Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset.

Toolbox Use
License
Creative Commons-PDDC
Recommended Citation
Welty JL, Jeffries MI, Arkle RS, Pilliod DS, Kemp SK. 2021. GIS Clipping and Summarization Toolbox: U.S. Geological Survey Software Release. https://doi.org/10.5066/P99X8558

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:

• Metadata is missing or incomplete for some layers at this time and will be continuously improved.
  
• We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.

Purpose

County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers (this dataset)
   • Without Coastal Buffers
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system
• Place Name: CDTFA incorporated (city) or county name
• County: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Accuracy

CDTFA"s source data notes the following about accuracy:

City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated

Santa Rosa Plain Programmatic Biological Opinion Parcels - Data "CDR_PARCELS" obtained from County of Sonoma GIS Central.

A distinction should be made with respect to this layer which includes GIS parcels and the official Assessor Parcels residing in the Assessor Map books at the Sonoma County Assessor Office. For official parcel records please contact the Sonoma County Assessor (707)565-1888. These parcels should NOT be represented as survey data, and the official record of survey takes precedence where there are discrepancies. It is the end user's responsibility to check the accuracy of the GIS data by comparing it with the published data from the Sonoma County Assessor / Recorder office. The Sonoma County parcel base was originally compiled from Assessor Parcel maps at a scale of 1:6000. The individual Assessor Parcel maps were enlarged or reduced in size using an electrostatic process to produce the maps at the 1:6000 scale, the maps were then fit together by hand and transcribed on to mylar. The mylar base consisted of 1:6000 USGS base map information typically found on the 7.5 USGS quad series. This base information consisted of Topography, Roads, Section, and Rancho lines to name some. Using this information, the Assessor Parcel maps were fit to the individual 1:6000 scale maps. Each 1:6000 scale map represents 1/6 (quad sixths) of a 7.5 minute USGS Quadrangle series map. In 1998 the State Board of Equalization provided the impetus to produce the Russian River Project for all of Planning Area 4. One aspect required for this project was a digital parcel base for Planning Area 4. This involved the conversion of the 1:6000 mylars with the transcribed parcels on them into a digital version of the parcels. The mylars where scanned and geo-referenced using the base map information originally included with the 1:6000 mylar base. The maps were geo-referenced to a digital version of the USGS 7.5 minute Quadrangle series available from the Teale Data Center. The original projection was California State Plane Zone 2 NAD 1927. County Staff then used AutoCAD software to heads up digitize each 1:6000 scale map in Planning Area 4. A custom application was created and used by GIS staff involving the use of Avenue and ArcView 3.2 to create a point for all the parcels in Planning Area 4, attributes included Assessor Parcel Number. The DWGs were then converted to shapefiles and then converted to ArcINFO coverages, the parcel tags were converted from shapefiles to ArcINFO coverages and the point coverage was merged with the polygon coverage with the IDENTITY command. An exhaustive process was involved to eliminate errors once the DWGs were converted to ArcINFO coverages so polygons could be generated. The coverages were then aggregated using the MAPJOIN command, the original boundary of the 1:6000 scale maps was removed using the REGIONDISSOLVE command to merge adjacent polygons with the same AP number. In 1999 the remainder of the planning areas were converted to digital form following the Russian River Project and the seamless base was completed in 2001. The seamless parcel base was maintained in ArcINFO until the release of ArcGIS 8.3, which included topology tools necessary for its maintenance. The seamless base prior to late 2002 was suitable for 1:100000 scale while the control points (the corners for the 1:6000 scale maps) were suitable for 1:24000 scale. Prior to rectification to the Merrick 2000 orthophotography, the parcel data were derived from 1:6000 scale maps (enlarged from USGS 7.5 minute quadrangle 1:24,000 series) and digitized in California State Plane, Zone II, NAD 27 coordinates (survey feet), but were converted to California State Plane, Zone II, NAD 83 coordinates (survey feet) as part of a rectification process now underway. The parcels used to use the USGS 7.5 minute quadrangle (1:24,000) series for coordinate control, but no guarantee is made for their spatial accuracy. The data were re-projected to NAD 83 coordinates to overlay the orthophotography, but the parcel boundaries will not correspond precisely with features in the images. The parcels were rectified to orthophotography flown in April - May 2000 using geo-referencing tools available in ArcGIS 8.3. This project was completed in July 2005. In general, the parcels meet National Accuracy Standards for 1:24,000 scale maps, and likely exceed that accuracy in urban areas. A complete description of the process is detailed in a series of documents located on a local file server: S:\COMMON\GIS\Documentation\Parcel Rectification & Update Process\Procedure - *. doc. A brief summary is as follows. Individual Assessor Parcel pages or CAD drawings are rectified to the orthophoto. COGO & survey data are used when available and in sufficient quantities to enable the bulk of an Assessor Parcel page to be digitized using said information. Polygons are generated directly from the COGO data, CAD dwg are exported to feature classes, where polygons are then generated, rectified Assessor Parcel pages are vectorized using ArcScan and subsequently polygons are generated. A spatial join is used to assign attributes to the newly generated polygons. Polygons are then assigned an accuracy rank based on source, quality of the fit to the orthophoto, and RMS error encountered during rectification (only the scanned Assessor maps will have and RMS error associated with them). See the fields RANK and DESCRIPTION for information on fit assessment. Areas that have been successfully updated as such have a reasonable expectation of accuracy of +/- 10 and possibly better, areas that have not been updated or are flagged in SCAMP under the GIS group Projects as Needs Survey Data, the original accuracy assessment of 1:100000 applies.

