Zonal summaries by county. Field descriptions and sources are described below:

Field names and descriptions:

GEOID: Unique county identifier (TIGER)

NAME: County Name

STATE_NAME: State Name

STATE_FIPS: State FIPS code

CNTY_FIPS: County FIPS code

FIPS: State and County FIPS Codes

Area (sq km): Area of county in square kilometers

pct_nodata: NLCD 2011 - Percentage of county classified as No Data

pct_opwat: NLCD 2011 - Percentage of county classified as Open Water

pct_ice_sn: NLCD 2011 - Percentage of county classified as Perennial Ice Snow

pct_dev_op: NLCD 2011 - Percentage of county classified as Developed Open Space

pct_dev_li: NLCD 2011 - Percentage of county classified as Developed Low Intensity

pct_dev_mi: NLCD 2011 - Percentage of county classified as Developed Medium Intensity

pct_dev_hi: NLCD 2011 - Percentage of county classified as Developed High Intensity

pct_barren: NLCD 2011 - Percentage of county classified as Barren Land

pct_de_for: NLCD 2011 - Percentage of county classified as Deciduous Forest

pct_ev_for: NLCD 2011 - Percentage of county classified as Evergreen Forest

pct_mi_for: NLCD 2011 - Percentage of county classified as Mixed Forest

pct_shrub: NLCD 2011 - Percentage of county classified as Shrub Scrub

pct_grass: NLCD 2011 - Percentage of county classified as Grassland Herbaceous

pct_pastur: NLCD 2011 - Percentage of county classified as Pasture Hay

pct_cultcr: NLCD 2011 - Percentage of county classified as Cultivated Crops

pct_woodyw: NLCD 2011 - Percentage of county classified as Woody Wetlands

pct_e_herb: NLCD 2011 - Percentage of county classified as Emergent Herbaceous Wetlands

pad_nodata: NLCD 2011 - Percentage of county classified as No Data within protected areas as defined in Protected Areas Database

pad_opwat: NLCD 2011 - Percentage of county classified as Open Water within protected areas as defined in Protected Areas Database

pad_ice_sn: NLCD 2011 - Percentage of county classified as Perennial Ice Snow within protected areas as defined in Protected Areas Database

pad_dev_op: NLCD 2011 - Percentage of county classified as Developed Open Space within protected areas as defined in Protected Areas Database

pad_dev_li: NLCD 2011 - Percentage of county classified as Developed Low Intensity within protected areas as defined in Protected Areas Database

pad_dev_mi: NLCD 2011 - Percentage of county classified as Developed Medium Intensity within protected areas as defined in Protected Areas Database

pad_dev_hi: NLCD 2011 - Percentage of county classified as Developed High Intensity within protected areas as defined in Protected Areas Database

pad_barren: NLCD 2011 - Percentage of county classified as Barren Land within protected areas as defined in Protected Areas Database

pad_de_for: NLCD 2011 - Percentage of county classified as Deciduous Forest within protected areas as defined in Protected Areas Database

pad_ev_for: NLCD 2011 - Percentage of county classified as Evergreen Forest within protected areas as defined in Protected Areas Database

pad_mi_for: NLCD 2011 - Percentage of county classified as Mixed Forest within protected areas as defined in Protected Areas Database

pad_shrub: NLCD 2011 - Percentage of county classified as Shrub Scrub within protected areas as defined in Protected Areas Database

pad_grass: NLCD 2011 - Percentage of county classified as Grassland Herbaceous within protected areas as defined in Protected Areas Database

pad_pastur: NLCD 2011 - Percentage of county classified as Pasture Hay within protected areas as defined in Protected Areas Database

pad_cultcr: NLCD 2011 - Percentage of county classified as Cultivated Crops within protected areas as defined in Protected Areas Database

pad_woodyw: NLCD 2011 - Percentage of county classified as Woody Wetlands within protected areas as defined in Protected Areas Database

