2025 Spring Aerials Lorain County Ohio.ECW FormatProjected Coordinate System NAD 1983 StatePlane Ohio North FIPS 3401 (US Feet)Projection Lambert Conformal ConicWKID 3734Previous WKID 102722Authority EPSGLinear Unit US Survey Feet (0.3048006096012192)False Easting 1968500.0False Northing 0.0Central Meridian -82.5Standard Parallel 1 40.43333333333333Standard Parallel 2 41.7Latitude Of Origin 39.66666666666666

Geographic analysis of impervious cover and demographic attributes by 2010 Census block group. Demographic attributes and Census block groups were downloaded from the EJScreen web page (www.epa.gov/ejscreen). EJScreen technical documentation is available at website; click on the technical information link on the landing page; the link of the documentation is in the center of the web page. NLCD2019 data for impervious cover were downloaded from www.mrlc.gov. The impervious cover for 2001 and 2019 in the NLCD2019 database were used in the analysis. The Census block groups downloaded from EJScreen were projected into Albers Conic Equal Area to match the NLCD2019 projection (Albers Conic Equal Area: central meridian=96.0 W; origin latitude=23.0 N; false easting=0.0; false northing=0.0; standard parallel 1=29.5 N; standard parallel 2=49.5 N; Geographic coordinate system=WGS84; WKID=4326) .

City Of Sacramento's Survey Division has developed a high accuracy GPS control point grid. This file currently contains data points for the entire City of Sacramento. The latitude and longitude values have an accuracy level of +/- .05 feet. Elevation data has accuracy of +/- .24 feet.

Field: GPSNUMBER Alias: Survey reference number Field Description: Reference to latitude/longitude minute

Field: NORTHINGFT Alias: False Northing, California State Plane, Zone II, Feet

Field: EASTINGFT Alias: False Easting, California State Plane, Zone II, Feet

Field: ELVORTHOFT Alias: Elevation Ortho (ft)- a preliminary ground elevation to which the orthometric leveling correction has been applied

Field: DFNGVD29FT Alias: Differential NGVD 29- elevation obtained by spirit leveling based on the national geodetic vertical datum of 1929

Field: STREET Alias: Street location of control point

Field: XSTREET Alias: Cross street or reference information

Field: MONTYPE Alias: Control point or monument type

Field: LAT_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: LONG_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: ELLIPSHT Alias: Ellipsoid Height- the distance, measured along the mormal, from the surface of the ellipsoid to a point

Field: CNVERGENCE Alias: The angle difference at a given location between grid north and astronomic north

Field: GRDSCLFCTR Alias: Grid Scale Factor- a multiplier for reducing a sea level lengths to grid lengths

Field: COMBNDFCTR Alias: Combined Factor- multiplier obtained from the product of the sea level and grid scale factor and applied to ground distance to obtain grid distance

Field: GEOIDHT Alias: Distance of the geoid above (positive) or below (negative) the mathematical reference spheroid

Field: ARCHIVELOC Alias: Use To be Determined Field Description: Associated with crossed out GPS No.-Point ID

