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1,465 results for “resilience”
Data archive for: Fire-regime variability and ecosystem resilience over four millennia in a Rocky Mountain subalpine watershed
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Mammalian resilience to megafire in western U.S. woodland savannas
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California Wildfire Resilience Core Metrics Rating Process and Results
The California Wildfire & Forest Resilience Task Force (Task Force) is producing a toolkit that can support organizations to prioritize, plan and implement actions to lessen wildfire risk to communities and improve broader statewide ecosystem resilience. As part of this effort, a core set of metrics needed to be identified to report resilience progress. The Task Force's Science Advisory Panel engaged in a rapid Delphi process, collecting expert opinion via surveys to provide content knowledge and science support for this process. This data archive provides content 1) for transparency, to share as much of our workflow as is feasible; 2) for others to pull from as an example if they want to do a similar process; 3) to provide all the detailed results on metrics if a reader wants to look into the ratings for and definition of a particular metric. The archive supports a report and paper summarizing and describing our Delphi method and the results regarding the specific metrics considered by our experts.
RESCCUE (RESilience to cope with Climate Change in Urban arEas) EU Project - WP1 Data
<p>These files contain the data generated for the Work Package 1 of the RESCCUE project. RESCCUE project was devised to analyse future urban impacts due to climate change so as to improve resilience of three target cities: Barcelona, Bristol and Lisbon. To achieve that, future climate projections and changes in extreme events were obtained at a local scale for the Work Package 1. Several past studies were analysed to identify all the climate variables and extreme events that could affect urban areas, e.g. heavy rainfall, heat waves and storm surge. All available meteorological observations in the considered areas were collected and filtered through several tests (general consistency, outliers and inhomogeneities) in order to handle datasets long enough and of good quality. As a way to obtain the best input possible, every valid station was extended in time by downscaling process with the ERA-Interim reanalysis.</p> <p>Future climate projections were obtained for ten different global climate models considering two of the main Representative Concentration Pathways (RCP4.5 and RCP8.5) established in the last IPCC report. These models were downscaled through a sophisticated statistical methods (analogous stratification and transfer functions among others) to project local climate according to the identified climate drivers: temperature, precipitation, wind, relative humidity, sea level pressure, potential evapotranspiration, snowfall, wave height and sea level; and for both climate and decadal timescales. Already downscaled models were first validated for the method and afterwards verified, obtaining small errors and good coherent simulations.</p> <p>Extreme events of the main climate drivers were obtained and analysed for both historical and future scenarios through the combination of several statistical methods as well as through the analysis of several teleconnectionpatterns. Derived events such as heat waves, drought, snowstorms, storm surges, wave height among others were afterwards inferred for climate, decadal and seasonal scale.</p> <p><strong>FILES</strong></p> <p>The data generated have been grouped into three different files, one for each studied area: the hydrological basin of the rivers Ter and Llobregat (the area that influences Barcelona), the geographical area between England and South Wales (the area that influences Bristol) and the Lisbon area.</p> <p>Each of the files contains a self-explanatory file detailing the structure of the information contained and the way in which it is provided.</p> <p><strong>ABOUT THE RESCCUE PROJECT</strong></p> <p>The RESCCUE project, Resilience to cope with Climate Change in Urban Areas, –a multisectorial approach focusing on water– aims to provide practical and innovative models and tools to end-users facing climate change challenges to build more resilient cities.</p> <p>The project provides tools to assess urban resilience from a multisectorial approach, for current and future climate scenarios and including multiple hazards. This holistic approach to urban resilience will enable city managers and urban systems operators to decide the optimal investments to cope with future situations.</p> <p>For more information, please visit <a href="http://www.resccue.eu/">www.resccue.eu</a></p>
Beef database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The beef database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets, and is further differentiated into All Beef, Breeders and Fatteners: BasicFarmType (18 rows), DetailedFarmType (75 rows), ClimateClass+BasicFarmType (270 rows), NUTS+BasicFarmType (2074 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
Dairy database for prediction of main environmental challenges to resilience and efficiency in cattle production systems at regional resolution
<p>The dairy database comprises average values for a wide range of variables (110 or 119), available in 4 worksheets: BasicFarmType (18 rows), DetailedFarmType (10 rows), ClimateClass+BasicFarmType (100 rows), NUTS+DetailedFarmType (1452 rows). Data are omitted when the sample size (n) is below 15, as per the confidentiality agreement under FADN data use rules.</p> <p>A combined farm characterisation database was constructed using two major data sources, the Farm Accountancy Data Network (FADN), and the Gridded Agro-Meteorological Data in Europe (AGRI4CAST). The database initially constructed was further enhanced through the addition of forage and crop yield data from the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) developed Agro-Ecological Zones (AEZ) methodology database (FAO, 2012). The data was processed and is presented in D1.2 as two databases (dairy and beef), as averages for a wide range of variables at basic or detailed farm types, and at NUTS2 regional scale.