pad_e_herb: NLCD 2011 - Percentage of county classified as Emergent Herbaceous Wetlands within protected areas as defined in Protected Areas Database

pwr_inslen: HSIP Gold 2013 - Transmission line density for each county - In-Service Transmission Length (km)

pwr_prolen: HSIP Gold 2013 - Transmission line density for each county - Proposed Transmission Length (km)

pwr_insden: HSIP Gold 2013 - Transmission line density for each county - In-Service Length Per Area (km/sq km)

pwr_proden: HSIP Gold 2013 - Transmission line density for each county - Proposed Length Per Area (km/sq km)

grass2crop: NLCD 2001-2011 percentage change - grassland herbaceous to cultivated crops

grass2dev: NLCD 2001-2011 percentage change - grassland herbaceous to developed (classes 21, 22, 23, 24)

pct_edge: NLCD 2011 cultivated crop edge/core - pct of county classified as cultivated crops within 30 meters of another class (crop edge)

pct_core: NLCD 2011 cultivated crop edge/core - pct of county classified as cultivated crops not within 30 meters of another class (crop core)

pct_edge2: NLCD 2011 cultivated crop edge/core - pct of cultivated crop total area within county classified as cultivated crops within 30 meters of another class (crop edge)

pct_core2: NLCD 2011 cultivated crop edge/core - pct of cultivated crop total area within county classified as cultivated crops not within 30 meters of another class (crop core)

rail_len: TIGER 2014 - Railroad Length (km) by county

rail_den: TIGER 2014 - Railroad Length Per Area (km/sq km) by county

crp_chg: Percentage of county that changed in CRP enrollment from 9/30/2007 to 10/31/2013 - USDA FSA

pct_corn02: Census of Agriculture 2002 - Percent of county corn for grain

pct_soyb02: Census of Agriculture 2002 - Percent of county soybeans for beans

pct_whea02: Census of Agriculture 2002 - Percent of county wheat for grain

pct_corn07: Census of Agriculture 2007 - Percent of county corn for grain

pct_soyb07: Census of Agriculture 2007 - Percent of county soybeans for beans

pct_whea07: Census of Agriculture 2007 - Percent of county wheat for grain

pct_corn12: Census of Agriculture 2012 - Percent of county corn for grain

pct_soyb12: Census of Agriculture 2012 - Percent of county soybeans for beans

pct_whea12: Census of Agriculture 2012 - Percent of county wheat for grain

corn02_07: Census of Agriculture - Percent change corn for grain 2002-2007 by county

soyb02_07: Census of Agriculture - Percent change soybeans for beans 2002-2007 by county

whea02_07: Census of Agriculture - Percent change wheat for grain 2002-2007 by county

corn07_12: Census of Agriculture - Percent change corn for grain 2007-2012 by county

soyb07_12: Census of Agriculture - Percent change soybeans for beans 2007-2012 by county

whea07_12: Census of Agriculture - Percent change wheat for grain 2007-2012 by county

cdl_var: NASS Cropland data layer - Variety of crop types by county

cdl_maj: NASS Cropland data layer - Majority crop type by county

s1100_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Primary road

s1200_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Secondary road

s1400_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Local Neighborhood Road, Rural Road, City Street

s1500_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Vehicular Trail (4WD)

s1630_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Ramp

s1640_rds: Tiger 2014 roads - Percent of county composed of rasterized road class -Service Drive usually along a limited access highway

s1710_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Walkway/Pedestrian Trail

s1720_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Stairway

s1730_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Alley

s1740_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Private Road for service vehicles (logging, oil fields, ranches, etc.)

