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Disentangling mechanical and sensory modules in the radiation of Noctilionoid bats
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Least-cost habitat linkages for American black bear, Rafinesque's big-eared bat, and timber rattlesnake.
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Effects of phylogenetic distance, niche overlap and habitat alteration on spatial co-occurrence patterns in Neotropical bats and birds
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The SONOZOTZ project: assembling an echolocation calls library for bats in a megadiverse country
<p><span><span><span><span><span><span><span><span><span><span><span>Bat acoustic libraries are important tools that assemble echolocation calls to allow the comparison and discrimination to confirm species identifications. The Sonozotz project represents the first nation-wide library of bat echolocation calls for a megadiverse country. It was assembled following a standardized recording protocol that aimed to cover different recording habitats, recording techniques, and call variation inherent to individuals. The Sonozotz project included 69 species of echolocating bats, a high species richness that represents 50% of bat species found in the country. We include recommendations on how the database can be used and how the sampling methods can be potentially replicated in countries with similar environmental and geographic conditions. To our knowledge, this represents the most exhaustive effort to date to document and compile the diversity of bat echolocation calls for a megadiverse country. This database will be useful to address a range of ecological questions including the effects of anthropogenic activities on bat communities through the analysis of bat sound.</span></span></span></span></span></span></span></span></span></span></span></p>
FIG. 2 in Acoustic identification of bats in the southern Western Ghats, India
FIG. 2. Echolocation calls of bats with a strong CF component
Resilience and virus ecology of paleotropical bats
<b>Description: </b><p>Emerging infectious diseases (EIDs) are a threat to human and animal health. Bats are known reservoir hosts for various highly fatal viruses associated with EIDs. Previously, outbreaks of some EIDs were directly associated with environmental conditions, namely an increased contact zone between reservoir hosts and humans resulting from bush-meat consumption and increasing anthropogenic land-use. <br>However, anthropogenic land-use, especially habitat logging and fragmentation, may also take effect via consequences upon wildlife communities, including population declines. Before effects manifest in decreased population sizes, individuals may show changes in physiological parameters. In this project, I investigated if habitat logging and fragmentation are associated with changes in body mass, levels of chronic stress, immunity and occurrence of viruses in bats. I sampled individuals of eight bat species of the genus Rhinolophus, Hipposideros and Kerivoula at the SASFE project in Sabah, Malaysia. I found that individuals of foliage-roosting bat species weighed less in currently logged and recently fragmented habitats and had lower white blood cell counts than their conspecifics from undisturbed forests. In a cave-roosting species (Rhinolophus borneensis) individuals from fragmented forests showed higher levels of chronic stress (indicated by the neutrophil to lymphocyte ratio) than conspecifics from actively logged forests. Overall, I conclude that foliage-roosting bat species may be particularly vulnerable to habitat fragmentation, affecting their overall health including cell-mediated immunity.<br>Further, I investigated if the detrimental effects of habitat logging and<br>fragmentation on the overall health of bats are reflected in increased detection rates of corona- and astroviruses in fecal samples of bats in the same study site. An increase in detection rates may increase the risk for pathogen spill-overs from bats to humans. Unexpectedly, the detection rates were not associated with habitat logging and fragmentation in any species. However, I identified the rainy season as a risk factor for increased astrovirus shedding. Further, individuals in poor body condition tended to have a higher risk for astrovirus shedding.