Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,742
datasets available to search
ShareScore release 0.9.0
Dataset results
1,742 results for “activity data”
Data from: Temporal partitioning of activity: rising and falling top-predator abundance triggers community-wide shifts in diel activity
Top predators cause avoidance behaviours in competitors and prey, which can lead to niche partitioning and facilitate coexistence. We investigate changes in partitioning of the temporal niche in a mammalian community in response to both the rapid decline in abundance of a top predator and its rapid increase, produced by two concurrent natural experiments: 1) the severe decline of the Tasmanian devil due to a transmissible cancer, and 2) the introduction of Tasmanian devils to an island, with subsequent population increase. We focus on devils, two mesopredators, and three prey species, allowing us to examine niche partitioning in the context of intra- and inter-specific competition, and predator-prey interactions. The most consistent shift in temporal activity occurred in devils themselves, which were active earlier in the night at high densities, presumably because of heightened intraspecific competition. When devils were rare, their closest competitor, the spotted-tailed quoll, increased activity in the early part of the night, resulting in increased overlap with the devil's temporal niche and suggesting release from interference competition. The invasive feral cat, another mesopredator, did not shift its temporal activity in response to either decreasing or increasing devil densities. Shifts in temporal activity of the major prey species of devils were stronger in response to rising than to falling devil densities. We infer that the costs associated with not avoiding predators when their density is rising (i.e., death) are higher than the costs of continuing to adopt avoidance behaviours as predator densities fall (i.e., loss of foraging opportunity), so rising predator densities may trigger more rapid shifts. The rapid changes in devil abundance provide a unique framework to test how the non-lethal effects of top predators affect community-wide partitioning of temporal niches, revealing that this top-predator has an important but varied influence on the diel activity of other species.
Dataset: Toothbrushing Data and Analysis of its Potential Use in Human Activity Recognition Applications
<p>This is the dataset that accompanies the paper 'Dataset: Toothbrushing Data and Analysis of its Potential Use in Human Activity Recognition Applications'.</p> <p>In this paper, we describe and analyze a time-series dataset from toothbrushing activity using brush-attached and wearable sensors. The data was collected from 17 participants when they brushed their teeth over one week in 5 different locations. The dataset consists of 62 toothbrushing sessions for each of the brush-attached and wearable sensor approaches, using both electric and manual brushes. The average duration of each session is 2 minutes. One sensor device was attached to the handle of the brush while the other was worn by the participants as a wrist-watch. We collected the data from a 3-axis accelerometer and a 3-axis gyroscope at a 200 Hz sampling rate.</p> <p>Accompanying code can be found at our GitHub repository <a href="https://github.com/icl-mq/toothbrushing-dataset">https://github.com/icl-mq/toothbrushing-dataset</a>. This repository contains example code demonstrating how to process the data along with the file metadata.</p>
Data supporting tables and figures in t ms Tidal influence on seismic activity during the 2011-2013 El Hierro volcanic unrest
<p>Introduction</p> <p>This set of files contains data supporting the tables and figures featured in the journal article.<br> <br> File Ts01.xlsx shows data from the earthquake cluster C1 defined in the manuscript, as well as tidal stress phases and amplitudes obtained for each event using the methodology explained in the text. <br> Files Ts02.xlsx, Ts03.xlsx and Ts04.xlsx are datasets analog to File Ts01.xlsx, but using data from clusters C2, C3 and C4 respectively. <br> Data of Files Ts01.xlsx, Ts02.xlsx, Ts03.xlsx and Ts04.xlsx have been used to compose Tables 1, 2, 3, 4, 5, 6 in the manuscript, Figures 3, 5, 8, 9 in the manuscript, <br> plus Figures S9, S10, S11, S12, S13, S14, S15, S16 in the Supporting Information.</p> <p>File Ts05.xlsx features tidal strain calculated for the setting of the shallow magma reservior in Phase 1 of the volcanic crisis, at two-hour intervals, between 2011-07-01 and 2011-10-31. <br> Data from File Ts05.xlsx has been used for composition of Figures 6 and 7 in the manuscript.<br> Files Ts06.xlsx, Ts07.xlsx and Ts08.xlsx are datasets analog to File Ts05.xlsx, but calculating tidal strain for the locations of events belonging to clusters C2, C3 and C4 respectively.<br> Data of Files Ts06.xlsx, Ts07.xlsx and Ts08.xlsx have been used to compose Figures S1, S2, S3, S4, S5, S6 in the Supporting Information. </p> <p>File Ts09.xlsx shows tidal confining stress values corresponding to the events in cluster C1. <br> File Ts10.xlsx features values of tidal stress taken hourly for the location corresponding to an event belonging to subcluster C1A. <br> File Ts11.xlsx features values of tidal stress taken hourly for the location corresponding to an event belonging to subcluster C1B. <br> Figure S7 in the Supporting Information has been produced using data from Files Ts09.xlsx, Ts10.xlsx and Ts11.xlsx. </p> <p>File Ts12.xlsx collects all events in four clusters C1-C4, and shows the amplitudes of the tidal confining stress half cycles in which the events occur, considering only ocean tides or only body tides. <br> These data were used for stating the predominance of ocean tidal loading against body tides in Chapter 5 - Discussion.</p> <p>File Ts13.xlsx shows data from the earthquake cluster C1 defined in the manuscript, as well as tidal stress phases and amplitudes, but considering only those events with M >= 2.<br> Files Ts14.xlsx, Ts15.xlsx and Ts16.xlsx are datasets analog to File Ts13.xlsx, but using data from clusters C2, C3 and C4 (events with M >= 2 only) respectively.<br> Data of Files Ts13.xlsx, Ts14.xlsx, Ts15.xlsx and Ts16.xlsx have been used to compose Table S3 in the Supporting Information.</p> <p>File Ts17.xlsx features the 4 declustered catalogs D1, D2, D3 and D4 which are described in Tables S4 and S5 in the Supporting Information.</p> <p>File Ts18.xlsx collects all events in four clusters C1-C4, and shows the results of tidal tilt (North-South and East-West components). <br> These data were used to compose Figures S27 and S28 in the Supporting Information. </p> <p>File Ts19.xlsx features tidal stress calculated for the setting of the shallow magma reservior in Phase 1 of the volcanic crisis, at two-hour intervals, between 2011-07-01 and 2011-10-31. <br> Data from File Ts19.xlsx has been used for composition of Figure S17 in the manuscript.