s1750_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Internal U.S. Census Bureau use

s1780_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Parking Lot Road

s1820_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Bike Path or Trail

s1830_rds: Tiger 2014 roads - Percent of county composed of rasterized road class - Bridle Path

crp_cp01: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp02: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp04D: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp04B: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp08: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp09: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp10: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp15: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp21: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp23BC: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp23FP: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp23NF: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp24: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp25: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp27: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp28: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp29: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp30: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp33: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp37: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp38: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp39: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp41: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_cp42: FSA - Potential pollinator/milkweed CRP practices - Percent of county

crp_all: FSA - Potential pollinator/milkweed CRP practices - Percent of county - All CRP practices

pest92gly: USGS Estimated Annual Agricultural Pesticide Use, 1992 (Glyphosate) - kg/sq km

pest93gly: USGS Estimated Annual Agricultural Pesticide Use, 1993 (Glyphosate) - kg/sq km

pest94gly: USGS Estimated Annual Agricultural Pesticide Use, 1994 (Glyphosate) - kg/sq km

pest95gly: USGS Estimated Annual Agricultural Pesticide Use, 1995 (Glyphosate) - kg/sq km

pest96gly: USGS Estimated Annual Agricultural Pesticide Use, 1996 (Glyphosate) - kg/sq km

pest97gly: USGS Estimated Annual Agricultural Pesticide Use, 1997 (Glyphosate) - kg/sq km

pest98gly: USGS Estimated Annual Agricultural Pesticide Use, 1998 (Glyphosate) - kg/sq km

pest99gly: USGS Estimated Annual Agricultural Pesticide Use, 1999 (Glyphosate) - kg/sq km

pest00gly: USGS Estimated Annual Agricultural Pesticide Use, 2000 (Glyphosate) - kg/sq km

pest01gly: USGS Estimated Annual Agricultural Pesticide Use, 2001 (Glyphosate) - kg/sq km

pest02gly: USGS Estimated Annual Agricultural Pesticide Use, 2002 (Glyphosate) - kg/sq km

pest03gly: USGS Estimated Annual Agricultural Pesticide Use, 2003 (Glyphosate) - kg/sq km

pest04gly: USGS Estimated Annual Agricultural Pesticide Use, 2004 (Glyphosate) - kg/sq km

pest05gly: USGS Estimated Annual Agricultural Pesticide Use, 2005 (Glyphosate) - kg/sq km

pest06gly: USGS Estimated Annual Agricultural Pesticide Use, 2006 (Glyphosate) - kg/sq km

pest07gly: USGS Estimated Annual Agricultural Pesticide Use, 2007 (Glyphosate) - kg/sq km

pest08gly: USGS Estimated Annual Agricultural Pesticide Use, 2008 (Glyphosate) - kg/sq km

pest09gly: USGS Estimated Annual Agricultural Pesticide Use, 2009 (Glyphosate) - kg/sq km

chg9509gly: USGS Estimated Annual Agricultural Pesticide Use, Change from 1995-2009 (Glyphosate) - kg/sq km

pest94imi: USGS Estimated Annual Agricultural Pesticide Use, 1994 (Imidacloprid) - kg/sq km - Neonicotinoid

pest95imi: USGS Estimated Annual Agricultural Pesticide Use, 1995 (Imidacloprid) - kg/sq km - Neonicotinoid

pest96imi: USGS Estimated Annual Agricultural Pesticide Use, 1996 (Imidacloprid) - kg/sq km - Neonicotinoid

pest97imi: USGS Estimated Annual Agricultural Pesticide Use, 1997 (Imidacloprid) - kg/sq km - Neonicotinoid

pest98imi: USGS Estimated Annual Agricultural Pesticide Use, 1998 (Imidacloprid) - kg/sq km - Neonicotinoid

pest99imi: USGS Estimated Annual Agricultural Pesticide Use, 1999 (Imidacloprid) - kg/sq km - Neonicotinoid

pest00imi: USGS Estimated Annual Agricultural Pesticide Use, 2000 (Imidacloprid) - kg/sq km - Neonicotinoid

pest01imi: USGS Estimated Annual Agricultural Pesticide Use, 2001 (Imidacloprid) - kg/sq km - Neonicotinoid

pest02imi: USGS Estimated Annual Agricultural Pesticide Use, 2002 (Imidacloprid) - kg/sq km - Neonicotinoid