</p> <p>Detailed FADN data (anonymised individual farm data) was requested for all ruminant and mixed farm types, over 10 years and the most recent data available at request (2011-2013) was utilised for the analysis. Following receipt of the data (~250k farms) this has been compiled into two consistent datasets, one for dairy (141,961) farms and one for beef farms (54,417). Each dataset comprises some values directly from the FADN data, but also a large number of calculated variables, to identify dairy or beef enterprise performance at per animal, per output product unit or per hectare. These values were calculated according to the respective dairy and beef enterprise allocation methodologies described by FADN. Further economic and structural variables have been calculated as necessary, as described in GenTORE D1.1 (Quiédeville et al., 2019).</p> <p>For each farm within the dataset, the structural, production and economic data from the FADN data is supplemented with the addition of meteorological data. The daily meteorological data was downloaded from the AGRI4STAT database web portal at a NUTS2 scale. For each NUTS2 region data was available for a number of weather stations. This large dataset was processed through scripts in STATA software to generate annual values for a wide range of climatic variables, including Temperature Humidity Index (THI), and indicators of drought and seasonality of weather. Furthermore, the altitude values per weather station allowed for a sub-grouping of weather station data by altitude zone (aligned with values available in the FADN dataset).</p> <p>Using a Latent Class Analysis process, the meteorological data was analysed to identify consistent environmental regions in Europe. Selected climatic variables, together with altitude zone, were utilised to statistically identify differing zones, and to classify each NUTS2 region to a zone, resulting in 6 lowland zones and 3 upland zones (above 600m) The LCA process enhanced an earlier method of manually overlaying the Metzger et al. (20054) pedo-climatic zone allocation, but closely correlates. Therefore for each farm in the dairy and beef datasets, meteorological and environmental zone data was allocated on a NUTS2 by altitude zone basis and this dataset has been subsequently assessed and submitted as papers; Quiédeville et al., (submitted May 2020) and Grovermann et al. (submitted May 2020).</p> <p>The GAEZ forage and crop yield data was downloaded from the GAEZ data portal as baseline and two future climate prediction periods: Baseline (1961-2000), 2020s (2011-2040), and 2050s (2041-2070), for the Hadley CM3 model and IPCC scenario A (the most extreme scenario). See: <a href="http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/">http://www.fao.org/nr/gaez/about-data-portal/agro-climatic-resources/en/#</a>). A zonal statistics was applied to the GAEZ layers to aggregate the data to NUT2 region and altitude zone (0-300m, 300-600m, 600m+) with raster package in R. The result is an average yield<a href="#_ftn1">[1]</a> for varying forages and crops for each altitude zone in each nuts2, for both the baseline and the future climate scenario. This data allows further analysis of the future impacts on cattle farming at both a regional scale, but also by farm type or system, which may be affected differently (Moakes et al. in preparation).</p> <p>All variable processing from FADN data is shown in the Annex, as performed in Stata software.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> The mean was performed on non-zero yield pixels in order to exclude non-suitable areas from average.</p>
DynaRev - Dynamic Coastal Protection: Resilience of Dynamic Revetments Under Sea Level Rise
<p>This dataset contains processed data from the DynaRev experiments carried out in the Large Wave Flume (Grosser Wellenkanal, GWK) from 14-08-2017 to 29-09-2017 as a Transnational Access project within the EU funded project HYDRALAB+ (654110). The dataset provided under this DOI includes the post-processed data detailed in the article submitted to Scientific Data titled "High-resolution, prototype-scale laboratory measurements of nearshore wave processes and morphological evolution of a sandy beach with and without a dynamic cobble berm revetment"</p> <p>The overall aim of this project was to construct a prototype-scale beach and investigate the response of the beach to a rising sea level and storms with and without a cobble berm dynamic revetment structure (a gravel or shingle ridge placed around the wave runup limit). The response of two beach configurations was investigated under erosive wave conditions (Hs=0.8m, Tp=6.0s) and a total sea-level rise, SLR = 0.4 m:</p> <p>i) an unmodified sand beach with an initially plane slope of 1:15, and<br> ii) a natural beach profile with a dynamic revetment installed at the location of the natural berm before imposing SLR.</p> <p>SLR was imposed in 4 steps of 0.1 m with an initial water depth of 4.5 m above the flume base. Each phase ran for a total of 58 hours, after which resilience testing under storm waves and accretive conditions was undertaken for a further 12 hours in each case.</p> <p>The changing profile of the sand beach and revetment was monitored throughout the experiments using a traditional profiler and a LiDAR array. Additional hydrodynamic measurements were obtained at the location of the offshore sandbar to investigate the process of bar formation and migration with SLR.</p> <p>The model set-up, experimental program and data structure are explained in the submitted Scientific Data paper. The data files are organised as detailed in "DynaRev_Data_Structure.doc".</p> <p> </p>
Resilience and virus ecology of paleotropical bats
<b>Description: </b><p>Emerging infectious diseases (EIDs) are a threat to human and animal health. Bats are known reservoir hosts for various highly fatal viruses associated with EIDs. Previously, outbreaks of some EIDs were directly associated with environmental conditions, namely an increased contact zone between reservoir hosts and humans resulting from bush-meat consumption and increasing anthropogenic land-use. <br>However, anthropogenic land-use, especially habitat logging and fragmentation, may also take effect via consequences upon wildlife communities, including population declines. Before effects manifest in decreased population sizes, individuals may show changes in physiological parameters. In this project, I investigated if habitat logging and fragmentation are associated with changes in body mass, levels of chronic stress, immunity and occurrence of viruses in bats. I sampled individuals of eight bat species of the genus Rhinolophus, Hipposideros and Kerivoula at the SASFE project in Sabah, Malaysia. I found that individuals of foliage-roosting bat species weighed less in currently logged and recently fragmented habitats and had lower white blood cell counts than their conspecifics from undisturbed forests. In a cave-roosting species (Rhinolophus borneensis) individuals from fragmented forests showed higher levels of chronic stress (indicated by the neutrophil to lymphocyte ratio) than conspecifics from actively logged forests. Overall, I conclude that foliage-roosting bat species may be particularly vulnerable to habitat fragmentation, affecting their overall health including cell-mediated immunity.