The daily cloudless MODIS Snow area ratio data set (2000-2015) of the Qinghai Tibet Plateau is based on MODIS daily snow product - mod10a1, which is obtained by using a cloud removal algorithm based on cubic spline interpolation. The data set is projected by UTM with spatial resolution of 500m, providing daily snow cover FSC results in the Tibetan Plateau. The data set is a day-to-day document, from 24 February 2000 to 31 December 2015. Each file is the result of snow area proportion on that day, the value is 0-100%, which is envi standard file, the naming rule is: yyyddd_fsc_0.5km.img, where yyyy represents the year, DDD represents Julian day (001-365 / 366). Files can be opened and viewed directly with envi or ArcMap. The original MODIS Snow data product for cloud removal comes from the mod10a1 product processed by the National Snow and Ice Data Center (NSIDC). This data set is in the format of HDF and uses the sinusional projection. The attributes of the daily cloudless MODIS Snow area ratio data set (2000-2015) on the Qinghai Tibet Plateau consist of the spatial-temporal resolution, projection information and data format of the data set. Temporal and spatial resolution: the temporal resolution is day by day, the spatial resolution is 500m, the longitude range is 72.8 ° ~ 106.3 ° e, and the latitude is 25.0 ° ~ 40.9 ° n. Projection information: UTM projection. Data format: envi standard format. File naming rules: "yyyyddd" + ". Img", where yyyy stands for year, DDD stands for Julian day (001-365 / 366), and ". Img" is the file suffix added for easy viewing in ArcMap and other software. For example, 2000055 ﹐ FSC ﹐ 0.5km.img represents the result on the 55th day of 2000. The envi file of this data set is composed of header file and body content. The header file includes row number, column number, band number, file type, data type, data record format, projection information, etc.; take 2000055 ﹣ FSC ﹣ 0.5km.img file as an example, the header file information is as follows: ENVI Description = {envi file, created [sat APR 27 18:40:03 2013]} Samples = 5760 Lines = 3300 Bands = 1 Header offset = 0 File type = envi standard Data type = 1: represents byte type Interleave = BSQ: data record format is BSQ Sensor type = unknown Byte order = 0 Map Info = {UTM, 1.500, 1.500, - 711320.359, 4526650.881, 5.0000000000e + 002, 5.0000000000e + 002, 45, north, WGS-84, units = meters} Coordinate system string = {projcs ["UTM [u zone [45N], geocs [" GCS [WGS [1984], data ["d [WGS [1984", organization ID ["WGS [1984", 6378137.0298.257223563]], prime ["Greenwich", 0.0], unit ["degree", 0.01745532925199433]]] project ["transfer [Mercator"]] parameter ["false [easting", 500000.0], parameter ["false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [false [false [easting ", 500000.0], parameter], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter [false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter ["false_northing", 0.0], parameter ["central_meridian", 87.0], parameter ["scale" _Factor ", 0.9996], parameter [" latitude ﹣ of ﹣ origin ", 0.0], unit [" meter ", 1.0]]} Wavelength units = unknown, band names = {2000055}

Continuous Wavelet Transform was applied to bed elevation profiles (BEP) and used in the study in order to recognise the spatial distribution of bedforms and discriminate between their hierarchical scales. In particular, the spatial distribution of the hierarchical scales is highlighted by averaging wavelet power spectra over different bands, and displayed as the wavelet variance of the BEP (see map). Four dune classes were defined, following Ashley (1990): small dunes (1-5 m), medium dunes (5-10 m), large dunes (10-100 m), and very large dunes (>100 m).

The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 m land use land change product for the contiguous United States (CONUS) generated from 5 years of consecutive growing season WELD inputs from April 15, 2006, to November 17, 2010. WELD LCLUC is offered in HDF format. This product includes the following bands: tree cover, bare ground, water surface, snow and ice, and number of good acquisitions which are composited over the 5-year period.

The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and South Dakota State University, Geospatial Sciences Center of Excellence (GSCE). The Land Processes Distributed Active Archive Center (LP DAAC) is responsible for archiving and distributing NASA WELD, which includes the WELD LCLUC product.

Data Set Characteristics: Projection: Albers Equal Area Datum: World Geodetic System 84 (WGS84) Geographic Extent: CONUS Tile size: 5000 x 5000 (rows/columns) Pixel size: 30 m Tile volume: 17.44 GB Tiles: 483 First standard parallel: 29.5° Second standard parallel: 45.5° Longitude of central meridian: -96.0° Latitude of projection origin: 23.0° False Easting: 0.0 False Northing: 0.0

This is the dataset for anticipated oil and gas well development in areas of high oil and gas potential as described in Copeland et al. (2009) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species, PLoSOne. (http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0007400) This dataset corresponds with results shown in Figure 2 of the publication. Please see paper in PLoSOne for more detailed methods. The dataset should be cited as: Copeland, H., K. Doherty, D. Naugle, A. Pocewicz, J. Kiesecker (2010) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species. The projection of this dataset is: US NAD83 Lambert Conformal Conic False easting 0 False northing 0 Central meridian -107.0 Standard parallel 1 33.0 Standard parallel 2 45.0 Latitude of origin 41.0