pest03imi: USGS Estimated Annual Agricultural Pesticide Use, 2003 (Imidacloprid) - kg/sq km - Neonicotinoid

pest04imi: USGS Estimated Annual Agricultural Pesticide Use, 2004 (Imidacloprid) - kg/sq km - Neonicotinoid

pest05imi: USGS Estimated Annual Agricultural Pesticide Use, 2005 (Imidacloprid) - kg/sq km - Neonicotinoid

pest06imi: USGS Estimated Annual Agricultural Pesticide Use, 2006 (Imidacloprid) - kg/sq km - Neonicotinoid

pest07imi: USGS Estimated Annual Agricultural Pesticide Use, 2007 (Imidacloprid) - kg/sq km - Neonicotinoid

pest08imi: USGS Estimated Annual Agricultural Pesticide Use, 2008 (Imidacloprid) - kg/sq km - Neonicotinoid

pest09imi: USGS Estimated Annual Agricultural Pesticide Use, 2009 (Imidacloprid) - kg/sq km - Neonicotinoid

chg9509imi: USGS Estimated Annual Agricultural Pesticide Use, Change from 1995-2009 (Imidacloprid) - kg/sq km - Neonicotinoid

pest04clo: USGS Estimated Annual Agricultural Pesticide Use, 2004 (Clothianidin) - kg/sq km - Neonicotinoid

pest05clo: USGS Estimated Annual Agricultural Pesticide Use, 2005 (Clothianidin) - kg/sq km - Neonicotinoid

pest06clo: USGS Estimated Annual Agricultural Pesticide Use, 2006 (Clothianidin) - kg/sq km - Neonicotinoid

pest07clo: USGS Estimated Annual Agricultural Pesticide Use, 2007 (Clothianidin) - kg/sq km - Neonicotinoid

pest08clo: USGS Estimated Annual Agricultural Pesticide Use, 2008 (Clothianidin) - kg/sq km - Neonicotinoid

pest09clo: USGS Estimated Annual Agricultural Pesticide Use, 2009 (Clothianidin) - kg/sq km - Neonicotinoid

pest05din: USGS Estimated Annual Agricultural Pesticide Use, 2005 (Dinotefuran) - kg/sq km - Neonicotinoid

pest06din: USGS Estimated Annual Agricultural Pesticide Use, 2006 (Dinotefuran) - kg/sq km - Neonicotinoid

pest07din: USGS Estimated Annual Agricultural Pesticide Use, 2007 (Dinotefuran) - kg/sq km - Neonicotinoid

pest08din: USGS Estimated Annual Agricultural Pesticide Use, 2008 (Dinotefuran) - kg/sq km - Neonicotinoid

pest09din: USGS Estimated Annual Agricultural Pesticide Use, 2009 (Dinotefuran) - kg/sq km - Neonicotinoid

pest00thi: USGS Estimated Annual Agricultural Pesticide Use, 2000 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest01thi: USGS Estimated Annual Agricultural Pesticide Use, 2001 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest02thi: USGS Estimated Annual Agricultural Pesticide Use, 2002 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest03thi: USGS Estimated Annual Agricultural Pesticide Use, 2003 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest04thi: USGS Estimated Annual Agricultural Pesticide Use, 2004 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest05thi: USGS Estimated Annual Agricultural Pesticide Use, 2005 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest06thi: USGS Estimated Annual Agricultural Pesticide Use, 2006 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest07thi: USGS Estimated Annual Agricultural Pesticide Use, 2007 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest08thi: USGS Estimated Annual Agricultural Pesticide Use, 2008 (Thiamethoxam) - kg/sq km - Neonicotinoid

pest09thi: USGS Estimated Annual Agricultural Pesticide Use, 2009 (Thiamethoxam) - kg/sq km - Neonicotinoid

coa_mval12: Census of Agriculture 2012 - Estimated market value of land and buildings \ Average per acre (dollars)

milk_rich: USDA Plants Database Milkweed (Asclepias) species richness by County

wort_rich: USDA Plants Database Swallowwort (Cynanchum) species richness by County

milk_richst: USDA Plants Database Milkweed (Asclepias) species richness by State

wort_richst: USDA Plants Database Swallowwort (Cynanchum) species richness by State