<br>Further, I investigated if the detrimental effects of habitat logging and<br>fragmentation on the overall health of bats are reflected in increased detection rates of corona- and astroviruses in fecal samples of bats in the same study site. An increase in detection rates may increase the risk for pathogen spill-overs from bats to humans. Unexpectedly, the detection rates were not associated with habitat logging and fragmentation in any species. However, I identified the rainy season as a risk factor for increased astrovirus shedding. Further, individuals in poor body condition tended to have a higher risk for astrovirus shedding.<br>In conclusion, foliage-roosting bat species may turn into a source for future viral spillover events if they are sufficiently resistant to remain in logged and fragmented habitats. Ongoing landscape fragmentation in Southeast Asia and worldwide will reduce the connectivity of remaining habitats, resulting in lower mobility and genetic diversity of bats within a species in future generations. These bat populations may have a higher susceptibility to contract and shed viruses especially during the rainy season. If <br>humans or their livestock live nearby, these viruses or other pathogens may spill over in new hosts.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/73"><b>Resilience and virus ecology of paleotropical bats</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>German Research Council (Research grant, DFG Priority Programm 1596; Vo890/23, DR772/10-1 and 2)</li><li>UK NERC Natural Environment Research Council (Research grant, HTMF Human-Modified Tropical Forests program under the LOMBoK Land-Use Options for Maintaining Biodiversity and Ecosystem Function consortium)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.3 (153))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.3 (317))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/3 JLD.2 (16))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3948443">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Seltmann_bat_data_V2.xlsx, Traps2014.2015.gpx</p><p><b>Seltmann_bat_data_V2.xlsx</b></p><p>This file contains dataset metadata and 9 data tables:</p><ol><li><p><b>ACTH_challenge</b> (described in worksheet ACTH_challenge)</p><p>Description: ACTH challenge</p><p>Number of fields: 17</p><p>Number of data rows: 15</p><p>Fields: </p><ul><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Cortisol</b>: Blood cortisol levels (Field type: numeric trait)</li><li><b>Recapture</b>: Is the bat a recapture (Field type: categorical trait)</li><li><b>Date</b>: Date of capture (Field type: date)</li><li><b>Trap</b>: Trap number (Field type: id)</li><li><b>Species</b>: Species of bat (Field type: taxa)</li><li><b>Sex</b>: Sex of bat (Field type: categorical trait)</li><li><b>Age</b>: Age of bat (A - adult) (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (PL - postlactating) (Field type: categorical trait)</li><li><b>Forearm_length</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Weight (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>T1</b>: Temperature before ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: Temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites observed on sampled bat (Field type: categorical trait)</li><li><b>W</b>: Wing punch (Field type: categorical trait)</li></ul></li><li><p><b>Viral_fecal_samples</b> (described in worksheet Viral_fecal_samples)</p><p>Description: Viral prevalences 03-04 2015</p><p>Number of fields: 31</p><p>Number of data rows: 790</p><p>Fields: </p><ul><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Pool_comments</b>: Pool ID (Field type: comments)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Astro</b>: Astrovirus prevalence (Field type: categorical trait)</li><li><b>COV</b>: Coronavirus prevalence (Field type: categorical trait)</li><li><b>Band_no</b>: Ring-Number (Field type: id)</li><li><b>R</b>: Recapture (uk - unknown) (Field type: categorical trait)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Sex</b>: Sex of bat sampled (Field type: categorical trait)</li><li><b>Age</b>: Age of bat sampled (A - adult, S - subadult, J - juvenile) (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical trait)</li><li><b>Forearm_length</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Biomass of bat (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites on sampled bats (Field type: comments)</li><li><b>W</b>: Wing punch taken (Field type: numeric trait)</li><li><b>Comments</b>: Comments (Field type: comments)</li><li><b>Dave</b>: Daves comments (Field type: comments)</li><li><b>File</b>: Orignal file name (Field type: comments)</li></ul></li><li><p><b>Overview_3</b> (described in worksheet Overview_3)</p><p>Description: Viral prevalences 03-04 2015</p><p>Number of fields: 4</p><p>Number of data rows: 7</p><p>Fields: </p><ul><li><b>Species</b>: Species of bat (Field type: taxa)</li><li><b>Scientific_name</b>: Scientific name of bat (Field type: comments)</li><li><b>Total_urine_samples</b>: Total number of urine samples (Field type: numeric trait)</li><li><b>Total_fecal_samples</b>: Total number of fecal samples (Field type: numeric trait)</li></ul></li><li><p><b>Sequences</b> (described in worksheet Sequences)</p><p>Description: Coronavirsu sequences</p><p>Number of fields: 3</p><p>Number of data rows: 5</p><p>Fields: </p><ul><li><b>ID</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Gene_seq</b>: Gene sequence code (Field type: comments)</li></ul></li><li><p><b>IgG_BKA</b> (described in worksheet IgG_BKA)</p><p>Description: IgGs and BKA</p><p>Number of fields: 28</p><p>Number of data rows: 44</p><p>Fields: </p><ul><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Band</b>: Ring-Number (Field type: id)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>R</b>: Recapture (Field type: categorical trait)</li><li><b>Sex</b>: Sex of bat (Field type: categorical