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Ecological niche models for American black bear, Rafinesque's big-eared bat, and timber rattlesnake
<p>This data set contains rasters that are predictive environmental suitability maps for three wildlife species: the American black bear (<i>Ursus americanus</i>), Rafinesque's big-eared bat (<i>Corynorhinus rafinesquii</i>), and Timber rattlesnake (<i>Crotalus horridus</i>). Rasters for each species include: individual prediction maps for each of 5 ENMs (GBM: generalized boosting model, GLM: generalized linear model, MARS: multivariate adaptive regression spline, MX: maximum entropy, and RF: random forest), as well as the ensemble prediction map from all five ecological niche models (ENMs).</p>
Figure 3 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica
Figure 3. Niche overlap values for Schoener's D and Hellinger's I compared to a null distribution: (a) Artibeus a. aztecus (yellow) vs. A. a. minor (blue), (b) A. a. aztecus vs.A. A. major (red), (c) A. a. minor vs. A. a. major.
Figure 2 in Ecological niche differentiation among Aztec fruit-eating bat subspecies (Chiroptera: Phyllostomidae) in Mesoamerica
Figure 2. Maxent predicted potential distribution for (a) Artibeus a. aztecus, (b) A. a. minor, and (c) A. a. major.
Figure 3 in Distribution and roosting ecology of the lesser mouse-tailed bat, Rhinopoma hardwickii Gray, 1831 (Chiroptera: Rhinopomatidae)
Figure 3. Map shows the distribution of lesser mouse-tailed bat, Rhinopoma hardwickii. The locations of roost sites were abbreviated and shown in the map: 1) AGC – Agra, 2) SFA – Sangam Fort (Allahabad), 3) NTB – Neelkanth Temple Kalinjar (Banda), 4) BAC – Banda, 5) PKC – Purani kotwali (Chitrakoot), 6) AFE – Awagdh Fort (Etah), 7) FBC – Faizabad, 8) FPC – Fatehpur, 9) EPF – Edalpur (Firozabad), 10) JFJ– Jaunpur Fort (Jaunpur), 11) BMJ – Bukhara, Mauranipur (Jhansi), 12) JHC – Jhansi, 13) LPC – Lalitpur, 14) TFL – Talbahte Fort (Lalitpur), 15) KKL – Kakori (Lucknow), 16) TTM– Tirthankar Temple (Mahowa), 17) MFM – Mirzapur Fort (Mirzapur), 18) KBP – Khusaroo bagh, 19) RBC – Raebareli, 20) SPC – Sultanpur, 21) UNC – Unnao.
The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes
<p>Ecological infrastructures (EI), defined as natural or semi-natural structural elements, are important to support biodiversity and could play a crucial role in counteracting the well-known impacts of intensive agriculture. Yet, the importance of EI remains largely unexplored in Mediterranean agricultural landscapes and for species providing essential ecosystem services such as bats. Here, we evaluated the role of different EI types – in terms of location (riparian vs terrestrial) and vegetation physiognomy (woody vs non-woody) – in shaping bat guild activity in crop fields located in the floodplains of the Iberian Peninsula. We recorded 60,732 bat sequences in 96 crop fields and characterized 106 EI patches via an adaptation of the Biodiversity Potential Index (BPI). We found that the activity of mid-range echolocators (MRE) and long-range echolocators (LRE) was twofold higher when the nearest EI patch was riparian (i.e., contiguous to a watercourse) than when it was terrestrial. When assessing changes in bat activity in crop fields in relation to a gradient distance from EI types, our results revealed both distinct and similar effects of the location and vegetation physiognomy of the EI on bat guilds. For instance, while only the LRE guild positively responded to the proximity of woody EI, both MRE and LRE showed a marked increase of activity when increasing distances to non-woody EI, thus suggesting low bat activity levels near these features. Our habitat quality assessment also revealed that woody EI and riparian EI had higher biodiversity potential and related habitat quality, thus contributing to our understanding of bat responses to EI type in crop fields. As riparian areas are rarely targeted in biodiversity-friendly measures in farmland, we strongly recommend including riparian EI (especially the woody type) in conservation planning as they are crucial for both biodiversity conservation and ecosystem functioning.</p>
Fig. 3 in Contribution To Ecology Of Brandt'S Bat, Myotis Brandtii (Chiroptera, Vespertilionidae) In The North-Eastern Ukraine: Comparison Of Local Summer And Winter Bat Assemblages
Fig. 3. Body mass (g) characteristic of females (F) and (M) of M. brandtii in periods of spring departure April (S_dep) and swarming August (Swarm) from the Tetlega mines (dot — mean value, line — median value, whiskers — min and max values, not filling circles — outliers).
