A GPS survey by Allan Riach (Communications Officer, AAD) in September 2001 near Casey Station, Antarctica. The survey was conducted in the western part of Bailey Peninsula. Data collected included the locations of masts, points at which wire feeders change direction, aerial anchor points, a transformer and the receiver hut. Data representing aerials was derived from the locations of masts and aerial anchor points. Data representing feeders was derived from the locations of masts, points at which wire feeders change direction and the receiver hut. This dataset consists of point and line data. The data are included in the GIS data for Casey, available for download from a Related URL below. Data belonging to this dataset has dataset_id = 12. The data are formatted according to the SCAR Feature Catalogue (see Related URL below).

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.

Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.

Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.html

This dataset includes the proposed field camp locations for the 2003/04 science expedition to Heard Island, the locations of the camp sites that were used during the expedition and the locations of some of the refuges on the island that were surveyed during the expedition. It is a point dataset stored in the Geographical Information System (GIS). The proposed field camp locations are shown in a map (refer to link below).

This category of planning priorities in the CEC 2023 Land-Use Screens provides an estimate of terrestrial landscape condition based on the extent to which human impacts such as agriculture, urban development, natural resource extraction, and invasive species have disrupted the landscape across the State of California. It is based on the open-source logic modeling framework Environmental Evaluation Modeling System (EEMS) developed by Conservation Biology Institute (CBI). This multicriteria evaluation model result, last updated in 2016 and resolved at 1-kilometer square, spans values ranging from -1 to 1. The higher end of the spectrum indicates areas that are relatively intact based on the more than 30 input variables, and values in the lower end of the spectrum indicate where these human impacts to disturb the landscape and ecological function are relatively high.¹

In the adapted version of the CBI Terrestrial Landscape Intactness given here, the dataset is partitioned into high and low categories based on the mean. Values of the dataset that lie above 0.3 are considered highly intact and are used as an exclusion. Values of the dataset that are less than or equal to 0.3 are allowed to remain in consideration for resource potential. Applying the partition at the mean allows for lands that are relatively more intact than disturbed to be considered for resource potential. The high category of landscape intactness given by this dataset is used as an exclusion in both the Core and SB 100 Terrestrial Climate Resilience Study screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

More information about this layer and its use in electric system planning is available in the Land Use Screens Staff Report in the CEC Energy Planning Library.

[1] Degagne, R., J. Brice, M. Gough, T. Sheehan, and J. Strittholt. 2016. “Terrestrial Landscape Intactness 1 kilometer, California.” Conservation Biology Institute.https://databasin.org/datasets/e3ee00e8d94a4de58082fdbc91248a65/

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from the gSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset SummaryPhenomenon Mapped: Soils of the United States and associated territoriesGeographic Extent: The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System: Web Mercator Auxiliary SphereVisible Scale: 1:144,000 to 1:1,000Source: USDA Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 What can you do with this layer?ArcGIS OnlineFeature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro.Below are just a few of the things you can do with a feature service in Online and Pro.Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-up ArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

