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opencc-by-4.0Mar 2023View details →

Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta

SUMMARY 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). DESCRIPTION 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. For additional details about the development and applications of these data, please see:   <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) DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits  </li> </ul> FUNDING STATEMENT 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. POINT OF CONTACT Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org SUGGESTED CITATION 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. DATA DISTRIBUTION Zenodo (https://doi.org/10.5281/zenodo.7504874) PROGRESS Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision. UPDATE FREQUENCY As Needed DATE 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). Literature Cited: <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> FIELD DEFINITIONS <ul> <li>METRIC_CATEGORY: Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li>METRIC: 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>UNIT: The units in which the METRIC is estimated</li> <li>CODE_NAME: The land cover class or subclass for which the METRIC is estimated</li> <li>LABEL: A more user-friendly version of CODE_NAME, useful for creating figures and tables</li> <li>SCORE_MEAN: The mean value of each METRIC estimated for each land cover class or subclass</li> <li>SCORE_SE: The standard error of the mean</li> </ul> ABBREVIATION DEFINITIONS <ul> <li>FTE: full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li>ha: hectares</li> <li>kg: kilograms</li> <li>USD: U.S. dollars</li> <li>yr: year</li> </ul> ACCESS & USE CONSTRAINTS CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/) KEYWORDS <ul> <li>Themes: agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li>Place: Sacramento-San Joaquin River Delta, Central Valley, California  </li> </ul>

opencc-by-4.0Mar 2023View details →

Multiple-benefit Conservation in Practice: Supplemental Spatial Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta

SUMMARY Spatial data representing climate, proximity to streams, and probability of flooding in the Sacramento-San Joaquin Delta. DESCRIPTION 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. For additional details about the development and applications of these data, please see:   <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, Sesser K, Reiter M, Shuford WD, Golet GH, Hickey C, Gardali T (In review) Priority Bird Conservation Areas in California’s Sacramento–San Joaquin Delta. </li> <li>Dybala KE (2023) DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta. R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits.</li> </ul> FUNDING STATEMENT 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. POINT OF CONTACT Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org SUGGESTED CITATION 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. DATA DISTRIBUTION Zenodo (https://doi.org/10.5281/zenodo.7672193) PROGRESS Complete UPDATE FREQUENCY None planned DATE 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). FIELD DEFINITIONS <ul> <li>bio_1: annual mean temperature (C), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li>bio_12: total annual precipitation (mm), 1970-2000 (WorldClim; Fick and Hijmans 2017)</li> <li>streamdist: square root of the distance to the nearest stream (m) (National Hydrography Dataset; USGS 2020)</li> <li>pwater_fall: mean probability of open surface water during the fall, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> <li>pwater_win: mean probability of open surface water during the winter, 2013-2019 (Point Blue Water Tracker; Reiter et al. 2018)</li> </ul> Literature Cited <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> ABBREVIATION DEFINITIONS N/A COORDINATE REFERENCE SYSTEM WGS 84 / UTM zone 10N (EPSG:32610) ACCESS & USE CONSTRAINTS CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/) KEYWORDS <ul> <li>Themes: climate, temperature, precipitation, hydrology, streams, water, flood, remote sensing </li> <li>Place: Sacramento-San Joaquin River Delta, Central Valley, California</li> </ul>

opencc-by-4.0Sep 2023View details →

Competence Centres and User Support Centres Landscaping Results

The dataset contains the data collected from the landscaping activity related to the Competence Centre and user support network for the D7.1 Report on Competence Centres landscape and user support activities.

opencc-by-4.0May 2019View details →

Cell metadata for "The emergent landscape of the mouse gut endoderm at single-cell resolution"

Cell metadata for the data published in "The emergent landscape of the mouse gut endoderm at single-cell resolution"

openCC (other)Jan 2021View details →

Landscape Flowering Phenology Field Data for Sites in the Vicinity of Crested Butte, CO

This dataset represents field observations of reproductive development (flowering phenology) in 135 species of flowering plants collected at 12 field sites in the vicinity of Crested Butte, Colorado starting in 2019. Sites were visited approximately weekly from early May until early August, and all species in flower were recorded in 25 segments along a 50m transect at each site, and species were recorded if they were within 1m of either side of the transect. Datasets included in this package are 1) cleaned field observations of flowering phenology, 2) taxonomic identity of all recorded species, 3) spatial data representing the location of the center of each transect segment, and 4) spatial data representing the segment polygons.

openCC (other)Jun 2021View details →

Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA

We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.

