This dataset contains records from bird point count and vegetation surveys conducted at 71 sites in southern Iowa in 2022 and 2023. All sites had recently implemented a management practice aimed at increasing habitat quality for northern bobwhite. We conducted surveys at 1-3 points per site and visited each point 3 times between May 15 and July 31 each year. Some sites were visited in both years and others were only surveyed in one year. We also collected vegetation data from points to assess fine-scale vegetation characteristics.

This dataset contains estimate for median household income in past 12 months for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Median household income is inflation adjusted based on last year in data collection period. Data is from the American Community Survey, Five Year Estimates, Table B19013.

Metadata and data derived from Philip A. DuMont Collection. Philip A. DuMont was an ornithologist and naturalist who spent much of his early life in Iowa. He published a monograph on Iowa birds and was hired by Jay N. 'Ding' Darling to assess potential wildlife areas in Iowa. He worked in the U.S. Fish and Wildlife Service from 1935 to 1972 and wrote hundreds of leaflets, bird and mammal lists, and brochures for the refuge system. This collection includes correspondence and other documents from DuMont's time in Iowa on topics including birds, avian specimen collections, and potential wildlife areas.

This measure counts the number of cities that have experience growth from the previous estimate from non-overlapping 60 month data collection period.

Three streamflow measurements are used to demonstrate the use of equations developed in Mueller (in review). All three measurements are from various locations on the Mississippi River. These data were not collected for the purpose of this paper but provide practical examples of the effect of heading errors. The use of data from the Mississippi River allows the collection of 500 or more ensembles in each transect which reduce the overall effect of random errors that could complicate the identification of effects due to heading errors. In addition, by using wide cross sections, the effect of GPS errors due to vegetation near the boundaries of the river are minimized. All measurements were collected with WinRiver II (Teledyne RD Instruments, 2016) and processed with QRev (Mueller, 2016). These three data sets represent three different situations: 1) availability of heading data from a GPS compass (Mississippi River near Hickman, KY), 2) transects intentionally collected at different speeds (Mississippi River near Vicksburg, MS), and 3) GPS data collected where there is minimal influence from a moving bed (Mississippi River near Clinton, IA). All data were collected using Teledyne RD Instruments Rio Grande ADCPs. All data were collected with Teledyne RD Instrument WinRiver II (Teledyne RD Instruments, 2016) and processed with QRev version 3.43 (Mueller, 2016). Due to the complexity of an ADCP data file and the various algorithms applied to compute the streamflow from ADCP data, these data are most useful in either 1) their original raw data format which can be opened and processed in either WinRiver II or QRev or 2) their processed format that can be opened and processed by QRev or opened by Matlab or any software that can read Matlab formatted files. Both WinRiver II and QRev are distributed free. WinRiver II can be obtained from: http://www.teledynemarine.com/rdi/support# QRev can be obtained from: https://hydroacoustics.usgs.gov/movingboat/QRev.shtml Each measurement consists of: 1) *.mmt file is an xml configuration file used by WinRiver II for setup, specific measurement data entry, and filenames of the raw transect data files (pd0) 2) *.pd0 files are the raw binary data collected by WinRiver II. The format for these files is defined in Teledyne RD Instruments (2016).
3) *.txt files contain raw ASCII data from external sensors such as GPS receivers. These data are not used by WinRiver II or QRev but provide the raw external data strings sent by the GPS receiver. 4) *.mat files are the saved data processed by QRev. These files can be opened and processed by QRev or loaded into Matlab or software that can read Matlab formatted files. The variable definitions are documented in Mueller (2016). 5) *.xml are summaries of the data processed by QRev. The variable definitions are documented in Mueller (2016).

This measure counts the number of cities that have experience population decline from the previous estimate from non-overlapping 60 month data collection period.

Analysis of ‘MCSP Monarch and Plant Monitoring - SOP 2 Pollard Walk 2018 Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/20d09488-9922-44cd-abcd-4bbfd9807417 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This record contains monarch butterfly observations collected using a pollard walk (following SOP 2; ServCat record 103367) during 2018 at custom 2017 GRTS draw sites within select monitoring areas. These data were collected as part of the Monarch Conservation Science Partnership (MCSP) monarch butterfly and habitat monitoring trial. Final verified data file includes butterfly counts for sites sampled in Legacy Region 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri.