<br>In conclusion, foliage-roosting bat species may turn into a source for future viral spillover events if they are sufficiently resistant to remain in logged and fragmented habitats. Ongoing landscape fragmentation in Southeast Asia and worldwide will reduce the connectivity of remaining habitats, resulting in lower mobility and genetic diversity of bats within a species in future generations. These bat populations may have a higher susceptibility to contract and shed viruses especially during the rainy season. If <br>humans or their livestock live nearby, these viruses or other pathogens may spill over in new hosts.</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/73"><b>Resilience and virus ecology of paleotropical bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>German Research Council (Research grant, DFG Priority Programm 1596; Vo890/23, DR772/10-1 and 2)</li><li>UK NERC Natural Environment Research Council (Research grant, HTMF Human-Modified Tropical Forests program under the LOMBoK Land-Use Options for Maintaining Biodiversity and Ecosystem Function consortium)</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 Council (Research licence JKM/MBS.1000-2/2 JLD.3 (153))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.3 (317))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/3 JLD.2 (16))</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=3948443">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Seltmann_bat_data_V2.xlsx, Traps2014.2015.gpx</p><p><b>Seltmann_bat_data_V2.xlsx</b></p><p>This file contains dataset metadata and 9 data tables:</p><ol><li><p><b>ACTH_challenge</b> (described in worksheet ACTH_challenge)</p><p>Description: ACTH challenge</p><p>Number of fields: 17</p><p>Number of data rows: 15</p><p>Fields: </p><ul><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Cortisol</b>: Blood cortisol levels (Field type: numeric trait)</li><li><b>Recapture</b>: Is the bat a recapture (Field type: categorical trait)</li><li><b>Date</b>: Date of capture (Field type: date)</li><li><b>Trap</b>: Trap number (Field type: id)</li><li><b>Species</b>: Species of bat (Field type: taxa)</li><li><b>Sex</b>: Sex of bat (Field type: categorical trait)</li><li><b>Age</b>: Age of bat (A - adult) (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (PL - postlactating) (Field type: categorical trait)</li><li><b>Forearm_length</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Weight (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>T1</b>: Temperature before ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: Temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites observed on sampled bat (Field type: categorical trait)</li><li><b>W</b>: Wing punch (Field type: categorical trait)</li></ul></li><li><p><b>Viral_fecal_samples</b> (described in worksheet Viral_fecal_samples)</p><p>Description: Viral prevalences 03-04 2015</p><p>Number of fields: 31</p><p>Number of data rows: 790</p><p>Fields: </p><ul><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Pool_comments</b>: Pool ID (Field type: comments)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Astro</b>: Astrovirus prevalence (Field type: categorical trait)</li><li><b>COV</b>: Coronavirus prevalence (Field type: categorical trait)</li><li><b>Band_no</b>: Ring-Number (Field type: id)</li><li><b>R</b>: Recapture (uk - unknown) (Field type: categorical trait)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Sex</b>: Sex of bat sampled (Field type: categorical trait)</li><li><b>Age</b>: Age of bat sampled (A - adult, S - subadult, J - juvenile) (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical trait)</li><li><b>Forearm_length</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Biomass of bat (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites on sampled bats (Field type: comments)</li><li><b>W</b>: Wing punch taken (Field type: numeric trait)</li><li><b>Comments</b>: Comments (Field type: comments)</li><li><b>Dave</b>: Daves comments (Field type: comments)</li><li><b>File</b>: Orignal file name (Field type: comments)</li></ul></li><li><p><b>Overview_3</b> (described in worksheet Overview_3)</p><p>Description: Viral prevalences 03-04 2015</p><p>Number of fields: 4</p><p>Number of data rows: 7</p><p>Fields: </p><ul><li><b>Species</b>: Species of bat (Field type: taxa)</li><li><b>Scientific_name</b>: Scientific name of bat (Field type: comments)</li><li><b>Total_urine_samples</b>: Total number of urine samples (Field type: numeric trait)</li><li><b>Total_fecal_samples</b>: Total number of fecal samples (Field type: numeric trait)</li></ul></li><li><p><b>Sequences</b> (described in worksheet Sequences)</p><p>Description: Coronavirsu sequences</p><p>Number