<br> Files Ts20.xlsx, Ts21.xlsx and Ts22.xlsx are datasets analog to File Ts19.xlsx, but calculating tidal stress for the locations of events belonging to clusters C2, C3 and C4 respectively.<br> Data of Files Ts20.xlsx, Ts21.xlsx and Ts22.xlsx have been used to compose Figures S18, S19 and S20 in the Supporting Information.</p> <p>File Ts23.xlsx shows data from the earthquake cluster C1 defined in the manuscript, as well as tidal stress phases and amplitudes obtained for each event using the methodology explained in the text,<br> but considering only ocean tides in the calculations. <br> Files Ts24.xlsx, Ts25.xlsx and Ts26.xlsx are datasets analog to File Ts23.xlsx, but using data from clusters C2, C3 and C4 respectively. <br> Data of Files Ts01.xlsx, Ts02.xlsx, Ts03.xlsx and Ts04.xlsx have been used to compose Figures S21 and S22 in Supporting Information.</p> <p>File Ts27.xlsx features horizontal tidal stress (Earth tides only) calculated for the setting of the shallow magma reservior in Phase 1 of the volcanic crisis, at two-hour intervals, <br> between 2011-07-01 and 2011-10-31. Data from File Ts27.xlsx has been used for composition of Figure S23 in the manuscript.<br> Files Ts28.xlsx, Ts29.xlsx and Ts30.xlsx are datasets analog to File Ts27.xlsx, but calculating horizontal tidal stress for the locations of events belonging to clusters C2, C3 and C4 respectively.<br> Data of Files Ts28.xlsx, Ts29.xlsx and Ts30.xlsx have been used to compose Figures S24, S25 and S26 in the Supporting Information.</p> <p>1. Ts01.xlsx Data used to detect tidal stress correlations in Phase 1 of the volcanic crisis.</p> <p>1.1 Column "Year", y.<br> 1.2 Column "Month", m.<br> 1.3 Column "Day", d.<br> 1.4 Column "Hour", h.<br> 1.5 Column "Minute", min.<br> 1.6 Column "Second", s.<br> 1.7 Column "Latitude", deg, latitude north of equator.<br> 1.8 Column "Longitude", deg, longitude east of Greenwich.<br> 1.9 Column "Depth", km.<br> 1.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 1.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 1.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 1.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 1.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 1.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 1.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 1.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 1.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 1.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 1.20 Column "Magnitude", earthquake magnitude.<br> 1.21 Column "Autonum", autonumeric code.</p> <p>2. Ts02.xlsx Data used to detect tidal stress correlations in Phase 2 of the volcanic crisis.</p> <p>2.1 Column "Year", y.<br> 2.2 Column "Month", m.<br> 2.3 Column "Day", d.<br> 2.4 Column "Hour", h.<br> 2.5 Column "Minute", min.<br> 2.6 Column "Second", s.<br> 2.7 Column "Latitude", deg, latitude north of equator.<br> 2.8 Column "Longitude", deg, longitude east of Greenwich.<br> 2.9 Column "Depth", km.<br> 2.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 2.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 2.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 2.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 2.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 2.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 2.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 2.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 2.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 2.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 2.20 Column "Magnitude", earthquake magnitude.<br> 2.21 Column "Autonum", autonumeric code.</p> <p>3. Ts03.xlsx Data used to detect tidal stress correlations in Phase 3 of the volcanic crisis.</p> <p>3.1 Column "Year", y.<br> 3.2 Column "Month", m.<br> 3.3 Column "Day", d.<br> 3.4 Column "Hour", h.<br> 3.5 Column "Minute", min.<br> 3.6 Column "Second", s.<br> 3.7 Column "Latitude", deg, latitude north of equator.<br> 3.8 Column "Longitude", deg, longitude east of Greenwich.<br> 3.9 Column "Depth", km.<br> 3.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 3.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 3.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 3.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 3.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 3.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 3.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 3.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 3.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 3.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 3.20 Column "Magnitude", earthquake magnitude.<br> 3.21 Column "Autonum", autonumeric code.</p> <p>4. Ts04.xlsx Data used to detect tidal stress correlations in Phase 4 of the volcanic crisis.</p> <p>4.1 Column "Year", y.<br> 4.2 Column "Month", m.<br> 4.3 Column "Day", d.<br> 4.4 Column "Hour", h.<br> 4.5 Column "Minute", min.<br> 4.6 Column "Second", s.<br> 4.7 Column "Latitude", deg, latitude north of equator.<br> 4.8 Column "Longitude", deg, longitude east of Greenwich.<br> 4.9 Column "Depth", km.<br> 4.10 Column "Phase_east-west_stress", deg, tidal phase angle assigned to the event, calculated for tidal east-west stress.<br> 4.11 Column "Amplitude_east-west_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal east-west stress.<br> 4.12 Column "Phase_north-south_stress", deg, tidal phase angle assigned to the event, calculated for tidal north-south stress.<br> 4.13 Column "Amplitude_north-south_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal north-south stress.<br> 4.14 Column "Phase_vertical_stress", deg, tidal phase angle assigned to the event, calculated for tidal vertical stress.<br> 4.15 Column "Amplitude_vertical_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal vertical stress.<br> 4.16 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 4.17 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 4.18 Column "Phase_confining_stress_rate", deg, tidal phase angle assigned to the event, calculated for tidal confining stress rate.<br> 4.19 Column "Amplitude_confining_stress_rate", Pa/h, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress rate.<br> 4.20 Column "Magnitude", earthquake magnitude.<br> 4.21 Column "Autonum", autonumeric code.</p> <p>5. Ts05.xlsx Tidal strain calculated for the shallow magma reservior between 2011-07-01 and 2011-10-31.</p> <p>5.1 Column "Latitude", deg, latitude north of equator.<br> 5.2 Column "Longitude", deg, longitude east of Greenwich.