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Dataset results
18 results for “farm connectivity”
Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region
<p><em>Context</em></p> <p>Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland-dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.</p> <p><em>Objectives</em></p> <p>This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (<em>Rana pipiens</em>) to: (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.</p> <p><em>Methods</em></p> <p>Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.</p> <p><em>Results</em></p> <p>STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.</p> <p><em>Conclusions</em></p> <p>While the <em>R. pipiens</em> populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.</p>
Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region
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Data from: Population genomics of the introduced and cultivated Pacific kelp Undaria pinnatifida: marinas — not farms — drive regional connectivity and establishment in natural rocky reefs
Ports and farms are well-known primary introduction hotspots for marine non-indigenous species (NIS). The extent to which these anthropogenic habitats are sustainable sources of propagules and influence the evolution of NIS in natural habitats was examined in the edible seaweed Undaria pinnatifida, native to Asia and introduced to Europe in the 1970s. Following its deliberate introduction 40 years ago along the French coast of the English Channel, this kelp is found in three contrasting habitat types: farms, marinas, and natural rocky reefs. In light of the continuous spread of this NIS, it is imperative to better understand the processes behind its sustainable establishment in the wild. In addition, developing effective management plans to curtail the spread of U. pinnatifida requires determining how the three types of populations interact with one another. In addition to an analysis using microsatellites, we developed, for the first time in a kelp, a ddRAD-sequencing technique to genotype 738 individuals sampled in 11 rocky reefs, 12 marinas, and 2 farms located along ca. 1000 km of coastline. As expected, the RAD-seq panel showed more power than the microsatellite panel for identifying fine-grained patterns. However, both panels demonstrated habitat-specific properties of the study populations. In particular, farms displayed very low genetic diversity and no inbreeding conversely to populations in marinas and natural rocky reefs. In addition, strong, but chaotic regional genetic structure, was revealed, consistent with human-mediated dispersal (e.g., leisure boating). We also uncovered a tight relationship between populations in rocky reefs and those in nearby marinas, but not with nearby farms, suggesting spill-over from marinas into the wild. Finally, a temporal survey (20 generations) showed that wild populations are self-sustaining, without local adaptation to any of the three habitats. These findings highlight that limiting the spread of U. pinnatifida requires management policies that also target marinas.
Data from: Population genomics of the introduced and cultivated Pacific kelp Undaria pinnatifida: marinas — not farms — drive regional connectivity and establishment in natural rocky reefs
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Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-05
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, WAN quality, latency captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of May 2024.</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-05
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, WAN quality, latency captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of May 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern Region farm 2024-05
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, WAN quality, latency captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of May 2024.</p>
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-06
<p>A collection of connectivity performance metrics, including data usage, location mapping, uptime/downtime, wan quality, latency, captured on a device-basis at each of 3 farms participation in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of June 2024</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-06
<p>A collection of connectivity performance metrics, including data usage, location mapping, uptime/downtime, wan quality, latency, captured on a device-basis at each of 3 farms participation in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of June 2024</p>
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-07
<p>A collection of connectivity performance metrics (including data usage, location mapping, uptime/downtime, wan quality) captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of July 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern Region farm 2024-07
<p>A collection of connectivity performance metrics (including data usage, location mapping, uptime/downtime, wan quality) captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of July 2024</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-07
<p>A collection of connectivity performance metrics (including data usage, location mapping, uptime/downtime, wan quality) captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of July 2024.</p>
Connectivity performance metrics Grain Automate GRDC Western region farm 2024-08
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality, captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of August 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern region farm 2024-08
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality captured on a device-basis at each of 3 farms participatiing in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of August 2024.</p>
Connectivity performance metrics Grain Automate GRDC Southern region farm 2024-08
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality, captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer to peer learning through the development of autonomous working farms, for the month of August 2024</p>
Connectivity performance metrics Grain Automate GRDC Western Region farm 2024-09
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the September 2024.</p>
Connectivity performance metrics Grain Automate GRDC Northern Region Farm 2024-09
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality, captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms for the month of September 2024.</p>
Connectivity performance metrics Grain Automate GRDC Southern Region farm 2024-09
<p>A collection of connectivity performance metrics including data usage, location mapping, uptime/downtime, wan quality captured on a device-basis at each of 3 farms participating in the Grain Automate Project 1 - peer-to-peer learning through the development of autonomous working farms, for the month of September 2024.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.