Feature layer generated from running the Overlay Layers analysis tool clip to Southern California. This layer was created as part of Esri’s Green Infrastructure Initiative and can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. Additional companion data pertaining to habitat fragments and landscape connectivity, including a Habitat Cost Surface, Habitat Connectors, and Intact Habitat Cores by Betweenness are also envisioned to be published in late 2023. This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2019 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 500,000 cores.Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (63 in total) related to the landscape characteristics found within. A detailed data and methods description can be found below and here: Detailed methodology for Intact Habitat Cores data creation. See this tile layer for a version that enables rapid visualization.The previous 2017 version can be found here for map packages and here for a map service.This layer utilized updated data inputs from the previous version, they include:New enrichment variables added: Areas of Unprotected Biodiversity Importance (NatureServe)Map of Biodiversity Importance (NatureServe) Updated data layers for cores geometry and enrichment: National Land Cover Database (2019)National Hydrology Dataset Plus Version 2.1 (2019)National Wetlands Inventory (2022)TIGER Paved Roads (2021)TIGER Railroads (2021)Protected Areas Database of the United States (PAD-US) Levels 1 – 4 (2022) All source data used to derive this layer and cores attribution is as follows:Areas of Unprotected Biodiversity Importance of Imperiled Species in the United StatesNatureServe Network. April 2021. The Map of Biodiversity Importance. Arlington, VA. U.S.A. NatureServe.Biodiversity Priority Index Areas: Endemic species, small home range size and low protection statusJenkins, C. N., Houtan, K. S. van, Pimm, S. L., & Sexton, J. O. (2015). US protected lands mismatch biodiversity priorities. Proceedings of the National Academy of Sciences , 112(16), 5081–5086. https://doi.org/10.1073/pnas.1418034112Ecological Land Units(Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D. Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard,M. Anderson, and P. Comer, 2014, A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages https://www.aag.org/wp-content/uploads/2021/12/AAG_Global_Ecosyst_bklt72.pdfEcologically Relevant LandformsTheobald DM, Harrison-Atlas D, Monahan WB, Albano CM, 2015, Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLOS ONE 10(12): e0143619 . https://doi.org/10.1371/journal.pone.0143619GAP Level 3 Ecological System BoundariesU.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022, GAP/LANDFIRE National Terrestrial Ecosystems 2011: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B .gSSURGOSoil Survey Staff. Gridded Soil Survey Geographic (gSSURGO), 2022, Database for the Conterminous United States. United States Department of Agriculture, Natural Resources Conservation Service , https://gdg.sc.egov.usda.gov/ .Human ModifiedTheobald, D.M., 2013, A general model to quantify ecological integrity for landscape assessments and US application. Landscape Ecol 28, 1859–1874. https://doi.org/10.1007/s10980-013-9941-6LCC Network AreasLandscape Conservation Cooperatives, 2015, Landscape Conservation Cooperatives, USA. Available online at https://www.sciencebase.gov/catalog/item/55b943ade4b09a3b01b65d78Local LandformsKaragulle, D, Frye, C, Sayre, R, et al. Modeling global Hammond landform regions from 250-m elevation data. Transactions in GIS . 2017; 21: 1040– 1060. https://doi.org/10.1111/tgis.12265*Scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.Map of Biodiversity Importance – Species RichnessNatureServe Network. April 2021. The Map of Biodiversity Importance. Arlington, VA. U.S.A. NatureServe.National Elevation Dataset (NED)Gesch, D. B., Evans, G. A., Oimoen, M. J., Arundel, S., & Sensing, A. S. for P. and R. (2018). The National Elevation Dataset (pp. 83–110). American Society for Photogrammetry and Remote Sensing. //pubs.er.usgs.gov/publication/70201572 NHDPlus Version 2.1Environmental Protection Agency and U.S. Geological Survey, 2019, National Hydrology Dataset Plus (Ver. 2.1. December 2019). Available online at https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus NLCD – National Land Cover Database (2019)Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021): U.S. Geological Survey data release. https://doi.org/10.5066/P9KZCM54NOAA C-CAP Coastal Change Analysis Program Regional Land Cover and Change (2016)National Oceanic and Atmospheric Administration, Office for Coastal Management, 2016, Coastal Change Analysis Program Regional (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Available online at www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/30m_lc/ .Number of Endemic SpeciesJenkins, C. N., van Houtan, K. S., Pimm, S. L., & Sexton, J. O. ,2015, US protected lands mismatch biodiversity priorities. Proceedings of the National Academy of Sciences , 112(16), 5081–5086. https://doi.org/10.1073/pnas.1418034112NWI – National Wetlands Inventory (2022)U.S. Fish & Wildlife Service, 2022, National Wetlands Inventory. U.S. Fish & Wildlife Service. Available online at https://data.nal.usda.gov/dataset/national-wetlands-inventory .PAD-US Protected Areas DatabaseU.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022, Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B .SSURGO (2021)Soil Survey Staff, 2021, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at //websoilsurvey.nrcs.usda.gov/.TIGER Paved Roads (2021)Geography Division, 2021, TIGER/Line Shapefiles, U.S. Census Bureau. Available online at https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2021&layergroup=Roads TIGER Rails (2021)Geography Division, 2021, TIGER/Line Shapefiles, U.S. Census Bureau. Available online at https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2021&layergroup=RailsTNC Terrestrial Ecoregions (Updated 2021)The Nature Conservancy, 2021, Terrestrial Ecoregions, The Nature Conservancy. Available online at https://geospatial.tnc.org/datasets/b1636d640ede4d6ca8f5e369f2dc368b/aboutUnique Ecological SystemsBased upon work by Aycrigg, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689.*New data constructed by Esri staff, using TNC Ecological Regions as summary areas. Other Reference Materials:Attribute table crosswalk between 2017 and 2023 Intact Habitat Cores layersDetailed methodology for Intact Habitat Cores data creation Data Coordinate System: WGS 1984 Web Mercator Evaluation: Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available at Esri's Green Infrastructure site. The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on biodiversity and uniqueness of ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.Scoring values:Default Weights:0.4, # Acres0.1, # Thickness0.05, # Topographic Diversity (Standard Deviation)0.1, # Biodiversity Priority Index (Species Richness in GIC original version) 0.05, # Percentage Wetland Cover0.03, # Ecological Land Unit – Shannon-Weaver Index (Soil Variety in GIC original version) 0.02, # Compactness Ratio (Area relative to the area of a circle with the same perimeter length) 0.1, # Stream Density (Linear Feet/Acre)0.05, # Ecological System Redundancy (Rare/Threatened/Endangered Species Abundance (Number of occurrences) in GIC original version)0.1, # Endemic Species Max (Rare/Threatened/Endangered Species Abundance (Number of unique species in a core) in GIC original version)Bio-Weights: 0.2, # Acres0.1, # Thickness0.05, # Topographic