<br> 5.3 Column "Depth", km.<br> 5.4 Column "Date", date in format yyyymmdd.<br> 5.5 Column "Time", time in format hour : minute : second.<br> 5.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 5.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 5.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 5.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>6. Ts06. Tidal strain calculated for the location of an event belonging to cluster C2.</p> <p>6.1 Column "Latitude", deg, latitude north of equator.<br> 6.2 Column "Longitude", deg, longitude east of Greenwich.<br> 6.3 Column "Depth", km.<br> 6.4 Column "Date", date in format yyyymmdd.<br> 6.5 Column "Time", time in format hour : minute : second.<br> 6.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 6.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 6.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 6.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>7. Ts07. Tidal strain calculated for the location of an event belonging to cluster C3.</p> <p>7.1 Column "Latitude", deg, latitude north of equator.<br> 7.2 Column "Longitude", deg, longitude east of Greenwich.<br> 7.3 Column "Depth", km.<br> 7.4 Column "Date", date in format yyyymmdd.<br> 7.5 Column "Time", time in format hour : minute : second.<br> 7.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 7.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 7.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 7.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>8. Ts08. Tidal strain calculated for the location of an event belonging to cluster C4.</p> <p>8.1 Column "Latitude", deg, latitude north of equator.<br> 8.2 Column "Longitude", deg, longitude east of Greenwich.<br> 8.3 Column "Depth", km.<br> 8.4 Column "Date", date in format yyyymmdd.<br> 8.5 Column "Time", time in format hour : minute : second.<br> 8.6 Column "Volume strain", nanostrain, tidal volume strain.<br> 8.7 Column "East-West strain", nanostrain, tidal East-West strain.<br> 8.8 Column "North-South strain", nanostrain, tidal North-South strain.<br> 8.9 Column "Vertical strain", nanostrain, tidal Vertical strain.</p> <p>9. File Ts09.xlsx Tidal confining stress values corresponding to the events in cluster C1.</p> <p>9.1 Column "Latitude", deg, latitude north of equator.<br> 9.2 Column "Longitude", deg, longitude east of Greenwich.<br> 9.3 Column "Depth", km.<br> 9.4 Column "Date", date in format yyyymmdd.<br> 9.5 Column "Time", time in format hour : minute : second.<br> 9.6 Column "Tides", Pa, tidal confining stress.</p> <p>10. Ts10.xlsx Hourly values of tidal confining stress obtained for the location of an earthquake belonging to subcluster C1A.</p> <p>10.1 Column "Year", y.<br> 10.2 Column "Month", m.<br> 10.3 Column "Day", d.<br> 10.4 Column "Hour", h.<br> 10.5 Column "Minute", min.<br> 10.6 Column "Second", s.<br> 10.7 Column "Latitude", deg, latitude north of equator.<br> 10.8 Column "Longitude", deg, longitude east of Greenwich.<br> 10.9 Column "Depth", m.<br> 10.10 Column "Tides", Pa, tidal confining stress.</p> <p>11. Ts11.xlsx Hourly values of tidal confining stress obtained for the location of an earthquake belonging to subcluster C1B.</p> <p>11.1 Column "Year", y.<br> 11.2 Column "Month", m.<br> 11.3 Column "Day", d.<br> 11.4 Column "Hour", h.<br> 11.5 Column "Minute", min.<br> 11.6 Column "Second", s.<br> 11.7 Column "Latitude", deg, latitude north of equator.<br> 11.8 Column "Longitude", deg, longitude east of Greenwich.<br> 11.9 Column "Depth", m.<br> 11.10 Column "Tides", Pa, tidal confining stress.</p> <p>12. Ts12.xlsx Data used to compare ocean tides to body tides</p> <p>12.1 Column "Cluster", number of the cluster (C1-C4).<br> 12.2 Column "Year", y.<br> 12.3 Column "Month", m.<br> 12.4 Column "Day", d.<br> 12.5 Column "Hour", h.<br> 12.6 Column "Minute", min.<br> 12.7 Column "Second", s.<br> 12.8 Column "Latitude", deg, latitude north of equator.<br> 12.9 Column "Longitude", deg, longitude east of Greenwich.<br> 12.10 Column "Depth", km.<br> 12.11 Column "Autonum", autonumeric code.<br> 12.12 Column "Ampl_ocean_hc", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress, ocean tides only.<br> 12.13 Column "Ampl_body_hc", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress, body tides only.</p> <p>13. Ts13.xlsx Data in Cluster C1 with M>=2</p> <p>13.1 Column "Year", y.<br> 13.2 Column "Month", m.<br> 13.3 Column "Day", d.<br> 13.4 Column "Hour", h.<br> 13.5 Column "Minute", min.<br> 13.6 Column "Second", s.<br> 13.7 Column "Latitude", deg, latitude north of equator.<br> 13.8 Column "Longitude", deg, longitude east of Greenwich.<br> 13.9 Column "Depth", km.<br> 13.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 13.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 13.12 Column "Magnitude", earthquake magnitude.<br> 13.13 Column "Autonum", autonumeric code.</p> <p>14. Ts14.xlsx Data in Cluster C2 with M>=2</p> <p>14.1 Column "Year", y.<br> 14.2 Column "Month", m.<br> 14.3 Column "Day", d.<br> 14.4 Column "Hour", h.<br> 14.5 Column "Minute", min.<br> 14.6 Column "Second", s.<br> 14.7 Column "Latitude", deg, latitude north of equator.<br> 14.8 Column "Longitude", deg, longitude east of Greenwich.<br> 14.9 Column "Depth", km.<br> 14.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 14.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 14.12 Column "Magnitude", earthquake magnitude.<br> 14.13 Column "Autonum", autonumeric code.</p> <p>15. Ts15.xlsx Data in Cluster C3 with M>=2</p> <p>15.1 Column "Year", y.<br> 15.2 Column "Month", m.<br> 15.3 Column "Day", d.<br> 15.4 Column "Hour", h.<br> 15.5 Column "Minute", min.<br> 15.6 Column "Second", s.<br> 15.7 Column "Latitude", deg, latitude north of equator.<br> 15.8 Column "Longitude", deg, longitude east of Greenwich.<br> 15.9 Column "Depth", km.<br> 15.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 15.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 15.12 Column "Magnitude", earthquake magnitude.<br> 15.13 Column "Autonum", autonumeric code.</p> <p>16. Ts16.xlsx Data in Cluster C4 with M>=2</p> <p>16.1 Column "Year", y.<br> 16.2 Column "Month", m.<br> 16.3 Column "Day", d.<br> 16.4 Column "Hour", h.<br> 16.5 Column "Minute", min.<br> 16.6 Column "Second", s.<br> 16.7 Column "Latitude", deg, latitude north of equator.<br> 16.8 Column "Longitude", deg, longitude east of Greenwich.<br> 16.9 Column "Depth", km.<br> 16.10 Column "Phase_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 16.11 Column "Amplitude_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 16.12 Column "Magnitude", earthquake magnitude.<br> 16.13 Column "Autonum", autonumeric code.</p> <p>17. Ts17.xlsx Declustered datasets D1, D2, D3 and D4</p> <p>17.1 Column "Dataset", number of the declustered dataset (D1-D4).