MXCropland: Total square kilometers of marginal land that is also classified as cropland

MXIdle: Total square kilometers of marginal land that is also classified as Idle

MXShrub: Total square kilometers of marginal land that is also classified as shrub

MXPasture: Total square kilometers of marginal land that is also classified as pasture

MXOther: Total square kilometers of marginal land that is also classified as other

I35_100mi: Counties centroid falls within buffer around I-35 (Source: TIGER roads)

mon_5_reg: Model regions made to facilitate the economic analysis for monarchs (Semmens)

mw_reg_fnl: Monarch regions from willingness to pay analysis (Diffendorfer)

pct_harv12: Census of Agriculture 2012 - Percent of county harvested cropland

County and zonal summary data sources:

Counties - 2014

Source

U.S. Department of Commerce, U.S. Census Bureau, Geography Division

Abstract

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.

The boundaries for counties and equivalent entities are mostly as of January 1, 2013, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS). However, some changes made after January 2013, including the addition and deletion of counties, are included.

Cropland Data Layer - 2014

Source

United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)

Abstract

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2014 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data. The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.

National Land Cover Dataset - 2011

Source

United States Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Research and Development Division (RDD), Geospatial Information Branch (GIB), Spatial Analysis Research Section (SARS)

Abstract

The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2014 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 8 OLI/TIRS sensor and the Disaster Monitoring Constellation (DMC) DEIMOS-1 and UK2 sensors collected during the current growing season. Agricultural training and validation data are derived from the Farm Service Agency (FSA) Common Land Unit (CLU) Program. The most current version of the NLCD is used as non-agricultural training and validation data. The purpose of the Cropland Data Layer Program is to use satellite imagery to (1) provide acreage estimates to the Agricultural Statistics Board for the state's major commodities and (2) produce digital, crop-specific, categorized geo-referenced output products.

Transmission Lines - 2013

Source

The HSIP Gold database is assembled by the National Geospatial-Intelligence Agency (NGA) in partnership with the Homeland Infrastructure Foundation-Level Data (HIFLD) Working Group for use by Homeland Defense (HD), Homeland Security (HLS), National Preparedness – Prevention, Protection, Mitigation, Response and Recovery (NP-PPMR&R) communities.

Abstract

The Transmission Lines feature class is a GIS file depicting market significant electric power transmission lines in North America. Included lines generally have a capacity of greater than 69 kilovolts. Features in this feature class often represent multiple circuits. Some features have been created in order to link major features from the Electric Power Generation Plants feature class to the electrical grid through the associated feature from the Substations feature class. The Transmission Lines feature class contains existing and proposed features. Features that are currently operational can be isolated by examining the PROPOSED field for an "In Service" attribute.

Roads - 2014

Source

U.S. Department of Commerce, U.S. Census Bureau, Geography Division

Abstract

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Rails - 2014

Source

U.S. Department of Commerce, U.S. Census Bureau, Geography Division

Abstract

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

The Rails Shapefile includes all features within the MTDB Super Class "Rail Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB tha begin with "R". This includes main lines such as spur lines, rail yards, mass transit rail lines such as carlines, streetcar track, monorail or other mass transit rail and special purpose rail lines such as cog rail lines, incline rail lines and trams.

Protected Areas Database of the United States - 2012

Source

US Geological Survey (USGS) - Gap Analysis Program (GAP)

Abstract

The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The geodatabase contains four feature classes such as, Marine Protected Areas (MPA) and Easements that each contains uniquely associated attributes. These two feature classes are combined with the PAD-US Fee feature class to provide a full inventory of protected areas in a common schema (i.e. Combined file).