Regional monitoring has become an important component of assessing the status of our coastal resources in the Southern California Bight (SCB). The Southern California Bight 2013 Regional Monitoring Program (Bight’13) is the fifth in a series of regional marine monitoring efforts beginning in 1994 and repeated again in 1998, 2003, and 2008. More than 90 different organizations encompassing regulatory, regulated, academic, and non-governmental agencies collaborated to create Bight‘13. Collectively, these organizations asked three primary questions: 1) What is the extent and magnitude of impact in the SCB?; 2) Does the extent and magnitude of impact vary among different habitats of interest?; and 3) What are the temporal trends in impacts?

Bight’13 had five components: Contaminant Impact Assessment, Water Column Nutrients, Shoreline Microbiology, Marine Protected Areas, and Trash and Marine Debris. The Contaminant Impact Assessment component included sediment chemistry and toxicity, benthic infauna, fish assemblages, and bioaccumulation. The focus of this data set is on invertebrate abundance.

Southern California Bight trawl caught fish and invertebrate communities were generally in good condition. The Bight ’13 survey provides a comprehensive regional characterization of the trawl-caught megabenthic invertebrate community, but it provides little in the way of biointegrity assessment for megabenthic invertebrates. This is largely because there is no reliable tool for invertebrates. In previous Bight surveys, the Megabenthic Invertebrate Response Index (MIRI) was utilized for biointegrity assessments. However, scientists have raised concerns about its use because MIRI responses are insensitive to sediment contamination. Because of the relative insensitivity of MIRI to pollution gradients, Bight scientists have opted to discontinue use of this potential assessment tool.