<br> 17.2 Column "Year", y.<br> 17.3 Column "Month", m.<br> 17.4 Column "Day", d.<br> 17.5 Column "Latitude", deg, latitude north of equator.<br> 17.6 Column "Longitude", deg, longitude east of Greenwich.<br> 17.7 Column "Magnitude", earthquake magnitude.<br> 17.8 Column "Depth", km.<br> 17.9 Column "Cluster". It takes the value "+" if the event does not belong to any cluster identified during the declustering process. <br> Otherwise, the event is the largest in a cluster identified by the code shown in Table S4 in Supporting Information. </p> <p><br> 18. Ts18.xlsx Data used to detect tidal tilt correlations</p> <p>18.1 Column "Cluster", number of the cluster (C1-C4).<br> 18.2 Column "Year", y.<br> 18.3 Column "Month", m.<br> 18.4 Column "Day", d.<br> 18.5 Column "Hour", h.<br> 18.6 Column "Minute", min.<br> 18.7 Column "Second", s.<br> 18.8 Column "Latitude", deg, latitude north of equator.<br> 18.9 Column "Longitude", deg, longitude east of Greenwich.<br> 18.10 Column "Depth", km.<br> 18.11 Column "Autonum", autonumeric code.<br> 18.12 Column "Phase_tilt_NS", deg, tidal phase angle assigned to the event, calculated for tidal tilt (North-South component).<br> 18.13 Column "Ampl_tilt_NS", nrad, amplitude of the tidal half cycle in which the event occurs, calculated for tidal tilt (North-South component).<br> 18.14 Column "Phase_tilt_EW", deg, tidal phase angle assigned to the event, calculated for tidal tilt (East-West component).<br> 18.15 Column "Ampl_tilt_NS", nrad, amplitude of the tidal half cycle in which the event occurs, calculated for tidal tilt (East-West component).</p> <p>19. Ts19.xlsx Tidal stress calculated for the shallow magma reservior between 2011-07-01 and 2011-10-31.</p> <p>19.1 Column "Latitude", deg, latitude north of equator.<br> 19.2 Column "Longitude", deg, longitude east of Greenwich.<br> 19.3 Column "Depth", km.<br> 19.4 Column "Date", date in format yyyymmdd.<br> 19.5 Column "Time", time in format hour : minute : second.<br> 19.6 Column "East-West stress", Pa, tidal East-West stress.<br> 19.7 Column "North-South stress", Pa, tidal North-South stress.<br> 19.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>20. Ts20.xlsx Tidal stress calculated for the location of an event belonging to cluster C2.</p> <p>20.1 Column "Latitude", deg, latitude north of equator.<br> 20.2 Column "Longitude", deg, longitude east of Greenwich.<br> 20.3 Column "Depth", km.<br> 20.4 Column "Date", date in format yyyymmdd.<br> 20.5 Column "Time", time in format hour : minute : second.<br> 20.6 Column "East-West stress", Pa, tidal East-West stress.<br> 20.7 Column "North-South stress", Pa, tidal North-South stress.<br> 20.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>21. Ts21.xlsx Tidal stress calculated for the location of an event belonging to cluster C3.</p> <p>21.1 Column "Latitude", deg, latitude north of equator.<br> 21.2 Column "Longitude", deg, longitude east of Greenwich.<br> 21.3 Column "Depth", km.<br> 21.4 Column "Date", date in format yyyymmdd.<br> 21.5 Column "Time", time in format hour : minute : second.<br> 21.6 Column "East-West stress", Pa, tidal East-West stress.<br> 21.7 Column "North-South stress", Pa, tidal North-South stress.<br> 21.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>22. Ts22.xlsx Tidal stress calculated for the location of an event belonging to cluster C4.</p> <p>22.1 Column "Latitude", deg, latitude north of equator.<br> 22.2 Column "Longitude", deg, longitude east of Greenwich.<br> 22.3 Column "Depth", km.<br> 22.4 Column "Date", date in format yyyymmdd.<br> 22.5 Column "Time", time in format hour : minute : second.<br> 22.6 Column "East-West stress", Pa, tidal East-West stress.<br> 22.7 Column "North-South stress", Pa, tidal North-South stress.<br> 22.8 Column "Vertical stress", Pa, tidal Vertical stress.</p> <p>23. Ts23.xlsx Data used to detect tidal stress correlations in Phase 1 of the volcanic crisis (ocean tides only).</p> <p>23.1 Column "Year", y.<br> 23.2 Column "Month", m.<br> 23.3 Column "Day", d.<br> 23.4 Column "Hour", h.<br> 23.5 Column "Minute", min.<br> 23.6 Column "Second", s.<br> 23.7 Column "Latitude", deg, latitude north of equator.<br> 23.8 Column "Longitude", deg, longitude east of Greenwich.<br> 23.9 Column "Depth", km.<br> 23.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 23.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 23.12 Column "Magnitude", earthquake magnitude.<br> 23.13 Column "Autonum", autonumeric code.</p> <p>24. Ts24.xlsx Data used to detect tidal stress correlations in Phase 2 of the volcanic crisis (ocean tides only).</p> <p>24.1 Column "Year", y.<br> 24.2 Column "Month", m.<br> 24.3 Column "Day", d.<br> 24.4 Column "Hour", h.<br> 24.5 Column "Minute", min.<br> 24.6 Column "Second", s.<br> 24.7 Column "Latitude", deg, latitude north of equator.<br> 24.8 Column "Longitude", deg, longitude east of Greenwich.<br> 24.9 Column "Depth", km.<br> 24.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 24.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 24.12 Column "Magnitude", earthquake magnitude.<br> 24.13 Column "Autonum", autonumeric code.</p> <p>25. Ts25.xlsx Data used to detect tidal stress correlations in Phase 3 of the volcanic crisis (ocean tides only).</p> <p>25.1 Column "Year", y.<br> 25.2 Column "Month", m.<br> 25.3 Column "Day", d.<br> 25.4 Column "Hour", h.<br> 25.5 Column "Minute", min.<br> 25.6 Column "Second", s.<br> 25.7 Column "Latitude", deg, latitude north of equator.<br> 25.8 Column "Longitude", deg, longitude east of Greenwich.<br> 25.9 Column "Depth", km.<br> 25.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 25.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 25.12 Column "Magnitude", earthquake magnitude.<br> 25.13 Column "Autonum", autonumeric code.</p> <p>26. Ts26.xlsx Data used to detect tidal stress correlations in Phase 4 of the volcanic crisis (ocean tides only).</p> <p>26.1 Column "Year", y.<br> 26.2 Column "Month", m.<br> 26.3 Column "Day", d.<br> 26.4 Column "Hour", h.<br> 26.5 Column "Minute", min.<br> 26.6 Column "Second", s.<br> 26.7 Column "Latitude", deg, latitude north of equator.<br> 26.8 Column "Longitude", deg, longitude east of Greenwich.<br> 26.9 Column "Depth", km.<br> 26.10 Column "Phase_ocean_confining_stress", deg, tidal phase angle assigned to the event, calculated for tidal confining stress.<br> 26.11 Column "Amplitude_ocean_confining_stress", Pa, amplitude of the tidal half cycle in which the event occurs, calculated for tidal confining stress.<br> 26.12 Column "Magnitude", earthquake magnitude.<br> 26.13 Column "Autonum", autonumeric code.</p> <p>27. Ts27.xlsx Horizontal tidal stress (Earth tides only) calculated for the shallow magma reservior between 2011-07-01 and 2011-10-31.