The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This dataset provides locations and values of water quality parameters from a four-day survey conducted between August 23, 2016 and August 26, 2016 using an Autonomous Underwater Vehicle (AUV) in Great South Bay, New York. Measured parameters include bottom dissolved oxygen (DO), salinity, specific conductance, water temperature, and pH. During the four day period, data was collected along 15 transects of the Great South Bay, totaling 60,480 observation points. From these point data, rasters showing the spatial distribution of bottom dissolved oxygen were generated using an interpolator in a GIS. A unique raster is provided for each day of the survey. All data files for download are available within 'Child Items' below. Observation point data are available as shapefiles while DO rasters are available as TIFFs. Both point data and DO rasters are made available as web mapping services. This allows for use in ArcGIS for Desktop, ArcGIS Online, and other web applications. For additional information on how to use web mapping services please visit http://server.arcgis.com/en/server/10.3/publish-services/linux/what-is-a-map-service.htm. Please note that the .sd files included are not meant to be open as standalone files, but rather were uploaded to generate the online web mapping service links provided.

Proportional change in effective area of similar ecological environments for Mammals as a function of land clearing within the present long term (30 year average) climate (1990 centred) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover.

This metric describes the effects of land clearing on the area of similar environments to each grid cell as a proportion. Each cell is compared with a sample of 60,000 points in both uncleared landscape and degraded landscape (pairwise similarities summed (e.g. a completely similar cell will contribute 1, a dissimilar cell 0, with a range of values in between). The contribution of each cell is then multiplied by a 0 (cleared) to 1 (intact) condition index based on the natural areas layer. By dividing the test area by the current area, we are able to quantify the reduction in area as a function of land use/climate change. Values less than one indicate a reduction, values of 1 no change, and values greater than 1 (rare cases in the north) show an increase in similar environments.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided in two forms: 1. Zipped ESRI float grids: Binary float grids (.flt) with associated ESRI header files (.hdr) and projection files (.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file. 2. ArcGIS layer package (.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.

Additionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.

Layers in this 9s series use a consistent naming convention: BIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS e.g. A_90_CAN85_S or R_90_MIR85_L where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants

Lineage: Proportional change in the area of similar ecological environments was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. Proportional change was calculated by taking the area of baseline ecological environments similar to each present cell as the denominator and the area of present cells with their contribution scaled by the natural areas condition index (0 degraded to 1 intact) as the numerator. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download. GDM Model: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ALA data extracted 28 February 2014 (GDM: REP_r3_v2) Climate data. Models were built and projected using: a) 9-second gridded climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment b) 9-second gridded climatology for continental Australia 2036-2065 CanESM2 RCP 8.5 (CMIP5): Summary variables with elevation and radiative adjustment Natural Areas Layer (intact to degraded land) Australian Government Department of the Environment (2014) Natural areas of Australia - 100 metre (digital dataset and metadata). Available at http://www.environment.gov.au/metadataexplorer/explorer.jsp and up to date information for Western Australia were provided at 25m Albers projection were reprojected to GDA94, merged and aggregated to a continuous measure of proportion of intact area per grid cell at 9s.