--- Original source retains full ownership of the source dataset ---

description: The United States Geological Survey (USGS), in collaboration with other state and federal agencies, industry, and academia, is conducting a National Geochemical Survey (NGS) to produce a database of geochemical information for the United States based primarily on stream sediments, analyzed using a single set of methods. This data set will comprise a national-scale geochemical coverage of the US, and will enable construction of geochemical maps, refine estimates of baseline chemical element concentrations in the sampled media, and provide context for a wide variety of geological and environmental studies. The goal of the NGS is to analyze at least one stream-sediment sample within every 289 km2 area across the US, using a consistent set of analytical methods, substituting soil samples where necessary. The survey incorporates geochemical data from a variety of sources, including existing analyses in USGS databases, reanalyses of samples in USGS archives, and analyses of recently collected samples. Currently, the NGS data covers ~71% of the land area of the US, and includes samples from all 50 states. The Iowa Geological Survey (IGS) of the Iowa Department of Natural Resources (IDNR) entered into an agreement with the USGS in September of 2002 whereby the IGS would design a database for field parameters and collect two soil sample sets. One set for the USGS to process and analyze, and one set to reposit. Field parameters included ambient site conditions, GPS location, elevation, landscape position, vegetation type, NRCS soil type, sample depth, and soil horizon, texture, color and moisture. Digital photographs were taken of the site and samples at each location. The samples were sieved to -100 mesh, then analyzed for 40 elements using inductively coupled plasma-atomic emission spectrometry/acid dissolution (ICP40), and 6 elements using atomic absorption spectrometry (AA). Arsenic (As) and gold (Au) were analyzed for using both methods. Analyses were performed by the USGS or an approved laboratory, using standard methods and an U.S.E.P.A. approved quality assurance/quality control plan. To maximize statistical reliability, sample collection in Iowa was based on a 17 km x 17 km grid, displayed on USGS 1:250,000 quad maps. Each grid or cell was identified by quad name and cell column and row position, and divided into four 72 km2 quadrants, and one was selected at random for sampling. The IGS selected specific sampling sites within the selected quadrants. To separate leached horizons from those accumulating CO3, one shallow (0-8 inches) and one deep (12-24 inches) sample were collected from 463 regular and 72 analysis of variance (AOV) sites from May through August of 2003 and shipped to the USGS in August. Randomly selected AOV sites were sampled to provide a data set for statistical analysis to test the adequacy of the samples to measure differences of sediment chemistry between cells, within cells, within sites, and between chemical analyses. "AOV1" was collected within the designated quadrant of the cell, then one of the three other quadrants of the cell were selected at random for "AOV2" and "AOV3" which were collected about 10 feet apart, preferably within the same soil type. The field data were described on data collection sheets and later transferred to the IGS network through an entry routine on a daily to weekly basis. The DBASE entry routine and database were developed and maintained by IGS personnel, then after the analyses were performed, the data were joined with the soil sample analyses by USGS personnel. The joined database can be accessed at the IGS website at http://www.igsb.uiowa.edu, and for a detailed description of the NGS, visit the USGS website at http://tin.er.usgs.gov/geochem/doc/home.htm; abstract: The United States Geological Survey (USGS), in collaboration with other state and federal agencies, industry, and academia, is conducting a National Geochemical Survey (NGS) to produce a database of geochemical information for the United States based primarily on stream sediments, analyzed using a single set of methods. This data set will comprise a national-scale geochemical coverage of the US, and will enable construction of geochemical maps, refine estimates of baseline chemical element concentrations in the sampled media, and provide context for a wide variety of geological and environmental studies. The goal of the NGS is to analyze at least one stream-sediment sample within every 289 km2 area across the US, using a consistent set of analytical methods, substituting soil samples where necessary. The survey incorporates geochemical data from a variety of sources, including existing analyses in USGS databases, reanalyses of samples in USGS archives, and analyses of recently collected samples. Currently, the NGS data covers ~71% of the land area of the US, and includes samples from all 50 states. The Iowa Geological Survey (IGS) of the Iowa Department of Natural Resources (IDNR) entered into an agreement with the USGS in September of 2002 whereby the IGS would design a database for field parameters and collect two soil sample sets. One set for the USGS to process and analyze, and one set to reposit. Field parameters included ambient site conditions, GPS location, elevation, landscape position, vegetation type, NRCS soil type, sample depth, and soil horizon, texture, color and moisture. Digital photographs were taken of the site and samples at each location. The samples were sieved to -100 mesh, then analyzed for 40 elements using inductively coupled plasma-atomic emission spectrometry/acid dissolution (ICP40), and 6 elements using atomic absorption spectrometry (AA). Arsenic (As) and gold (Au) were analyzed for using both methods. Analyses were performed by the USGS or an approved laboratory, using standard methods and an U.S.E.P.A. approved quality assurance/quality control plan. To maximize statistical reliability, sample collection in Iowa was based on a 17 km x 17 km grid, displayed on USGS 1:250,000 quad maps. Each grid or cell was identified by quad name and cell column and row position, and divided into four 72 km2 quadrants, and one was selected at random for sampling. The IGS selected specific sampling sites within the selected quadrants. To separate leached horizons from those accumulating CO3, one shallow (0-8 inches) and one deep (12-24 inches) sample were collected from 463 regular and 72 analysis of variance (AOV) sites from May through August of 2003 and shipped to the USGS in August. Randomly selected AOV sites were sampled to provide a data set for statistical analysis to test the adequacy of the samples to measure differences of sediment chemistry between cells, within cells, within sites, and between chemical analyses. "AOV1" was collected within the designated quadrant of the cell, then one of the three other quadrants of the cell were selected at random for "AOV2" and "AOV3" which were collected about 10 feet apart, preferably within the same soil type. The field data were described on data collection sheets and later transferred to the IGS network through an entry routine on a daily to weekly basis. The DBASE entry routine and database were developed and maintained by IGS personnel, then after the analyses were performed, the data were joined with the soil sample analyses by USGS personnel. The joined database can be accessed at the IGS website at http://www.igsb.uiowa.edu, and for a detailed description of the NGS, visit the USGS website at http://tin.er.usgs.gov/geochem/doc/home.htm

