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42 results for “aerial insectivore”
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
<p><span>Edge effects - abiotic and biotic changes associated with habitat boundaries - are key drivers of community change in fragmented landscapes. Their influence is heavily modulated by matrix composition. With over half of the world's tropical forests predicted to become forest edge by the end of the </span><span>century, it is paramount that conservationists gain a better understanding of how tropical biota is impacted by edge gradients. Bats comprise a large fraction of tropical mammalian fauna and are demonstrably sensitive to habitat modification. Yet, </span><span>knowledge about how bat assemblages are affected by edge effects remains scarce</span><span>. Capitalizing on a whole-ecosystem manipulation in the Central Amazon, the aims of this study were to i) assess the consequences of edge effects for twelve aerial insectivorous bat species across the interface of primary and secondary forest and ii) investigate if the activity levels of these species differed between the understory and canopy and if they were modulated by distance from the edge</span><span>. Acoustic surveys were conducted along four 2-km transects each traversing equal parts of primary and ca. 30-year-old secondary forest. Five models were used to assess the changes in the relative activity of forest specialists (three species), flexible forest foragers (three species), and edge foragers (six species). Modelling results revealed no evidence of edge effects, except for forest specialists in the understory. No significant differences in activity were found between the secondary or primary forest but most species exhibited pronounced vertical stratification. Our study highlights that forest specialist bats are more edge-sensitive than both flexible forest and edge foraging bats and suggests that the influence of edge effects on aerial insectivorous bats may exceed 2 km. The absence of pronounced edge effects and the comparable activity levels between primary and old secondary forests indicates that old secondary forest can help ameliorate the consequences of fragmentation on tropical aerial insectivorous bats. </span></p>
Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest
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Population structure, patterns of natal dispersal, and demographic history in a declining aerial insectivore, the purple martin Progne subis
<p>Genetic variation is a fundamental component of biodiversity, and studying population structure, gene flow, and demographic history can help guide conservation strategies for many species. Like other aerial insectivores, the purple martin (<em>Progne subis</em>) is in decline, and yet their genetic background remains largely unknown. To address this knowledge gap, we assessed population structure in the nominate eastern subspecies (<em>P. s. subis</em>) with relation to natal dispersal and examined historical genetic patterns in all three subspecies (<em>P. s. subis, P. s. arboricola, P. s. hesperia</em>) across their North American breeding range by estimating effective population sizes over time. We used next-generation sequencing strategies for genomic analyses, integrating whole-genome resequencing data with continent-wide band encounter records to examine natal dispersal. We documented population structure across <em>P. s. subis</em>, with the highest differentiation between the northern (Alberta) and more southern colonies and following patterns of isolation-by-distance. Consistent with spatial patterns of genetic differentiation, we also found greater longitudinal than latitudinal natal dispersal distances, signifying potential latitudinal constraints on gene flow. Earlier contractions in effective population sizes in the western <em>P. s. arboricola</em> and <em>P. s. hesperia</em> compared to the eastern <em>P. s. subis</em> subspecies suggest these subspecies originated from two different glacial refugia. Together, these findings support latitudinal distinction in <em>P. s. subis</em>, and elucidate the origin of subspecies differentiation, highlighting the importance to conserve populations across the range to maximize genetic diversity and adaptive potential in the purple martin.</p>
No apparent trade-off between the quality of nest grown feathers and time spent in the nest in an aerial insectivore, the tree swallow
<p>Life history theory provides a framework for understanding how trade-offs generate negative trait associations. Among nestling birds, time spent in the nest, risk of predation, and lifespan covary, but some associations are only found within species while others are only observed between species. A recent comparative study suggests that allocation trade-offs may be alleviated by disinvestment in ephemeral traits, such as nest-grown feathers, that are quickly replaced. However, direct resource allocation trade-offs cannot be inferred from inter-specific trait-associations without complementary intra-specific studies. Here, we asked whether there is evidence for a within-species allocation trade-off between feather quality and time spent in the nest in tree swallows (<em>Tachycineta bicolor</em>). Consistent with the idea that ephemeral traits are deprioritized, nest-grown feathers had lower barb density than adult feathers. However, despite substantial variation in fledging age among nestlings, there was no evidence for a negative association between time in the nest and feather quality. Furthermore, accounting for differences in resource availability by considering provisioning rate and a nest predation treatment did not reveal a trade-off that was masked by variation in resources. Our results are most consistent with the idea that the inter-specific association between development and feather quality arises from adaptive specialization, rather than from a direct allocation trade-off.</p>
Figure 5 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 5. The tongue of Vespertilionoidea, family Natalidae, Natalus macrourus: (a) Apex covered by flaky-shaped filiform papillae; (b) Salience (arrow) on mid-dorsal region of the tongue; and (c) Posterior region of the tongue with three circumvallate papillae, one anterior (VA) and two posteriorly placed (VM) and pointed basal filiform papillae (B).
