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opencc-by-4.0Aug 2022View details →

Combined U-series and in situ U-Pb dating of fault-related carbonates for reconstructing a long-term history of fault activity in response to SE Tibetan Plateau brittle deformation

Table S1. Analytical conditions for LA-ICP-MS U-Pb dating Table S2. Analytical conditions for LA-ICP-MS elemental mapping Table S3. In situ calcite LA-ICAPMS U-Pb dating data

embargoedcc-by-4.0Dec 2021View details →

Data and metadata of soil microbial community structure, enzyme activities, functional genes and earthworms derived from H2020 Diverfarming project

Soil data and metadata of soil microbial community structure, enzyme activities (dehydrogenase, β-glucosidase, leucine-aminopeptidase, alkaline phosphatase and arylsusfatase activities), N functional genes and earthworms from the different cases studies and long terms from WP4 "Impact of crop diversification on biodiversity", derived from H2020 Diverfarming project. The main objective of workpackage is to provide a scientific understanding of the link between diversified cropping systems, above- and belowground biodiversity, and the resulting ecosystem services provided by soil microorganisms, soil invertebrates and vegetation in agro-ecosystems. Soil organisms contribute to all biogeochemical cycles, Soil organic matter mineralization and stabilization, shape soil structure and have associations with plant species promoting growth and development. http://www.diverfarming.eu.

Data from: Interactive effects of wildfires, season, and predator activity shape mule deer movements

Wildfires are increasing in size, frequency, and severity due to climate change and fire suppression, but the direct and indirect effects on wildlife remain largely unresolved. Fire removes forest canopy, which can improve forage for ungulates but also reduce snow interception, leading to a deeper snowpack and potentially increased vulnerability to predation in winter. If ungulates exhibit predator-mediated foraging, burns should generally be selected for in summer to access high-quality forage and avoided in winter to reduce predation risk in deep snow. Fires also typically increase the amount of deadfall and initiate growth of dense understory vegetation, creating obstacles that may confer a hunting advantage to stalking predators and a disadvantage to coursing predators. To minimize risk, ungulates may therefore avoid burns when and where stalking predators are most active, and use burns when and where coursing predators are most active. We used telemetry data from GPS-collared mule deer (Odocoileus hemionus), cougars (Puma concolor), and wolves (Canis lupus) to develop step selection functions to examine how mule deer navigated species-specific predation risk across a landscape in northern Washington, USA that has experienced substantial wildfire activity during the past several decades. We considered a diverse array of wildfire impacts, accounting for both the severity of the fire and time since the burn (1 to 35 years) in our analyses. We observed support for the predator mediating foraging hypothesis: mule deer generally selected for burned areas in summer and avoided burns in winter. In addition, deer increased use of burned areas when and where wolf activity was high and avoided burns when and where cougar use was high in winter, suggesting the hunting mode of resident predators mediated the seasonal response of deer to burns. Deer were not more likely to die by predation in burned than in unburned areas, indicating that they adequately manage fire-induced changes to predation risk. As fire activity increases with climate change, our findings indicate the impact on ungulates will depend on tradeoffs between enhanced summer forage and functionally reduced winter range, mediated by characteristics of the predator community.

Data from: Empirically testing the influence of light regime on diel activity patterns in a marine predator reveals complex interacting factors shaping behaviour

Diel cycles in marine predator diving behaviour centre around the light-mediated diel vertical migration (DVM) of prey, and are considered critical for optimizing foraging and limiting competition across global seascapes. Yet our understanding of predator diel behaviour is based primarily on examining relative depth usage between constant day/night cycles with no formal investigation of how varying light regimes interact with abiotic factors to shape diel activity. The extreme seasonal light regimes (midnight sun, polar night, day/night cycle) in the Arctic provide a unique natural experimental setting to empirically investigate the occurrence and intensity of diel behaviour in marine predators relative to changing light levels while concomitantly assessing interacting abiotic factors. Depth time series data from satellite-linked tags deployed on six belugas for up to 12 months were used to quantify diel behaviour by calculating dissimilarity in time-at-depth between periods of low and high solar altitude on each day. Generalized additive mixed effects models were used to examine the influence of hours of daylight across extreme light cycles, coupled with bathymetry and sea ice concentration; focal diel patterns were further examined relative to the thermal structure of the water column. As predicted, belugas exhibited cathemerality during the midnight sun, and initiated diel behaviour with the onset of the fall day/night cycle, with a marked increase in its intensity with the progression to equal day/night length. Occurrence of diel patterns, however, was complex; ceasing in regions with seafloor depths < 700 m, and occurring with greatest intensity when the water column was thermally homogeneous within the upper 150 m. Through empirical investigation, this study demonstrates that the onset of day/night light cycles and presumably associated prey DVM can modulate predator diel dive behaviour under certain circumstances, but highlights how the complex interaction of abiotic factors with light regime shape dynamic spatiotemporal patterns. These findings, building on a body of recent work, emphasize that the traditional view of the ubiquitous occurrence of diel behaviour tied to DVM at the base of the food web oversimplifies vertical predator-prey interactions, identifying the need for more structured investigation.