With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

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We cover all states and cities in the country : Example covered.

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A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

This dataset provides information and locations for Iowa DOT stations where citizens can obtain, renew, or modify an Iowa Driver's License.

This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 ½ minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. The second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot division of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class. Special Surveys are non-PLSS survey areas from BLM survey records which represent federal parcels.

Water quality in the Barnegat Bay-Little Egg Harbor estuary along the New Jersey coast is the focus of a multidisciplinary research project begun in 2011 by the U.S. Geological Survey (USGS) in partnership with the New Jersey Department of Environmental Protection. This narrow estuary is the drainage for the Barnegat Watershed and flushed by just three inlets connecting it to the Atlantic Ocean, is experiencing degraded water quality, algal blooms, loss of seagrass, and increases in oxygen -depletion events, seaweed, stinging nettles, and brown tide. The scale of the estuary and the scope of the problems within it necessitate a multidisciplinary approach that includes characterizing its physical characteristics (for example, depth, magnitude and direction of tidal currents, distribution of seafloor and subseafloor sediment) and modeling how the physical characteristics interact to affect the estuary's water quality. Scientists from USGS Coastal and Marine Geology Program offices in Woods Hole, Massachusetts, and St. Petersburg, Florida, began mapping the seafloor of the Barnegat Bay-Little Egg Harbor estuary in November 2011 and completed in September 2013. With funding from the New Jersey Department of Environmental Protection and logistical support from the USGS New Jersey Water Science Center, they collected data with a suite of geophysical tools, including swath bathymetric sonar for measuring seafloor depth, a sidescan sonar for collecting acoustic-backscatter data (which provides information about seafloor texture and sediment type), subbottom profiler for imaging sediment layers beneath the floor of the estuary, and sediment samples with bottom photographs for ground validation of the acoustic data. 2011-041-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2011-041-FA 2012-003-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2012-003-FA 2013-014-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2013-014-FA 2013-030-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2013-030-FA

The USGS compiles online access to water-resources data collected at approximately 1.5 million sites in all 50 States, the District of Columbia, Puerto Rico, the Virgin Islands, Guam, American Samoa and the Commonwealth of the Northern Mariana Islands.

This filtered view contains most current population estimate and percent change from prior non-overlapping data collection period for individual cities in Iowa. Data is from the American Community Survey, Five Year Estimates, Table B02001.

This filtered view provides population estimates, population change, and percent change from the most current 60 month data collection period for cities with a population increase between 250 to 500 people. Data is from the American Community Survey, Five Year Estimates, Table B02001.

This filtered view provides population estimates and change rate from the most current 60 month data collection period for cities greater than 20,000 persons. Data is from the American Community Survey, Five Year Estimates, Table B02001.