Active high intensity gas seepage is documented for the first time at the seaward edge of the salt occurrence in the southern Lower Congo Basin. Microbial methane release from the seafloor occurs on the crests of two 800 m high ridges formed by fault-propagation folding. Intense uplift is documented since the end of the Miocene by distinct onlapping reflections on the landward flank of these ridges. A paleo-pockmark structure suggests an onset of seepage coincident with this deformation period. High-resolution seismic imaging reveals methane migration along strata from Oligocene/Miocene fan deposits towards the ridge crests where large gas accumulations form beneath a discontinuous Bottom Simulating Reflection (BSR). Detailed mapping revealed that free gas and gas hydrate occurrences below and above the base of the gas hydrate stability zone are closely linked to sedimentary strata in the flanks of topographic ridges. Gas transport through the gas hydrate stability zone originates from the shallowest area of the BSR directly beneath the seafloor seep sites, suggesting pressure controlled venting. These sites represent the most seaward salt-related gas seepage features documented in the area and illustrate the initiation of long-lasting seepage at the front of an area of compressional tectonics at a passive continental margin.

Israeli_butterfly_move_dataPrimary data for estimating movement rates. UniqueID combines the site name, track number and flight step number. Species are identified in Supplemental Information, Table S3. Field types are Wheat and Olives. Locations are Wheat, Olive, Nature and (Field) Margin. See Figure 1 for visual of field types vs locations. Projected coordinate system is GRS_1980_Transverse_Mercator with False Easting = 219529.584 and False Northing = 626907.390. Each move consisted of a move length, measured as the distance between turning or stopping points i and i+1, and a turning angle θi , measured as the angle between move i-1 and i. The file contains Time (seconds), Length (m) and Turn (Cosine of turning angle). Note that steps starting in edge regions, defined as 10m outside the Margin towards Nature or Fields, were omitted from the analysis (see text for details). However, if a move step starts in one of the included Locations and ends in edge region, the step and associat...

TIF files: ---> DEM, ---> Lat, Lon, ---> Mask, ---> DayMET precipitation Daymet (annual) precipitation SVR processing example Daymet data set provides annual and summary climate data for minimum and maximum temperature, precipitation, and vapor pressure (Thornton et al. 2014). Daymet data consider the total accumulated precipitation over the annual period of the daily total precipitation. Precipitation is the sum of all forms of precipitation converted to water equivalent (mm/yr) (Thornton et al. 2014). The Daymet data layers are produced on a 1-km x 1-km gridded surface over the conterminous United States in Lambert Conformal Conic projection (units are meters) with the following parameters, a) horizontal datum: WGS 84, b) 1st standard parallel= 25o, 2nd standard parallel= 60o, c) Central meridian= -100o, and Latitude of origin= 42.5o, d) false easting= 0, false northing= 0. X is in the range -1949774 to -725054 m, and Y is in the range -1144097 to 222385 m. So the data is projected to a rectangular grid instead of a geographic grid. Thus, the 3 independent variables will be h, X (Easting) and Y (Northing) instead of h, ö and ë. The 12 Daymet gridded annual precipitation (P) images for the period 2003 to 2014 are used. Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., Wei, Y., Devarakonda, R., & Cook, R.B. (2014), Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1277.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "LBA-IA Ceramic Compositional Data from Troy" data publication.

In the recent past, there has been continuing growth in using GIS and related technologies by many organizations engaged in planning and management of the Kathmandu Valley. As a result, the demand for accurate and homogenous spatial data of the Valley has been realized by government as well as research and development organizations.

    This study attempts to build a comprehensive GIS Database of the Kathmandu
    Valley with an aim to bridge the important data gaps in the Valley. The study
    employs a fresh approach in constructing a GIS database with the available maps
    and integrates many different kinds of satellite imageries. The maps presented
    in this publication visualize the different scenarios and raise the awareness
    of exiting digital database. The application presented in this publication
    shall increase awareness about the usefulness of digital database and
    demonstrate what can be achieved with the GIS and related technologies. The
    database thus developed shall improve the availability of information of the
    Kathmandu Valley and assist different stakeholders engaged in planning and
    management of the Valley.

