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opencc-by-4.0Aug 2021View details →

FIG. 4 in Mammalian Diversity And Matses Ethnomammalogy In Amazonian Peru Part 4: Bats

FIG. 4. Photographs of A, an adult female (MUSM 37068) Diclidurus isabella captured at Lago Preto; B, an adult female Peropteryx leucoptera captured at Tahuayo Farm; and C, an adult P. macrotis captured at Frog Valley. Photographs by Mark Bowler (A) and Brock Fenton (B, C).

Fig. 6 in Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland

Fig. 6. Close-up views of the lower mandible of Plecotus bats illustrating the shape of the chin pad in the three species. Pictures were taken from genetically identified adult long-eared bats from Switzerland or France. In the first column is P. auritus, in the middle column P. macrobullaris and in the third P. austriacus. Notice the particular shape of the chin pad of P. macrobullaris, with elongated tip and distinctly concave sides.

Fig. 5 in Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland

Fig. 5. Alignment of typical 16S sequences of different Plecotus lineages obtained with the MamP007 primer pair (framed); the expected amplicon size is 110 bp (including primers). Alignment dots represent identical nucleotides.

Fig. 4 in Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland

Fig. 4. Bivariate plot of the length of upper tooth row (CM3) versus diameter of tympanic bulla (DBT) of 194 skulls of Plecotus. Blue squares represent skulls of P. auritus, violet circles skulls of P. macrobullaris and orange triangles skulls of P. austriacus. Plain symbols indicate genetically identified individuals, while hollow ones are from animals examined for skull morphology only. Coloured boxes indicate the species-specific measurement ranges given by Benda & Ivanova (2003) for Central European Plecotus and the grey bars represent the limit values of the two cranial measurements proposed by Blant et al. (2008) to identify the three species.

Fig. 3 in Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland

Fig. 3. Map of Switzerland depicting the six biogeographical regions occurring in this country (Gonseth et al., 2001) and the occurrences of 700 genetically identified Plecotus samples. Plain symbols represent locations of P. auritus (in blue), P. austriacus (in orange) and P. macrobullaris (in violet). Symbols with more than one colour represent areas of sympatry. Map produced by the Centre Suisse de Cartographie de la Faune.

Fig. 2 in Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland

Fig. 2. Species-specific patterns of amplification of 16S fragments obtained in a single PCR cocktail. These fragments were resolved on a 1.6% agarose gel run for about 30 min at 60 V/m. A 100 bp molecular ladder was run on each side of the pictured agarose gel. Amplification products of diagnostic sizes appear on lane 1 for P. macrobullaris (at about 400 bp), on lane 2 for P. austriacus (350 bp), on lane 3 for the 'west' clade of P. auritus (300 bp) and on lane 4 for the 'east' clade of P. auritus (two bands at about 300 and 400 bp, respectively).

Fig. 1 in Novel molecular tools to identify Plecotus bats in sympatry and a review of their distribution in Switzerland

Fig. 1. Skull drawing of a Plecotus austriacus (specimen MHNG 1704.016) illustrating the two cranial measurements examined in this study (DBT and CM3).

opencc-by-4.0May 2019View details →

Data and code for: Roost selection by male northern long-eared bats (Myotis septentrionalis) in a managed fire-adapted forest

Data and code for: Roost selection by male northern long-eared bats (Myotis septentrionalis) in a managed fire-adapted forest

Fig. 5 in Multiple mortality events in bats: a global review

Fig. 5. Intentional killing of bats for food. (a) Dead bats on the ground outside a cave in Nagaland, India, where villagers kill several thousand bats as an annual event extending back perhaps 150 years (see Appendix S1; photograph by Pilot Dovih). (b) PteROPUS species for sale at a market in Manado, Northern Sulawesi, Indonesia (photograph by D.T.S. Hayman).

Fig. 4 in Multiple mortality events in bats: a global review

Fig. 4. Causes of multiple mortality events in bats, expressed as percentages of all events (n), for each geographic region.

Fig. 3 in Multiple mortality events in bats: a global review

Fig. 3. Percentages of reported multiple mortality events in bats by causal category, before 2000 (n = 409 events) and from 2000 and thereafter (n = 771 events).

Fig. 2 in Multiple mortality events in bats: a global review

Fig. 2. Cumulative (1790–2015) frequencies of 1180 reported multiple mortality events in bats, by causal category.