This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 ½ minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. The second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot division of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class. The second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot division of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

This layer details Important Areas (IAs) relevant to important geographic features in the Strait of Georgia (SOG) ecoregion. This data was mapped to inform the selection of marine Ecologically and Biologically Significant Areas (EBSA). Experts have indicated that these areas are relevant based upon their high ranking in one or more of three criteria (Uniqueness, Aggregation, and Fitness Consequences). The distribution of IAs within ecoregions is used in the designation of EBSAs. Canada’s Oceans Act provides the legislative framework for an integrated ecosystem approach to management in Canadian oceans, particularly in areas considered ecologically or biologically significant. DFO has developed general guidance for the identification of ecologically or biologically significant areas. The criteria for defining such areas include uniqueness, aggregation, fitness consequences, resilience, and naturalness. This science advisory process identifies proposed EBSAs in Canadian Pacific marine waters, specifically in the Strait of Georgia (SOG), along the west coast of Vancouver Island (WCVI, southern shelf ecoregion), and in the Pacific North Coast Integrated Management Area (PNCIMA, northern shelf ecoregion). Initial assessment of IAs in PNCIMA was carried out in September 2004 to March 2005 with spatial data collection coordinated by Cathryn Clarke. Subsequent efforts in WCVI and SOG were conducted in 2009, and may have used different scientific advisors, temporal extents, data, and assessment methods. WCVI and SOG IA assessment in some cases revisits data collected for PNCIMA, but should be treated as a separate effort. Other datasets in this series detail IAs for birds, cetaceans, coral and sponges, fish, invertebrates, and other vertebrates. Though data collection is considered complete, the emergence of significant new data may merit revisiting of IAs on a case by case basis.

This layer details Important Areas (IAs) relevant to key invertebrate species (which are not corals or sponges) in the Strait of Georgia (SOG) ecoregion. This data was mapped to inform the selection of marine Ecologically and Biologically Significant Areas (EBSA). Experts have indicated that these areas are relevant based upon their high ranking in one or more of three criteria (Uniqueness, Aggregation, and Fitness Consequences). The distribution of IAs within ecoregions is used in the designation of EBSAs. Canada’s Oceans Act provides the legislative framework for an integrated ecosystem approach to management in Canadian oceans, particularly in areas considered ecologically or biologically significant. DFO has developed general guidance for the identification of ecologically or biologically significant areas. The criteria for defining such areas include uniqueness, aggregation, fitness consequences, resilience, and naturalness. This science advisory process identifies proposed EBSAs in Canadian Pacific marine waters, specifically in the Strait of Georgia (SOG), along the west coast of Vancouver Island (WCVI, southern shelf ecoregion), and in the Pacific North Coast Integrated Management Area (PNCIMA, northern shelf ecoregion). Initial assessment of IA's in PNCIMA was carried out in September 2004 to March 2005 with spatial data collection coordinated by Cathryn Clarke. Subsequent efforts in WCVI and SOG were conducted in 2009, and may have used different scientific advisors, temporal extents, data, and assessment methods. WCVI and SOG IA assessment in some cases revisits data collected for PNCIMA, but should be treated as a separate effort. Other datasets in this series detail IAs for birds, cetaceans, coral and sponges, fish, geographic features, and other vertebrates. Though data collection is considered complete, the emergence of significant new data may merit revisiting of IA's on a case by case basis.

This layer details Important Areas (IAs) relevant to coral, sponge, and reef-building species in the West Coast Vancouver Island (WCVI) ecoregion. This data was mapped to inform the selection of marine Ecologically and Biologically Significant Areas (EBSA). Experts have indicated that these areas are relevant based upon their high ranking in one or more of three criteria (Uniqueness, Aggregation, and Fitness Consequences). Canada’s Oceans Act provides the legislative framework for an integrated ecosystem approach to management in Canadian oceans, particularly in areas considered ecologically or biologically significant. DFO has developed general guidance for the identification of ecologically or biologically significant areas. The criteria for defining such areas include uniqueness, aggregation, fitness consequences, resilience, and naturalness. This science advisory process identifies proposed EBSAs in Canadian Pacific marine waters, specifically in the Strait of Georgia (SOG), along the west coast of Vancouver Island (WCVI, southern shelf ecoregion), and in the Pacific North Coast Integrated Management Area (PNCIMA, northern shelf ecoregion). Initial assessment of IA's in PNCIMA was carried out in September 2004 to March 2005 with spatial data collection coordinated by Cathryn Clarke. Subsequent efforts in WCVI and SOG were conducted in 2009, and may have used different scientific advisors, temporal extents, data, and assessment methods. WCVI and SOG IA assessment in some cases revisits data collected for PNCIMA, but should be treated as a separate effort. Though data collection is considered complete, the emergence of significant new data may merit revisiting of IA's on a case by case basis. Other datasets in this series detail IAs for birds, cetaceans, fish, geographic features, invertebrates, and other vertebrates. This package also includes project documentation and tech reports relevant to the IA process and its role within the selection of EBSAs.