Biochar and nitrogen fertilizer promote rice yield by altering soil enzyme activity and microbial community structure

Biochar can significantly change soil properties and improve soil quality. However, the effects of long-term combined application of biochar (B) and nitrogen (N) fertilizer on relationships between soil enzyme activity, microbial community structure and crop yield are still obscure. We characterized these relationships in a long-term (8 years) field experiment with rice, two biochar rates of 0 and 13.5 t ha-1 year-1 (B0 and B) and two N fertilizer rates of 0 and 300 kg N ha-1 year-1 (N0 and N). The repeated, long-term combined applications of biochar and N fertilizer significantly increased microbial biomass carbon and nitrogen (MBC and MBN), but biochar decreased the abundance of total bacteria, fungi, actinomycetes, Gram-positive and Gram-negative bacteria as well as the amount of total phospholipid fatty acids. The activity of leucine aminopeptidase (LAP) decreased significantly in the biochar-amended and N fertilized treatment, but the LAP activity either remained unchanged or increased with biochar amendment at N0. The relative abundance of bacterial phylum Chloroflexi was increased in the combined biochar and N fertilizer treatment. The changes in soil organic matter and the activity of α-1,4-xylosidase were the major properties influencing soil bacterial community composition, whereas the structure of fungal community was governed by MBC, MBN and LAP activity. In addition, long-term biochar and N fertilizer applied together significantly increased rice yield (more than biochar and nitrogen fertilizer applied alone). Yield was significantly positively correlated with LAP activity, but significantly negatively correlated with the relative abundance of Chloroflexi. In conclusion, long-term biochar and nitrogen fertilizer applications increased rice yield, which was associated with altered soil microbial community and enhanced activity of some enzymes.

Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019

1. PUBLICATION CORRESPONDING TO THESE DATA Sloan, Sean*; Locatelli, Bruno; Andela, Niels; Cattau, Megan E.; Gaveau, David; Tacconi, Luca. 2022 'Declining Severe Fire Activity on Managed Lands in Equatorial Asia'. Communications Earth & Environment. DOI: 10.1038/s43247-022-00522-6. *Corresponding author email: sean.sloan@viu.ca 2. ABSTRACT OF THE DATA The GIS data and corresponding attribute data described here pertain to discrete fire events, their severity, and their ignitions, as derived on the basis of daily MODIS Collection 6 MCD14ML active-fire detections (AFDs). Data on fire events and their ignitions are provided separately, as two data files. These data files on fire events and ignitions may however be linked to each other by the data user. Fire-event severity is quantified per fire event and reported in the data file for fire events. A fire event is a cluster of MODIS Collection 6 MCD14ML active-fire detections (AFDs) wherein each AFD has a spatial (<=1-km) and temporal (<=4-day) proximity to another AFD in the same fire event, inferring thus a relational co-occurrence amongst AFDs in time and space. In other words, a fire event is considered a likely occasion of burning wherein all constituent AFDs are related to each other in time and space, either directly (as for proximate AFDs) or indirectly (as in the case of a large area of fire activity that spread progressively over time and space from an initial source). Each fire event has a designated ignition AFD, being the AFD of the fire event with the earliest detection date. A given fire event can have more than one ignition AFD if the ignitions all share same earliest detection date. The ignition AFD(s) is the nominal initial source of the burning described by the corresponding fire event. All other, non-ignition AFDs of a fire event are deemed its 'propagation' AFDs, since these AFDs follow from the ignitions, temporally and spatially. See Figure 4 in the publication by Sloan et al. for an illustration of the geography of fire events and their ignition AFDs. Fire events and their ignitions were derived from standard science-quality MODIS Collection 6 MCD 14ML AFD data, commonly referred to as fire 'hotspot' data. Data were detected by both the Terra and Aqua satellite sensors daily for Indonesia between July 2002 and December 2019. Information on these input data are provide by the two citations below. The publication of Sloan et al. provides methodological details on how the MODIS Collection 6 MCD 14ML AFD data were processed into discrete fire events and ignitions. EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019). Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31-41, (2016). 3. DATA FILES Two data files are distributed here – one for discrete fire events, and another for the ignition AFDs of each fire event. The data files are provided in a GIS-compatible format, and also as a generic text format, as described below. 3.1 GIS VERSION Data files in GIS-compatible format are provided as 'feature classes' within an ArcGIS file geodatabase 'Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb'. These data files can be viewed and manipulated using either ArcGIS Desktop or ArcGIS Pro software. There is one feature class for fire events, and another file for ignitions. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_joins_sp_LC This file pertains to fire events. All AFDs of a given fire event are included, without differentiation as to whether the AFDs are ignition AFDs or other (propagation) AFDs. Fire events are assigned unique ID values and basic attribute data. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb\nfire4_all_spatial_fire_2002_2019_igs_sp_LC This file pertains to ignitions. Only ignition AFDs are included for a given fire event. Fire events corresponding to the ignitions are assigned unique ID values and basic attribute data. 3.2 CSV TEXT VERSION Both data files are also supplied as comma-separated value (CSV) text files for viewing and manipulation in non-GIS software, such as Excel, text editors, or any statistical software. The text files can also be read into various GIS software. CSV-formatted files have the same file name and attribute fields as the corresponding GIS-formatted data files. These CSV-formatted data files (as well as the GIS-formatted data files) include attribute data on the latitude and the longitude of each AFD. Attribute field names are included as the first row of values in a CSV file. No 'text qualifiers' like quotations (" ") or inverted commas ('') are used to designate text/string values within the CSV file. Text values appear directly between commas in the CSV data file, e.g., …,Kalimantan_Southern,… . Note two points of caution for working with these CSV data: i) Microsoft Excel may be used for a partial view of the data file nfire4_all_spatial_fire_2002_2019_joins_sp_LC.csv, but it is not recommended for working with this data file. This is because the number of records/rows in this csv file slightly exceeds that maximum that may be read by Excel, which is just over 1 million. This limitation does not apply to the other csv file, however. ii) The GIS-formatted data files employ 'null values' in their attribute tables, and so the corresponding 'values' in the CSV-formatted data files are similarly null. For null values, no value whatsoever is ascribed, not even 0. In the syntax of a CSV file (apparent upon opening the file in any text editor like Microsoft Notepad), a null value is denoted by two consecutive commas without any value, text, or space between them. If a CSV file were opened in Excel, a cell assigned a null value would be blank, not 0 or otherwise. This denotes the correct transcription of the GIS-formatted data. This feature will not impede the correct reading of these CSV data by whatever software. Users are made aware of this feature merely to ensure the proper input of these data into whatever software. 4. DATA STRUCTURE / GEOGRAPHY The GIS-formatted data files are 'point data', i.e., they map the geography of AFDs as individual 'points', in keeping with how these MODIS MCD14ML AFD data were originally structured. For the GIS-formatted data files, each record/row in its corresponding attribute tables corresponds geographically to single AFD 'point', regardless of whether that AFD belongs to a fire event comprised of many AFDs. In the parlance of GIS files, the files depict 'single-part' point features. The unique ID field [nfireID2] serves to denote the fire event to which a given AFD belongs. Similarly, for the CSV-formatted data files, each record/row of values corresponds to a single AFD. There are 1,232,377 records for the data file 'nfire4_all_spatial_fire_2002_2019_joins_sp_LC'. There are 720795 records for the data file 'nfire4_all_spatial_fire_2002_2019_igs_sp_LC'. 