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194 results for “Agricultural landscapes”
North Temperate Lakes LTER: Patterns of Soil Phosphorus Across an Urbanizing Agricultural Landscape 2000 - 2001
Understanding the magnitude and location of soil phosphorus (P) accumulation in watersheds is a critical step toward managing runoff of this pollutant to aquatic ecosystems. Here, we examined the usefulness of urban-rural gradients (URGs), an emerging paradigm in urban ecology, for predicting soil P concentrations across a rapidly urbanizing agricultural watershed in southern Wisconsin. We compared several measures of an urban-rural gradient to predictors of soil P such as soil type, slope, topography, land use, land cover, and fertilizer and manure use. Most of the factors that were expected to drive differences in soil P concentrations were not found to be good predictors of soil P; while there were several significant relationships, most explained only a small proportion of the variation. There was a significant relationship between soil P concentration and each of the urban-rural gradients, but these relationships explained only a small amount of the variation in soil P concentrations. Soil P concentration, unlike some other ecosystem properties, is not well predicted by urban-rural gradients Additional Chemical Analyses: These additional analyses were done to provide comparisons to Bray-1 P. Specifically, we wanted to know whether, in Dane County, there was a consistent relationship between total P and Bray-1 P. For sample sites on private property, specific site location information, such as GPS coordinates, is not included in these datasets. If you have a need for this information, please get in touch with the contact person listed above Number of sites: 334; 20 of these sites with additional chem analyses
Data from: Spatial and host-related variation in prevalence and population density of wheat curl mite (Aceria tosichella) cryptic genotypes in agricultural landscapes
<p><strong>Filename: coord.csv</strong></p> <p>Names of the sampling locations and their geographic coordinates.</p> <ol> <li>Name - sampling locality identifier</li> <li>Lat - latitude</li> <li>Long - longitude</li> </ol> <p> </p> <p><strong>Filename: lineages.csv</strong></p> <ol> <li>id.sample - sample identifier</li> <li>host - host species (Arrela=<em>Arrhenantherum elatius</em>, Avesat=<em>Avena sativa</em>, Broine=<em>Bromus inermis</em>, Elyres=<em>Elymus repens</em>, Horvul=<em>Hordeum vulgaris</em>, Seccer=<em>Secale cereale</em>, Triaes=<em>Triticum aestivum</em>, Tririm=<em>Triticale rimpaui</em></li> <li>x, y - geodetic coordinates</li> <li>stems - no. of stems in a sample</li> <li>leaves - no. of leaves in a sample</li> <li>MT.01 to MT.27 - no. of mites belonging to each genetic lineage</li> </ol>
[Dataset] Plant–pollinator in a highly intensive agricultural landscape LTSER Zone Atelier Plaine & Val de Sèvre
<p>We built bipartite networks formed by pollinators and the flowers they forage on, using data collected in the Long Term Socio-Ecological Research site "Zone Atelier Plaine & Val de Sèvre" (Bretagnolle et al. 2028). We compiled a six-year monitoring dataset of plant–pollinator interactions, sampling by sweep-nets along transects in the main crop types of this intensive agricultural plain. </p> <p>The dataset contained all the "pollinator-plant" pair observed in each crop samples.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Figure 7 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina
Figure 7. Mean number of (a) above-ground nesting bee individuals, (b) floral specialist bee individuals, (c) oligolectic bee individuals and (d) oil-collecting bee individuals in cropped area (n = 28 points) and semi-natural area (n = 11 points). ns indicates a non-significant result. Asterisks indicate that means are significantly different (Wilcoxon rank sum test, ** = P <0.01). Bars show SEs.
Figure 2 in Diversity and life-history traits of wild bees (Insecta: Hymenoptera) in intensive agricultural landscapes in the Rolling Pampa, Argentina
Figure 2. Semi-natural area of the study site: (a) semi-natural grassland; (b) the stream 'Arroyo Dulce' and its banks (Photos: Violette Le Féon).