</p> <p>27.1 Column "Latitude", deg, latitude north of equator.<br> 27.2 Column "Longitude", deg, longitude east of Greenwich.<br> 27.3 Column "Depth", km.<br> 27.4 Column "Date", date in format yyyymmdd.<br> 27.5 Column "Time", time in format hour : minute : second.<br> 27.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p> <p>28. Ts28.xlsx Horizontal tidal stress (Earth tides only) calculated for the location of an event belonging to cluster C2.</p> <p>28.1 Column "Latitude", deg, latitude north of equator.<br> 28.2 Column "Longitude", deg, longitude east of Greenwich.<br> 28.3 Column "Depth", km.<br> 28.4 Column "Date", date in format yyyymmdd.<br> 28.5 Column "Time", time in format hour : minute : second.<br> 28.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p> <p>29. Ts29.xlsx Horizontal tidal stress (Earth tides only) calculated for the location of an event belonging to cluster C3.</p> <p>29.1 Column "Latitude", deg, latitude north of equator.<br> 29.2 Column "Longitude", deg, longitude east of Greenwich.<br> 29.3 Column "Depth", km.<br> 29.4 Column "Date", date in format yyyymmdd.<br> 29.5 Column "Time", time in format hour : minute : second.<br> 29.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p> <p>30. Ts30.xlsx Horizontal tidal stress (Earth tides only) calculated for the location of an event belonging to cluster C4.</p> <p>30.1 Column "Latitude", deg, latitude north of equator.<br> 30.2 Column "Longitude", deg, longitude east of Greenwich.<br> 30.3 Column "Depth", km.<br> 30.4 Column "Date", date in format yyyymmdd.<br> 30.5 Column "Time", time in format hour : minute : second.<br> 30.6 Column "Horizontal stress", Pa, tidal Horizontal stress.</p>
Data from: Drivers of assemblage-wide calling activity in tropical anurans and the role of temporal resolution
<p>1. Temporal scale in animal communities is often associated with seasonality, despite the large variation in species activity during a diel cycle. A gap thus remains in understanding the dynamics of short-term activity in animal communities.</p> <p>2. Here we assessed calling activity of tropical anurans and addressed how species composition varied during night activity in assemblages along gradients of local and landscape environmental heterogeneity.</p> <p>3. We investigated 39 anuran assemblages in the Pantanal wetlands (Brazil) with passive acoustic monitoring during the peak of one breeding season and first determined changes in species composition between night periods (early, mid, and late) using two temporal resolutions (1-hour and 3-hour intervals). Then, we addressed the role of habitat structure (local and landscape heterogeneity variables from field-based and remote sensing metrics) and ecological context (species richness and phylogenetic relatedness) in determining changes in species composition (i) between night periods and (ii) across days.</p> <p>4. Nocturnal calling activity of anuran assemblages varied more within the 1-hour resolution than the 3-hour resolution. Differences in species composition between early and late-night periods were related to local habitat structure and phylogenetic relatedness, while a low variation in compositional changes across days was associated with low-heterogeneous landscapes. None of these relationships were observed using the coarser temporal resolution (3-hour).</p> <p>5. Our findings on the variation of calling activity in tropical anuran assemblages suggest potential trades-off mediated by fine-temporal partitioning. Local and landscape heterogeneity may provide conditions for spatial partitioning, while the relatedness among co-signaling species provides cues on the ecological overlap of species with similar requirements. These relationships suggest a role of niche dimensional complementarity on the structuring of these anuran assemblages over fine temporal scales. We argue that fine-temporal differences between species in breeding activity can influence the outcome of species interaction and that addressing temporal scaling issues can improve our understanding of dynamics of animal communities.</p>
Data from: Combining correlative and mechanistic niche models with human activity data to elucidate the invasive potential of a sub-Antarctic insect
<p>Aim</p> <p>Correlative Species Distribution Models (SDMs) are subject to substantial spatio-temporal limitations when historical occurrence records of data-poor species provide incomplete and outdated information for niche modelling. Complementary mechanistic modelling techniques can, therefore, offer a valuable contribution to underpin more physiologically-informed predictions of biological invasions, the risk of which is often exacerbated by climate change. In this study we integrate physiological and human pressure data to address the uncertainties and limitations of correlative SDMs and to better understand, predict, and manage biological invasions.</p> <p>Location</p> <p>Western archipelagos of the Southern Ocean and martime Antarctica</p> <p>Taxon</p> <p>Eretmoptera murphyi (Chironomidae), invertebrates.</p> <p>Methods</p> <p>Mahalanobis Distances were used for correlative SDM construction for a species with few records. A mechanistic SDM was built around different fitness components (larval survival and life stage progression) as a function of temperature. SDM predictions were combined with human activity levels in Antarctica to generate a site vulnerability index to the colonization of E. murphyi. Future scenarios of ecophysiological suitability were built around the warming trends in the region.<br> Results Both SDMs converge to predict high environmental suitability in the species' native and introduced ranges. However, the mechanistic model indicates a slightly larger invasive potential based on larval performance at different temperatures. Human activity levels across the Antarctic Peninsula play a key role in discerning site vulnerabilities. Niche suitability in Antarctica grows considerably under long-term climate scenarios, leading to a substantially higher invasive threat to the Antarctic ecosystems. In turn changing conditions result on growing physiological mismatches with the environment in the native range on South Georgia.</p> <p>Main conclusions</p> <p>Long-term studies of invasion potential under climate benefit from integrating correlative predictions with physiological experiments, as the invasion potential varies depending on the area and the timescale examined. This study also highlights a conservation paradox whereby the accidental introduction of an insect represents a threat to the Antarctic ecoystems that contrasts with its endangered status at the native range.</p>
Data supplement for "Phase-field crystal description of active crystallites: Elastic and inelastic collisions"
