.
Diet analysis of bats killed at wind turbines suggest large-scale losses of trophic interactions
<p>Agricultural practice has led to landscape simplification and biodiversity decline, yet recently, energy producing infrastructures, such as wind turbines, have been added to these simplified agroecosystems, turning them into multi-functional energy-agroecosystems. Here, we studied the trophic interactions of bats killed at wind turbines using a DNA metabarcoding approach to shed light on how turbine-related bat fatalities may possibly feedback on local habitats. Specifically, we identified insect DNA in the stomachs of common noctule bats (<em>Nyctalus noctula</em>) killed by wind turbines in Germany to infer in which habitats these bats hunted. Common noctule bats consumed a wide variety of insects from different habitats, ranging from aquatic to terrestrial ecosystems (e.g. wetlands, farmland, forests, and grasslands). Agricultural and silvicultural pest insects made up about 20% of insect species consumed by the studied bats. Our study suggests that the potential damage of wind energy production goes beyond the loss of bats and the decline of bat populations. Bat fatalities at wind turbines may lead to the loss of trophic interactions and ecosystem services provided by bats, which may add to the functional simplification and impaired crop production, respectively, in multi-functional ecosystems.</p>
Seasonal weighted percentage of occurrence (wPOO) of different arthropod orders in the diet of bats of northern Madagascar
<p>This table (updated from previous version) 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 Table A4.2 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 illustrates the weighted percentage of occurrence (wPOO) of each arthropod order in the diet of the different bat species for each season (dry and wet). The values in brackets next to each species name are the total samples collected for that species.</p> <p>Methodology: </p> <p>When focusing on the different bat species, bat diet was assessed using the weighted percentage of occurrence data (wPOO), which is similar to POO, but it weights each occurrence according to the number of food items in the sample, since it may be more biologically realistic than using POO or FOO. As discussed by Deagle et al. (2019), POO data is useful because it shows each food taxon's percentage of the total diet (unlike FOO, which does not sum to 100%). However, wPOO may be more accurate because it weights each sample equally, preventing samples with many food taxa from having stronger influence. wPOO values were also calculated for each season (dry/wet) independently, in order to perceive changes in prey preferences depending on the season. </p>
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>
Bats generate lower affinity, but higher diversity antibody responses compared to mice, an effect that can be manipulated with diet
<p><span>Bats are reservoirs of many zoonotic viruses that are fatal in humans but do not cause disease in bats. Moreover, bats generate low neutralizing antibody titers in response to experimental viral infection, although more robust antibody responses have been observed in wild caught bats during times of food stress. Here we compared the antibody titers and B cell receptor (BCR) diversity of Jamaican fruit bats (<em>Artibeus jamaicensis</em>; JFB) and BALB/c mice generated in response to T-dependent and T-independent antigens. We then manipulated the diet of JFBs and challenged them with H18N11 influenza A-like virus or a replication incompetent Nipah virus VSV (Nipah-riVSV). Under standard housing conditions, JFBs generated a lower avidity antibody response and possessed more BCR mRNA diversity compared to BALB/c mice. However, withholding protein from JFBs improved serum neutralization in response to Nipah-riVSV and improved serum antibody titers specific to H18 but reduced BCR mRNA diversity. </span></p>
Data from: Who's for dinner? High-throughput sequencing reveals bat diet differentiation in a biodiversity hotspot where prey taxonomy is largely undescribed
Effective management and conservation of biodiversity requires understanding of predator–prey relationships to ensure the continued existence of both predator and prey populations. Gathering dietary data from predatory species, such as insectivorous bats, often presents logistical challenges, further exacerbated in biodiversity hot spots because prey items are highly speciose, yet their taxonomy is largely undescribed. We used high-throughput sequencing (HTS) and bioinformatic analyses to phylogenetically group DNA sequences into molecular operational taxonomic units (MOTUs) to examine predator–prey dynamics of three sympatric insectivorous bat species in the biodiversity hotspot of south-western Australia. We could only assign between 4% and 20% of MOTUs to known genera or species, depending on the method used, underscoring the importance of examining dietary diversity irrespective of taxonomic knowledge in areas lacking a comprehensive genetic reference database. MOTU analysis confirmed that resource partitioning occurred, with dietary divergence positively related to the ecomorphological divergence of the three bat species. We predicted that bat species' diets would converge during times of high energetic requirements, that is, the maternity season for females and the mating season for males. There was an interactive effect of season on female, but not male, bat species' diets, although small sample sizes may have limited our findings. Contrary to our predictions, females of two ecomorphologically similar species showed dietary convergence during the mating season rather than the maternity season. HTS-based approaches can help elucidate complex predator–prey relationships in highly speciose regions, which should facilitate the conservation of biodiversity in genetically uncharacterized areas, such as biodiversity hotspots.
Fig. 2 in Variation in diet of frugivorous bats in fragments of Brazil's Atlantic Forest associated with vegetation density
Fig. 2.—Carbon and nitrogen isotopic ratios for each bat population presented as mean and standard deviation. Each panel represents one species–season pairing: Al-H is Artibeus lituratus in the Humid season; Cp-H is Carollia perspicillata in the Humid season; Cp-S is C. perspicillata in the super-humid season; Sl-S is Sturnira lilium in the super-humid season. Fragments and population designations correspond to fragments in Brazil's Atlantic Forest in Fig. 1. Shades correspond to fragment where the sample was collected ordered by area. Darker greens are largest fragments; darkest purple are smallest fragments.
Fig. 1 in Variation in diet of frugivorous bats in fragments of Brazil's Atlantic Forest associated with vegetation density
Fig. 1.—Map of study sites situated in Brazil's Atlantic Forest, Rio de Janeiro State, Brazil. Sites in Reserva Ecologica de Guapiacu ("REGUA") and fragments (F) adjacent to this area are indicated as points. Thirteen areas were sampled and allocated as REGUA, REGUA2, REGUA3 for those sampled in the reserve (considered repeated efforts sampling in the same fragment), and 10 fragments designated as F1 through F10. This figure was constructed using ESRI base maps and existing maps available through Instituto Brasileiro de Geografia (IBGE) (SOS Mata Atlântica (2009) (www.sosma.org). Used under commons licence). Figure is adapted from Teixeira (2019).
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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.