of fields: 3</p><p>Number of data rows: 5</p><p>Fields: </p><ul><li><b>ID</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Gene_seq</b>: Gene sequence code (Field type: comments)</li></ul></li><li><p><b>IgG_BKA</b> (described in worksheet IgG_BKA)</p><p>Description: IgGs and BKA</p><p>Number of fields: 28</p><p>Number of data rows: 44</p><p>Fields: </p><ul><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Band</b>: Ring-Number (Field type: id)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>R</b>: Recapture (Field type: categorical trait)</li><li><b>Sex</b>: Sex of bat (Field type: categorical trait)</li><li><b>Age</b>: Age of bat (A - adult) (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical trait)</li><li><b>Forearm length</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Biomass of bat (Field type: numeric trait)</li><li><b>BC</b>: Body condition (mass divided by forearm) (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>W</b>: Wing punch (Field type: numeric trait)</li><li><b>Iggs1</b>: IgGs (optical density) measured before blood injected with hormone ACTH intraperitoneal (Field type: numeric trait)</li><li><b>Iggs2</b>: IgGs (optical density) measured 2.5 hours after blood injected with hormone ACTH intraperitoneal (Field type: numeric trait)</li><li><b>BKA1</b>: "The BKA is a constitutive innate marker of the immune system and measures humoral and cellular components in function of the sample used. While using whole blood is possible to quantify the overall constitutive innate immunity of an individual44, with serum or plasma samples only the humoral part is measured45,46. We assessed the bacterial killing activity (BKA) of the plasma against E. coli in vitro following the method of Schneeberger et al.47. The BKA of plasma is a functional marker of the humoral part of the constitutive innate immunity45,46. Plasma samples were diluted 1:50 in sterile PBS and we added 10 µl of a suspension of living E. coli (ATCC #8739) to each diluted sample (140 µl). The bacterial suspension was adjusted to a concentration of ~200 colonies per 50 µl plasma-bacteria mixture. The mixtures were then incubated for 30 min at 37°C. After incubation, 50 µl aliquot of the vortexed mixture was spread onto Tryptic Soy Agar plates in duplicate, followed by overnight incubation at 37°C. To obtain the initial number of bacteria that we had before starting to interact with the plasma, we diluted 140 µl PBS with bacterial suspension and plated in similar ways. On the following day, the colony-forming units were counted and the bacterial killing activity was defined as percent of the killed bacteria, which was calculated as 1-(average of viable bacteria after incubation / the initial number of bacteria47). 45 Heinrich, S. K. et al. Feliform carnivores have a distinguished constitutive innate immune response. Biol. Open 5, 550-555 (2016). 46 Heinrich, S. K. et al. Cheetahs have a stronger constitutive innate immunity than leopards. Sci. Rep. 7, 44837 (2017). 47 Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Measures of the constitutive immune system are linked to diet and roosting habits of Neotropical bats. PLoS One 8, e54023 (2013). (Field type: numeric trait)</li><li><b>BKA2</b>: "The BKA is a constitutive innate marker of the immune system and measures humoral and cellular components in function of the sample used. While using whole blood is possible to quantify the overall constitutive innate immunity of an individual44, with serum or plasma samples only the humoral part is measured45,46. We assessed the bacterial killing activity (BKA) of the plasma against E. coli in vitro following the method of Schneeberger et al.47. The BKA of plasma is a functional marker of the humoral part of the constitutive innate immunity45,46. Plasma samples were diluted 1:50 in sterile PBS and we added 10 µl of a suspension of living E. coli (ATCC #8739) to each diluted sample (140 µl). The bacterial suspension was adjusted to a concentration of ~200 colonies per 50 µl plasma-bacteria mixture. The mixtures were then incubated for 30 min at 37°C. After incubation, 50 µl aliquot of the vortexed mixture was spread onto Tryptic Soy Agar plates in duplicate, followed by overnight incubation at 37°C. To obtain the initial number of bacteria that we had before starting to interact with the plasma, we diluted 140 µl PBS with bacterial suspension and plated in similar ways. On the following day, the colony-forming units were counted and the bacterial killing activity was defined as percent of the killed bacteria, which was calculated as 1-(average of viable bacteria after incubation / the initial number of bacteria47). 