openCC (other)Feb 2022View details →

Everglades Landscape Model (ELM) Code and Documentation

The Everglades Landscape Model (ELM) is an application instance of the generalized Ecological Landscape Modeling code package. For applications within the Florida Coastal Everglades (FCE) LTER, the ELM is one of the simulation modeling tools used to a) explore hypotheses of ecosystem processes in heterogenous spatial landscapes, b) extrapolate field-scale research findings across space and time, and c) predict the evolution of the Everglades landscape in response to plausible future scenarios. For ELM v.2.8.3-4, the 2 zip-archive packages here contains 1a) all (C) source code and unix (Bourne) shell scripts used to build and run the ELM (& includes Doxygen-generated hyperlinked source code documentation of every file/function/struct/parameter), 1b) all Everglades-specific input data used in historical (1981-2000) simulations; and 2) a complete documentation report with chapters including the Introduction&Goals, Model Data, Model Structure, Model Performance, and Model User's Guide. The ELM is being applied and updated routinely. Such updated information can be found at http://www.ecolandmod.com .

openCustomJan 2020View details →

Mechanisms mediating plant distributions across estuarine landscapes in a low-latitude tidal estuary

Understanding of how plant communities are organized and will respond to global changes requires an understanding of how plant species respond to multiple environmental gradients. We examined the mechanisms mediating the distribution patterns of tidal marsh plants along an estuarine gradient in Georgia using a combination of field transplant experiments and monitoring. Our results could not be fully explained by the “competition-to-stress hypothesis” (the current paradigm explaining plant distributions across estuarine landscapes). This hypothesis states that the upstream limits of plant distributions are determined by competition, and the downstream limits by abiotic stress. We found that competition was generally strong in freshwater and brackish marshes, and that conditions in brackish and salt marshes were stressful to freshwater marsh plants, results consistent with the competition-to-stress hypothesis. Four other aspects of our results, however, were not explained by the competition-to-stress hypothesis. First, several halophytes found the freshwater habitat stressful, and performed best (in the absence of competition) in brackish or salt marshes. Second, the upstream distribution of one species was determined by the combination of both abiotic and biotic (competition) factors. Third, marsh productivity (estimated by standing biomass) was a better predictor of relative biotic interaction intensity (RII) than was salinity or flooding, suggesting that productivity is a better indicator of plant stress than salinity or flooding gradients. Fourth, facilitation played a role in mediating the distribution patterns of some plants. Our results illustrate that even apparently simple abiotic gradients can encompass surprisingly complex processes mediating plant distributions.

openCC0Jan 2023View details →

CBM01 Plains bison movement patterns in an experimental heterogeneous landscape at Konza Prairie

This GPS-collar data set was used to evaluate the factors that influence where bison choose to graze and how grazing and space use patterns affect ecosystem function and structure. Our objectives were to quantify space use and movement patterns of adult female Plains bison in the context of selection for specific prescribed burn frequencies and topographical features in the bison-grazed watersheds at Konza Prairie. We hypothesized bison would track post-prescribed burn forage productivity and we predicted watersheds burned for the first time in several years would be used to a greater extent than watersheds burned more frequently.

openCC (other)Mar 2024View details →

STREAMS Project: Emergent landscape patterns in stream ecosystem processes resulting from groundwater/surface water interactions

This Data Set is hosted by the Luquillo LTER Program (LUQ) and owned by a LUQ's investigator. Our primary objective is to understand the linkage between surface-subsurface water interactions and ecosystem processes in neotropical lowland streams over an extended time frame (>25 yrs). Proposed research will occur at La Selva Biological Reserve in Costa Rica, which is owned and operated by the Organization for Tropical Studies In tectonically active regions of Central America, it is common for solute-rich groundwater to emerge at gradient breaks within the complex volcanic topography of mountains and foothills which intergrade with the coastal plain. These groundwaters can significantly influence solute chemistry and related ecological and ecosystem-level processes in receiving surface waters. Many solute-rich groundwaters are associated with underlying volcanic activity which has altered the chemistry of receiving streams throughout Central America. Geothermally-modified groundwaters, surfacing at the gradient break between the Central Mountain range and the coastal plain at La Selva Biological Station, have high levels of P (up to 400 mg SRP L-1) and other solutes (Ca, Cl, Mg, SO4) but are not elevated in temperature. Spatial patterns in stream solute chemistry are determined by geomorphic features of the volcanic landscape that include: upland lavas drained by P-poor streams; a gradient break (~50 m.a.s.l.), at or near where P-rich springs emerge; and lowland alluvial areas drained by streams that are both P-rich and P-poor depending on whether they receive the input of solute-rich springs. Our project is the first to determine long-term effects of nutrient enrichment in a detrital-based stream within the wet tropics. We will continue to build upon our long-term(1988-present) data set on stream solute chemistry, which is the only one that we are aware of for lowland primary rainforest of Central America. The proposed project will build on 18 years of past resear

openCC (other)Apr 2020View details →

MCR LTER: Coral Reef: Landscape-scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts, Adam et al., Ecol. Appl.