Fig. 1 in Multiple mortality events in bats: a global review

Fig. 1. Numbers and proportions of reported multiple mortality events in bats over time (1790–2015). All 1180 events included in this review are shown.

FIGURE 3 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 3 Variation in HeV AUC from flying foxes. (A) The forest plot displays annual estimates and 95% confidence intervals ordered by latitude and year; points are scaled by the inverse sampling variance. The horizontal axis uses a modulus transformation to accommodate wide upper bounds of some confidence intervals. (B) Fitted values and 95% confidence intervals for the top GAM, with raw data (scaled by inverse sampling variance) and modelled means coloured by roost type. Transparency denotes AUC derived from truncated time series (≤20 weeks)

FIGURE 2 Fitted HeV urine pool prevalence and 95 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 2 Fitted HeV urine pool prevalence and 95% confidence intervals from the most parsimonious GAMM with week, seasonal interactions with roost type and previous food shortages, and an adjustment for relative abundance of Pteropus alecto. Weekly data are overlaid, coloured by roost type, and sized by corresponding P. alecto relative abundance. Thin lines show the fitted curves from the random factor smooth including each roost per year

FIGURE 1 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 1 Spatiotemporal variation in HeV shedding for the nine Australian flying fox roosts sampled from 2012 through 2014. Curve height indicates the weekly proportion of HeV-positive urine pools, with roosts shown in order of latitude and coloured by roost type. Ticks show sampling time points. Dark grey shading indicates regional acute food shortage events, and dashed lines with light grey shading indicate the Austral winter (i.e. June through August)

FIGURE 4 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 4 Spatiotemporal variation in regional HeV spillover events during the flying fox surveillance period (2012–2014) and its relationship with HeV AUC. (a) Maps display the annual distributions of spillovers (coloured by year) in relation to the nine analysed roosts. (b) Modelled relationships between AUC and spillover counts are shown with fitted values and 95% confidence intervals from GAMs for 50, 100, 200, 300, 400 and 500 km buffers of each roost. Raw data are overlaid and scaled by the inverse of the sampling variance for AUC

opencc-by-4.0Nov 2022View details →

Pond bat capture and diet data from the Netherlands

The goal of this study was to describe the spatial segregation and diet of Pond bats (Myotis dasycneme). 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.  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 MgCl2, 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.   Description of the data files:   pelletsMicroscopy.csv Details of individual faecal pellets with prey remains analysed using microscopic analysis. ID: pellet ID sex: sex of the caught Pond bat individual age: age class of the bat date: capture night province: province in which the bat was captured x and y: spatial coordinates within the Netherlands waterDepth: water depth in meters at the capture location pelletWeight: weight of the pellet in grams year: capture year dayOfYear: day of the year, since January 1 period: I: end of hibernation till May 20, II May 21- June 29, III: June 30- July 30 and IV: July 31 till hibernation meanPreyWeight: average weight of prey in mg peat: whether the capture site was located in peatland or not propPupae: the proportion of pupae of chironomids: the number of pupae divided by the total number of organisms of all species in a pellet evenness: Pielou’s evenness of the abundance of prey items in a pellet shannon: Shannon index of the diversity of prey items in a pellet saFemale and saMale: sexual activity status of females and males at the time of capture temp: mean temperature (in 0.1 degrees Celsius) during the first two hours after sunset during the capture night wind: mean wind speed (in 0.1 m/s) during the first two hours after sunset during the capture night nPupae: number of Chironomidae pupae found in a pellet nPrey: total number of prey items found in a pellet   pelletsMetabarcoding.csv Details of individual faecal pellets analysed using metabarcoding.   ID: pellet ID sex: sex of the caught Pond bat individual reproductiveState: reproductive status of the bat age: age class of the bat mature: sexually mature (1) or not (0) date: capture night peat: whether the capture site was located in peatland or not province: province in which the bat was captured x and y: spatial coordinates within the Netherlands waterDepth: water depth in meters at the capture location pelletWeight: weight of the pellet in grams   preyWeight.csv Mean weight (milligram) of taxonomic and developmental prey groups, including information on length (mm) and width (mm) of each group.   orderPellet.csv 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.    taxaCountsMicroscopy.csv 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.   taxaCountsMetabarcoding.csv 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.

dryad40/100

Data for: Predicting habitat suitability for Townsend's big-eared bats across California in relation to climate change

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend's big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species' distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species' responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions.

opencc-zeroDec 2022View details →