45 Heinrich, S. K. et al. Feliform carnivores have a distinguished constitutive innate immune response. Biol. Open 5, 550-555 (2016). 46 Heinrich, S. K. et al. Cheetahs have a stronger constitutive innate immunity than leopards. Sci. Rep. 7, 44837 (2017). 47 Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Measures of the constitutive immune system are linked to diet and roosting habits of Neotropical bats. PLoS One 8, e54023 (2013). (Field type: numeric trait)</li></ul></li><li><p><b>Leukocytes</b> (described in worksheet Leukocytes)</p><p>Description: Leukocyte counts</p><p>Number of fields: 37</p><p>Number of data rows: 57</p><p>Fields: </p><ul><li><b>Site</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Transect</b>: Transect within SAFE block (Field type: replicate)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Band</b>: Ring-Number (Field type: id)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Recapture</b>: Recapture (uk - unknown) (Field type: categorical trait)</li><li><b>Age</b>: Age of bat sampled (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical trait)</li><li><b>Sex</b>: Sex of bat sampled (Field type: categorical trait)</li><li><b>Forearm</b>: Forearm length (Field type: numeric trait)</li><li><b>Body mass</b>: Biomass of bat (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites on bat (Field type: comments)</li><li><b>W</b>: Wing punch (Field type: numeric trait)</li><li><b>CommentsEnglish</b>: Blood sample comments (Field type: comments)</li><li><b>Eosinophiles</b>: Eosinophiles (Field type: numeric trait)</li><li><b>Basophiles</b>: Basophiles (Field type: numeric trait)</li><li><b>Neutrophiles</b>: Neutrophiles (Field type: numeric trait)</li><li><b>Monocytes</b>: Monocytes (Field type: numeric trait)</li><li><b>Lymphocytes</b>: Lymphocytes (Field type: numeric trait)</li><li><b>CommentsGerman</b>: Comments in german (Field type: comments)</li><li><b>NL-ratio</b>: NL-ratio (Field type: numeric trait)</li><li><b>Monolayers</b>: Monolayers (Field type: numeric trait)</li><li><b>Leukocytes</b>: Leukocytes (Field type: numeric trait)</li><li><b>Mean_leukocytes_monolayer</b>: Proportion of monolayers with leukocytes (Field type: numeric trait)</li></ul></li><li><p><b>PCR</b> (described in worksheet PCR)</p><p>Description: PCR analysis</p><p>Number of fields: 10</p><p>Number of data rows: 78</p><p>Fields: </p><ul><li><b>ID</b>: Bat ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Ct_bIL-6</b>: ? (Field type: numeric trait)</li><li><b>Ct_bSTAT1</b>: ? (Field type: numeric trait)</li><li><b>Ct_bMAPK</b>: ? (Field type: numeric trait)</li><li><b>Ct_bActin_B</b>: ? (Field type: numeric trait)</li><li><b>Ct_bIL-6-2</b>: ? (Field type: numeric trait)</li><li><b>Ct_bSTAT1-2</b>: ? (Field type: numeric trait)</li><li><b>Ct_bMAPK-2</b>: ? (Field type: numeric trait)</li></ul></li><li><p><b>Bat data</b> (described in worksheet Bat_data)</p><p>Description: Collection of bat data</p><p>Number of fields: 26</p><p>Number of data rows: 891</p><p>Fields: </p><ul><li><b>Locations</b>: Location of trap (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Band_number</b>: Ring-Number (Field type: id)</li><li><b>R</b>: Recapture (Field type: categorical)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Sex</b>: Sex of bat (Field type: categorical trait)</li><li><b>Age</b>: Age of bat (A - adult, J - juvenile) (Field type: categorical)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical)</li><li><b>Forearm lenght</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Biomass of bat (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites present on bat (Field type: comments)</li><li><b>W</b>: Wing punch (Field type: numeric trait)</li><li><b>Comments</b>: Additional comments (Field type: comments)</li></ul></li><li><p><b>Harp_trap</b> (described in worksheet Harp_trap)</p><p>Description: Harp trap data </p><p>Number of fields: 13</p><p>Number of data rows: 322</p><p>Fields: </p><ul><li><b>Date_harp_open</b>: Date that harp trap was open (Field type: date)</li><li><b>Date_harp_closed</b>: Date the harp was closed (Field type: date)</li><li><b>Location</b>: Location of harp trap transect (Field type: location)</li><li><b>Night_number</b>: night number (Field type: id)</li><li><b>Trap_opened</b>: Time trap as opened (Field type: time)</li><li><b>Trap_closed</b>: Time trap was closed (Field type: time)</li><li><b>Trap_hrs</b>: Numbers of hours the trap was open (Field type: numeric)</li><li><b>Trap_nights</b>: Number of nights traps was open (Field type: numeric)</li><li><b>Rain</b>: Rain conditions (Field type: categorical)</li><li><b>Wind</b>: Wind conditions (Field type: categorical)</li><li><b>Moon</b>: Moon conditions (Field type: categorical)</li><li><b>Other</b>: Other notes about the weather conditions (Field type: comments)</li><li><b>Comments</b>: Other comments (Field type: comments)</li></ul></li></ol><p><b>Traps2014.2015.gpx</b></p><p>Description: Trap locations gps locations</p><p><b>Date range: </b>2014-01-31 to 2015-09-04</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> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Nycteridae <br> -  -  -  -  -  -  <i>Nycteris</i> <br> -  -  -  -  -  -  -  <i>Nycteris tragata</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros doriae</i> <br> -  -  -  -  -  -  -  <i>Hipposideros dyacorum</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br> -  -  -  -  -  -  -  <i>Hipposideros doriae</i> <br> -  -  -  -  -  Vespertilionidae <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 papillosa</i> <br> -  -  -  -  -  -  -  <i>Kerivoula pellucida</i> <br> -  -  -  -  -  -  <i>Myotis</i> <br> -  -  -  -  -  -  -  <i>Myotis muricola</i> <br> -  -  -  -  -  -  <i>Pipistrellus</i> <br> -  -  -  -  -  -  -  <i>Pipistrellus tenuis</i> <br> -  -  -  -  -  -  <i>Phoniscus</i> <br> -  -  -  -  -  -  -  <i>Phoniscus atrox</i> <br> -  -  -  -  -  -  <i>Murina</i> <br> -  -  -  -  -  -  -  <i>Murina aenea</i> <br> -  -  -  -  -  -  -  <i>Murina cyclotis</i> <br> -  -  -  -  -  -  -  <i>Murina suilla</i> <br> -  -  -  -  -  Pteropodidae <br> -  -  -  -  -  -  <i>Cynopterus</i> <br> -  -  -  -  -  -  -  <i>Cynopterus brachyotis</i> <br> -  -  -  -  -  -  <i>Rousettus</i> <br> -  -  -  -  -  -  -  <i>Rousettus spinalatus</i> <br> -  -  -  -  -  -  <i>Balionycteris</i> <br> -  -  -  -  -  -  -  <i>Balionycteris maculata</i> <br> -  -  -  -  -  Emballonuridae <br> -  -  -  -  -  -  <i>Emballonura</i> <br> -  -  -  -  -  -  -  <i>Emballonura alecto</i> <br> -  -  -  -  -  -  -  <i>Emballonura monticola</i> <br></div><p></p>
Data from: Bat ensembles differ in response to use zones in a tropical biosphere reserve
<p>Biosphere reserves, designated under The United Nations Education, Scientific and Cultural Organization's (UNESCO) Man and Biosphere Programme, aim to sustainably integrate protected areas into the biological and economic landscape around them by buffering strictly protected habitats with zones of limited use. However, the effectiveness of biosphere reserves and the contribution of the different zones of use to protection is poorly known. We assessed the diversity and activity of bats in the Crocker Range Biosphere Reserve (CRBR) in Sabah, Malaysia, using harp traps, mist nets and acoustic surveys in each zone—core, buffer, transition and in agricultural plots outside of the reserve. We captured 30 species, bringing the known bat fauna of CRBR to 50 species, half of Borneo's bat species. Species composition and acoustic activity varied among zones and by foraging ensemble, with the core and buffer showing particular importance for conserving forest-dependent insectivorous bats. Frugivorous bats were found in all zones but were the most abundant and most species-rich ensemble within agricultural sites. Although sampling was limited, bat diversity and activity was low in the transition zone compared to other zones, indicating potential for management practices that increase food availability and enhance biodiversity value. We conclude that, collectively, the zones of the CRBR effectively protect diversity, but the value of the transition zone can be improved.</p>
Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system
<p>Seven decades of research on the "cognitive map", the allocentric representation of space, have yielded key neurobiological insights, yet we still lack field evidence from free-ranging wild animals. Using a system capable of tracking dozens of animals simultaneously at high accuracy and resolution, we assembled a large dataset of 172 foraging Egyptian fruit bats comprising >18M localizations collected over 3,449 bat-nights across 4 years. Detailed track analysis, combined with translocation experiments, revealed that wild bats seldom exhibit random search but instead repeatedly forage in goal-directed, long and straight flights that include frequent shortcuts. Alternative non-map-based strategies were ruled out by simulations, time-lag embedding and other trajectory analyses. Our results are consistent with expectations from cognitive map-like navigation and support previous neurobiological evidence from captive bats. </p>
