Historical as well as current data on species distributions are needed to track changes in biodiversity. Species distribution data are found in a variety of sources but is likely that they include different biases towards certain time periods or places. By collating a large historical database of ~170,000 records of species in the avian order Galliformes dating back over two centuries and covering Europe and Asia, we investigate patterns of spatial and temporal bias in five sources of species distribution data; museum collections, the scientific literature, ringing records, ornithological atlases and website reports from 'citizen scientists'. Museum data were found to provide the most comprehensive historical coverage of species' ranges but often proved extremely time-expensive to collect. Literature records have increased in their number and coverage through time whereas ringing, atlas and website data are almost exclusively restricted to the last few decades. Geographically, our data were biased towards Western Europe and Southeast Asia. Museums were the only data source to provide reasonably even spatial coverage across the entire study region. In the last three decades, literature data have become increasingly focussed towards threatened species and protected areas and currently no source is providing reliable baseline information, a role once filled by museum collections. As well as securing historical data for the future, and making it available for users, the sampling biases will need to be understood and addressed if we are to obtain a true picture of biodiversity change.

This data package, LAGOS-NE-LIMNO v1.087.3, is 1 of 5 data packages associated with the LAGOS-NE database-- the LAke multi-scaled GeOSpatial and temporal database. With this release, only this data package is being updated and users are expected to use prior releases of the other types of data. Please see the attached additional documentation for a full description of the changes that have been made for this new release.The data packages that make up LAGOS-NE include the following information on lakes and reservoirs in 17 lake-rich states in the Northeastern and upper Midwestern U.S. (1) LAGOS-NE-LOCUS v1.01: lake location and physical characteristics for all lakes greater than one hectare. (2) LAGOS-NE-GEO v1.05: ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes and for all spatial resolutions, also called ‘zones’ (i.e., ecoregions, states, counties). These geospatial data were created by processing national-scale and publicly-accessible datasets to quantify numerous metrics at multiple spatial resolutions. (3) LAGOS-NE-LIMNO v1.087.3: in-situ measurements of lake water quality from the past three decades for approximately 2,600-12,000 lakes, depending on the variable. This module was created by harmonizing 87 water quality datasets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This module includes variables that are most commonly measured by state agencies and researchers for studying eutrophication. For each water quality data value, we also include metadata related to the sampling program, methods, qualifiers with data flags from the original program (qual, not standardized for LAGOS-NE), censor codes from our quality control procedures (censorcode, standardized for LAGOS-NE), and the date of each sample. (4) LAGOS-NE-GIS v1.0: the GIS data layers for lakes, wetlands, and streams, as well as the spatial resolutions that were used to create the LAGOS-NE-GEO module. (5) LAGOS-NE-RAWDATA: the original 87 datasets of lake water quality prior to processing, the R code that converts the original data formats into LAGOS-NE data format, and the log file from this procedure to create LAGOS-NE. This latter data package supports the reproducibility of the LAGOS-NE-LIMNO data module. Citation for the full documentation of this database: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 https://doi.org/10.1186/s13742-015-0067-4 Citation for the data paper for this database: Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell, C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr, K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M.Collins, J.D. Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk, M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C. Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K. Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S. King, S.A. Kishbaugh, J.F. Lapierre, B. Lathrop, J.A. Latimore, Y. Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B. Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson, A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry, K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K. Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L. Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P. Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E. Webster, J.D. White, M.K. Wilmes, S. Yuan. 2017. LAGOS-NE: A multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. lakes. Gigascience 6(12) https://doi.org/10.1093/gigascience/gix101