These data and analyses code were generated in support of the manuscript: Adam TC, Burkepile DE, Holbrook SJ, Carpenter RC, Claudet J, Loiseau C, Thiault L, Brooks, AJ, Washburn L, and RJ Schmitt, Ecological Applications We investigated the potential role of anthropogenic nutrient loading in driving recent coral-to-macroalgae phase shifts on reefs in the lagoons surrounding Moorea, French Polynesia. We used nitrogen (N) tissue content and stable isotopes (δ15N) in an abundant macroalga (Turbinaria ornata) together with empirical models of nutrient discharge to describe spatial and temporal patterns of nutrient enrichment in the lagoons. Turbinaria ornata were collected at 190 sites around Moorea in January, May, and August 2016. These sampling periods corresponded with distinct seasonal shifts in rainfall and wave forcing. Our results revealed that patterns of N enrichment were linked to rainfall, wave-driven circulation, and distance from anthropogenic nutrient sources, especially human sewage. In addition to describing high resolution patterns of N enrichment from 2016, we also analyzed core MCR time series on N tissue content in Turbinaria ornata from three habitats (fringing reef, back reef, and reef crest) at the six core MCR LTER sites between 2007 and 2013. These data showed that fringing reefs have been consistently enriched in N relative to back reefs, which are enriched relative to the reef crest. Further, these patterns mirror long-term patterns of nitrate and nitrite concentrations in the water column. We also analyzed core MCR time series on benthic communities and fishes and found that back reef sites that were consistently enriched in N between 2007 and 2013 experienced large increases in macroalgae while macroalgae remained much less abundant at back reef sites with lower N. These phase shifts to macroalgae occurred despite island-wide increases in the density and biomass of herbivorous fishes over the time period. Together, these results indicate th

opencc-by-4.0Mar 2020View details →

The Research Software Alliance (ReSA) and the community landscape

The Research Software Alliance (ReSA)’s mission is to bring research software communities together to collaborate on the advancement of research software. ReSA has formed taskforces and one of them revolves around a software landscape analysis aiming to answer the question "How can we identify the different communities and topics of interest for the research software community (e.g., preservation, RSEs, citation, productivity, sustainability)?" Here we include text describing the work of the taskforce to date (see <a href="https://zenodo.org/api/files/5a1e0c32-cbf7-4b9a-9c02-139b2ce66e85/2020-03-11-ReSA-landscape.md?versionId=ffedcfe9-33f4-4aba-9d6b-9975fc1c548d">2020-03-11-ReSA-landscape.md</a>), as well as plans for the future, and an invitation to readers to contribute to the ReSA list of research software communities. We are a;sp including the current version of the list in a CSV file (see <a href="https://zenodo.org/api/files/5a1e0c32-cbf7-4b9a-9c02-139b2ce66e85/2020-03-11-ReSA-landscape.csv?versionId=be4bbcdd-79a8-4444-a764-98bf0d548922">2020-03-11-ReSA-landscape.csv </a>), and we welcome contributions on the live spreadsheet that can be found via this <a href="https://docs.google.com/spreadsheets/d/15JHqOxR4HIKHYe821IPvbxIuXP1zMjXKGEIJwB-GPqE/edit#gid=0">link</a>.

opencc-by-4.0Jun 2020View details →

The Cassandra retrotransposon landscape in sugar beet (Beta vulgaris): Recombination and re-shuffling leads to a high structural variability

Here we provide supplementary data for our study of non-autonomous Cassandra terminal-repeat retrotransposons in miniature (TRIMs) in sugar beet and related genomes. Cassandra sequences are distributed across the plant kingdom and share a unique feature: conserved 5S rDNA promoter motifs within their long terminal repeats (LTRs). This dataset contains two multiple sequence alignments and a sequence list of tandemly-arranged (TA) Cassandra sequences in FASTA format. Alignments cover LTR and internal regions of all Amaranthaceae Cassandra (Ama-Cassandra) from our study. This includes Cassandra full-length sequences from B. vulgaris (Ama_Cassandra_Beet_full-length) and C. quinoa (Ama_Cassandra_Quinoa_full-length). Sequence names include information on host plant, subfamily classification, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation. For the tandemly-arranged Cassandra sequences from sugar beet, we provide a sequence list of twelve sequences (Ama_Cassandra_TA_Beet_list). Here, sequence names refer to TA copy number, host, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation. All sequences were identified in the recent genome assemblys of B. vulgaris (RefBeet1.2; Dohm et al. 2014) and C. quinoa (ASM168347v1; Jarvis et al., 2017).

