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Data from: Descriptive multi-agent epidemiology via molecular screening on Atlantic salmon farms in the northeast Pacific Ocean
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Isotopic signatures of methane emissions from dairy farms in California's San Joaquin Valley
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Ukulinga Farms, South Africa: Plant Species List
The distribution, structure and function of mesic savanna grasslands are strongly driven by fire regimes, grazing by large herbivores, and their interactions. This research addresses a general question about our understanding of savanna grasslands globally: Is our knowledge of fire and grazing sufficiently general to enable us to make accurate predictions of how these ecosystems will respond to changes in these drivers over time? Some evidence suggests that fire and grazing influence savanna grassland structure and function differently in South Africa (SA) compared to North America (NA). These differences have been attributed to the contingent factors of greater biome age, longer evolutionary history with fire and grazing, reduced soil fertility, and greater diversity of plants and large herbivores in SA. An alternative hypothesis is that differences in methods and approaches used to study these systems have led to differing perspectives on the role of these drivers. If the impacts of shared ecosystem drivers truly differ between NA and SA, this calls into question the generality of our understanding of these ecosystems and our ability to forecast how changes in key drivers will affect savanna grasslands globally. Since 2006, an explicitly comparative research program has been conducted to determine the degree of convergence in ecosystem (productivity, N and C cycling) and plant community (composition, diversity, dynamics) responses to fire and grazing in SA and NA. Thus far, initial support has been found for convergence at the ecosystem level and divergence at the community level in response to alterations in both fire regimes and grazing. However, there have also been two unexpected findings (1) the ways in which fire and grazing interact differed between NA and SA, and (2) the rate of change in communities when grazers were removed was much greater in NA than in SA. These unexpected findings raise a number of important new questions: (Q1) Will exclusion of grazi
Hull Springs Farm Weather Data 2019-11-11 to 2019-12-11
<p>General Metadata for Hull Springs Farm Atmospheric Sampling Station</p> <p> </p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HSF_weather_YYYY-MM-DD_metadata.txt </code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p> </p> <p>File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p> </p> <p>File Modified</p> <p> </p> <p>Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p> </p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes </code></pre> <p> </p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N) </code></pre>
Hull Springs Farm Weather Data 2019-12-27 to 2020-01-31
<p>General Metadata for Hull Springs Farm Atmospheric Sampling Station</p> <p> </p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>HSF_weather_YYYY-MM-DD_metadata.txt </code></pre> <p>Where YYYY-MM-DD is the date that the sampling period ended.</p> <p> </p> <p>File Created</p> <ul> <li>2019-10-27 by KF</li> </ul> <p> </p> <p>File Modified</p> <p> </p> <p>Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at <a href="https://zenodo.org/communities/leo/">https://zenodo.org/communities/leo/</a></p> <p> </p> <p>Station Specifics</p> <p>The specific at each site are:</p> <pre><code>* Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300 * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300 * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor * Data Collection - Campbell Scientific CR200 Data Logger * The sensors are sampled every 15 minutes </code></pre> <p> </p> <p>Measurement Parameters, units, and Variable Names</p> <pre><code>* DATE - the date that the record was collected (YYYY-MM-DD) * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS) * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Avg - The average battery voltage (Volts) * BattV - The battery voltage at the time of the sampling (Volts) * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg) * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg) * BP_mmHg - The barometric pressure at the time of the sampling (mmHg) * Rain_mm_Tot - The total rainfall during the sampling interval (mm) * AirTC_Avg - The average air temperature during the sampling interval (dC) * AirTC_Std - The standard deviation of the average air temperature (dC) * AirTC - the air temperature at the time of the sampling (dC) * RH - the relative humidity at the time of the sampling (%) * RH_Min - the minimum relative humidity recorded (%) * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RH_Max - the maximum relative humidity recorded (%) * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data. * BattV_Min - the minimum battery voltage (Volts) * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2) * SlrkW - the light flux density at the time of the sampling (kW/m^2) * SlrMJ_Tot - the total light flux (MJ/m^2) * WS_ms_Avg - the average wind speed during the sampling interval (m/s) * WS_ms_Std - the standard deviation of the average wind speed (m/s) * WS_ms - the wind speed at the time of the sampling event (m/s) * WindDir - the wind direction at the time of the sampling event (degrees from true N) * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s) * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N) * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N) </code></pre>
Supplementary material 1 from: Auliya M, Hofmann S, Segniagbeto GH, Assou D, Ronfot D, Astrin JJ, Forat S, Koffivi K. Ketoh G, D'Cruze N (2020) The first genetic assessment of wild and farmed ball pythons (Reptilia, Serpentes, Pythonidae) in southern Togo. Nature Conservation 38: 37-59. https://doi.org/10.3897/natureconservation.38.49478
: Data type: molecular data
Dataset for "Effect of visual environmental enrichment on the welfare and development of juvenile farmed rainbow trout (Oncorhynchus mykiss)"
<p>This is the dataset used for the inaugural dissertatoin thesis "Effect of Visual Environmental Enrichment on the Welfare and Development of Juvenile Farmed Rainbow Trout (Oncorhynchus mykiss)".</p> <p>Submittetd by Melchior Isler.</p>
Model Outputs for Lobster Farm location Statistics
<p>Results of the Delft3D model for summer and winter scenarios to look at how the current velocities vary between the different areas of the lobster farm, results compared using ANOVA and post hoc statistical tests.</p>
Supplementary material 1 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Specimen identifications
Figure 5 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 5 AEchinanthera undulataBErythrolapmprus aesculapiiCE. miliarisDHelicops carinicaudusEImantodes cenchoa (photo: Ricardo J. Sawaya) FOxyrhopus clathratusGSiphlophis pulcherHSordellina punctata.