Sentinel-2, 10, 20, and 60m Multispectral, Multitemporal, 13-band imagery is rendered on-the-fly and available for visualization. This imagery layer pulls directly from the Sentinel-2 on AWS collection and is updated daily with new imagery.This imagery layer can be applied across a number of industries, scientific disciplines, and management practices. Some applications include, but are not limited to, land cover and environmental monitoring, climate change, deforestation, disaster and emergency management, national security, plant health and precision agriculture, forest monitoring, watershed analysis and runoff predictions, land-use planning, tracking urban expansion, highlighting burned areas and estimating fire severity.Geographic CoverageGlobalContinental land masses from 65.4° South to 72.1° North, with these special guidelines:All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean SeaTemporal CoverageThe revisit time for each point on Earth is every 5 days.This layer is updated daily with new imagery.This imagery layer includes a rolling collection of imagery acquired within the past 14 months.The number of images available will vary depending on location.Product LevelThis service provides Level-1C Top of Atmosphere imagery.Alternatively, Sentinel-2 Level-2A is also available.Image Selection/FilteringThe most recent and cloud free images are displayed by default.Any image available within the past 14 months can be displayed via custom filtering.Filtering can be done based on attributes such as Acquisition Date, Estimated Cloud Cover, and Tile ID.Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence]. More…Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).The DRA version of each layer enables visualization of the full dynamic range of the images.Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions.Various pre-defined Raster Functions can be selected or custom functions created.Available renderings include: Agriculture with DRA, Bathymetric with DRA, Color-Infrared with DRA, Natural Color with DRA, Short-wave Infrared with DRA, Geology with DRA, NDMI Colorized, Normalized Difference Built-Up Index (NDBI), NDWI Raw, NDWI - with VRE Raw, NDVI – with VRE Raw (NDRE), NDVI - VRE only Raw, NDVI Raw, Normalized Burn Ratio, NDVI Colormap.Multispectral BandsBandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional NotesOverviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available.NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request.Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of their Registry of Open Data. Users can access the imagery from Sentinel-2 on AWS , or alternatively access EarthExplorer or the Copernicus Data Space Ecosystem to download the scenes.For information on Sentinel-2 imagery, see Sentinel-2.

The survey area consists of a 101 square kilometer polygon over an eastern portion of Yosemite National Park. Data were collected to study spatial and temporal patterns of nitrogen export and land use change in a mountainous watershed. hese data were collected by the National Center for Airborne Laser Mapping (NCALM) on behalf of Steve Martel, University of Hawaii

This data release contains four data files that were used to evaluate the conditions during which erosion occurred and the magnitude of erosional events. A single shapefile contains shoreline positions for five sites measured during the beginning and end of the project and projected shoreline positions based on the observed erosion rates. There are three .csv files that contain measured erosion pin data, photo-electric erosion pin data, and acoustic energy and water level data.

These geospatial data characterize the potential for geographic overlap among land-use practices and between land-use and climate change on the Colorado Plateau—a dryland region experiencing rapid changes in land-use and facing aridification. They were used to characterize spatial patterns and temporal trends in aridification, land-use, and recreation at the county and 10-km2 grid scales. Increasing trends and overlapping areas of high intensity for use, including oil and gas development and recreation, and climate drying, suggest areas with high potential to experience detrimental effects to the recreation economy, water availability, vegetation and wildlife habitat, and spiritual and cultural resources. Patterns of overlap in high-intensity land-use and climate drying differ from the past, indicating the potential for novel impacts and suggesting that land managers and planners may require new strategies to adapt to changing conditions. This analytical framework for assessing the potential impacts of overlapping land-use and climate change could be applied with other drivers of change or to other regions to create scenarios at various spatial scales in support of natural resource planning efforts.

GALLIFORM: WPA Eurasian Database v 1.0.Records refer to species occurrence data collected from museum collections, the literature, ornithological atlases, bird ringing programmes or ornithological trip report websites. Records which could be at least approximately dated and georeferenced are contained in a comma delimited text file. The file gives information on the data source, the year of the record, the species the record relates to, the threat status of the species, the country the record is from and whether the record came from inside a protected area. The column names are mostly self-explanatory. In cases where an exact year of record was not known, a date range is given. For example, ‘Pre 1980’ would indicate a record from 1979 or earlier. Similarly, ‘Post 1980’ would indicate a record from 1981 or later. For the column ‘Threatened?’, ‘0’ indicates a non-threatened species and ‘1’ a threatened species. For the column ‘Inside a Protected Area?’ ‘0’ indicates the record is ...

The LAGOS-US GEO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The GEO module contains data on the geospatial and temporal ecological setting (e.g., land use, terrain, soils, climate, hydrology, atmospheric deposition, and human influence) quantified at multiple spatial divisions (e.g., equidistant buffers around lakes, watersheds, hydrologic basins, political boundaries, and ecoregions) relevant to the LAGOS-US lake population defined in the LAGOS-US LOCUS module. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.

The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. The database consists of 11 tables; one raw data table plus ten related meta data tables. For further information please see our associated data paper.