<p>This dataset contains data related to the publication:</p> <p>Lukas Ophaus and Johannes Kirchner and Svetlana V. Gurevich and Uwe Thiele, "Phase-field crystal description of active crystallites: Elastic and inelastic collisions" Chaos<strong> 30</strong>, 123149 (2020); <a href="https://doi.org/10.1063/5.0019426">https://doi.org/10.1063/5.0019426</a></p> <p>The set contains (i) all figures in pdf format and (ii) complete data files accompanied by python plot scripts for selected figures.</p> <p> </p>
Data from: Dung beetle activity had no positive effect on nutrient concentration or performance of established rainforest seedlings
<p>All five datasets from this study correspond to field observations collected for seedlings of six tree species (<i>Brosimum alicastrum</i>, <i>Calophyllum brasiliense</i>, <i>Cymbopetalum baillonii</i>, <i>Diospyros digyna</i>, <i>Omphalea oleifera</i> and <i>Poulsenia armata</i>) in a tropical rainforest (Los Tuxtlas, Mexico). We estimated the concentrations of foliar nitrogen (N) and phosphorus (P), resource allocation (the ratio of biomass allocated to shoot vs root), seedling survival, and growth. Experimental seedlings were subject to three treatment levels: (a) feces added and beetles active, (b) feces added and beetles excluded, and (c) no feces added (and consequently no beetles active). We analyzed data at two levels: community (all plant species together) and individual species. For resource allocation (ESM_Dataset1_RootShoot) we weighed aerial parts (stem and leaves) and roots, separately, and calculated the root/shoot biomass ratio for each seedling. For foliar nutrients (ESM_Dataset2_FoliarNutrients) we obtained N and P concentrations using a Kjeldahl wet digestion and colorimetric analysis; for the analyses four outlier values were removed (for <em>Brosimum</em> one P value; for <i>Calophyllum</i> two P values and one N value). We analyzed foliar nutrients for all plant species. In the case of seedling survival (ESM_Dataset3_Survival) we registered seedling survival weekly during six weeks for <i>Cymbopetalum</i>, and during 26 weeks for the other five species. The response variable was the number of days elapsed until death; seedlings alive by the end of the experiment were included as right-censored data. For seedling growth in height (ESM_Dataset4_GrowthHeight; measured in all plant species except <i>Cymbopetalum</i>) we measured seedling height to the nearest centimeter every four or five weeks during 26 weeks. For this variable we calculated net growth in height (final height minus initial height). Finally, for the growth in the number of leaves (ESM_Dataset5_GrowthLeaves; measured in all plant species except <i>Cymbopetalum</i>) we counted the number of leaves lost and number of new leaves, every four or five weeks during 26 weeks; the response variable was the net growth in the number of leaves (final number of leaves minus initial number of leaves). All analyses were conducted in R. Detailed information about dataset structure and contents can be found in file ESM_Metadata.</p>
Kinesin-1 activity recorded in living cells with a precipitating dye_FIB-SEM_data_2_part1
<p>Kinesin-1 activity recorded in living cells with a precipitating dye_FIB-SEM_data_2_part1</p>
Kinesin-1 activity recorded in living cells with a precipitating dye_FIB-SEM_data_2_part2
<p>Kinesin-1 activity recorded in living cells with a precipitating dye_FIB-SEM_data_2_part2</p>
Data from: On the scaling of activity in tropical forest mammals
<p>Activity range – the amount of time spent active per day – is a fundamental aspect contributing to the optimization process by which animals achieve energetic balance. Based on their size and the nature of their diet, theoretical expectations are that larger carnivores need more time active to fulfil their energetic needs than do smaller ones and also more time active than similar-sized non-carnivores. Despite the relationship between daily activity, individual range and energy acquisition, large-scale relationships between activity range and body mass among wild mammals have never been properly addressed. This study aimed to understand the scaling of activity range with body mass, while controlling for phylogeny and diet. We built simple empirical predictions for the scaling of activity range with body mass for mammals of different trophic guilds and used a phylogenetically controlled mixed model to test these predictions using activity records of 249 mammal populations (128 species) in 19 tropical forests (in 15 countries) obtained using camera traps. Our scaling model predicted a steeper scaling of activity range in carnivores (0.21) with higher levels of activity (higher intercept), and near-zero scaling in herbivores (0.04). Empirical data showed that activity ranges scaled positively with body mass for carnivores (0.061), which also had higher intercept value, but not for herbivores, omnivores and insectivores, in general, corresponding with the predictions. Despite the many factors that shape animal activity at local scales, we found a general pattern showing that large carnivores need more time active in a day to meet their energetic demands.</p>
Data from: Twelve years of repeated wild hog activity promotes population maintenance of an invasive clonal plant in a coastal dune ecosystem
Invasive animals can facilitate the success of invasive plant populations through disturbance. We examined the relationship between the repeated foraging disturbance of an invasive animal and the population maintenance of an invasive plant in a coastal dune ecosystem. We hypothesized that feral wild hog (Sus scrofa) populations repeatedly utilized tubers of the clonal perennial, yellow nutsedge (Cyperus esculentus) as a food source and evaluated whether hog activity promoted the long-term maintenance of yellow nutsedge populations on St. Catherine's Island, Georgia, United States. Using generalized linear mixed models, we tested the effect of wild hog disturbance on permanent sites for yellow nutsedge culm density, tuber density, and percent cover of native plant species over a 12-year period. We found that disturbance plots had a higher number of culms and tubers and a lower percentage of native live plant cover than undisturbed control plots. Wild hogs redisturbed the disturbed plots approximately every 5 years. Our research provides demographic evidence that repeated foraging disturbances by an invasive animal promote the long-term population maintenance of an invasive clonal plant. Opportunistic facultative interactions such as we demonstrate in this study are likely to become more commonplace as greater numbers of introduced species are integrated into ecological communities around the world.