    Furthermore, the study advocates a building block approach to development,
    management and revision of database in a complementary way and it hopes to
    avoid duplication of efforts in costly production of digital data. The study
    hopes to sensitise senior executives and decision-makers about the need for a
    sound policy on database sharing, development and standards. Such a policy, at
    the national level known as National Spatial Database Infrastructure (NSDI)
    should evolve in order to benefit from the prevailing GIS technology. In using
    GIS and related technologies, the study facilitated the establishment of
    Spatial Data Infrastructure of the Kathmandu Valley in a concrete manner.


    Members informations:
    Attached Vector(s):
     MemberID: 1
    Vector Name: Contours
    Source Map Name: topo sheets
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Projection: transverse mercator
    Projection_desc: origin 87E/ 0N, false easting=900000, scale=0.9999
    Projection_meas: Meter
    Feature_type: lines
    Vector 
    Contours digitized from topo sheets

    Members informations:
    Attached Vector(s):
     MemberID: 2
    Vector Name: Roads
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Projection: see member1
    Feature_type: lines
    Vector 
    Road Network

    Members informations:
    Attached Vector(s):
     MemberID: 3
    Vector Name: Drainage
    Source Map Name: topo sheets
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Projection: see member 1
    Feature_type: lines
    Vector 
    Drainage Network

    Members informations:
    Attached Vector(s):
     MemberID: 4
    Vector Name: Land use 78
    Source Map Name: LRMP
    Source Map Scale: 50000
    Source Map Date: 1905-05-31
    Feature_type: polygon
    Vector 
    Land use

    Members informations:
    Attached Vector(s):
     MemberID: 5
    Vector Name: Land use 1995
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Feature_type: polygon
    Vector 
    Land cover


    Members informations:
    Attached Vector(s):
     MemberID: 6
    Vector Name: Administrative boundaries
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Feature_type: polygon
    Vector 
    District and VDC boundaries and various socio-economic data

    Attached Report(s)
    Member ID: 7
    Report Name: Kathmandu Valley GIS database
    Report Authors: B. Shrestha & S. Pradhan
    Report Publisher: ICIMOD
    Report Date: 2000-02-01
    Report 
    Report

Datasets used in: Young, A.M., Higuera, P.E., Duffy, P.A., and F.S. Hu. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. In Review at Ecography as of 10/2015.

----------------------- Description ----------------------------------

These data are comprised of 60 individual GeoTIFF files that indicate whether fire is estimated to haved occurred in a given pixel for a given year. Each map is representative of one calendar year. These data are gridded versions of the vector geographic data provided by the Alaska Interagency Coordination Center (AICC; http://fire.ak.blm.gov/). Converting the polygon (i.e., vector) datasets to raster was done using ESRI software in ArcMap 10.1. Specifically, we used the function "Feature to Raster" under the Conversion Toolbox. A given pixel was classified as "fire occurrence" if the center of the pixel overlapped

with a fire polygon from the AICC dataset.

----------------------- Resolution -----------------------------------

Spatial Resolution: 2 km Temporal Resolution: Annual

Time Coverage: 1950-2009

------------------------ File Naming ---------------------------------

'fire' - fire occurrence data. 1 = fire occurrence -999 = No Data/No Fire '_xxxx' - year (1950-2009)

'.tif' - file extention

------------------ Geographic Information ----------------------------

Rows: 725 Columns: 687 Spatial Extent (in meters) - - Top: 2390439.786 - Left: -656204.44 - Right: 717795.56 - Bottom: 940439.786 Spatial Reference: Albers Equal Area Datum: North American 1983 False Easting: 0 False Northing: 0 Central Meridian: -154 Standard Parallel 1: 55 Standard Parallel 2: 65 Latitude of Origin: 50

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "The Eastern Korinthia Archaeological Survey" data publication.

