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22 results for “virus ecology”
Virophage replication mode drives ecological and evolutionary shifts in a host-virus-virophage system
<p>We studied how virophage replication modes affect the ecological dynamics of a host-virus-virophage system and the virophage’s evolutionary responses. By manipulating the level of virophage (Mavirus) integration into the host (Cafeteria burkhardae) alongside the Cafeteria roenbergensis virus (CroV), we found that higher integration improved host survival but decreased virophage reactivation. These communities had lower population densities and fewer fluctuations in host and virus populations, while virophage fluctuations increased. The virophage’s dual replication mode plays a key role in maintaining microbial community stability.</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
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>
Ecological determinants of rabies virus dynamics in vampire bats and spillover to livestock
<p>Data and scripts for the publication</p>
Ecological and socioeconomic factors associated with reported tick-borne viruses
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The paradoxical impact of drought on West Nile virus risk: Insights from long-term ecological data
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Ecological drivers of African swine fever virus persistence in wild boar populations: insight for control
Environmental sources of infection can play a primary role in shaping epidemiological dynamics, however the relative impact of environmental transmission on host-pathogen systems is rarely estimated. We developed and fit a spatially-explicit model of African swine fever virus (ASFV) in wild boar to estimate what proportion of carcass-based transmission is contributing to the low-level persistence of ASFV in Eastern European wild boar. Our model was developed based on ecological insight and data from field studies of ASFV and wild boar in Eastern Poland. We predicted that carcass-based transmission would play a substantial role in persistence, especially in low-density host populations where contact rates are low. By fitting the model to outbreak data using Approximate Bayesian Computation, we inferred that between 53 to 66% of transmission events were carcass-based – i.e., transmitted through contact of a live host with a contaminated carcass. Model fitting and sensitivity analyses showed that the frequency of carcass-based transmission increased with decreasing host density, suggesting that management policies should emphasize the removal of carcasses and consider how reductions in host densities may drive carcass-based transmission. Sensitivity analyses also demonstrated that carcass-based transmission is necessary for the autonomous persistence of ASFV under realistic parameters. Autonomous persistence through direct transmission alone required high host densities; otherwise re-introduction of virus periodically was required for persistence when direct transmission probabilities were moderately high. We quantify the relative role of different persistence mechanisms for a low-prevalence disease using readily collected ecological data and viral surveillance data. Understanding how the frequency of different transmission mechanisms vary across host densities can help identify optimal management strategies across changing ecological conditions.
Data from: Flies on the move: an inherited virus mirrors Drosophila melanogaster's elusive ecology and demography
Vertically transmitted parasites rely on their host's reproduction for their transmission, leading to the evolutionary histories of both parties being intimately entwined. Parasites can thus serve as a population genetic magnifying glass for their host's demographic history. Here, we study the fruitfly Drosophila melanogaster's vertically transmitted sigma virus DMelSV. The virus has a high mutation rate and low effective population size, allowing us to reconstruct at a fine scale how the combined forces of the movement of flies and selection on the virus have shaped its migration patterns. We found that the virus is likely to have spread to Europe from Africa, mirroring the colonization route of Drosophila. The North American DMelSV population appears to be the result of a recent single immigration from Europe, invading together with its host in the late 19th century. Across Europe, DMelSV migration rates are low and populations are highly genetically structured, likely reflecting limited fly movement. Despite being intolerant of extreme cold, viral diversity suggests that fly populations can persist in harsh continental climates and that recolonisation from the warmer south plays a minor role. In conclusion, studying DMelSV can provide insights into the poorly understood ecology of D. melanogaster, one of the best-studied organisms in biology.
Representative giant virus genomes for "Resolving environmental drivers and ecological patterns of active viruses infecting protists in the Southern Ocean"
<p>Nucleotide FASTA files of each representative giant virus genomes used in "Resolving environmental drivers and ecological patterns of active viruses infecting protists in the Southern Ocean". </p>
Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
<p>The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment, which have been hypothesized to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyze three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasizes the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.</p>
Data for: Metagenomics show high spatiotemporal virus diversity and ecological compartmentalisation: virus infections of melon, Cucumis melo, crops and adjacent wild communities
<p>Emergence of viral diseases results from novel transmission dynamics between wild and crop plant communities. The bias of studies towards pathogenic viruses of crops has distracted from knowledge of non-antagonistic symbioses in wild plants. Here we implemented a high throughput approach to compare the viromes of melon (<em>Cucumis melo</em>)<em>, </em>and wild plants of crop (Crop) and adjacent boundaries (Edge). Each of the 41-plant species examined was infected by at least one virus. The interactions of 104 virus operational taxonomic units (OTUs) with these hosts occurred largely within ecological compartments of either Crop or Edge, Edge having traits of a reservoir community. The positive correlation of virus and plant richness at each site, the tendency for increased specialist host use through seasons, and specialist host use by OTUs observed only in Melon, characterised local-scale patterns of infection. In this study of systematically sampled viromes of crop and adjacent wild communities most hosts showed no disease symptoms, suggesting non-antagonistic symbioses are common. The coexistence of viruses within species-rich ecological compartments of agro-systems might promote the evolution of a diversity of virus strategies for survival and transmission. These communities, including those suspected as reservoirs, are subject to sporadic changes in assemblages, and so too are the conditions that favour the emergence of disease.</p>
Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts
Open the record for dataset details and reuse information.
Ecological drivers of African swine fever virus persistence in wild boar populations: insight for control
Open the record for dataset details and reuse information.
Data from: The ecology of avian influenza viruses in wild dabbling ducks (Anas spp.) in Canada
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Data from: Flies on the move: an inherited virus mirrors Drosophila melanogaster’s elusive ecology and demography
Open the record for dataset details and reuse information.
Data for: Metagenomics show high spatiotemporal virus diversity and ecological compartmentalisation: virus infections of melon, Cucumis melo, crops and adjacent wild communities
Open the record for dataset details and reuse information.
Data from: Inference of Japanese encephalitis virus ecological and evolutionary dynamics from passive and active virus surveillance
A comprehensive monitoring strategy is vital for tracking the spread of mosquito-borne Japanese encephalitis virus (JEV), the leading cause of viral encephalitis in Asia. Virus detection consists of passive surveillance of primarily humans and swine, and/or active surveillance in mosquitoes, which may be a valuable proxy in providing insights into ecological processes underlying the spread and persistence of JEV. However, it has not been well characterized whether passive surveillance alone can capture the circulating genetic diversity to make reasonable inferences. Here, we develop phylogenetic models to infer JEV host changes, spatial diffusion patterns, and evolutionary dynamics from data collected through active and passive surveillance. We evaluate the feasibility of using JEV sequence data collected from mosquitoes to estimate the migration histories of genotypes GI and GIII. We show that divergence times estimated from this dataset were comparable to estimates from all available data. Increasing the amount of data collected from active surveillance improved time of most recent common ancestor estimates and reduced uncertainty. Phylogenetic estimates using all available data and only mosquito data from active surveillance produced similar results, showing that GI epidemics were widespread and diffused significantly faster between regions than GIII. In contrast, GIII outbreaks were highly structured and unlinked suggesting localized, unsampled infectious sources. Our results show that active surveillance of mosquitoes can sufficiently capture circulating genetic diversity of JEV to confidently estimate spatial and evolutionary patterns. While surveillance of other hosts could contribute to more detailed disease tracking and evaluation, comprehensive JEV surveillance programs should include systematic surveillance in mosquitoes to infer the most complete patterns for epidemiology, and risk assessment.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.