5. ATTRIBUTE FIELDS In the data files, while some attribute fields pertain to the individual AFD as the unit of observation (e.g., the land-cover class coincident with the AFD), other attribute fields correspond to the larger 'fire event' to which the individual AFD belongs (e.g., the total duration of fire activity for the fire event). Accordingly, for certain attribute fields pertaining to the fire event as a whole, their values will appear 'duplicated' in the data file amongst those individual AFDs (records) that constitute the fire event in question. Whether a given attribute field pertains to the individual AFD or to its constituent fire event is denoted below for each field. Each AFD is assigned a unique ID field denoting its constituent fire event, [nfireID2]. This field is consistent between both data files, so that attribute data for a given fire event may be 'matched' to attribute data for its corresponding ignition AFD(s), and vice versa, on the basis of the common value of the field [nfireID2]. Note that many attributes below are as originally defined/measured by the input MCD 14ML data, or are derived directly thereof. 5.1 DATASET nfire4_all_spatial_fire_2002_2019_joins_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> MIN_CONFIDE </td> <td> Fire Event </td> <td> The minimum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MAX_CONFIDE </td> <td> Fire Event </td> <td> The maximum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> MEAN_CONFIDE </td> <td> Fire Event </td> <td> The mean detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> STD_CONFIDE </td> <td> Fire Event </td> <td> The standard deviation of detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. </td> </tr> <tr> <td> SUM_FRP </td> <td> Fire Event </td> <td> The sum total of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_FRP </td> <td> Fire Event </td> <td> The mean of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MIN_FRP </td> <td> Fire Event </td> <td> The minimum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MAX_FRP </td> <td> Fire Event </td> <td> The maximum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. </td> </tr> <tr> <td> MEAN_LATITUD </td> <td> Fire Event </td> <td> The mean latitude of all AFDs in the fire event. </td> </tr> <tr> <td> MEAN_LONGITU </td> <td> Fire Event </td> <td> The mean longitude of all AFDs in the fire event. </td> </tr> <tr> <td> MIN_ACQ_DAT </td> <td> Fire Event </td> <td> The minimum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MAX_ACQ_DAT </td> <td> Fire Event </td> <td> The maximum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MAX_acq_mnt </td> <td> Fire Event </td> <td> The month in which the latest AFD of the fire event was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> MAX_yday </td> <td> Fire Event </td> <td> The detection day of year of the latest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> RANGE_yday </td> <td> Fire Event </td> <td> The number of days of duration of the fire event, defined as [MAX_yday]-[MIN_yday] </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> AF_Count </td> <td> Fire Event </td> <td> Number of AFDs in the fire event. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> Island </td> <td> AFD </td> <td> Numerical values coding for major Indonesian islands/region: 1000000=Sumatra; 2000000=Kalimantan; 3000000=Sulawesi; 4000000=Papua. </td> </tr> <tr> <td> FRP_Days </td> <td> Fire Event </td> <td> The severity of the fire event, as defined by Sloan et al., equal to [Sum_FRP] * ([Range_yday]+1). </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event </td> </tr> <tr> <td> CCI_LC2002 </td> <td> </td> <td> The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> </td> </tr> <tr> <td> CCI_LC2004 </td> <td> </td> </tr> <tr> <td> CCI_LC2005 </td> <td> </td> </tr> <tr> <td> CCI_LC2006 </td> <td> </td> </tr> <tr> <td> CCI_LC2007 </td> <td> </td> </tr> <tr> <td> CCI_LC2008 </td> <td> </td> </tr> <tr> <td> CCI_LC2009 </td> <td> </td> </tr> <tr> <td> CCI_LC2010 </td> <td> </td> </tr> <tr> <td> CCI_LC2011 </td> <td> </td> </tr> <tr> <td> CCI_LC2012 </td> <td> </td> </tr> <tr> <td> CCI_LC2013 </td> <td> </td> </tr> <tr> <td> CCI_LC2014 </td> <td> </td> </tr> <tr> <td> CCI_LC2015 </td> <td> </td> </tr> <tr> <td> CCI_LC2016 </td> <td> </td> </tr> <tr> <td> CCI_LC2017 </td> <td> </td> </tr> <tr> <td> CCI_LC2018 </td> <td> </td> </tr> <tr> <td> CCI_LC2019 </td> <td> </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.2 DATASET nfire4_all_spatial_fire_2002_2019_igs_sp_LC <table class="MsoTableGrid"> <tbody> <tr> <td> Field Name </td> <td> Geography of Attribute Value </td> <td> Definition </td> </tr> <tr> <td> OID </td> <td> AFD </td> <td> Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. </td> </tr> <tr> <td> IG_Count </td> <td> Fire Event </td> <td> Number of ignition AFDs in the fire event. </td> </tr> <tr> <td> MIN_acq_yer </td> <td> Fire Event </td> <td> The year in which the earliest AFD of the fire event was detected. </td> </tr> <tr> <td> MIN_acq_mnt </td> <td> Fire Event </td> <td> The month in which the earliest AFD of the fire event (i.e., ignition AFD) was detected. Months are coded numerically, e.g., 1=January, 12=December. </td> </tr> <tr> <td> FIRST_subst_r </td> <td> AFD </td> <td> Text labels denoting the Indonesian island/region of data processing, e.g., Kalimantan, Papua. </td> </tr> <tr> <td> MIN_yday </td> <td> Fire Event </td> <td> The detection day of year of the earliest AFD of the fire event, i.e., ignition AFD. Day of year is denoted numerically, where 1=January 1 and 365=December 31. </td> </tr> <tr> <td> nfireID2 </td> <td> Fire Event </td> <td> The unique ID of the fire event to which the AFD belongs. </td> </tr> <tr> <td> CCI_LC2002 </td> <td> AFD </td> <td> The land-cover class coincident with the AFD. The class is coded by a numerical value as per the left-most column in the table in Section 5.3. The class is that observed for the calendar year in which the AFD occurred, where the year #### is denoted in the field name 'CCI_LC####'. The land-cover data source is as described in Section 5.3. </td> </tr> <tr> <td> CCI_LC2003 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2004 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2005 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2006 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2007 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2008 