FIG. 5 in Annotated checklist of bats (Mammalia: Chiroptera) of Mount Cameroon, southwestern Cameroon
FIG. 5. — Megaloglossus woermanni Pagenstecher, 1885. Photo: © Aaron Manga Mongombe.
FIG. 3 in Annotated checklist of bats (Mammalia: Chiroptera) of Mount Cameroon, southwestern Cameroon
FIG. 3. — Eidolon helvum (Kerr, 1792). Photo: © Aaron Manga Mongombe.
FIG. 4 in Annotated checklist of bats (Mammalia: Chiroptera) of Mount Cameroon, southwestern Cameroon
FIG. 4. — Epomops franqueti (Tomes, 1860). Photo: © Aaron Manga Mongombe.
Data from: Beta diversity patterns of bats in the Atlantic Forest: how does the scale of analysis affect the importance of spatial and environmental factors?
<p>Aim: Environmental and spatial factors are broadly recognized as important predictors of beta diversity patterns. However, the scale at which beta diversity patterns are evaluated will affect the outcoming results. For example, studies at larger scales will usually find spatial processes as the main predictor of beta diversity patterns. In this study we evaluate how beta diversity patterns change when analyses are conducted at different scales by reducing the scale of analysis in a hierarchical manner.</p> <p>Taxon: Chiroptera.</p> <p>Location: Atlantic Forest biome.</p> <p>Methods: Information on the occurrence of 59 bat species were obtained from the Atlantic Bats and Species Link database. We partitioned beta diversity into its two components (nestedness and turnover), and calculated these indexes hierarchically: the biome in its entirety (all ecoregions); between larger regions (north, central and south); and between ecoregions within each region. We performed a Generalized Dissimilarity Model (GDM) to identify and predict the turnover of bat species in the Atlantic Forest based on geo-climatic predictors. We obtained 19 geo-climatic data from AMBDATA, an environmental dataset based on different data sources commonly used in species distribution modeling.</p> <p>Results: We found that turnover was the main component influencing a latitudinal gradient when the biome was analysed in its entirety. However, when the scale of the analysis was reduced, we found that species loss (nestedness component) had a large effect in determining beta diversity dissimilarity. We also found that nestedness was the main pattern explaining beta diversity dissimilarity along a longitudinal gradient.</p> <p>Main conclusions: Beta diversity patterns changed with the scale of analysis, which indicates that bat species composition does not follow the same pattern throughout the Atlantic Forest. This corroborates the importance of analysing beta diversity patterns at different scales in order to understand how environmental dissimilarity across geographic space can influence species distribution patterns.</p>
Tracking sickness effects on social encounters via continuous proximity-sensing in wild vampire bats
Sickness behaviors can slow the spread of pathogens across a social network. We conducted a field experiment to investigate how sickness behavior affects individual connectedness over time using a dynamic social network created from high-resolution proximity data. After capturing adult female vampire bats (Desmodus rotundus) from a roost, we created 'sick' bats by injecting a random half of bats with the immune-challenging substance, lipopolysaccharide, while the control group received saline injections. Over the next three days, we used proximity sensors to continuously track dyadic associations between 16 'sick' bats and 15 control bats under natural conditions. Compared to control bats, 'sick' bats associated with fewer bats, spent less time near others, and were less socially connected to more well-connected individuals (sick bats had on average a lower degree, strength, and eigenvector centrality). High-resolution proximity data allow researchers to flexibly define network connections (association rates) based on how a particular pathogen is transmitted (e.g., contact duration of >1 vs >60 minutes, contact proximity of <1 vs <10 meters). Therefore, we inspected how different ways of measuring association rates changed the observed effect of LPS. How researchers define association rates influences the magnitude and detectability of sickness effects on network centrality.