trait)</li><li><b>Age</b>: Age of bat (A - adult) (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical trait)</li><li><b>Forearm length</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Biomass of bat (Field type: numeric trait)</li><li><b>BC</b>: Body condition (mass divided by forearm) (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>W</b>: Wing punch (Field type: numeric trait)</li><li><b>Iggs1</b>: IgGs (optical density) measured before blood injected with hormone ACTH intraperitoneal (Field type: numeric trait)</li><li><b>Iggs2</b>: IgGs (optical density) measured 2.5 hours after blood injected with hormone ACTH intraperitoneal (Field type: numeric trait)</li><li><b>BKA1</b>: "The BKA is a constitutive innate marker of the immune system and measures humoral and cellular components in function of the sample used. While using whole blood is possible to quantify the overall constitutive innate immunity of an individual44, with serum or plasma samples only the humoral part is measured45,46. We assessed the bacterial killing activity (BKA) of the plasma against E. coli in vitro following the method of Schneeberger et al.47. The BKA of plasma is a functional marker of the humoral part of the constitutive innate immunity45,46. Plasma samples were diluted 1:50 in sterile PBS and we added 10 µl of a suspension of living E. coli (ATCC #8739) to each diluted sample (140 µl). The bacterial suspension was adjusted to a concentration of ~200 colonies per 50 µl plasma-bacteria mixture. The mixtures were then incubated for 30 min at 37°C. After incubation, 50 µl aliquot of the vortexed mixture was spread onto Tryptic Soy Agar plates in duplicate, followed by overnight incubation at 37°C. To obtain the initial number of bacteria that we had before starting to interact with the plasma, we diluted 140 µl PBS with bacterial suspension and plated in similar ways. On the following day, the colony-forming units were counted and the bacterial killing activity was defined as percent of the killed bacteria, which was calculated as 1-(average of viable bacteria after incubation / the initial number of bacteria47). 45 Heinrich, S. K. et al. Feliform carnivores have a distinguished constitutive innate immune response. Biol. Open 5, 550-555 (2016). 46 Heinrich, S. K. et al. Cheetahs have a stronger constitutive innate immunity than leopards. Sci. Rep. 7, 44837 (2017). 47 Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Measures of the constitutive immune system are linked to diet and roosting habits of Neotropical bats. PLoS One 8, e54023 (2013). (Field type: numeric trait)</li><li><b>BKA2</b>: "The BKA is a constitutive innate marker of the immune system and measures humoral and cellular components in function of the sample used. While using whole blood is possible to quantify the overall constitutive innate immunity of an individual44, with serum or plasma samples only the humoral part is measured45,46. We assessed the bacterial killing activity (BKA) of the plasma against E. coli in vitro following the method of Schneeberger et al.47. The BKA of plasma is a functional marker of the humoral part of the constitutive innate immunity45,46. Plasma samples were diluted 1:50 in sterile PBS and we added 10 µl of a suspension of living E. coli (ATCC #8739) to each diluted sample (140 µl). The bacterial suspension was adjusted to a concentration of ~200 colonies per 50 µl plasma-bacteria mixture. The mixtures were then incubated for 30 min at 37°C. After incubation, 50 µl aliquot of the vortexed mixture was spread onto Tryptic Soy Agar plates in duplicate, followed by overnight incubation at 37°C. To obtain the initial number of bacteria that we had before starting to interact with the plasma, we diluted 140 µl PBS with bacterial suspension and plated in similar ways. On the following day, the colony-forming units were counted and the bacterial killing activity was defined as percent of the killed bacteria, which was calculated as 1-(average of viable bacteria after incubation / the initial number of bacteria47). 45 Heinrich, S. K. et al. Feliform carnivores have a distinguished constitutive innate immune response. Biol. Open 5, 550-555 (2016). 46 Heinrich, S. K. et al. Cheetahs have a stronger constitutive innate immunity than leopards. Sci. Rep. 7, 44837 (2017). 47 Schneeberger, K., Czirják, G. Á. & Voigt, C. C. Measures of the constitutive immune system are linked to diet and roosting habits of Neotropical bats. PLoS One 8, e54023 (2013). (Field type: numeric trait)</li></ul></li><li><p><b>Leukocytes</b> (described in worksheet Leukocytes)</p><p>Description: Leukocyte counts</p><p>Number of fields: 37</p><p>Number of data rows: 57</p><p>Fields: </p><ul><li><b>Site</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Transect</b>: Transect within SAFE block (Field type: replicate)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Band</b>: Ring-Number (Field type: id)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Recapture</b>: Recapture (uk - unknown) (Field type: categorical trait)</li><li><b>Age</b>: Age of bat sampled (Field type: categorical trait)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical trait)</li><li><b>Sex</b>: Sex of bat sampled (Field type: categorical trait)</li><li><b>Forearm</b>: Forearm length (Field type: numeric trait)</li><li><b>Body mass</b>: Biomass of bat (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites on bat (Field type: comments)</li><li><b>W</b>: Wing punch (Field type: numeric trait)</li><li><b>CommentsEnglish</b>: Blood sample comments (Field type: comments)</li><li><b>Eosinophiles</b>: Eosinophiles (Field type: numeric trait)</li><li><b>Basophiles</b>: Basophiles (Field type: numeric trait)</li><li><b>Neutrophiles</b>: Neutrophiles (Field type: numeric trait)</li><li><b>Monocytes</b>: Monocytes (Field type: numeric trait)</li><li><b>Lymphocytes</b>: Lymphocytes (Field type: numeric trait)</li><li><b>CommentsGerman</b>: Comments in german (Field type: comments)</li><li><b>NL-ratio</b>: NL-ratio (Field type: numeric trait)</li><li><b>Monolayers</b>: Monolayers (Field type: numeric trait)</li><li><b>Leukocytes</b>: Leukocytes (Field type: numeric trait)</li><li><b>Mean_leukocytes_monolayer</b>: Proportion of monolayers with leukocytes (Field type: numeric trait)</li></ul></li><li><p><b>PCR</b> (described in worksheet PCR)</p><p>Description: PCR analysis</p><p>Number of fields: 10</p><p>Number of data rows: 78</p><p>Fields: </p><ul><li><b>ID</b>: Bat ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Location</b>: Location within the SAFE landscape (Field type: location)</li><li><b>Ct_bIL-6</b>: ? (Field type: numeric trait)</li><li><b>Ct_bSTAT1</b>: ? (Field type: numeric trait)</li><li><b>Ct_bMAPK</b>: ? (Field type: numeric trait)</li><li><b>Ct_bActin_B</b>: ? (Field type: numeric trait)</li><li><b>Ct_bIL-6-2</b>: ? (Field type: numeric trait)</li><li><b>Ct_bSTAT1-2</b>: ? (Field type: numeric trait)</li><li><b>Ct_bMAPK-2</b>: ? (Field type: numeric trait)</li></ul></li><li><p><b>Bat data</b> (described in worksheet Bat_data)</p><p>Description: Collection of bat data</p><p>Number of fields: 26</p><p>Number of data rows: 891</p><p>Fields: </p><ul><li><b>Locations</b>: Location of trap (Field type: location)</li><li><b>Capture</b>: Bat ID. Start with year, e.g. 140001 is the first bat caught in 2014. Numbers starting with Q follow Dave Bennett's and Victoria Kemp's consecutive capture numbers, numbers starting without a lettter or with "L" only appear in my dataset. (Field type: id)</li><li><b>Band_number</b>: Ring-Number (Field type: id)</li><li><b>R</b>: Recapture (Field type: categorical)</li><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Trap</b>: Trap ID (Field type: id)</li><li><b>Species</b>: Species of bat sampled (Field type: taxa)</li><li><b>Sex</b>: Sex of bat (Field type: categorical trait)</li><li><b>Age</b>: Age of bat (A - adult, J - juvenile) (Field type: categorical)</li><li><b>Rep</b>: Reproductive status (L - lactating, PL - postlacting, RPL - recently postlactating, Pr - Pregnant, NR - non reproductive) (Field type: categorical)</li><li><b>Forearm lenght</b>: Forearm length (Field type: numeric trait)</li><li><b>Weight</b>: Biomass of bat (Field type: numeric trait)</li><li><b>Fed</b>: Feeding status (Field type: categorical trait)</li><li><b>O</b>: Oral swab (Field type: numeric trait)</li><li><b>U</b>: Urine swab (Field type: numeric trait)</li><li><b>F</b>: fecal sample (Field type: numeric trait)</li><li><b>FS</b>: fecal swab (Field type: numeric trait)</li><li><b>BS</b>: blood smear (Field type: numeric trait)</li><li><b>B1</b>: blood sample before ACTH injection (Field type: numeric trait)</li><li><b>T1</b>: temperature before ACTH injection (Field type: numeric trait)</li><li><b>B2</b>: blood sample after ACTH injection (Field type: numeric trait)</li><li><b>T2</b>: temperature after ACTH injection (Field type: numeric trait)</li><li><b>Parasites</b>: Parasites present on bat (Field type: comments)</li><li><b>W</b>: Wing punch (Field type: numeric trait)</li><li><b>Comments</b>: Additional comments (Field type: comments)</li></ul></li><li><p><b>Harp_trap</b> (described in worksheet Harp_trap)</p><p>Description: Harp trap data </p><p>Number of fields: 13</p><p>Number of data rows: 322</p><p>Fields: </p><ul><li><b>Date_harp_open</b>: Date that harp trap was open (Field type: date)</li><li><b>Date_harp_closed</b>: Date the harp was closed (Field type: date)</li><li><b>Location</b>: Location of harp trap transect (Field type: location)</li><li><b>Night_number</b>: night number (Field type: id)</li><li><b>Trap_opened</b>: Time trap as opened (Field type: time)</li><li><b>Trap_closed</b>: Time trap was closed (Field type: time)</li><li><b>Trap_hrs</b>: Numbers of hours the trap was open (Field type: numeric)</li><li><b>Trap_nights</b>: Number of nights traps was open (Field type: numeric)</li><li><b>Rain</b>: Rain conditions (Field type: categorical)</li><li><b>Wind</b>: Wind conditions (Field type: categorical)</li><li><b>Moon</b>: Moon conditions (Field type: categorical)</li><li><b>Other</b>: Other notes about the weather conditions (Field type: comments)</li><li><b>Comments</b>: Other comments (Field type: comments)</li></ul></li></ol><p><b>Traps2014.2015.gpx</b></p><p>Description: Trap locations gps locations</p><p><b>Date range: </b>2014-01-31 to 2015-09-04</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Chiroptera <br> -  -  -  -  -  Rhinolophidae <br> -  -  -  -  -  -  <i>Rhinolophus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus acuminatus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus borneensis</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus sedulus</i> <br> -  -  -  -  -  -  -  <i>Rhinolophus trifoliatus</i> <br> -  -  -  -  -  Nycteridae <br> -  -  -  -  -  -  <i>Nycteris</i> <br> -  -  -  -  -  -  -  <i>Nycteris tragata</i> <br> -  -  -  -  -  Hipposideridae <br> -  -  -  -  -  -  <i>Hipposideros</i> <br> -  -  -  -  -  -  -  <i>Hipposideros cervinus</i> <br> -  -  -  -  -  -  -  <i>Hipposideros diadema</i> <br> -  -  -  -  -  -  -  <i>Hipposideros doriae</i> <br> -  -  -  -  -  -  -  <i>Hipposideros dyacorum</i> <br> -  -  -  -  -  -  -  <i>Hipposideros ridleyi</i> <br> -  -  -  -  -  -  -  <i>Hipposideros doriae</i> <br> -  -  -  -  -  Vespertilionidae <br> -  -  -  -  -  -  <i>Hesperoptenus</i> <br> -  -  -  -  -  -  -  <i>Hesperoptenus blanfordi</i> <br> -  -  -  -  -  -  <i>Kerivoula</i> <br> -  -  -  -  -  -  -  <i>Kerivoula hardwickii</i> <br> -  -  -  -  -  -  -  <i>Kerivoula intermedia</i> <br> -  -  -  -  -  -  -  <i>Kerivoula papillosa</i> <br> -  -  -  -  -  -  -  <i>Kerivoula pellucida</i> <br> -  -  -  -  -  -  <i>Myotis</i> <br> -  -  -  -  -  -  -  <i>Myotis muricola</i> <br> -  -  -  -  -  -  <i>Pipistrellus</i> <br> -  -  -  -  -  -  -  <i>Pipistrellus tenuis</i> <br> -  -  -  -  -  -  <i>Phoniscus</i> <br> -  -  -  -  -  -  -  <i>Phoniscus atrox</i> <br> -  -  -  -  -  -  <i>Murina</i> <br> -  -  -  -  -  -  -  <i>Murina aenea</i> <br> -  -  -  -  -  -  -  <i>Murina cyclotis</i> <br> -  -  -  -  -  -  -  <i>Murina suilla</i> <br> -  -  -  -  -  Pteropodidae <br> -  -  -  -  -  -  <i>Cynopterus</i> <br> -  -  -  -  -  -  -  <i>Cynopterus brachyotis</i> <br> -  -  -  -  -  -  <i>Rousettus</i> <br> -  -  -  -  -  -  -  <i>Rousettus spinalatus</i> <br> -  -  -  -  -  -  <i>Balionycteris</i> <br> -  -  -  -  -  -  -  <i>Balionycteris maculata</i> <br> -  -  -  -  -  Emballonuridae <br> -  -  -  -  -  -  <i>Emballonura</i> <br> -  -  -  -  -  -  -  <i>Emballonura alecto</i> <br> -  -  -  -  -  -  -  <i>Emballonura monticola</i> <br></div><p></p>
The Resilience of Habitable Climates Around Circumbinary Stars: 3D climate model data Part 2
<p>Climate modeling outputs used in the paper, "The Resilience of Habitable Climates Around Circumbinary Stars", to be published JGR-Planets Special Edition on Exoplanets. Files contain 4 Earth years of hourly time cadence outputs of basic climate fields. Hourly time-cadence is needed in order to grasp the temporal variations of circumbinaries. </p>