Conservation Reserve Program - 2014

Source

US Department of Agriculture (USDA) - Farm Service Agency (FSA) Aerial Photography Field Office

Abstract

The common land unit (CLU) dataset consists of digitized farm tract and field boundaries and associated attribute data. The USDA Farm Service Agency (FSA) defines farm fields as agricultural land that is delineated by natural and man-made boundaries such as road ways, tree lines, waterways, fence lines, etc. Field boundaries are visible features that can be identified and delineated on aerial photography and digital imagery. Farm tracts are defined by FSA as sets of contiguous fields under single ownership. Common land units are used to administer USDA farm commodity support and conservation programs in a GIS environment.

Gridded SSURGO (gSSURGO) National Commodity Crop Productivity Index (NCCPI) Version 2 - 2012

Source

Soil Survey Staff. National Value Added Look Up (valu) Table Database for the Gridded Soil Survey Geographic (gSSURGO) Database for the United States of America and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. United States Department of Agriculture, Natural Resources Conservation Service.

Abstract

This dataset is called the National Value Added Look Up (valu) Table database. The valu1 table resides in the valu database and is comprised of 57 pre-summarized or “ready to map” attributes derived from the official SSURGO database. These attribute data are pre-summarized to the map unit level using best-practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. These themes were prepared to better meet the mapping needs of users of soil survey information and can be used with both SSURGO and gridded SSURGO (gSSURGO) datasets. Below is a partial list of the data found in the valu1 table. National Commodity Crop Productivity Index (NCCPI) Version 2 - weighted average index for major components (Dobos, Sinclair, and Robotham, 2012)

Estimated Annual Agricultural Pesticide Use for Counties of the Conterminous United States, 1992–2009

Source

U.S. Geological Survey

Abstract

These data were estimated annual agricultural pesticide use for the four most commonly used neonictinoid pesticides for counties of the conterminous United States from 1992 through 2009, following the methods described in Thelin and Stone (2013). As described in Thelin and Stone (2013), U.S. Department of Agriculture county-level data for harvested-crop acreage were used in conjunction with proprietary Crop Reporting District (CRD)-level pesticide-use data to estimate county-level pesticide use. Estimated pesticide use (EPest) values were calculated with both the EPest-high and EPest-low methods. The estimates of annual agricultural pesticide use were provided in tab-delimited files and organized by compound, year, state Federal Information Processing Standard (FIPS) code, county FIPS code, and kg (amount in kilograms).

Thelin, G.P., and Stone, W.W., 2013, Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992–2009: U.S. Geological Survey Scientific Investigations Report 2013-5009, 54 p.

Census of Agriculture – 2002-2012

Source

United States Department of Agriculture, National Agricultural Statistics Service

Abstract

The Census of Agriculture is the leading source of facts and figures about American agriculture. Conducted every five years, the Census provides a detailed picture of U.S. farms and ranches and the people who operate them. It is the only source of uniform, comprehensive agricultural data for every state and county in the United States. Participation by every farmer and rancher, regardless of the size or type of operation, is vitally important. By responding to the Census, producers are helping themselves, their communities and all of U.S. agriculture.

The 2012 Census of Agriculture collected information concerning all areas of farming and ranching operations, including production expenses, market value of products, and operator characteristics. This information is used by everyone who provides services to farmers and rural communities - including federal, state and local governments, agribusinesses, and many others. Census data is used to make decisions about many things that directly impact farmers, including: community planning, store/company locations, availability of operational loans and other funding, location and staffing of service centers, farm programs and policies.

PLANTS Database - 2015

Source

United States Department of Agriculture, Natural Resources Conservation Service

Abstract

The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories. It includes names, plant symbols, checklists, distributional data, species abstracts, characteristics, images, crop information, automated tools, onward Web links, and references. This information primarily promotes land conservation in the United States and its territories, but academic, educational, and general use is encouraged. PLANTS reduces government spending by minimizing duplication and making information exchange possible across agencies and disciplines.

