Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
212
datasets available to search
ShareScore release 0.7.1
Dataset results
212 results for “Landscape change”
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta.</p> <p><strong>DESCRIPTION</strong><br> These data were compiled as predictors of the distribution of riparian landbird species and groups of waterbird species, to facilitate projecting the probability of species or group presence across a given landscape. They were used to identify Priority Bird Conservation Areas and in analyses of the impacts of scenarios representing habitat restoration and perennial crop expansion on suitable habitat. These data are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (<em>In review</em>) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta</li> <li>Dybala KE, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (<em>In review</em>) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta</em>. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7672193.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7672193)</p> <p><strong>PROGRESS</strong><br> Complete</p> <p><strong>UPDATE FREQUENCY</strong><br> None planned</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from WorldClim (representing 1970-2000), National Hydrography Dataset (published 2020), and Point Blue's Water Tracker (representing 2013-2019).</p> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>bio_1: </strong>annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>bio_12:</strong> total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li><strong>streamdist: </strong>square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li><strong>pwater_fall:</strong> mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li><strong>pwater_win:</strong> mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> <p><strong>Literature Cited</strong></p> <ul> <li>Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol. 37:4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a> </li> <li>Reiter ME, Elliott NK, Barbaree B, Moody D. 2018. An automated open surface water tracking system for California’s Central Valley. Report to the U.S. Fish and Wildlife Service. Petaluma, California: Point Blue Conservation Science. Available from: <a href="https://data.pointblue.org/apps/autowater/ ">https://data.pointblue.org/apps/autowater/ </a></li> <li>[USGS] United States Geological Survey. 2020. National Hydrography Dataset Best Resolution (NHD) for Hydrologic Units (HU) 4 - 1802, 1803, 1804, 1805. Reston (VA): U.S. Geological Survey. Available from: <a href="https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products ">https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products </a></li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong><br> N/A</p> <p><strong>COORDINATE REFERENCE SYSTEM</strong><br> WGS 84 / UTM zone 10N (EPSG:32610)</p> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li><strong>Place: </strong>Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>
Map of 1992-2015 landscape change trajectories
<p>Map of 1992-2015 landscape change trajectories. Landscapes are colored depending on their change trajectories and a percentage of changed area; small < 10%, medium (10% to 30%), and large (> 30%).</p> <p>To access and visualize the map use: <a href="https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c"><strong>https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c</strong></a></p> <p>Creation of this map is explained in details at <a href="https://www.sciencedirect.com/science/article/pii/S0303243418305841">https://www.sciencedirect.com/science/article/pii/S0303243418305841</a> (preprint at <a href="https://eartharxiv.org/k3rmn/">https://eartharxiv.org/k3rmn/</a>).</p>
Figure 1 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 1. Location of Knuckles forest reserve within Kandy and Matale Districts (left) and the four study sites [two at Riverston (1 and 2), Hunasgiriya (3) and Deanston (4)] within the reserve (right).
Figure 3 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 3. Comparison of climatic and structural parameters among the four habitat types during the dry (dashed line) and wet (solid line) seasons. Data from both locations with lizards and random locations are considered in combination. (C = Cardamom plantations, M = Mixed cardamom forests, N = Natural forests, P = Pine plantations.)
Figure 2 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 2. Mean number of sightings of Ceratophora tennentii within three habitat types at Knuckles Range, Sri Lanka.
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>
Рис. 2. Изменение виΑового богатства и таксономической структуры зонаΛьных фаун прямокрыΛых северо-востока Русской равнины. Fig. 2. Changes in species richness and taxonomic structure of Orthoptera fauna of the Russian Plain. in Fauna and landscape-zonal distribution of Orthoptera in the Komi Republic (Russia)
Рис. 2. Изменение виΑового богатства и таксономической структуры зонаΛьных фаун прямокрыΛых северо-востока Русской равнины. Fig. 2. Changes in species richness and taxonomic structure of Orthoptera fauna of the Russian Plain.
Land use change converts temperate dryland landscape into a net methane source
<p>Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12% respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally.</p>
Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces
<p>Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.</p>
Fig. 6 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 6. Abundance (mean number of burrows/100 × 5 m transect) of S. citellus in 4 colonies in the study area in summer (for the period 2017–2021) N = Luda Yana; –– l –– = Belotrup; ---- l ---- = Panagyurski kolonii; u = Beli Manastiri
Fig. 5 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 5. Changes in the habitat suitability in the study area (white – not suitable, black – high suitability) of European souslik assessed by maxent modelling based on data from 2006–2018 (B) and extrapolated for the period 1985–2005 (A). The results are presented in
Fig. 3 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 3. Negative and positive anomalies (white and black bars) of the Mean Annual Temperature time series for the period of 1985–2018 (data from the meteorological station Sofia)
Fig. 2 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 2. Changes in the number of grazing livestock in the southern central Bulgarian planning region for the period 2001–2018
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. 2 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis
Fig. 2. Map of Tasmania showing blood collection sites for the three native carnivore species and the introduced feral cat. Places identified are those referred to in the text.
Fig. 1 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis
Fig. 1. Map of Tasmania showing average cat densities from individual spotlighting districts over 8 years and blood collection sites for the Tasmanian pademelon. Positive T. gondii sites are those where at least one sample tested positive to IgG antibodies. Negative sites are those where no evidence of exposure to T. gondii was found in any sample.
Fig. 3 in Wildlife disease ecology in changing landscapes: Mesopredator release and toxoplasmosis
Fig. 3. Prevalence of IgG antibodies of Tasmanian mammals to T. gondii by trophic level; n represents the total number of samples tested. Standard error bars are shown.
Fig. 5 in Historical biogeography highlights the role of Miocene landscape changes on the diversification of a clade of Amazonian tree frogs
Fig. 5 Dorsolateral views of Osteocephalus. a O. leprieurii sensu stricto, Kaw mountain, French Guiana. b O. leprieurii, AF1824, Mana, French Guiana. c O. yasuni sensu stricto, QCAZ69010, Estación Cientí- fica Yasuní, Orellana, Ecuador. d O. yasuni, QCAZ55994, Lorocachi, Pastaza, Ecuador. e O. deridens, QCAZ56031, Lorocachi, Pastaza, Ecuador. f O. fuscifacies, QCAZ59887, Parque Nacional Llanganates, Pastaza, Ecuador. g O. aff. leoniae 2, AF4513 (sequenced), San Martín, Peru. h O. planiceps, QCAZ55881, Lorocachi, Pastaza, Ecuador. Photographs: a–b, g by Antoine Fouquet; c–f, h by Santiago Ron (BIOWEB, Licence: CC BY-NC-ND 4.0)
Fig. 4 in Historical biogeography highlights the role of Miocene landscape changes on the diversification of a clade of Amazonian tree frogs
Fig. 4 Dorsolateral and ventral views of Osteocephalus. a O. cabrerai, CORBIDI120, Tara- poa, Sucumbíos, Ecuador. b O. helenae, AF2427 (sequenced), Nassau, Suriname. c O. helenae, Amapá, Brazil. d O. helenae, Voltaire, French Guiana. e–f O. aff. helenae 2 'morph cabrerai', AF2357 (sequenced), Mapaou, French Guiana. g O. aff. helenae 2 'morph cabrerai', AF3358 (sequenced), Bakhuis, Surinam. h O. aff. helenae 2 'morph cabrerai', St. Georges, French Guiana. Photographs: a by Pablo Venegas (BIOWEB, Licence: CC BY-NC-ND 4.0); b–h by Antoine Fouquet
ScienceDex guides
Understand access before you commit
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.