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2009 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2010 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2011 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2012 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2013 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2014 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2015 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2016 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2017 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2018 </td> <td> AFD </td> </tr> <tr> <td> CCI_LC2019 </td> <td> AFD </td> </tr> <tr> <td> Region </td> <td> AFD </td> <td> Text labels denote whether the AFD occurs within one of the two focal regions of Sloan et al.: Southern Kalimantan, or Central South Sumatra. </td> </tr> <tr> <td> Peat </td> <td> AFD </td> <td> Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. </td> </tr> <tr> <td> POINT_X </td> <td> AFD </td> <td> Longitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Y </td> <td> AFD </td> <td> Latitude, given as decimal degrees with WGS84 datum. </td> </tr> <tr> <td> POINT_Z </td> <td> -- </td> <td> Ignore. </td> </tr> <tr> <td> POINT_M </td> <td> -- </td> <td> Ignore. </td> </tr> </tbody> </table> 5.3. LAND-COVER ATTRIBUTE DATA As noted in Section 5.1 and Section 5.2, the nominal values of the attribute fields 'CCI_LC####' correspond to column 1 of the table below. These hierarchical values, and their corresponding land-cover classes labels in column 2 of the table, pertain to the land-cover classification of the Copernicus Climate Change Initiative Land-Cover Product of the European Space Agency. This classification has an annual temporal resolution and 300-meter spatial resolution. Pertinent citations for these land-cover data are below: ESA. Annual land-cover product, 1992 to 2019/present, based on MERIS 300-m and ancillary SPOT, AVHRR, Sentinel-3 and PROB-V satellite data. European Space Agency (ESA) European Centre for Medium-Range Weather Forecasts (ECMFW) Copernicus Climate Change Service (C3S) Climate Change Initiative (CCI), <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview">https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview</a>; <a href="http://maps.elie.ucl.ac.be/CCI/viewer/download.php">http://maps.elie.ucl.ac.be/CCI/viewer/download.php</a>; <a href="http://www.esa-landcover-cci.org/">http://www.esa-landcover-cci.org/</a> (2020). Pérez-Hoyos, A., Rembold, F., Kerdiles, H. & Gallego, J. Comparison of global land cover datasets for cropland monitoring. Remote Sensing 9, (2017). Columns 3 and 4 in the table below illustrate how the original land-cover classes of the Copernicus Climate Change Initiative Land-Cover Product were reclassified for analysis in Sloan et al. <table class="MsoNormalTable"> <tbody> <tr> <td> 1. CCI-LC Class Value </td> <td> 2. CCI-LC Class Label </td> <td> 3. New Class Value for Sloan et al. </td> <td> 4. New Class Label for Sloan et al. </td> </tr> <tr> <td> 0 </td> <td> No Data </td> <td> 0 </td> <td> No Data </td> </tr> <tr> <td> 10 </td> <td> Cropland, rainfed </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 11 </td> <td> Herbaceous cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 12 </td> <td> Tree or shrub cover </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 20 </td> <td> Cropland, irrigated or post‐flooding </td> <td> 1 </td> <td> Cleared/Cultivated </td> </tr> <tr> <td> 30 </td> <td> Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%) </td> <td> 2 </td> <td> Mosaic Cropland </td> </tr> <tr> <td> 40 </td> <td> Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) </td> <td> 3 </td> <td> Mosaic Natural Veg </td> </tr> <tr> <td> 50 </td> <td> Tree cover, broadleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 60 </td> <td> Tree cover, broadleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 61 </td> <td> Tree cover, broadleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 62 </td> <td> Tree cover, broadleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 70 </td> <td> Tree cover, needleleaved, evergreen, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 71 </td> <td> Tree cover, needleleaved, evergreen, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 72 </td> <td> Tree cover, needleleaved, evergreen, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 80 </td> <td> Tree cover, needleleaved, deciduous, closed to open (>15%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 81 </td> <td> Tree cover, needleleaved, deciduous, closed (>40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 82 </td> <td> Tree cover, needleleaved, deciduous, open (15‐40%) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 90 </td> <td> Tree cover, mixed leaf type (broadleaved and needleleaved) </td> <td> 4 </td> <td> Forest </td> </tr> <tr> <td> 100 </td> <td> Mosaic tree and shrub (>50%) / herbaceous cover (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 110 </td> <td> Mosaic herbaceous cover (>50%) / tree and shrub (<50%) </td> <td> 5 </td> <td> Mosaic Shrubland </td> </tr> <tr> <td> 120 </td> <td> Shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 121 </td> <td> Evergreen shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 122 </td> <td> Deciduous shrubland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 130 </td> <td> Grassland </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 140 </td> <td> Lichens and mosses </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 150 </td> <td> Sparse vegetation (tree, shrub, herbaceous cover) (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 151 </td> <td> Sparse tree (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 152 </td> <td> Sparse shrub (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 153 </td> <td> Sparse herbaceous cover (<15%) </td> <td> 6 </td> <td> Low/Sparse Vegetation </td> </tr> <tr> <td> 160 </td> <td> Tree cover, flooded, fresh or brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 170 </td> <td> Tree cover, flooded, saline water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 180 </td> <td> Shrub or herbaceous cover, flooded, fresh/saline/brakish water </td> <td> 7 </td> <td> Flooded Vegetation </td> </tr> <tr> <td> 190 </td> <td> Urban areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 200 </td> <td> Bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 201 </td> <td> Consolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 202 </td> <td> Unconsolidated bare areas </td> <td> 8 </td> <td> Other </td> </tr> <tr> <td> 210 </td> <td> Water bodies </td> <td> 8 </td> <td> Other </td> </tr> </tbody> </table>