Data and code for: Time of night and moonlight structure vertical space use by insectivorous bats in a Neotropical rainforest: an acoustic monitoring study
<p>Abstract</p> <p>Previous research has shown diverse vertical space use by various taxa, highlighting the importance of forest canopy. Yet, we often fail to explore how this three-dimensional space use changes over time. Here we use canopy tower systems in French Guiana to monitor neotropical bat activity above and below the forest canopy throughout nine nights in the wet season. We show that different bats use both canopy and understory space differently, and that this can change throughout the night. We find that bats are overall more active in the canopy, but multiple species/acoustic complexes are more active in the understory. We also find that species that do not seem to prefer understory or canopy, when data are aggregated by night, do show temporally changing preferences in hourly activity. This work highlights the need to consider temporal axes in studies of space use, both throughout daily cycles and across seasons.</p>
Data from: External temperature and distance from nearest entrance influence microclimates of cave and culvert roosting tri-colored bats Perimyotis subflavus
<p>Many North American bat species hibernate in both natural and artificial roosts. Although hibernacula can have high internal climate stability, they still retain spatial variability in their thermal regimes, resulting in various 'microclimates' throughout the roost that differ in their characteristics (e.g., temperature, air moisture). These microclimate components can be influenced by factors such as the number of entrances, the depth of the roost, and distance to the nearest entrance of the roost. Tri-colored bats are commonly found roosting in caves in winter, but they can also be found roosting in large numbers in culverts, providing the unique opportunity to investigate factors influencing microclimates of bats in both natural and artificial roost sites. As tri-colored bats are currently under consideration for federal listing, information of this type could be useful in aiding in the conservation and management of this species through a better understanding of what factors affect the microclimate near roosting bats. We collected data on microclimate temperature and microclimate actual water vapor pressure (AWVP) from a total of 760 overwintering tri-colored bats at 18 caves and 44 culverts. Using linear mixed models analysis, we found that variation in bat microclimate temperatures was best explained by external temperature and distance from nearest entrance in both caves and culverts. External temperature had a greater influence on microclimate temperatures in culverts than caves. We found that variation in microclimate AWVP was best explained by external temperature, distance from nearest entrance and proportion from entrance (proportion of the total length of the roost from the nearest entrance) in culvert roosting bats. Variation in microclimate AWVP was best explained by external temperature and proportion from entrance in cave roosting bats. Our results suggest that bat microclimate temperature and AWVP are influenced by the similar factors in both artificial and natural roosts, although the relative contribution of these factors differ between roost types.</p>
Reproductive notes of Phyllostomid bats in Ecuador
<p><strong>Reproductive notes of Phyllostomid bats in Ecuador</strong> (data_reproductive bats_ecuador.xlsx). Es el producto de seis estudios ambientales realizados en cinco provincias y seis localidades del Ecuador continental. Los muestreos se realizaron en los años: 2009, 2011, 2013, 2019 y 2020. Esta base de datos se usó para evaluar el estado reproductivo de individuos hembra de murciélagos filostómidos, previa publicación en <em>Ecotrópicos</em>, Revista de la Sociedad Venezolana de Ecología.</p>
The functional ecology of bat pollination in the African sausage tree Kigelia africana (Bignoniaceae)