Figure 6 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 6 ATaeniophallus bilineatusBT. persimilisCThamnodynastes nattereriDTomodon dorsatusETropidodryas serraFXenodon neuwiediiGMicrurus corallinusHTropidophis paucisquamis.
Figure 4 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 4 AClelia plumbeaBDipsas albifronsCD. alternans (photo: Arthur Abegg) DD. indica petersiED. neuwiediFD. variegataGEchinanthera cephalostriataHE. cyanopleura (photograph Marcos Di-Bernardo).
Figure 2 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 2 Satellite images (source: Google Earth) A the region where the study area (light orange rectangle) is located in the southern São Paulo State and the group of continuous protected areas (in light green) that encompasses most of the primary forests of this region (AR = Parque Estadual Turístico do Alto Ribeira; CB = Parque Estadual Carlos Botelho; IV = Parque Estadual Intervales; XI = Estação Ecológica de Xituê) as well as the location of the Etá Farm region (rectangle) B the region of the Etá Farm, Sete Barras Municipality where 1 indicates the Etá Farm administrative buildings, 2–4 indicate areas of forest, 5 and 6 indicate peach palm plantations; 7 indicates abandoned banana plantation, and 8 indicates an agricultural settlement.
Figure 3 from: Fiorillo BF, Silva BR, Menezes FA, Marques OAV, Martins M (2020) Composition and Natural History of Snakes from Etá Farm region, Sete Barras, south-eastern Brazil. ZooKeys 931: 115-153. https://doi.org/10.3897/zookeys.931.46882
Figure 3 ACorallus cropaniiBC. hortulanusCChironius bicarinatus (photo: Arthur Abegg) DC. exoletusEC. foveatusFC. fuscusGC. laevicollisHSpilotes pullatus.
Data from: Factors affecting carcass detection at wind farms using dogs and human searchers
<p>1. The use of detection dogs to effectively monitor bird and bat fatalities at wind farms is becoming increasingly popular. All studies to date agree that dogs outperform human searchers at finding bird and bat carcasses around wind turbines; however, it remains unclear how particular conditions during the search may influence carcass detection.</p> <p>2. We investigate the effect of carcass size, habitat characteristics and weather conditions on carcass detection probability, for both dogs and humans, using data from the monitoring program of a wind farm in Spain.</p> <p>3. A generalized linear model reveals a high performance of dogs (~80% detection probability), with no clear influence of any of the variables analysed. Humans, on the contrary, were markedly affected by the size of the carcass and to some extent, by the vegetation structure. Humans performed poorly at detecting small carcasses (~20% detection probability), more so in dense vegetation.</p> <p>4. Synthesis and applications. Our results provide evidence that dogs perform at a high level under a wide range of environmental conditions. They are particularly well-suited for the monitoring of fatalities of small, rare or inconspicuous species in cluttered environments. Humans, by contrast, are very poor at detecting all but the largest carcasses.16-Jun-2020</p>
Ecological and social outcomes of urbanization in farming systems
<p>This dataset contains the data extracted for a systematic review on ecological and social outcomes of urbanization in farming systems. It is based on the papers selected after scooping and screeing done in February 2019.</p> <p>The file also contains the four factors- continents, national GDP per-capita, city/town population size and dominant farming systems of the cases which potentially influenced those ecological and social outcomes.</p>
Data from: Can variation in standard metabolic rate explain context-dependent performance of farmed salmon offspring?