Data from: Active anemosensing hypothesis: How flying insects could estimate ambient wind direction

Estimating the direction of ambient fluid flow is a crucial step during chemical plume tracking for flying and swimming animals. How animals accomplish this remains an open area of investigation. Recent calcium imaging with tethered flying Drosophila has shown that flies encode the angular direction of multiple sensory modalities in their central complex: orientation, apparent wind (or airspeed) direction, and direction of. Here we describe a general framework for how these three sensory modalities can be integrated over time to provide a continuous estimate of ambient wind direction. After validating our framework using a flying drone, we use simulations to show that ambient wind direction can be most accurately estimated with trajectories characterized by frequent, large magnitude turns. Furthermore, sensory measurements and estimates of their derivatives must be integrated over a period of time that incorporates at least one of these turns. Finally, we discuss approaches that insects might use to simplify the required computations and present a list of testable predictions. Together, our results suggest that ambient flow estimation may be an important driver underlying the zigzagging maneuvers characteristic of plume tracking animals' trajectories.

Figure 3–Figure Supplement 4 of the paper 'A Feed-forward Pathway Drives LRRK2 kinase Membrane Recruitment and Activation'

Raw immunoblotting and quantitation data for Figure 3–Figure Supplement 4 of the paper "A Feed-forward Pathway Drives LRRK2 kinase Membrane Recruitment and Activation" (Edmundo G. Vides, Ayan Adhikar, Claire Y. Chiang, Pawel Lis, Elena Purlyte, Charles Limouse, Justin L. Shumate, Elena Spínola-Lasso, Herschel S. Dhekne,  Dario R. Alessi, and Suzanne R. Pfeffer) File descriptions: Figure 3–Figure Supplement 4A Fig.3.S4A_700(tRab10;tLRRK2)_High.tif - Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; high contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4A_700(tRab10;tLRRK2)_Low.tif - Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; low contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4A_800(pRab10;pLRRK2)_High.tif - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4A_800(pRab10;pLRRK2)_Low.tif - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4A_HA.tif - ChemiDoc Scan (ECL) showing HA blot Figure 3–Figure Supplement 4B Fig.3.S4B_700(tRab10;tLRRK2)_High.tif - Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; high contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4B_700(tRab10;tLRRK2)_Low.tif - Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; low contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4B_800(pRab10;pLRRK2)_High.tif - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4B_800(pRab10;pLRRK2)_Low.tif - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast Panel 1 - top left membrane Panel 2 - top right membrane Panel 3 - bottom left membrane Panel 4 - bottom right membrane Fig.3.S4B_HA.tif - ChemiDoc Scan (ECL) showing HA blot Fig.3.S4_immunoblotting_numerical_data.xlsx - Excel sheet containing raw quatitation values from Licor scans as well as calculated pRab10/Rab10 ratios (values used in Figure 3C and 3D)