PLANTS is a collaborative effort of the USDA NRCS National Plant Data Team (NPDT), the USDA NRCS Information Technology Center (ITC), The USDA National Information Technology Center (NITC), and many other partners. Much of the PLANTS data and design is developed at NPDT, and the Web application is programmed at ITC and NITC and served through the USDA Web Farm. Here’s more information about who does what on the PLANTS Team, our Partners, and our Data Contributors.

USDA, NRCS. 2015. The PLANTS Database (http://plants.usda.gov, 25 September 2015). National Plant Data Team, Greensboro, NC 27401-4901 USA.

Biota of North America Program, North American Plant Atlas – 2015

Source

Biota of North America Program

Kartesz, J.T., The Biota of North America Program (BONAP). 2015. North American Plant Atlas. (http://bonap.net/napa). Chapel Hill, N.C. [maps generated from Kartesz, J.T. 2015. Floristic Synthesis of North America, Version 1.0. Biota of North America Program (BONAP). (in press)].

Abstract

The North American Plant Atlas represents the first comprehensive attempt to provide state- and county-level distribution maps of all vascular plant taxa found within the study area. It also provides multiple unique maps depicting unique soil and substrate types, climates and temperature zones, along with vegetation maps for the continent.

Monarch regions

Source

Diffendorfer, J. E., L. Ries, J. B. Loomis, K. Oberhauser, L. Lopez-Hoffman, B. Semmens, B. Butterfield, D. Semmens, K. Bagstad, J. Goldstein, R. Wiederholt, J. Dubovsky, B. Mattsson, and W. E. Thogmartin. 2014. National valuation of monarch butterflies indicates an untapped potential for incentive-based conservation. Conservation Letters 7:253-262.

See USGS press release at http://goo.gl/rXh3bj

Abstract

Monarch regions were developed and used in the study defined as; West breeding, East summer, East spring, Florida, California overwintering and Mexico overwintering.

Asclepias and monarch distribution models under moderate and severe climate change scenarios

Source

Lemoine, N. P. 2015. Climate change may alter breeding ground distributions of eastern migratory monarchs (Danaus plexippus) via range expansion of Asclepias host plants. PLoS ONE 10(2): e0118614. doi:10.1371/journal.pone.0118614 http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118614

Abstract

Given that monarchs largely depend on the genus Asclepias as larval host plants, the effects of climate change on monarch northward migrations will most likely be mediated by climate change effects on Asclepias. Lemoine used MaxEnt species distribution modeling to assess potential changes in Asclepias and monarch distributions under moderate and severe climate change scenarios. Model predictions suggest Asclepias, and consequently monarchs, should undergo expanded northern range limits in summer months while encountering reduced habitat suitability throughout the northern migration.

Core monarch breeding boundary identified by isotope analysis

Source

Wassenaar, L. I., and K. A. Hobson. 1998. Natal origins of migratory monarch butterflies at wintering colonies in Mexico: New isotopic evidence. Proceedings of the National Academy of Sciences, USA 95:15436-15439. http://www.pnas.org/content/95/26/15436.full

Abstract

Each year, millions of monarch butterflies from eastern North America migrate to overwinter in 10–13 discrete colonies located in the Oyamel forests of central Mexico. For decades, efforts to track monarch migration have relied on observations and tag-recapture methods. Stable hydrogen (δD) and carbon (δ13C) isotope ratios of wintering monarchs were used to evaluate natal origins on the summer breeding range. Stable-hydrogen and carbon isotopic values of 597 wintering monarchs from 13 wintering roost sites were compared with isotopic patterns measured in individuals at natal sites across their breeding range over a single migration cycle. Wassenaar and Hobson determined that all monarch wintering colonies were composed of individuals originating mainly from the Midwest, United States, thereby providing evidence for a panmictic model of wintering colony composition. The figure in this paper depicting the primary breeding area for monarch butterflies in North America was digitized for use as a coarse filter in prioritization scenarios.

Tagged monarch capture frequencies

Source

Chip Taylor, Monarch Watch

Abstract

Monarch frequencies, larger number denotes lower frequency of capture during migration (Min. 800 - Max. 12)