This data consists of several elements:

• BioTIMESQL_02_04_2018.sql - an SQL file for the full public version of BioTIME which can be imported into any mySQL database.
• BioTIMEQuery_02_04_2018.csv - data file, although too large to view in Excel, this can be read into several software applications such as R or various database packages.
• BioTIMEMetadata_02_04_2018.csv - file containing the meta data for all studies.
• BioTIMECitations_02_04_2018.csv - file containing the citation list for all studies.
• BioTIMECitations_02_04_2018.xlsx - file containing the citation list for all studies (some special characters are not supported in the csv format).
• BioTIMEInteractions_02_04_2018.Rmd - an r markdown page providing a brief overview of how to interact with the database and associated .csv files (this will not work until field paths and database connections have been added/updated).

Please note: any users of any of this material should cite the associated data paper in addition to the DOI listed here.

To cite the data paper use the following:

Dornelas M, Antão LH, Moyes F, Bates, AE, Magurran, AE, et al. BioTIME: A database of biodiversity time series for the Anthropocene. Global Ecol Biogeogr. 2018; 27:760 - 786. https://doi.org/10.1111/geb.12729

This folder contains the data and codes used in "Spatial and Temporal Analysis of Precipitation and Effective Rainfall using Gauge Observations, Satellite, and Gridded Climate Data for Agricultural Water Management in the Upper Colorado River Basin" paper.

This dataset includes lake total phosphorus (TP), true water color, and chlorophyll a (CHLa) concentrations from summer, epilimnetic water samples and is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.040.0 for lake water chemistry data and LAGOSGEO version 1.02 for lake catchment geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. Lake catchments, defined as 'The area of land that drains directly into a lake, and into all upstream-connected, permanent streams to that lake exclusive of any upstream lake watersheds for lakes greater than or equal to 10 ha that are connected via permanent streams', were delineated for lakes greater than or equal to 4 ha. Lake-stream connectivity type was assigned to lakes greater than or equal to 4 ha using GIS tools that use the National Hydrology Dataset (See Soranno et al. 2015 for LAGOS geographic processing steps). A subset of lake and geographic data was created to examine spatial variation in TP and water color relationships with CHLa across broad geographic extents using spatially-varying coefficient models with a Bayesian framework. Lakes were selected that had complete records for summer epilimnetic total TP, true water color, and CHLa. In addition we selected lakes with surface area greater than or equal to 4 ha and less than 10,000 ha to exclude very small and very large lakes from the analyses. The resulting dataset includes 838 lakes in Wisconsin, Michigan, New York, and Maine with 7395 observations. The majority of lakes in the data subset have only one water chemistry observation (~72% of lakes). There are 228 lakes with more than one water chemistry observation taken on different sampling occasions over time (average of 29 observations per lake with repeated measures). The dataset reports the original, individual measurements. The proportion of agriculture and wetlands in the lake catchment were derived from land cover and land use data in the National Land Cover Dataset (2006). For the analyses we withheld ten percent of the observations for model validation and to assess prediction accuracy. The remaining observations were used in the model building steps. The 'dataset' column in the data indicates whether the observation belongs to the model-building ('mb') or hold-out dataset ('h').

The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. The database consists of 11 tables; one raw data table plus ten related meta data tables. For further information please see our associated data paper.

This data consists of several elements:

• BioTIMESQL_11_07_2017.sql - an SQL file for the full public version of BioTIME which can be imported into any mySQL database.
• BioTIMEQuery_06_07_2017.csv - data file, although too large to view in Excel, this can be read into several software applications such as R or various database packages.
• BioTIMEMetadata_11_07_2017.csv - file containing the meta data for all studies.
• BioTIMECitations_11_07_2017.csv - file containing the citation list for all studies.
• InteractingWithBioTIME.html - a brief tutorial on using BioTIME in the form of an r markdown HTML page.

Please note: any users of any of this material should cite the associated data paper in addition to the DOI listed here.

Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

The 2020 Violence Early Warning System (ViEWS) Prediction Competition challenged participants to produce predictive models of violent political conflict at high spatial and temporal resolutions. This paper presents a convolutional long short-term memory (ConvLSTM) recurrent neural network capable of forecasting the log change in battle-related deaths resulting from state-based armed conflict at the PRIO-GRID cell-month level. The ConvLSTM outperforms the benchmark model provided by the ViEWS team and performs comparably to the best models submitted to the competition. In addition to providing a technical description of the ConvLSTM, I evaluate the model's out-of-sample performance and interrogate a selection of interesting model forecasts. I find that the model relies heavily on lagged levels of battle-related fatalities to forecast future decreases in violence. The model struggles to forecast escalations in violence and tends to underpredict the magnitude of escalation while overpredicting the spatial spread of escalation. This repository contains the supplemental_data required for a full (from scratch) replication of "High Resolution Conflict Forecasting with Spatial Convolutions and Long Short-Term Memory." See the guidance available at https://github.com/benradford/views2020.