<p>Plants often interact with a wide range of animal floral visitors that can vary in their pollination effectiveness. Flowers of the African sausage tree Kigelia africana are be visited by bats and bush babies during the night and by birds during the day. We studied floral traits (phenophases, scent, colour and nectar chemistry) and the visitation frequency and pollination effectiveness of different flower visitors to determine if K. africana is functionally specialized for bat-pollination. We found that flower opening corresponds with bat activity, flowers emit scent dominated by aliphatic esters and alcohols, and that nectar is produced in copious amounts accessible to bats. Pollen deposition on stigmas was twenty-fold greater per-visit by bats than it was per-visit by birds, likely a result of the close morphological fit between snouts of bats and the flowers. However, bat visits appear to be rare at some sites and the delayed senescence of flowers that are open throughout the morning provides an opportunity for additional pollination by birds. We conclude that K. africana is primarily adapted for bat pollination, but is also able</p>
Data for: Phantom rivers filter birds and bats by acoustic niche
<p>Natural sensory environments, despite strong potential for structuring systems, have been neglected in ecological theory. Here, we test the hypothesis that intense natural acoustic environments shape animal distributions and behavior by broadcasting whitewater river noise in montane riparian zones for two summers. We find that both birds and bats avoid areas with high sound levels, while birds avoid frequencies that overlap with birdsong, and bats avoid higher frequencies more generally. Behaviorally, intense sound levels decrease foraging in birds, whereas bats appear to switch hunting strategies from passive listening to aerial hawking as sound levels increase. Natural acoustic environments are an underappreciated niche axis, a conclusion that serves to escalate the urgency of mitigating human-created noise.</p>
Bats use live fences to move between tropical dry forest remnants
<p>Linear features can benefit wildlife by assisting animal movement. We captured bats along barbed-wire and live-tree fences connecting Tropical Dry Forest patches in Nicaragua. Bat species richness and captures were higher along live fences but we noted differences in sex ratios, richness, and species composition compared to surrounding natural forests.</p>
Data from: Genetic diversity, infection prevalence, and possible transmission routes of Bartonella spp. in vampire bats
Bartonella spp. are globally distributed bacteria that cause endocarditis in humans and domestic animals. Recent work has suggested bats as zoonotic reservoirs of some human Bartonella infections; however, the ecological and spatiotemporal patterns of infection in bats remain largely unknown. Here we studied the genetic diversity, prevalence of infection across seasons and years, individual risk factors, and possible transmission routes of Bartonella in populations of common vampire bats (Desmodus rotundus) in Peru and Belize, for which high infection prevalence has previously been reported. Phylogenetic analysis of the gltA gene for a subset of PCR-positive blood samples revealed sequences that were related to Bartonella described from vampire bats from Mexico, other Neotropical bat species, and streblid bat flies. Sequences associated with vampire bats clustered significantly by country but commonly spanned Central and South America, implying limited spatial structure. Stable and nonzero Bartonella prevalence between years supported endemic transmission in all sites. The odds of Bartonella infection for individual bats was unrelated to the intensity of bat flies ectoparasitism, but nearly all infected bats were infested, which precluded conclusive assessment of support for vector-borne transmission. While metagenomic sequencing found no strong evidence of Bartonella DNA in pooled bat saliva and fecal samples, we detected PCR positivity in individual saliva and feces, suggesting the potential for bacterial transmission through both direct contact (i.e., biting) and environmental (i.e., fecal) exposures. Further investigating the relative contributions of direct contact, environmental, and vector-borne transmission for bat Bartonella is an important next step to predict infection dynamics within bats and the risks of human and livestock exposures.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.