Fig. 2 in Contribution To Ecology Of Brandt'S Bat, Myotis Brandtii (Chiroptera, Vespertilionidae) In The North-Eastern Ukraine: Comparison Of Local Summer And Winter Bat Assemblages
Fig. 2. Forearm length (mm) of females (F) and males (M) of M. brandtii from Tetlega mines (dot — mean value, line — median value, whiskers — min and max values).
Fig. 1 in Contribution To Ecology Of Brandt'S Bat, Myotis Brandtii (Chiroptera, Vespertilionidae) In The North-Eastern Ukraine: Comparison Of Local Summer And Winter Bat Assemblages
Fig. 1. Allocation of M. brandtii (Mbra) and M. daubentonii (Mdau) inside the Tetlega mines from November to April (n — number of counted bats); A — in crevices or open, B — on walls or ceiling.
Fig. 1 in Ecology of bat flies in Singapore: A study on the diversity, infestation bias and host specificity (Diptera: Nycteribiidae)
Fig. 1. Pteropodid fruit bats in Singapore with their ectoparasitic Nycteribiidae bat flies. Cynopterus brachyotis (a) and Leptocyclopodia ferrarii (b); Eonycteris spelaea (c) and Eucampsipoda sundaica (d); Penthetor lucasi (e), Eucampsipoda penthetoris (f), and Archinycteribia octophthalma (g).
Fig. 2 in Ecology of bat flies in Singapore: A study on the diversity, infestation bias and host specificity (Diptera: Nycteribiidae)
Fig. 2. Posterior mean intensity of the three species of bats and 89% HDPI (High Density Posterior Interval).
Data from: Andriollo T., Michaux J.R., Ruedi M. (2021). Food for everyone: differential feeding habits of cryptic bat species inferred from DNA metabarcoding. Molecular Ecology
<p><strong>Supporting data for:</strong> Andriollo T., Michaux J.R., Ruedi M. (2021). Food for everyone: differential feeding habits of cryptic bat species inferred from DNA metabarcoding. Molecular Ecology. https://doi.org/ 10.1111/mec.16073</p> <p>Raw DNA sequences of prey of <em>Plecotus auritus</em>, <em>P. austriacus</em> and <em>P. macrobullaris</em> with complete sampling information and taxonomic assignations. Data separated by semicolums as follows:</p> <p>Sample name; Dataset; Colony; Bat species; Date; Season; Read numbers (Size); DNA sequence; Lowest taxonomic identification (ID_MOTU); Family; Order; Class; Is the sequence attributable to the diet or not (Diet)</p>
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
Ecological niche models for American black bear, Rafinesque's big-eared bat, and timber rattlesnake
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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>
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>
Foraging shifts and visual preadaptation in ecologically diverse bats
<p>Changes in behaviour may initiate shifts to new adaptive zones, with physical adaptations for novel environments evolving later. While new mutations are commonly considered engines of adaptive change, sensory evolution enabling access to new resources might also arise from standing genetic diversity, and even gene loss. We examine the relative contribution of molecular adaptations, measured by positive and relaxed selection, acting on eye expressed genes associated with shifts to new adaptive zones in ecologically diverse bats from the superfamily Noctilionoidea. Collectively, noctilionoids display remarkable ecological breadth, from highly divergent echolocation to flight strategies linked to specialized insectivory, the parallel evolution of diverse plant-based diets (e.g., nectar, pollen, and fruit) from ancestral insectivory, and –unusually for echolocating bats– often have large, well-developed eyes. We report contrasting levels of positive selection in genes associated with the development, maintenance, and scope of visual function, tracing back to the origins of noctilionoids and Phyllostomidae (the bat family with most dietary diversity), instead of during shifts to novel diets. Generalized plant visiting was not associated with exceptional molecular adaptation, and exploration of these novel niches took place in an ancestral phyllostomid genetic background. In contrast, evidence for positive selection in vision genes was found at subsequent shifts to either nectarivory or frugivory. Thus, neotropical noctilionoids that use visual cues for identifying food and roosts, as well as for orientation, were effectively preadapted, with subsequent molecular adaptations in nectar-feeding lineages and the Stenodermatinae subfamily of fig-eating bats fine-tuning pre-existing visual adaptations for specialized purposes.</p>
Figure 3 in Ilha Grande, one of the locations with the most records of bat species (Mammalia, Chiroptera) in Rio de Janeiro state: results of a long-term ecological study
Figure 3. Relationship between number of species in the Phyllostomidae family (A) and number of captures in the Phyllostomidae family, (B) and capture effort (h.m²) in bat inventories in Rio de Janeiro state. Numbers correspond to locations listed in Table IV. Dots: cross = study 1 on Ilha Grande; square = study 2 on Ilha Grande; star = study 3 on Ilha Grande; triangle = study 4 on Ilha Grande; black circle = study 5 on Ilha Grande; grey circle = other studies in Rio de Janeiro state.
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.