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Data from: Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes
<p>Data underlying the publication "Bees go up, flowers go down: Increased resource limitation from late spring to summer in agricultural landscapes". Site coordinates are excluded from this dataset for data protection.</p>
Oilseed rape plant phytometers in an agricultural landscape in France - LTSER Zone Atelier Plaine & Val de Sèvre
<p>Fruit set as a proxy of pollination efficiency measured using oilseed rape plant phytometers placed in grasslands, cereals and oilseed rape fields in the LTSER Zone Atelier Plaine & Val de Sèvre. The individual contributions of different processes to pollination were determined using a bagging experiment (large-, small- and osmolux) on plant phytometers.</p> <p>Landscape metrics are available upon reasonable request</p>
Riparian buffers maintain aquatic trophic structure in agricultural landscapes
<p>Supporting data and R code for the publication entitled "Riparian buffers maintain aquatic trophic structure in agricultural landscapes".</p> <p> </p>
Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity
<p> </p> <p><strong>Short description</strong></p> <p>This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology.</p> <p>For further details please see the original article and its supplementary materials.</p> <p> </p> <p><strong>Organization of the data</strong></p> <p>The repository contains two main folders:</p> <p> 1. Target indicators & spatial analysis</p> <p><em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay).</p> <p><em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm.</p> <p><em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site.</p> <p><em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework.</p> <p> 2. CoMOLA input & parameterisation</p> <p>The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA</p> <p><em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings)</p> <p><em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file.</p> <p><em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.</p> <p> </p>
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
<p>The amount of semi-natural habitat surrounding farm fields is a common but inconsistent predictor of natural enemy populations and predation services. Standard land cover metrics may not accurately capture the actual availability of limiting resources for natural enemies and can miss important dynamics across space and time. Theory from animal movement and landscape ecology predicts that regions with more, spatio-temporally continuous resources (i.e. food, shelter) should have larger predator populations and enhanced biological control. To test these predictions empirically, we designed a study measuring aphids, lady beetles, and predation services in agricultural landscapes in Wisconsin, USA. In two study years, we sampled lady beetles and aphids in 336 crop fields (corn, soybean, alfalfa, and small grains) and adjacent semi-natural habitat patches (grasslands and woodlands) across 24 1.5 km buffer landscapes at 4–7 time points each, and in one year we assessed predation rates with sentinel egg cards. We used aphid counts to model habitat-specific aphid phenologies, from which we calculated landscape indices of prey amount and continuity. These indices, along with semi-natural habitat area, were used to predict lady beetle abundance. While there were strong differences in the abundance and timing of aphids by habitat, semi-natural habitat amount was still a better predictor of lady beetle counts and sentinel egg predation than either aphid amount or continuity indices in these landscapes.</p> <p>Synthesis and application: Our findings confirm the robust relationship between lady beetles and semi-natural habitat in agricultural landscapes, and highlight the complexities of measuring fine-scale resource heterogeneity in real landscapes. Retaining or adding woodland and grassland patches in agricultural landscapes is likely to support larger lady beetle populations and enhance predation in crop fields. Our results suggest that these habitats may be more important for shelter than prey continuity, though this mechanism warrants further investigation. Future work should continue to refine experimental methods for the successful integration of landscape ecology and animal behavior to support conservation goals.</p>
Fig. 1 in Habitat Preference In Territories Of The Red-Backed Shrike Lanius Collurio And Their Food Richness In An Extensive Agriculture Landscape
Fig. 1. Mean numbers of more important invertebrate taxa of 4–10 mm in length in four types of habitat
Agricultural landscape simplification affects wild plant fitness indirectly through herbivore-mediated changes in floral display
<p>As natural landscapes are modified and converted into simplified agricultural landscapes, the community composition and interactions of organisms persisting in these modified landscapes are altered. While many studies examine the consequences of these changing interactions for crops, few have evaluated the effects on wild plants. Here, we examine how pollinator and herbivore interactions affect fitness for wild resident and phytometer plants at sites along a landscape gradient ranging from natural to highly simplified. We tested the direct and indirect effects of landscape composition on plant traits and fitness mediated by insect interactions. For phytometer plants exposed to herbivores, we found that greater landscape complexity corresponded with elevated herbivore damage, which reduced total flower production but increased individual flower size. Though larger flowers increased pollination, the reduction in flowers ultimately reduced plant fitness. Herbivory was also higher in complex landscapes for resident plants, but overall damage was low and therefore did not have a cascading effect on floral display and fitness. This work highlights that landscape composition directly affects patterns of herbivory with cascading effects on pollination and wild plant fitness. Further, the absence of fitness consequences for resident plants suggests that they may be adapted to their local insect community. </p>
Fig. 3 in Circadian activity patterns of the Red fox (Vulpes vulpes) and the Stone marten (Martes foina) in agricultural landscape of Northwestern Bulgaria during autumn-winter period
Fig. 3. Stone marten (Martes foina) and Red fox (Vulpes vulpes) daily activity patterns in protected area "Zlatiyata", Northwestern Bulgaria.
Fig. 2 in Circadian activity patterns of the Red fox (Vulpes vulpes) and the Stone marten (Martes foina) in agricultural landscape of Northwestern Bulgaria during autumn-winter period
Fig. 2. Stone marten, Martes foina (left) and Red fox, Vulpes vulpes (right) captured in protected area "Zlatiyata", Northwestern Bulgaria.
Figure 4 in Coexistence of Syrian Woodpecker Dendrocopos syriacus and Great Spotted Woodpecker Dendrocopos major in nonforest tree stands of the agricultural landscape in SE Poland
Figure 4. Frequency of the Syrian Woodpecker's (open dots and dashed line) and Great Spotted Woodpecker's (filled dots and continuous line) park occupancy depending on the area of the tree stand within the park.
Figure 3 in Coexistence of Syrian Woodpecker Dendrocopos syriacus and Great Spotted Woodpecker Dendrocopos major in nonforest tree stands of the agricultural landscape in SE Poland
Figure 3. Frequency of the Syrian Woodpecker's (open dots and dashed line) and Great Spotted Woodpecker's (filled dots and continuous line) park occupancy depending on the density of Syrian Woodpeckers in the study area (breeding pairs/10 km2).
Figure 1 in Coexistence of Syrian Woodpecker Dendrocopos syriacus and Great Spotted Woodpecker Dendrocopos major in nonforest tree stands of the agricultural landscape in SE Poland
Figure 1. Correlation between the density (number of breeding pairs/10 km2) of the Syrian Woodpecker and Great Spotted Woodpecker in nonforest tree stands of the agricultural landscape in SE Poland.
Figure 2 in Coexistence of Syrian Woodpecker Dendrocopos syriacus and Great Spotted Woodpecker Dendrocopos major in nonforest tree stands of the agricultural landscape in SE Poland
Figure 2. Locations of Syrian Woodpecker and Great Spotted Woodpecker territories in nonforest tree stands of the agricultural landscape in SE Poland. Legend: gray bars – territories with parks, white bars – territories without parks.
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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.