The Resilience of Tropical Forest Invertebrates to Microclimate Change
<b>Description: </b><p>This dataset examines the thermal physiology of ants accross the SAFE project, with the goal of understanding how changing microclimates affect communities of invertebrates in disturbed landscapes. Tropical invertebrates are expected to already live close to their upper thermal tolerances, and so the rapid changes to microclimate brought about by logging may be a powerful determinant of the emergent communites in disturbed forests. The worksheet contains the upper critical temperature (CTmax) of individual ants identified to genus level. Ants were collected from the ground or soil layer unless specified as arboreal. CTmax was determined using a ramping procedure whereby temeprature was increased from 32 degrees upwards at a rate of 0.2 degrees per minuted until individuals lost motor control. Ants were found by searching opportunistically throughout entire blocks, therefore for locations we have simply inputted one large fractal order from each sampling block used.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/135"><b>The Resilience of Tropical Forest Invertebrates to Microclimate Change</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=4297673">here</a></p><p><b>Files: </b>This consists of 1 file: MJWB_SAFE_CTmax_Upload.xlsx</p><p><b>MJWB_SAFE_CTmax_Upload.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Ant.CTmax</b> (described in worksheet Ant.CTmax)</p><p>Description: The worksheet contains the upper critical temperature (CTmax in degrees centigrade) of individual ants identified to genus level. Ants were collected from the ground or soil layer unless specified as arboreal. CTmax was determined using a ramping procedure whereby temeprature was increased from 32 degrees upwards at a rate of 0.2 degrees per minuted until individuals lost motor control. Ants were found by searching opportunistically throughout entire blocks, therefore for locations we have simply inputted one large fractal order from each sampling block used.</p><p>Number of fields: 4</p><p>Number of data rows: 2359</p><p>Fields: </p><ul><li><b>CTmax</b>: Critical upper thermal tolerance in degrees centigrade (Field type: numeric)</li><li><b>Genus</b>: Genus name of ant (Field type: taxa)</li><li><b>Location</b>: SAFE Project sampling block (Field type: location)</li><li><b>Arboreal</b>: Comment on if the ant was sampled from the ground or arboreal layer (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2015-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.6380 to 4.7412</p><p><b>Longitudinal extent: </b>116.9568 to 117.6245</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Hymenoptera <br> -  -  -  -  -  Formicidae <br> -  -  -  -  -  -  <i>Acanthomyrmex</i> <br> -  -  -  -  -  -  <i>Aenictus</i> <br> -  -  -  -  -  -  <i>Bothriomyrmex</i> <br> -  -  -  -  -  -  <i>Camponotus</i> <br> -  -  -  -  -  -  <i>Cardiocondyla</i> <br> -  -  -  -  -  -  <i>Carebara</i> <br> -  -  -  -  -  -  <i>Cataulacus</i> <br> -  -  -  -  -  -  <i>Centromyrmex</i> <br> -  -  -  -  -  -  <i>Crematogaster</i> <br> -  -  -  -  -  -  <i>Cryptopone</i> <br> -  -  -  -  -  -  <i>Diacamma</i> <br> -  -  -  -  -  -  <i>Dolichoderus</i> <br> -  -  -  -  -  -  <i>Echinopla</i> <br> -  -  -  -  -  -  <i>Euprenolepis</i> <br> -  -  -  -  -  -  <i>Hypoponera</i> <br> -  -  -  -  -  -  <i>Iridomyrmex</i> <br> -  -  -  -  -  -  <i>Lepisiota</i> <br> -  -  -  -  -  -  <i>Leptogenys</i> <br> -  -  -  -  -  -  <i>Lophomyrmex</i> <br> -  -  -  -  -  -  <i>Lordomyrma</i> <br> -  -  -  -  -  -  <i>Monomorium</i> <br> -  -  -  -  -  -  <i>Myrmecina</i> <br> -  -  -  -  -  -  <i>Myrmicaria</i> <br> -  -  -  -  -  -  <i>Nylanderia</i> <br> -  -  -  -  -  -  <i>Ochetellus</i> <br> -  -  -  -  -  -  <i>Odontomachus</i> <br> -  -  -  -  -  -  <i>Odontoponera</i> <br> -  -  -  -  -  -  <i>Oecophylla</i> <br> -  -  -  -  -  -  <i>Pachycondyla</i> <br> -  -  -  -  -  -  <i>Paraparatrechina</i> <br> -  -  -  -  -  -  <i>Paratopula</i> <br> -  -  -  -  -  -  <i>Paratrechina</i> <br> -  -  -  -  -  -  <i>Pheidole</i> <br> -  -  -  -  -  -  <i>Pheidologeton</i> <br> -  -  -  -  -  -  <i>Philidris</i> <br> -  -  -  -  -  -  <i>Plagiolepis</i> <br> -  -  -  -  -  -  <i>Polyrhachis</i> <br> -  -  -  -  -  -  <i>Ponera</i> <br> -  -  -  -  -  -  <i>Prenolepis</i> <br> -  -  -  -  -  -  <i>Prionopelta</i> <br> -  -  -  -  -  -  <i>Pristomyrmex</i> <br> -  -  -  -  -  -  <i>Pseudolasius</i> <br> -  -  -  -  -  -  <i>Rhoptromyrmex</i> <br> -  -  -  -  -  -  <i>Rhytidoponera</i> <br> -  -  -  -  -  -  <i>Tapinoma</i> <br> -  -  -  -  -  -  <i>Technomyrmex</i> <br> -  -  -  -  -  -  <i>Tetramorium</i> <br> -  -  -  -  -  -  <i>Tetraponera</i> <br> -  -  -  -  -  -  <i>Vollenhovia</i> <br></div><p></p>
Measures of Freight Network Resiliency During the Covid-19 Pandemic
<p>Recent headlines depict significant shifts in operations within the freight community in particular, e.g., HOS laws suspended at a national level for the first time in 82 years1; national carriers shifting operations completely to grocery supply chains2; fleet operators laying off employees in response to manufacturing closures3. As a result of the current COVID-19 pandemic, there is a great need to capture freight movement data (not otherwise collected) to measure the effects of the COVID-19 response and recovery practices on freight network resiliency. In this project, we consider an expanded definition of the freight network, beyond roads and warehouses, to include truck drivers and driver support systems. </p> <p>Driver support systems include physical infrastructure like public and private rest stops as well as operational protections like Hours of Service (HOS). COVID-19 responses by public agencies and private citizens have affected drivers and driver support systems by three mechanisms. First, increased demand for medical supplies, food and packaged goods creates a need for more trucks and drivers, and the increased need for quick shipments promotes an environment in which speeding and unsafe driving practices may prevail. Second, with HOS restrictions lifted by the National Highway Transportation Safety Administration (NHTSA) driver fatigue may occur at greater frequency leading to unsafe driving conditions and higher likelihood of accidents. Third, the effects of social distancing mandates can lead to closures of critical, but oft forgotten, freight infrastructure like rest areas and truck stops, leaving drivers without necessary rest opportunities. While any single mechanism has detrimental effects on driver health and safety, the economy, and national recovery efforts, when combined, the system can be pushed to failure. Pandemic responses have only exacerbated critical industry issues like driver shortages, lack of available parking, and HOS compliance issues stemming from electronic logbooks. The purpose of this work was to develop and implement a driver health and safety survey during the pandemic. </p>