Figure 4 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 4. Tongues of Noctilionoidea, families Mormoopidae, Thyropteridae, and Furipteridae; (a) Oval and grooved fungiform (F) surrounded by crown filiform papillae (C) in Pteronotus rubiginosus; (b) Globular fungiform (F) surrounded by short and pointed strictly filiform papillae (FL) in Thyroptera wynneae; (c) Posterior region with large medial circumvallate with prominent sulcus (S) and surrounding integument (I), and triangular filiform papillae (T) in Furipterus horrens; and (d) Striclty filiform papillae at the apex in F. horrens.
Figure 3 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 3. Tongues of Emballonuridae (Emballonuroidea): (a) Circumvallate papillae (V) with the groove and surrounding incipient tegument and fungiform papillae (F) to the left of the circumvallate papillae in Peropteryx kappleri; (b) Lateral fungiform papillae (F) and low basal filiform papillae (B) in P. kappleri; (c) Strictly filiform papillae concentrically arranged at the middle region of the tongue in Rhynchonycteris naso; and (d) Bifid filiform tubular-shaped papillae at the apex in P. kappleri.
Figure 2 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 2. Types of lingual papillae observed in Neotropical aerial insectivore bats: (a) Circumvallate with remarkable sulcus and integument, surrounded by pointed basal filiform (arrows); (b) Fungiform with notable sulcus (arrow); (c) Bifid filiform; (d) Strictly filiform; (e) Flacky-like filiform, note the layered structure with dentate keratinous plates; (f) Giant filiform (center), note the bigger size than the surrounding papillae; (g) Digitiform filiform; (h) Crown-shaped filiform, note the bulbous base and delicate filamentous projections at the apical edge; (i) Scale-like filiform, note the rectangular-shaped and dorsally concave structure; and (j) Triangular filiform.
Figure 7 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 7. Constrained bat phylogeny and the fittest distribution of characters and their respective state (in parentheses).The coding of characters among the taxa is inTable 2.
Figure 1 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 1. Dorsal surface of the tongue showing the general division in three regions to facilitate the description and distribution of the papillae.
Figure 6 in Comparative morphology of tongue surface in Neotropical aerial insectivore bats (Mammalia: Chiroptera)
Figure 6. The tongue of Vespertilionoidea, family Vespertilionidae: (a) Posterior region of the tongue with a pair of circumvallate papillae (V) and the conical basal papillae (B) in Histiotus velatus. Note the naked central portion between circumvallate papillae and the glottis (G); (b) Middle portion of the tongue with remarkable salience covered by scale-like filiform papillae with large fungiform papillae in Eptesicus furinalis; (c) Circumvallate papilla, note the lobed surface of the papilla in Eptesicus brasiliensis; (d) Scale-like filiform papillae on the mid-dorsal salience in H. velatus.