Datasets used in: Young, A.M., Higuera, P.E., Duffy, P.A., and F.S. Hu. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. In Review at Ecography as of 10/2015. ---------------------------------------------------------------------- ----------------------- Description ---------------------------------- ---------------------------------------------------------------------- These data are gridded maps of Alaska containing the projected 30-yr probabiltiy of fire occurrence for three different time periods in the 21st century: 2010-2039, 2040-2069, 2070-2099. We use downscaled GCM climate data under the RCP6.0 scenario. We provide projections for all 100 models for each of the three different spatial domains: AK, BOREAL, and TUNDRA. Gridded GCM data were processed and provided by the Scenarios Network for Alaska and Arctic Planning (https://www.snap.uaf.edu/). For further details regarding these archived data please refer to YOung et al. (In Review). ---------------------------------------------------------------------- ----------------------- Resolution ----------------------------------- ---------------------------------------------------------------------- Spatial Resolution: 2 km Temporal Resolution: Decadal Time Coverage: 2010-2099 ---------------------------------------------------------------------- ------------------------ File Naming --------------------------------- ---------------------------------------------------------------------- Example: AK_CCSM4_rcp60_pred_map_2010_2039_1.tif 'AK_' - Either models build for all of Alaska (AK), boreal forest only (BOREAL), tundra only (TUNDRA) 'CCSM4' - Specific GCM '_rcp60' - Climate change scenario '_pred_map' - predicted (or projected) 30-yr probabilities of fire occurrence [0-1]. '_2010_2039' - time period for projection '_x' - gbm model. corresponds to 'gbm_x.RData' and historical predictions '.tif' - file extention ---------------------------------------------------------------------- ------------------ Geographic Information ---------------------------- ---------------------------------------------------------------------- Rows: 725 Columns: 687 Spatial Extent (in meters) - - Top: 2390439.786 - Left: -656204.44 - Right: 717795.56 - Bottom: 940439.786 Spatial Reference: Albers Equal Area Datum: North American 1983 False Easting: 0 False Northing: 0 Central Meridian: -154 Standard Parallel 1: 55 Standard Parallel 2: 65 Latitude of Origin: 50

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Differentiating local from nonlocal ceramic production at Late Bronze Age/Iron Age Kinet Höyük using NAA" data publication.

This dataset provides information about the number of properties, residents, and average property values for Easting Road cross streets in Buzzards Bay, MA.

Datasets used in: Young, A.M., Higuera, P.E., Duffy, P.A., and F.S. Hu. Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change. In Review at Ecography as of 10/2015. Description: The 'AK_VEG.tif' file contains vegetation classifications for each pixel in Alaska. There are five different vegetation types. Values 1-5 represent the following vegetation classifications: 1 - Wetland Tundra 2 - Shrub Tundra 3 - Graminoid Tundra 4 - Barren Tundra 5 - Boreal Forest Methods to use these maps to create the 'AK_VEG.tif' file are described in Young et al. (In Review). The 'ecor.tif' file is a map that classifies pixels in Alaska by ecoregion. The original ecoregion map is from (Nowacki et al. 2001). The main modification to the original ecoregions map was the addition of the Noatak River Watershed. The spatial coverage of the Noatak River Watershed was obtained from the perimeter of the Noatak National Preserve, available from: (). The original ecoregions map of Alaska is is in vector data format (i.e., polygons). We converted these data to a raster format using the "Feature to Raster" conversion tool in the "Conversion Toolbox" in ESRI Software ArcMap 10.1 Ecoregion identifications can be found in Fig. 1 of Young et al. (In Review). The 'TR.tif' file is a map of topographic ruggedness measured in meters. Methods used to create the topographic ruggedness are described in Young et al. (In Reveiw). Resolution: Spatial Resolution: 2 km Temporal Resolution: NA Time Coverage: NA Geographic Information: Rows: 725 Columns: 687 Spatial Extent (in meters) - - Top: 2390439.786 - Left: -656204.44 - Right: 717795.56 - Bottom: 940439.786 Spatial Reference: Albers Equal Area Datum: North American 1983 False Easting: 0 False Northing: 0 Central Meridian: -154 Standard Parallel 1: 55 Standard Parallel 2: 65 Latitude of Origin: 50