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

Note: This description is taken from a draft report entitled "Creation of a Database of Lakes in the St. Johns River Water Management District of Northeast Florida" by Palmer Kinser. Introduction“Lakes are among the District’s most valued resources. Their aesthetic appeal adds substantially to waterfront property values, which in turn generate tax revenues for local governments. Fish camps and other businesses, that provide lake visitors with supplies and services, benefit local economies directly. Commercial fishing on the District’s larger lakes produces some income, , but far greater economic benefits are produced from sport fishing. Some of the best bass fishing lakes in the world occur in the District. Trophy fishing, guide services and high-stakes fishing tournaments, which they support, also generate substantial revenues for local economies. In addition, the high quality of District lakes has allowed swimming, fishing, and boating to become among the most popular outdoor activities for many District residents and attracts many visitors. Others frequently take advantage of the abundant opportunities afforded for duck hunting, bird watching, photography, and other nature related activities.”(from likelihood of harm to lakes report).ObjectiveThe objective of this work was to create a consistent database of natural lake polygon features for the St. Johns River Water Management District. Other databases examined contained point features only, polygons representing a wide range of dates, water bodies not separated or coded adequately by feature type (i.e. no distinctions were made between lakes, rivers, excavations, etc.), or were incomplete. This new database will allow users to better characterize and measure the lakes resource of the District, allowing comparisons to be made and trends detected; thereby facilitating better protection and management of the resource.BackgroundPrior to creation of this database, the District had 2 waterbody databases. The first of these, the 2002 FDEP Primary Lake Location database, contained 3859 lake point features, state-wide, 1418 of which were in SJRWMD. Only named lakes were included. Data sources were the Geographic Names Information System (GNIS), USGS 1:24000 hydrography data, 1994 Digital orthophoto quarter quadrangles (DOQQs), and USGS digital raster graphics (DRGs). The second was the SJRWMD Hydrologic Network (Lake / Pond and Reservoir classes). This data base contained 42,002 lake / pond and reservoir features for the SJRWMD. Lakes with multiple pools of open water were often mapped as multiple features and many man-made features (borrow pits, reservoirs, etc.) were included. This dataset was developed from USGS map data of varying dates.MethodsPolygons in this new lakes dataset were derived from a "wet period" landcover map (SJRWMD, 1999), in which most lake levels were relatively high. Polygons from other dates, mostly 2009, were used for lakes in regionally dry locations or for lakes that were uncharacteristically wet in 1999, e.g. Alachua Sink. Our intension was to capture lakes in a basin-full condition; neither unusually high nor low. To build the data set, a selection was made of polygons coded as lakes (5200), marshy lakes (5250, enclosed saltwater ponds in salt marsh (5430), slough waters (5600), and emergent aquatic vegetation (6440). Some large, regionally significant or named man-made reservoirs were also included, as well as a small number of named excavations. All polygons were inspected and edited, where appropriate, to correct lake shores and merge adjacent lake basin features. Water polygons separated by marshes or other low-ground features were grouped and merged to form multipart features when clearly associated within a single lake basin. The initial set of lake names were captured from the Florida Primary Lake Location database. Labels were then moved where needed to insure that they fell within the water bodies referenced. Additional lake names were hand entered using data from USGS 7.5 minute quads, Google Maps, MapQuest, Florida Department of Transportation (FDOT) county maps, and other sources. The final dataset contains 4892 polygons, many of which are multi-part.Operationally, lakes, as captured in this data base, are those features that were identified and mapped using the District’s landuse/landcover scheme in the 5200, 5250, 5430, 5600 classes referenced above; in addition to some areas mapped tin the 6440 class. Some additional features named as lakes, ponds, or reservoirs were also included, even when not currently appearing to be lakes. Some are now very marshy or even dry, but apparently held deeper pools of water in the past. A size limit of 1 acre or more was enforced, except for named features, 30 of which were smaller. The smallest lake was Fox Lake, a doline of 0.04 acres in Orange county. The largest lake, Lake George covered 43,212.8 acres.The lakes of the SJRWMD are a diverse set of features that may be classified in many ways. These include: by surrounding landforms or landcover, by successional stage (lacustrine to palustrine gradient), by hydrology (presence of inflows and/or outflows, groundwater linkages, permanence, etc.), by water quality (trophic state, water color, dissolved solids, etc.), and by origin. We chose to classify the lakes in this set by origin, based on the lake type concepts of Hutchinson (1957). These types are listed in the table below (Table 1). We added some additional types and modified the descriptions to better reflect Florida’s geological conditions (Table 2). Some types were readily identified, others are admittedly conjectural or were of mixed origins, making it difficult to pick a primary mechanism. Geological map layers, particularly total thickness of overburden above the Floridan aquifer system and thickness of the intermediate confining unit, were used to estimate the likelihood of sinkhole formation. Wind sculpting appears to be common and sometimes is a primary mechanism but can be difficult to judge from remotely sensed imagery. For these and others, the classification should be considered provisional. Many District lakes appear to have been formed by several processes, for instance, sinkholes may occur within lakes which lie between sand dunes. Here these would be classified as dune / karst. Mixtures of dunes, deflation and karst are common. Saltmarsh ponds vary in origin and were not further classified. In the northern coastal area they are generally small, circular in outline and appear to have been formed by the collapse and breakdown of a peat substrate, Hutchinson type 70. Further south along the coast additional ponds have been formed by the blockage of tidal creeks, a fluvial process, perhaps of Hutchinson’s Type 52, lateral lakes, in which sediments deposited by a main stream back up the waters of a tributary. In the area of the Cape Canaveral, many salt marsh ponds clearly occupy dune swales flooded by rising ocean levels. A complete listing of lake types and combinations is in Table 3. TypeSub-TypeSecondary TypeTectonic BasinsMarine BasinTectonic BasinsMarine BasinCompound dolineTectonic BasinsMarine BasinkarstTectonic BasinsMarine BasinPhytogenic damTectonic BasinsMarine BasinAbandoned channelTectonic BasinsMarine BasinKarstSolution LakesCompound dolineSolution LakesCompound dolineFluvialSolution LakesCompound dolinePhytogenicSolution LakesDolineSolution LakesDolineDeflationSolution LakesDolineDredgedSolution LakesDolineExcavatedSolution LakesDolineExcavationSolution LakesDolineFluvialSolution LakesKarstKarst / ExcavationSolution LakesKarstKarst / FluvialSolution LakesKarstDeflationSolution LakesKarstDeflation / excavationSolution LakesKarstExcavationSolution LakesKarstFluvialSolution LakesPoljeSolution LakesSpring poolSolution LakesSpring poolFluvialFluvialAbandoned channelFluvialFluvialFluvial Fluvial PhytogenicFluvial LeveeFluvial Oxbow lakeFluvial StrathFluvial StrathPhytogenicAeolianDeflationAeolianDeflationDuneAeolianDeflationExcavationAeolianDeflationKarstAeolianDuneAeolianDune DeflationAeolianDuneExcavationAeolianDuneAeolianDuneKarstShoreline lakesMaritime coastalKarst / ExcavationOrganic accumulationPhytogenic damSalt Marsh PondsMan madeExcavationMan madeDam