Benign effects of logging on aerial insectivorous bats in Southeast Asia revealed by remote sensing technologies
<b>Description: </b><p>Number of bat calls recorded by SongMeter bat 2 detectors set to record continuously on a trigger. Counts are classified into 21 acoustic call types, including 13 species.</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/101"><b>Impacts of forest modification on bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>UK Natural Environment Research Council (NERC) (Human Modified Tropical Forests programme & a PhD scholarship jointly funded by University of Kent & NERC & EnvEast DTP scholarship, NE/L002582/1)</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>Economic Planning Unit of the Malaysian Government and the Sabah Biodiversity Council (Research licence UPE: 40/200/19/2723)</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=7740421">here</a></p><p><b>Files: </b>This consists of 1 file: SAFE_data_archive_Yoh2.xlsx</p><p><b>SAFE_data_archive_Yoh2.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>All data </b> (described in worksheet MasterData)</p><p>Description: All auto and manual identifications for bat passes across a disturbance gradient, identified to functional group or species when possible</p><p>Number of fields: 13</p><p>Number of data rows: 134920</p><p>Fields: </p><ul><li><b>LOCATION</b>: Where the data was collected (Field type: location)</li><li><b>DATE</b>: Date surveyed (Field type: date)</li><li><b>TIME</b>: Time of recording (Field type: time)</li><li><b>AUTO_ID</b>: Taxa as identified using the automatic classifier (Field type: taxa)</li><li><b>ACCURACY</b>: Confidence value for auto identification results (Field type: numeric)</li><li><b>THRESLEVEL</b>: Whether the data met the desired auto-identification confidence value (Field type: categorical)</li><li><b>MANUAL_ID_CLEAN</b>: Taxa as identified manually (Field type: taxa)</li><li><b>FINAL_ID</b>: Final taxa label considering both the auto and manual ID (Field type: taxa)</li><li><b>TREATMENT</b>: Habitat type (Field type: categorical)</li><li><b>fc_100m</b>: Forest extent within 100m buffer of the survey location (Field type: numeric)</li><li><b>chm_100m</b>: Average canopy height within 100m buffer from survey location (Field type: numeric)</li><li><b>shape_100m</b>: Forest shape within 100m buffer of survey location (Field type: numeric)</li><li><b>TRI_100m</b>: Topographic ruggedness within 100m buffer of survey location (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2011-04-01 to 2012-06-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</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> -  -  -  -  -  [CF_CROB] <br> -  -  -  -  -  [CF_H140] <br> -  -  -  -  -  [FMQCF1] <br> -  -  -  -  -  [FMQCF2] <br> -  -  -  -  -  [FMQCF3] <br> -  -  -  -  -  [FMQCF4] <br> -  -  -  -  -  [FMQCF5] <br> -  -  -  -  -  [FMQCF6] <br> -  -  -  -  -  [QCF] <br> -  -  -  -  -  [FM] <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 philippinensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ater</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros galeritus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br></div><p></p>
The positive influence of wetlands on reproductive success and body mass in an aerial insectivore is more pronounced in intensively cropped agroecosystems
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Population structure, patterns of natal dispersal, and demographic history in a declining aerial insectivore, the purple martin Progne subis
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Data from: Forecasting the cumulative effects of multiple stressors on breeding habitat for a steeply declining aerial insectivorous songbird, the olive-sided flycatcher (Contopus cooperi)
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No apparent trade-off between the quality of nest grown feathers and time spent in the nest in an aerial insectivore, the tree swallow
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Data from: Heat dissipation capacity influences reproductive performance in an aerial insectivore
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Data for: Combining radio-telemetry and radar measurements to test optimal foraging in an aerial insectivore bird
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Data from: Responses of aerial insectivorous bats to local and landscape-level features of coffee agroforestry systems in Western Ghats, India
Shade coffee has shown great promise in providing crucial habitats for biodiversity outside formal protected areas. Insectivorous bats have been understudied in coffee, although they may provide pest control services. We investigated the influence of local and landscape-level features of coffee farms on aerial insectivorous bats in Chikmagalur district in the Western Ghats biodiversity hotspot, India. Bats were monitored in 20 farm sites using ultrasound detectors, and the response of bat species richness and activity to changes in tree density, proportion of built-up area in the neighborhood, and distance of farm from forest areas quantified. We examined if models built to explain the species richness and activity could also predict them in nine additional sites. We detected nine phonic types/species in the study area. The quantified predictors had no effect on assemblage-level species richness and activity of bats. Responses of edge-space and cluttered-space forager guilds mirrored those of the overall assemblage, but some species vulnerable to forest conversion like *Rhinolophus beddomei* were detected rarely. Best models explained up to 20% and 15% variation in assemblage-level species richness and activity respectively, and were poor predictors of both response variables. We conclude that coffee farms in our study area offer an important commuting space for insectivorous bats across a gradient of shade management. Further research should include species-specific responses to management decisions for at-risk species and quantification of ecosystem services like natural pest control to inform biodiversity conservation initiatives in the Western Ghats coffee landscapes.
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.
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International Brain Laboratory public data
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