China's land cover data set includes 5 products: 1) glc2000_lucc_1km_China.asc, a Chinese subset of global land cover data based on SPOT4 remote sensing data developed by the GLC2000 project. The data name is GLC2000.GLC2000 China's regional land cover data is directly cropped from global cover data. For data description, please refer to http : //www-gvm.jrc.it/glc2000/defaultGLC2000.htm 2) igbp_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR remote sensing data supported by IGBP-DIS, the data name is IGBPDIS; IGBPDIS data was prepared using the USGS method, using April 1992 to March 1992 The AVHRR data developed global land cover data with a resolution of 1km. The classification system adopts a classification system developed by IGBP, which divides the world into 17 categories. Its development is based on continents. Applying AVHRR for 12 months to maximize synthetic NDVI data, 3) modis_lucc_1km_China_2001.asc, a subset of MODIS land cover data products in China, the data name is MODIS; MODIS China's regional land cover data is directly cropped from global cover data, and its data description please refer to http://edcdaac.usgs.gov/ modis / mod12q1v4.asp. 4. umd_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR data produced by the University of Maryland, the data name is UMd; the five bands of UMd based on AVHRR data and NDVI data are recombined to suggest a data matrix, using Methodology carried out global land cover classification. The goal is to create data that is more accurate than past data. The classification system largely adopts the classification scheme of IGBP. 5) westdc_lucc_1km_China.asc, China ’s 2000: 100,000 land cover data organized and implemented by the Chinese Academy of Sciences, combined with Yazashi conversion (the largest area method), and finally obtained a land use data product of 1km across the country, data name WESTDC. WESTDC China's regional land cover data is based on the results of a 1: 100,000 county-level land resource survey conducted by the Chinese Academy of Sciences. The land use data were merged and converted into a vector (the largest area method). The Chinese Academy of Sciences resource and environment classification system is adopted. 2: Data format: ArcView GIS ASCII 3: Mesh parameters: ncols 4857 nrows 4045 xllcorner -2650000 yllcorner 1876946 cellsize 1000 NODATA_value -9999 4: Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 / * 1st standard parallel 47 00 0.000 / * 2nd standard parallel 105 00 0.000 / * central meridian 0 0 0.000 / * latitude of projection's origin 0.00000 / * false easting (meters) 0.00000 / * false northing (meters)

Velocity-based macrorefugia for boreal passerine birds

Citation for dataset

Stralberg, D. Velocity-based macrorefugia for boreal passerine birds. Boreal Avian Modelling Project. Edmonton, Alberta, Canada. DOI: 10.5281/zenodo.1299880
https://doi.org/10.5281/zenodo.1299880

Data layers

Refugia layers represent mid-century (2041-2070) and end-of-century (2071-2100) conditions for the SRES A2 emissions scenario at 4-km resolution

Combined index for 53 species (clipped to Brandt's boreal region):
_refbrandt53_YYYYZZZZ

Species-specific indices:
XXXX_refYYYY

where:
YYYY = Time period (2050s or 2080s)
ZZZZ = weighted or unweighted
XXXX = Songbird Species Code (see Birdlookup.csv)

Percentile values of refugia indices for mapping purposes
0.01 0.1 0.25 0.5 0.75 0.9 0.99 "2050s, weighted " 0.032 0.243 0.317 0.399 0.484 0.589 0.779 "2080s, weighted" 0.002 0.09 0.137 0.2 0.281 0.386 0.675 "2050s, unweighted" 0.006 0.108 0.159 0.218 0.292 0.358 0.421 "2080s, unweighted" 0.001 0.055 0.083 0.123 0.185 0.241 0.297

Projection information

"""+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs"""

Projection LAMBERT
Spheroid GRS80
Units METERS
Zunits NO
Xshift 0.0
Yshift 0.0
Parameters
49 0 0.0 /* 1st standard parallel
77 0 0.0 /* 2nd standard parallel
-95 0 0.0 /* central meridian
0 0 0.0 /* latitude of projection's origin
0.0 /* false easting (meters)
0.0 /* false northing (meters)