Despite the lack of reliable biointegrity tools for invertebrates, potential impacts to trawl invertebrates were identified during Bight ’13 by examining biogeographic changes in populations of sensitive species. Specifically, sea urchin distributions were examined relative to depth and the encroachment of decreased pH/low dissolved oxygen bottom waters. Results compiled over the last four regional surveys spanning 20 years indicated potential habitat compression in sensitive urchin species, and the habitat expansion of less sensitive species. Species-specific population impacts like these may be a harbinger of future regionwide impacts due to ocean acidification. Recommendations for the future included the following actions:

• a critical review and update of the Fish Response Index to enhance assessments

• improved information management to increase accuracy and efficiency

• further investigation of linkages between biological and oceanographic condition

• continued support of regional taxonomic societies that improve the comparability and quality of the organisms species identifications amongst regional Bight survey participants

• evaluation of additional potential indicators of contaminant impacts

Supplemental Information: A stratified random sampling design was selected to ensure an unbiased sampling approach to provide areal assessments of environmental condition. There were 6 strata selected for the trawlbased study including three continental shelf strata (5-30 m, 30-120 m, 120-200 m), upper slope (200-500 m), and an embayment stratum. One new stratum, marine protected areas, was introduced in Bight ’13.

A total of 165 trawl stations were sampled, capturing over 75,000 fishes from 127 species, and over 165,000 invertebrates from 229 species. Overall, trawls in 2013 had greater average abundance, greater biomass, and reduced average species count compared to previous Bight trawl surveys.

Trawl samples were collected according to standard methods described in the Contaminant Impact Assessment Field Operations Manual (Bight ’13 Contaminant Impact Assessment Committee 2013a). Stations were located by global positioning system (GPS) via Android tablet, or input via the research vessel’s differential global positioning system (DGPS). If a station could not be trawled or was too deep, it was relocated up to 100 meters from the nominal location (not to exceed 10% of the nominal station depth).

Samples were collected with 7.6-m head-rope semi-balloon otter trawls with a 1.3 cm cod-end mesh. Trawls were towed along isobaths for 10 minutes (5-10 minutes in Bays & Harbors) at 0.8- 1.0 m/sec (1.5-2 kts) as determined by GPS/DGPS. These tows covered an estimated distance of 300 and 600 m for 5- and 10-minute trawls, respectively (Bight ’13 Contaminant Impact Assessment Committee 2013a). Agencies used a pressure-temperature (PT) sensor attached to one of the otter trawl boards throughout the survey to provide net on-bottom data. Stations were re-trawled if the on-bottom time, as measured by the PT sensor, was less than 8 minutes.

Fish and megabenthic invertebrates from the trawls were identified and processed. Allowable invertebrates included megabenthic species with a minimum dimension of 1 cm; specimens less than 1 cm were excluded from the analysis. Other excluded species were pelagic, infaunal, and colonial, as well as unattached fish parasites (e.g., leeches, cymothoid isopods). 6 Fishes and invertebrates were identified, individuals were counted, and species were batchweighed to the nearest 0.1 kg using spring scales. Species weighing less than 0.1 kg were recorded as “<0.1 kg”. These <0.1 kg species were then weighed together with all other <0.1 kg specimens from the same sample to provide a composite weight for fishes and a separate composite weight for invertebrates. These weights were then used to calculate the total biomass of the fish and invertebrate catches.

Lengths of individual fish were measured to centimeter size class on measuring boards. Bony fish were measured for standard length (anterior tip of head to end of caudal peduncle at the posterior border of the hypural plate). Cartilaginous fish size measurements were total lengths, from the anterior end of the head to the posterior end of the tail. In addition, wingspan was measured for skates and stingrays.

Each organism was also examined for gross external anomalies. Targeted fish anomalies included fin erosion, tumors, external parasites, ambicoloration, albinism, diffuse pigmentation, skeletal deformities, and lesions. Targeted invertebrate anomalies included burnspot disease, echinoderm wasting disease, structural deformities, and external parasites.

It should be noted that over 3,300 individual fish were not measured for size class, nor examined for occurrence of health anomalies. These fish are not included in the results for either measurement. The abundance of these individuals was estimated using an aliquot method and did not have a size estimation, nor health assessment component. Two of the species not measured were in the top 10 for abundance. These included one sample each of California Lizardfish (over 1,700 in the Middle Shelf) and Halfbanded Rockfish (over 1,450 in the Middle Shelf). A third species so treated was the Slough Anchovy (155 individuals in the Bays & Harbors stratum).

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