Public Defense of Doctoral Thesis: "Improving the Resilience of the Constrained Internet of Things" (raw video)
<p>This is the raw video footage of Renzo E. Navas' public PhD Thesis Defense: "Improving the Resilience of the Constrained Internet of Things". Original date: Wednesday 9th of December 2020.</p>
Reproducibility Package for TACAS'21 Paper Resilient Capacity-Aware Routing
<p>This package contains all the necessary information for the reproduction of the experimental results in the paper "Resilient Capacity-Aware Routing" accepted for TACAS'21. In particular we provide all the python scripts that we used, their dependencies and the shell scripts for running the experiments or its subset.</p>
Synchronization of seasonal acclimatization and short-term heat hardening improves physiological resilience in a changing climate
<p><b>Summary</b></p> <p>1. Animal survival and species distribution in the face of global warming and increasing occurrences of heatwave largely depend on how heat tolerance shifts with plastic responses at different spatiotemporal scales, including long-term acclimation/acclimatization and short-term heat hardening. However, knowledge about the interaction of these plastic responses is still unclear.</p> <p>2. To understand how plastic responses at different timescales work together to adjust heat tolerance of organisms, we examined the effect of heat hardening on the upper thermal limits of an intertidal mudflat bivalve, the razor clam <i>Sinonovacula constricta</i>, for different seasons by using heart rate as a proxy.</p> <p>3. We observed a stronger heat hardening response of<i> S. constricta</i> in warm seasons, implying that heat hardening worked synchronously with seasonal acclimatization to increase resistance of the clams to high temperatures in warm seasons. In warm seasons, heat hardening increased heat tolerance by 2-4<sup>°</sup>C and showed a 24-h temporal dependence, suggesting an adaptation to the diel fluctuation of thermal regimes in summer.</p> <p>4. Furthermore, thermal stress resembling seasonal maximum environmental temperature induced stronger heat hardening effects, indicating that heat hardening is an essential plastic response to extreme hot weather, complementing seasonal acclimatization.</p> <p>5. Our results suggest that high temperature risk can be alleviated jointly by seasonal acclimatization and heat hardening, and emphasize the importance of considering physiological plasticity on both long-term and short-term temporal scales in evaluating and forecasting vulnerability of organisms to climate change.</p>
Data from: Where and how to restore in a changing world: a demographic-based assessment of resilience
Managers are increasingly looking to apply concepts of resilience to better anticipate and understand conservation and restoration in a changing environment. In this study, we explore how information on demography (recruitment, growth and survival) and competitive effects in different environments and with different starting species abundances can be used to better understand resilience. We use observational and experimental data to better understand dynamics between native Stipa pulchra and exotic Avena barbata and fatua, grasses characteristic of native and invaded grasslands in California, at three different levels of nitrogen (N) representative of a range of pollution via atmospheric deposition. A modelling framework that incorporates this information on demography and competition allows us to forecast dynamics over time. Our results showed that resilience of native grasslands depends on N inputs, where natural recovery should be possible at low N levels whereas native persistence would be difficult at high N levels. Hysteresis was evident at moderate N levels, where the starting conditions mattered. Synthesis and applications. The resilience of both invaded and native grasslands is influenced by nitrogen inputs. Our modelling approach gives direction about how best to allocate limited management resources as baselines shift: where natural recovery is possible, where best to allocate active restoration efforts, and where native remnants may be most vulnerable.
Data from: Resilience of seed production to a severe El Niño‐induced drought across functional groups and dispersal types
More frequent and severe El Niño Southern Oscillations (ENSO) are causing episodic periods of decreased rainfall. Although the effects of these ENSO-induced droughts on tree growth and mortality have been well studied, the impacts on other demographic rates such as reproduction are less well known. We use a four-year seed rain dataset encompassing the most severe ENSO-induced drought in more than 30 years to assess the resilience (i.e. resistance and recovery) of the seed composition and abundance of three forest types in a tropical dry forest. We found that forest types showed distinct differences in the timing, duration and intensity of drought during the ENSO event, which likely mediated seed composition shifts and resilience. Drought-deciduous species were particularly sensitive to the drought with overall poor resilience of seed production, whereby seed abundance of this functional group failed to recover to pre-drought levels even two years after the drought. Liana and wind-dispersed species were able to maintain seed production both during and after drought suggesting that ENSO events promote early successional species or species with a colonization strategy. Combined, these results suggest that ENSO-induced drought mediates the establishment of functional groups and dispersal types suited for early successional conditions with more open canopies and reduced competition among plants. The effects of the ENSO-induced drought on seed composition and abundance were still evident two years after the event suggesting the recovery of seed production requires multiple years that may lead to shifts in forest composition and structure in the long-term, with potential consequences for higher trophic levels like frugivores.