Data from: Avoidance of windfarms by harbour seals is limited to pile driving activities
As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement.
Data from: Asynchrony between ant seed dispersal activity and fruit dehiscence of myrmecochorous plants
Phenological mismatch has received attention in plant-pollinator interactions, but less so in seed dispersal mutualisms. We investigated whether the seasonal availability of myrmecochorous seeds is well matched to the seasonal activity patterns of seed-dispersing ants. Methods We compared seasonal timing of seed removal by a keystone seed-dispersing ant, Aphaenogaster rudis, and fruit dehiscence of several species of plants whose seeds it disperses in a deciduous forest in southern Ontario, Canada. We examined the timing of elaiosome 'robbing' by the non-native slug, Arion subfuscus, and tested whether seed removal by ants declines in response to supplementation with additional elaiosome-bearing seeds (ant "satiation"). Key Results Seed removal from experimental depots peaked early in the season for all plant species, and that seed removal correlated with temperature. In contrast, elaiosome robbing by slugs increased late in the season and thus may disproportionately affect plants with late-dehiscing fruits. Ant colonies removed seeds at similar rates regardless of seed supplementation, indicating that satiation likely does not impact seasonal patterns of seed dispersal in this system. Fruits of the five myrmecochorous plant species in our study dehisced at discrete intervals throughout the season, with minimal overlap among species. Peak dehiscence did not overlap with peak seed removal for any plant species. Conclusions Fruit dehiscence of myrmecochorous plants and peak ant seed dispersal activity occur asynchronously. Whether future climate warming will shift ant and plant phenologies in ways that have consequences for seed dispersal remains an open question. In compliance with data protection regulations, please contact the publication office if you would like to have your personal information removed from the database.
Data from: Activation of the Arabidopsis thaliana immune system by combinations of common ACD6 alleles
A fundamental question in biology is how multicellular organisms distinguish self and non-self. The ability to make this distinction allows animals and plants to detect and respond to pathogens without triggering immune reactions directed against their own cells. In plants, inappropriate self-recognition results in the autonomous activation of the immune system, causing affected individuals to grow less well. These plants also suffer from spontaneous cell death, but are at the same time more resistant to pathogens. Known causes for such autonomous activation of the immune system are hyperactive alleles of immune regulators, or epistatic interactions between immune regulators and unlinked genes. We have discovered a third class, in which the Arabidopsis thaliana immune system is activated by interactions between natural alleles at a single locus, ACCELERATED CELL DEATH 6 (ACD6). There are two main types of these interacting alleles, one of which has evolved recently by partial resurrection of a pseudogene, and each type includes multiple functional variants. Most previously studies hybrid necrosis cases involve rare alleles found in geographically unrelated populations. These two types of ACD6 alleles instead occur at low frequency throughout the range of the species, and have risen to high frequency in the Northeast of Spain, suggesting a role in local adaptation. In addition, such hybrids occur in these populations in the wild. The extensive functional variation among ACD6 alleles points to a central role of this locus in fine-tuning pathogen defenses in natural populations.
Data from: Social context affects thermoregulation but not activity level during avian immune response
Determining how an animal's social context alters its immune responses will help us understand how pathogens impact individual health and spread within groups. Several studies have shown that group-housed animals can suppress components of the acute phase immune response, specifically sickness behaviors like lethargy. However, we do not know whether individuals alter sickness behaviors or other components of the acute phase response, including thermoregulation, in response to the infection status of other group members. We used automated radio telemetry on captive house sparrows (Passer domesticus) to test whether sickness behaviors and thermoregulation differed during immune challenge under 2 social contexts: 1) all of the flock inoculated with lipopolysaccharide (LPS), a non-replicating component of gram-negative bacterial cell walls, or 2) half of the flock inoculated. We predicted that with half of the flock inoculated, LPS-treated birds would be under more pressure to maintain competitive behaviors, so would suppress components of the acute phase response. As we predicted, LPS-inoculated birds showed less pronounced heterothermia (fever) when housed with a mixture of inoculated and healthy flockmates. In contrast, LPS-inoculated birds exhibited similar degrees of lethargy regardless of the infection status of their flockmates. Our results show that the infection status of an individual's social group did exert an effect on the acute phase response but surprisingly did not impact the expression of lethargy, a canonical sickness behavior. Determining the mechanisms underlying these responses will require testing additional social contexts with different ratios of infected to uninfected birds.
Data from: An environmental DNA-based method for monitoring spawning activity: a case study, using the endangered Macquarie perch (Macquaria australasica)
Determining the timing and location of reproductive events is critical for efficient management of species. However, methods currently used for aquatic species are costly, time intensive, biased and often require destructive or injurious sampling. Hence, developing a non-invasive sampling method to accurately determine the timing and location of reproduction for aquatic species would be extremely valuable. We conducted an experimental and field study to determine the influence of spawning, and the mass release of spermatozoa in particular, on environmental DNA (eDNA) concentrations. Using a quantitative PCR approach we monitored changes in nuclear and mitochondrial eDNA concentrations over time. The data from the experimental study and the field survey supported our hypothesis that spawning events are characterized by higher concentrations of nuclear relative to mitochondrial eDNA. Outside of the reproductive period, we find that nuclear and mitochondrial DNA fragments are equally abundant in environmental water samples. We have shown that changes in the relative abundance of nuclear and mitochondrial eDNA can be used to monitor spawning activity of the endangered Macquarie perch. Our method is likely to be transferrable to other aquatic species and can be particularly useful to increase our understanding of the spawning biology of cryptic, rare or threatened species as well as design and evaluate environmental management actions and determine species establishment.