Escaped farmed Atlantic salmon interbreed with wild Atlantic salmon, leaving offspring that often have lower success in nature than pure wild salmon. On top of this, presence of farmed salmon descendants can impair production of wild type recruits. We hypothesize that both these effects connect with farmed salmon having acquired higher standard metabolic rates (SMR, the energetic cost of self-maintenance) during domestication. Furthermore, fitness related advantages of phenotypic traits associated with both high SMR and farmed salmon (e.g. social dominance) depend on environmental conditions, such as food availability. We hypothesize that farmed offspring have an advantage at high food availability due to e.g. dominance behavior but suffer increased risks of starvation when food is scarce because this behavior is energy-demanding. To test these hypotheses, we first compare embryo SMR of pure farmed, farmed-wild hybrids and pure wild offspring. Next, we test early life performance (in terms of survival and growth) of hybrids relative to that of their wild half-siblings, as well as their competitive abilities, in semi-natural conditions of high and low food availability. Finally, we test how SMR affects early life performance at high and low food availability. We find inconclusive support for the hypothesis that domestication has induced increased SMR. Further, wild and hybrid juveniles had similar survival and growth in the semi-natural streams. Yet, the presence of hybrids led to decreased survival of their wild half-siblings. Contrary to our hypothesis about context-dependency, these effects were not modified by food availability. However, wild juveniles with high SMR had decreased survival when food was scarce, but there was no such effect at high food availability. This study provides further proof that farmed salmon introgression may compromise the viability of wild salmon populations. We cannot, however, conclude that this is connected to alterations in the metabolic phenotype of farmed salmon.
Data from: Cohabitation with farm animals rather than breeding effort increases the infection with feather-associated bacteria in the barn swallow Hirundo rustica
Feather-associated bacteria are widespread inhabitants of avian plumage. However, the determinants of the between-individual variation in plumage bacterial loads are less well understood. Infection intensities can be determined by ecological factors, such as breeding habitat, and can be actively regulated by hosts via preening. Preening, yet, is a resource intensive activity, and thus might be traded-off against reproductive investment in breeding birds. Here, we studied barn swallows Hirundo rustica to assess the bacterial cost of reproduction in relation to nesting site micro-habitats. Barn swallows prefer to breed in the company of large-sized farm animals, although the presence of mammalian livestock in barns assures a warm and humid micro-climate that favours bacterial proliferation. Thus, we experimentally manipulated brood sizes of birds breeding in barns with, or without, farm animals and measured total cultivable bacteria (TCB) and feather-degrading bacteria (FDB) from the plumage. We found that the abundance of feather-associated bacteria (i.e. both TCB and FDB) in females, but not males, breeding in barns with livestock were significantly higher than in conspecifics breeding in empty barns. Plumage bacterial loads, however, were not affected by brood size manipulations in either sex. In addition, we report a negative relationship between both TCB and FDB and hatching date in females, and several sex and seasonal differences in plumage bacterial abundances. Our study is the first to show that breeding micro-habitat (i.e. livestock co-tenancy) has consequences for the abundance of feather-associated bacteria.
Data from: Long term on-farm participatory maize breeding by stratified mass selection retains molecular diversity while improving agronomic performance
Modern maize breeding programs gave rise to genetically uniform varieties that can affect maize's capacity to cope with increasing climate unpredictability. Maize populations, genetically more heterogeneous, can evolve and better adapt to a broader range of edaphic-climatic conditions. These populations usually suffer from low yields; it is therefore desirable to improve their agronomic performance while maintaining their valuable diversity levels. With this objective, a long-term participatory breeding/on-farm conservation program was established in Portugal. In this program maize populations were subject to stratified mass selection. The current work aims to estimate the effect of on-farm stratified mass selection on the agronomic performance, quality, and molecular diversity of two historical maize populations. Multi-location field trials, comparing the initial populations with the derived selection cycles, showed that this selection methodology led to agronomic improvement for one of the populations. The molecular diversity analysis, using microsatellites, revealed that overall genetic diversity in both populations was maintained throughout selection. The comparison of quality parameters between the initial populations and the derived selection cycles was done using kernel from a common garden experiment. This analysis showed that the majority of the quality traits evaluated progressed erratically over time. In conclusion, this breeding approach, through simple and low-cost methodologies, proved to be an alternative strategy for genetic resources' on-farm conservation.
Data from: Bird services and disservices to strawberry farming in Californian agricultural landscapes
1.Bird conservation in agricultural settings can be controversial. While some bird species damage some crops, others suppress insect pests. Few studies have simultaneously compared bird services and disservices to assess their net impact. 2.Using an exclusion experiment in six California strawberry farms, we show that bird suppression of berry damage by insect pests (about 3.8% of berries) is similar in magnitude to the damage birds inflict on strawberries (about 3.2% of berries). 3.Across 27 farms, we found that bird species richness and the relative abundance of insectivorous birds increased, while the relative abundance of strawberry‐eating birds and bird damage decreased on farms with more semi‐natural land cover in the surrounding landscapes (1000m radius). 4.Relative to homogeneous farms, those that implemented diversification practices, such as hedgerows, flower strips, or increased crop diversity, had greater bird species richness, total relative abundance, insectivore abundance, and strawberry‐eating bird abundance. 5.Synthesis and applications. Conserving semi‐natural land cover in the surrounding landscape benefits bird species richness locally and aids farmers through reduced abundance of strawberry‐eating birds and bird damage. These results highlight the need to consider both the services and disservices of birds when making management decisions.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.