A GeoNEX-based 1km hourly land surface downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) product

The dataset is produced by employing a well-established physical-based look-up table (LUT) approach to the GeoNEX gridded top-of-atmosphere bidirectional reflectance factor data acquired by the Advanced Himawari Imager (AHI) and Advanced Baseline Imager (ABI) sensors. It consists of DSR and PAR over both AHI and ABI coverage at an hourly temporal step with a 1km spatial resolution. Full details about the work are under review and can be cited as  Li, R., Wang, D., Wang, W., and Nemani, R.: A GeoNEX-based high spatiotemporal resolution product of land surface downward shortwave radiation and photosynthetically active radiation, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-319,  in review, 2022. Due to the file size restrictions, only some sample files are shared here. The full dataset can be accessed through the NASA Advanced Supercomputing Division GeoNEX data portal, using the following URLs: <a href="https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/">https://data.nas.nasa.gov/geonex/geonexdata/GOES16/GEONEX-L2/DSR-PAR/</a> <a href="https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/">https://data.nas.nasa.gov/geonex/geonexdata/HIMAWARI8/GEONEX-L2/DSR-PAR/</a>

Use of A Molecular Switch Probe to Activate or Inhibit GIRK1 Heteromers In Silico Reveals a Novel Gating Mechanism

GIRK channel structure models (PDB structure files) used for Molecular Dynamics simulations.

mmWave Radar and RGB-D Camera Sensor Data for Human Activity Recognition

This is a human activity recognition dataset with measurements from both mmWave radar and camera sensor. Meanwhile, we set multiple people scenario to mimic more realistic scenes. The other dataset collected in non-LOS(line-of-sight) environment, you can visit https://zenodo.org/record/7096889#.YynBvuhBwQ8 to get it. The mmWave radar sensors used in our experiments are composed of TI IWR6843ISK-ODS, eradar ESRR(corner radar), eradar EMRR(front radar). We appreciate the support of the eradar company, that provides corner radars and front radars for us, you can visit  http://en.eradartech.com/ to get more information.

opencc-zeroSep 2022View details →

A test for plasticity in sperm motility activation

Evolutionary theory predicts that selection will favour phenotypic plasticity in sperm traits that maximise fertilisation success in dynamic fertilisation environments. In species with external fertilisation, osmolality of the fertilisation medium is known to play a critical role in activating sperm motility, but evidence for osmotic-induced sperm plasticity is limited to euryhaline fish and marine invertebrates. Whether this capacity extends to freshwater taxa remains unknown. Here, we provide the first test for plasticity in sperm-motility activation in response to osmotic environment in an anuran amphibian. Unexpectedly, there was no detectable shift in the optimal osmolality for sperm-motility activation after approximately 13 weeks of acclimation (a period reflecting the duration of the winter breeding season). However, in both the low and high acclimation treatments, the optimal osmolality for sperm-motility activation mirrored the osmolality at the natural breeding site, indicating a phenotypic match to the local environment. Previously it has been shown that C. signifera display among-population covariation between environmental osmolality and sperm performance. Coupled with this finding, the results of the present study suggest that inter-population differences reflect genetic divergence and local adaptation. We discuss the need for experimental tests of osmotic-induced sperm plasticity in more freshwater taxa to better understand the environmental and evolutionary contexts favouring adaptive plasticity in sperm-motility activation.

opencc-zeroSep 2022View details →

Cytosolic peptides encoding CaV1 C-termini downregulate the calcium channel activity-neuritogenesis coupling

L-type Ca2+ (CaV1) channels transduce channel activities into nuclear signals critical to neuritogenesis. Also, standalone peptides encoded by CaV1 DCT (distal carboxyl-terminus) act as nuclear transcription factors reportedly promoting neuritogenesis. Here, by focusing on exemplary CaV1.3 and cortical neurons under basal conditions, we discover that cytosolic DCT peptides downregulate neurite outgrowth by the interactions with CaV1's apo-calmodulin binding motif. Distinct from nuclear DCT, various cytosolic peptides exert a gradient of inhibitory effects on Ca2+ influx via CaV1 channels and neurite extension and arborization, and also the intermediate events including CREB activation and c-Fos expression. The inhibition efficacies of DCT are quantitatively correlated with its binding affinities. Meanwhile, cytosolic inhibition tends to facilitate neuritogenesis indirectly by favoring Ca2+-sensitive nuclear retention of DCT. In summary, DCT peptides as a class of CaV1 inhibitors specifically regulate the channel activity-neuritogenesis coupling in a variant-, affinity-, and localization-dependent manner.