This data set consists of density ratings of submersed aquatic vegetation and Bithynia tentaculata abundance in Pool 8 of the UMR in 2015.

This dataset was created for the following publication: Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.-F. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. Under review. Creating multi-themed ecological regions for macrosystems ecology: Testing a flexible, repeatable, and accessible clustering method. Submitted to Ecology and Evolution July 2016. This dataset includes lake total phosphorus (TP) and Secchi data from summer, epilimnetic water samples, as well as 52 geographic variables at the HU-12 scale; it is a subset of the larger LAGOS-NE database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS-NE compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.054.1 for lake water chemistry data and LAGOSGEO version 1.03 for geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. The dataset is a subset of the following integrated databases: LAGOSLIMNO v.1.054.1 and LAGOSGEO v.1.03. For full documentation of these databases, please see the publication below: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 doi:10.1186/s13742-015-0067-4 .

This dataset includes four streams with daily CO2 data at five sites labeled daily data. There is an additional data set labeled LTM sites that has daily CO2 and stream chemistry at 4 sites. Finally, there is a file labeled reach characteristics that has the number of seeps, the slope, and other reach related measurements.

The distribution and abundance of macromoth species is strongly influenced by geographical (region-neighboring plots) scale, elevation, aspect, plant community, management regime, and time of year. Noctural macromoths have been observed at a total of 263 sample sites throughout the Andrews Forest watershed since 1994. Only a limited subset of these sites is sampled each year. From 2004 to 2008, 20 sites were sampled consistently using a hierarchical sampling design stratified by elevation and vegetation type. Moths are sampled using blacklight traps deployed for one night every two weeks at each site from April through October. A total of 503 species have been observed, and approximately 300 species may be observed in any given year. The watershed can be divided into 13 distinct zones. The northwest ridge above the Andrews headquarters has the highest number of species (n = 321) and the lowest number of species occurred at upper Lookout Creek (n = 239). Each of 13 zones is missing ca. 200 of the 500 resident species, suggesting that heterogeneity in the landscape is important. A breakdown of the species into functional groups based on larval feeding habits: conifers, hardwood, herb, mix, unknown shows that 43% of Andrews species rely on a hardwoods and 63% rely on hardwoods and herbaceous angiosperms. Conifer-feeders only represent 8% of moth species. However, moths associated with conifer hosts are the most abundant; for instance, in the zone representing the midlevel of Carpenter Mountain 67% of moth individuals are conifer feeders, but only 14% of the species feed on conifers. In contrast, within the zone represented by the Headquarters site, only 32% of the individual moths feed on conifers whereas 56% feed on hardwoods. Moth biogeographic zones correspond to elevation zones and to potential vegetation.

Environmental conditions are the major drivers of species distribution, and terrestrial Antarctica arguably presents the most dramatic challenges for its inhabitants. Many animals rely on acclimation to enhance their stress tolerance to face unfavorable conditions. Some animals can also rely on their phenotypic plasticity to respond to these unfavorable conditions without the need to slowly experience increasing levels of stress to enhance their stress tolerance (i.e., acclimate). Belgica antarctica can rely on both types of strategies, but since they evolved to live in a habitat with such dramatic environmental changes as Antarctica, they are very sensitive to any type of stress (e.g., a sudden drop in temperature, or a bout of high-speed wind). Studying the extent to which B. antarctica rely on each of these strategies to survive and how environmental variation can shape this species’ biology across distinct populations (i.e., that might experience distinct selective pressures) is important to help us better understand how polyextremophiles adapt and evolve while inhabiting extreme environments. This project focused on studying freeze tolerance in B. antarctica populations populations within Cormorant Island that inhabited three distinct microhabitats over the course of the summer season (January-March).

The data set contains the monthly statistics for the TMP2m variable (2-m above ground temperature) of the North American Land Data Assimilation System Version 2 (NLDAS-2) model. The period of analysis is from 1979-01-02 to 2013-12-31. The statistics for each calendar month are the mean, standard deviation, minimum, maximum, and percentiles in 0.05 interval. The data set also includes a p-value per calendar month of the Kolmogorov-Smirnov (KS) test. The p-value of the KS test shows if the computed empirical cumulative distribution function (CDF) comes from a fitted normal distribution