Data from: Achromatic plumage brightness predicts stress resilience and social interactions in tree swallows (Tachycineta bicolor)
Theory suggests that signal honesty may be maintained by differential costs for high and low quality individuals. For signals that mediate social interactions, costs can arise from the way that a signal changes the subsequent social environment via receiver responses. These receiver-dependent costs may be linked with individual quality through variation in resilience to environmental and social stress. Here, we imposed stressful conditions on female tree swallows (Tachycineta bicolor) by attaching groups of feathers during incubation to decrease flight efficiency and maneuverability. We simultaneously monitored social interactions using an RFID network that allowed us to track the identity of every individual that visited each nest for the entire season. Prior to treatments, plumage coloration was correlated with baseline and stress-induced corticosterone. Relative to controls, experimentally challenged females were more likely to abandon their nest during incubation. Overall, females with brighter white breasts were less likely to abandon, but this pattern was only significant under stressful conditions. In addition to being more resilient, brighter females received more unique visitors at their nest box and tended to make more visits to other active nests. In contrast, dorsal coloration did not reliably predict abandonment or social interactions. Taken together, our results suggest that females differ in their resilience to stress and that these differences are signaled by plumage brightness, which is in turn correlated with the frequency of social interactions. While we do not document direct costs of social interaction, our results are consistent with models of signal honesty based on receiver-dependent costs.
Data from: Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans
Coral reefs have great biological and socioeconomic value, but are threatened by ocean acidification, climate change, and local human impacts. The capacity for corals to adapt or acclimatise to novel environmental conditions is unknown but fundamental to projected reef futures. The coral reefs of Kāne'ohe Bay, Hawai'i were devastated by anthropogenic insults from the 1930s-1970s. These reefs experience naturally reduced pH and elevated temperature relative to many other Hawaiian reefs which are not expected to face similar conditions for decades. Despite catastrophic loss in coral cover due to human disturbance, these reefs recovered under low pH and high temperature within 20 years after sewage input was diverted. We compare the pH and temperature tolerances of three dominant Hawaiian coral species from within Kāne'ohe Bay to conspecifics from a nearby control site and show that corals from Kāne'ohe are far more resistant to acidification and warming. These results show that corals can have different pH and temperature tolerances among habitats and understanding the mechanisms by which coral cover rebounded within two decades under projected future ocean conditions will be critical to management. Together these results indicate that reducing human stressors offers hope for reef resilience and effective conservation over coming decades.
Data from: How neighbourhood interactions control the temporal stability and resilience to drought of trees in mountain forests
<p>1. Over the coming decades, the predicted increase in frequency and intensity of extreme events such as droughts is likely to have a strong effect on forest functioning. Recent studies have shown that species mixing may buffer the temporal variability of productivity. However, most studies have focused on temporal stability of productivity, while species mixing may also affect forest resilience to extreme events. Our understanding of mechanisms underlying species mixing effects on forest stability and resilience remains limited because we ignore how changes from intraspecific to interspecific interactions in the neighbourhood of a given tree might affect its stability and resilience to extreme drought (i.e. response during and after this drought). This is crucial to better understand forests' response to climate change and how diversity may help maintain forest functioning.</p> <p>2. Here we analysed how local intra‐ or interspecific interactions may affect the temporal stability and resilience to drought of individual trees in French mountain<br> forests, using basal area increment data over the previous 20 years for Fagus sylvatica, Abies alba and Quercus pubescens. We analysed the effect of interspecific<br> competition on (a) the temporal stability and (b) the resilience to drought (resistance and recovery) of individual tree radial growth.</p> <p>3. We found no significant interspecific competition effect on temporal stability, but species‐specific effects on tree growth resilience to drought. There was a positive<br> effect of heterospecific proportion on the drought resilience of Q. pubescens, a negative effect for A. alba and no effect for F. sylvatica. These differences may be<br> related to interspecific differences in water use or rooting depth.</p> <p>4. Synthesis: In this study, we showed that stand composition influences individual tree growth resilience to drought, but this effect varied depending on the species<br> and its physiological responses. Our study also highlighted that a lack of biodiversity effect on long‐term stability might hide important effects on short‐term<br> resilience to extreme climatic events. This may have important implications in the face of climate change.</p>
Data from: Resilience of lake biogeochemistry to boreal-forest wildfires during the late Holocene
Novel fire regimes are expected in many boreal regions, and it is unclear how biogeochemical cycles will respond. We leverage fire and vegetation records from a highly flammable ecoregion in Alaska and present new lake-sediment analyses to examine biogeochemical responses to fire over the past 5300 years. No significant difference exists in δ13C, %C, %N, C:N, or magnetic susceptibility between pre-fire, post-fire, and fire samples. However, δ15N is related to the timing relative to fire (Χ2=19.73, p<0.0001), with higher values for fire-decade samples (3.2±0.3‰) than pre-fire (2.4±0.2‰) and post-fire (2.2±0.1‰) samples. Sediment δ15N increased gradually from 1.8±0.6‰ to 3.2±0.2‰ over the late Holocene, probably as a result of terrestrial-ecosystem development. Elevated δ15N in fire decades likely reflects enhanced terrestrial nitrification and/or deeper permafrost-thaw depths immediately following fire. Similar δ15N values before and after fire decades suggest that N cycling in this lowland-boreal watershed was resilient to fire disturbance. However, this resilience may diminish as boreal ecosystems approach climate-driven thresholds of vegetation structure, permafrost thaw, and fire.
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