opencc-by-4.0Nov 2020View details →

Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"

Methylome of the fresh water snail Biomphalaria glabrata. DNA was extracted from the feet of 10 individuals of B. glabrata originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol  (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments. BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of B. glabrata.  Wiggle files were generated for CpG pairs only. Produced at IHPE (http://ihpe.univ-perp.fr/)

opencc-by-4.0Dec 2020View details →

Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation

The data refers to the paper "Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities" The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them. The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.   The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes. <ul> <li> <code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA. </li> <li> <code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA. </li> </ul>

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Contrasting effects of landscape composition on crop yield mediated by specialist herbivores

Landscape composition not only affects a variety of arthropod-mediated ecosystem services, but also disservices, such as herbivory by insect pests that may have negative effects on crop yield. Yet, little is known about how different habitats influence the dynamics of multiple herbivore species, and ultimately their collective impact on crop production. Using cabbage as a model system, we examined how landscape composition influenced the incidence of three specialist cruciferous pests (aphids, flea beetles, and leaf-feeding Lepidoptera), lepidopteran parasitoids, and crop yield across a gradient of landscape composition in New York, USA. We expected that landscapes with a higher proportion of cropland and lower habitat diversity would lead to an increase in pest pressure of the specialist herbivores and a reduction in crop yield. However, results indicated that neither greater cropland area nor lower landscape diversity influenced pest pressure or yield. Rather, pest pressure and yield were best explained by the presence of non-crop habitats (i.e. meadows) in the landscape. Specifically, cabbage was infested with fewer Lepidoptera in landscapes with a higher proportion of meadows likely resulting from increased parasitism. Conversely, cabbage was infested with more flea beetles and aphids as the proportion of meadows in the landscape increased, suggesting that these pests benefit from non-crop habitats. Furthermore, path analysis confirmed that these landscape-mediated effects on pest populations can have either positive or negative cascading effects on crop yield. Our findings illustrate how different pest species within the same cropping system show contrasting responses to landscape composition with respect to both the direction and spatial scale of the relationship. Such tradeoffs resulting from the complex interaction between multiple-pests, natural enemies, and landscape composition must be considered, if we are to manage landscapes for pest suppression benefits.

opencc-zeroDec 2017View details →

opencc-by-sa-4.0Feb 2021View details →

Towards an open-source landscape for 3D CSEM modelling

Accompanying data to journal article <blockquote> Werthmüller, D., R. Rochlitz, O. Castillo-Reyes, and L. Heagy, 2021, Towards an open-source landscape for 3D CSEM modelling: Geophysical Journal International; ggab238, DOI: <a href="https://doi.org/10.1093/gji/ggab238">10.1093/gji/ggab238</a>. </blockquote> <ul> <li>Official article: <a href="https://doi.org/10.1093/gji/ggab238">https://doi.org/10.1093/gji/ggab238</a></li> <li>GitHub repo: <a href="https://github.com/swung-research/3d-csem-open-source-landscape">https://github.com/swung-research/3d-csem-open-source-landscape</a></li> <li>arXiv.org: <a href="https://arxiv.org/abs/2010.12926">https://arxiv.org/abs/2010.12926</a></li> </ul> The Marlim R3D model can be found at: <ul> <li>Original, fine resistivity model: <a href="https://doi.org/10.5281/zenodo.400233">https://doi.org/10.5281/zenodo.400233</a></li> <li>Upscaled computational model: <a href="https://doi.org/10.5281/zenodo.3748491">https://doi.org/10.5281/zenodo.3748491</a></li> <li>CSEM data set: <a href="https://doi.org/10.5281/zenodo.1256786">https://doi.org/10.5281/zenodo.1256786</a></li> <li>Noise-free CSEM data set: <a href="https://doi.org/10.5281/zenodo.1807134">https://doi.org/10.5281/zenodo.1807134</a></li> </ul>

opencc-by-4.0Mar 2017View details →

The Landscape of Research Data Repositories in 2015. A re3data Analysis

The attached data sets provides an overview of the landscape of research data repositories in 2015. They are based on an analysis of the re3data - registry of research data repositories from December 2015.

openmit-licenseJul 2017View details →

Porto Santo landscape features and endemic lichens occurrence data

Landscape features of Porto Santo island of and observation data of endemic lichens belonging to Sparrius et al. 2017, Bryologist.