opencc-zeroSep 2022View details →

Pollinator data from: Pollinator movement activity influences genetic diversity and differentiation of spatially isolated populations of clonal forest herbs

In agricultural landscapes, forest herbs live in small, spatially isolated forest patches. For their long-term survival, their populations depend on animals as genetic linkers that provide pollen- or seed-mediated gene flow among different forest patches. However, whether insect pollinators serve as genetic linkers among spatially isolated forest herb populations in agricultural landscapes remains to be shown. Here, we used population genetic methods to analyze: (A) the genetic diversity and genetic differentiation of populations of two common, slow-colonizing temperate forest herb species (Polygonatum multiflorum (L.) All. and Anemone nemorosa L.) in spatially isolated populations within three agricultural landscapes in Germany and Sweden and (B) the movement activity of their most relevant associated pollinator species, i.e., the bumblebee Bombus pascuorum (Scopoli, 1763) and the hoverfly Melanostoma scalare (Fabricus, 1794), respectively, which differ in their mobility. We tested whether the indicated pollinator movement activity affected the genetic diversity and genetic differentiation of the forest herb populations. Bumblebee movement indicators that solely indicated movement activity between the forest patches affected both genetic diversity and genetic differentiation of the associated forest herb P. multiflorum in a way that can be explained by pollen-mediated gene flow among the forest herb populations. In contrast, movement indicators reflecting the total movement activity at a forest patch (including within-forest patch movement activity) showed unexpected effects for both plant-pollinator pairs that might be explained by accelerated genetic drift due to enhanced sexual reproduction. Our integrated approach revealed that bumblebees serve as genetic linkers of associated forest herb populations, even if they are more than 2 km apart from each other. No such evidence was found for the forest-associated hoverfly species which showed significant genetic differentiation among forest patches itself. Our approach also indicated that a higher within-forest patch movement activity of both pollinator species might enhance sexual recruitment and thus diminishes the temporal buffer that clonal growth provides against habitat fragmentation effects.

Catalogues of active galactic nuclei from Gaia and unWISE data (updated to Gaia DR3 astrometric solution)

This is a minor update to the Gaia-unWISE AGN catalogue published in Shu et al. 2019, MNRAS, 489, 4741. It contains the same 2.7 million AGN candidates selected from the Gaia DR2 and unWISE data using a random forest classifier. In addition to the original columns (which are explained in detail in Shu et al. 2019), a few new columns that correspond to Gaia DR3 astrometric solutions are appended, i.e. dr3_source_id, dr3_pmra, dr3_pmra_err, dr3_pmdec, dr3_pmdec_err, dr3_plx, dr3_plx_err, dr3_pmsig, and dr3_plxsig.

opencc-by-4.0Jul 2022View details →

Figs 1–3 in Effects of diet and feed composition on antibacterial activity of hemolymph of saproxylic beetles: A case study of Zophobas atratus (Coleoptera: Tenebrionidae)

Figs 1–3. Results of disk-diffusive test. Effects of artificial fungi-based and standard

opencc-by-4.0Jul 2022View details →

Figs 6, 7 in Effects of diet and feed composition on antibacterial activity of hemolymph of saproxylic beetles: A case study of Zophobas atratus (Coleoptera: Tenebrionidae)

Figs 6, 7. Results of photometric bacterial test. Viability of gram-negative E. сoli ATCC

opencc-by-4.0Jul 2022View details →

Figs 4, 5 in Effects of diet and feed composition on antibacterial activity of hemolymph of saproxylic beetles: A case study of Zophobas atratus (Coleoptera: Tenebrionidae)

Figs 4, 5. Results of photometric bacterial test. Dynamics of effects of artificial and

opencc-by-nc-4.0Mar 2022View details →

Figure 1 in Circadian activity patterns and temporal overlap among cracids (Aves: Cracidae) within a vegetation mosaic in the Pantanal of Rio Negro, Brazil

Figure 1. Study area and location of the 90 camera trap stations in the Pantanal of Rio Negro, Aquidauana, Mato Grosso do Sul, Brazil.