Data from: The evolution of colour polymorphism in British winter‐active Lepidoptera in response to search image use by avian predators
Phenotypic polymorphism in cryptic species is widespread. This may evolve in response to search image use by predators exerting negative frequency‐dependent selection on intraspecific colour morphs, "apostatic selection". Evidence exists to indicate search image formation by predators and apostatic selection operating on wild prey populations, though not to demonstrate search image use directly resulting in apostatic selection. The present study attempted to address this deficiency, using British Lepidoptera active in winter as a model system. It has been proposed that the typically polymorphic wing colouration of these species represents an anti‐search image adaptation against birds. To test (a) for search image driven apostatic selection, dimorphic populations of artificial moth‐like models were established in woodland at varying relative morph frequencies and exposed to predation by natural populations of birds. In addition, to test (b) whether abundance and degree of polymorphism are correlated across British winter‐active moths, as predicted where search image use drives apostatic selection, a series of phylogenetic comparative analyses were conducted. There was a positive relationship between artificial morph frequency and probability of predation, consistent with birds utilising search images and exerting apostatic selection. Abundance and degree of polymorphism were found to be positively correlated across British Lepidoptera active in winter, though not across all taxonomic groups analysed. This evidence is consistent with polymorphism in this group having evolved in response to search image driven apostatic selection and supports the viability of this mechanism as a means by which phenotypic and genetic variation may be maintained in natural populations.
Data from: A case for considering individual variation in diel activity patterns
There is a growing recognition of the role of individual variation in patterns emerging at higher levels of biological organization. Despite the importance of the temporal configuration of ecological processes and patterns, intraspecific individual variation in diel activity patterns is almost never accounted for in behavioral studies at the population level. We used individual-based monitoring data from 98 GPS-collared brown bears in Scandinavia to estimate diel activity patterns before the fall hunting season. We extracted 7 activity measures related to timing and regularity of activity from individual activity profiles. We then used multivariate analysis to test for the existence of distinct activity tactics and their environmental determinants, followed by generalized linear regression to estimate the extent of within-individual repeatability of activity tactics. We detected 4 distinct activity tactics, with a high degree of individual fidelity to a given tactic. Demographic factors, availability of key foraging habitat, and human disturbance were important determinants of activity tactics. Younger individuals and those with higher bear and road densities within their home range were more nocturnal and more likely to rest during the day. Good foraging habitat and increasing age led to more diurnal activity patterns and nocturnal resting periods. We did not find evidence of diel activity tactics influencing survival during the subsequent hunting season. We conclude that individual variation in activity deserves greater attention than it currently receives, as it may help account for individual heterogeneity in fitness and could facilitate within-population niche partitioning that can have population- or community-level consequences.
Data from: Genetic evidence for the uncoupling of local aquaculture activities and a population of an invasive species – a case study of Pacific oysters (Crassostrea gigas)
Human-mediated introduction of non-native species into coastal areas via aquaculture is one of the main pathways that can lead to biological invasions. To develop strategies to counteract invasions it is critical to determine whether populations establishing in the wild are self-sustaining or based on repeated introductions. Invasions by the Pacific oyster (Crassostrea gigas) have been associated with the growing oyster aquaculture industry worldwide. In this study, temporal genetic variability of farmed and wild oysters from the largest enclosed bay in Ireland was assessed to reconstruct the recent biological history of the feral populations using seven anonymous and seven microsatellites linked to expressed sequence tags (ESTs). There was no evidence of EST-linked markers showing footprints of selection. Allelic richness was higher in feral than in aquaculture samples (p=0.003, paired t-test). Significant deviations from Hardy-Weinberg equilibrium (HWE) due to heterozygote deficiencies were detected for almost all loci and samples, most likely explained by the presence of null-alleles. Relatively high genetic differentiation was found between aquaculture and feral oysters (largest pairwise multilocus FST 0.074, p < 0.01) and between year classes of oysters from aquaculture (largest pairwise multilocus FST 0.073, p < 0.01), which was also confirmed by the strong separation of aquaculture and wild samples using Bayesian clustering approaches. A ten-fold higher effective population size (Ne) – and a high number of private alleles – in wild oysters suggest an established self-sustaining feral population. The wild oyster population studied appears demographically independent from the current aquaculture activities in the estuary and alternative scenarios of introduction pathways are discussed.
Data from: Salivary gland ultrasonography as a predictor of clinical activity in Sjögren's syndrome
Purpose: Primary Sjögren's syndrome is a multisystem autoimmune disease characterized by hypofunction of salivary and lacrimal glands and possible multi-organ system manifestations. Over the past 15 years, three sets of diagnostic criteria have been proposed, but none has included salivary gland ultrasonography. However, recent studies support its role in the diagnosis and prognostic evaluation of patients with Sjögren's syndrome. This study aimed to determine the value of salivary gland ultrasonography in the diagnosis and prognosis of Sjögren's syndrome by relating ultrasonography severity scores to clinical and laboratory data. Methods: Seventy patients who fulfilled the 2002 American-European Consensus Group diagnostic criteria for primary Sjögren's syndrome were selected from 84 patients receiving care in specialized outpatient clinics at our institution from November 2013 to May 2016. Their serology, European League Against Rheumatism Sjögren's syndrome disease activity index (ESSDAI), salivary flow rate, immunoglobulin G, and salivary and serum beta-2 microglobulin levels were measured. Salivary gland ultrasonography was performed by an experienced radiologist, using scores of 1-4 to classify salivary gland impairment. Results: Salivary gland ultrasonography scores of 1 or 2 were associated with an ESSDAI < 5. Ultrasonography scores of 3 or 4 were associated with an ESSDAI ≥ 5 (p=0.064), a positive antinuclear antibody test (p=0.006), positive anti-Ro/SSA antibodies (p=0.003), positive anti-La/SSB antibodies (p=0.077), positive rheumatoid factor (p=0.034), and immunoglobulin G levels > 1600 mg/dL (p=0.077). Salivary flow rate was lower in patients with scores 3 or 4 (p=0.001). Conclusion: This study provides further evidence that salivary gland ultrasonography can be used not only for diagnosis but also for prognostic evaluation of primary Sjögren's syndrome. These findings confirm what has been reported in the literature. However, further analyses involving larger matched samples are required to support this finding and include salivary gland ultrasonography as part of the diagnostic criteria for Sjögren's syndrome.
ScienceDex guides
Understand access before you commit
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.