This dataset summarises 40 years of seagrass data collection (1983-2022) within Torres Strait and the Gulf of Carpentaria into two GIS shapefiles: (1) a point shapefile that includes survey data for 48,612 geolocated sites, and (2) a polygon geopackage describing seagrass at 641 individual or composite meadows.

Managing seagrass resources in northern Australia requires adequate baseline information on where seagrass is (presence/absence), the mapped extent of meadows, what species are present, and date of collection. This baseline is particularly important as a reference point against which to compare seagrass loss or change through time. The scale of northern Australia and the remoteness of many seagrass meadows from human populations present a challenge for research and management agencies reporting on the state of seagrass ecological indicators. Broad-scale and repeated surveys/studies of areas are logistically and financially impractical. However seagrass data is being collected through various projects which, although designed for specific reasons, are amenable to collating a picture of the extent and state of the seagrass resource.

In this project we compiled seagrass spatial data collected during surveys in Torres Strait and the Gulf of Carpentaria into a standardised form with point-specific and meadow-specific spatial and temporal information. We revisited, evaluated, simplified, standardised, and corrected individual records, including those collected several decades ago by drawing on the knowledge of one of our authors (RG Coles) who led the early seagrass data collection and mapping programs. We also incorporate new data, such as from photo records of an aerial assessment of mangroves in the Gulf of Carpentaria in 2017. This project was funded by the National Environmental Science Programme (NESP) Marine and Coastal Hub and Torres Strait Regional Authority (TSRA) in partnership with the Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University. The project follows on from TropWATER’s previous work compiling 35 years of seagrass spatial point data and 30 years of seagrass meadow extent data for the Great Barrier Reef World Heritage Area (GBRWHA) and adjacent estuaries, funded through successive NESP Tropical Water Quality Hub Projects 3.1 (2015-2016) and 5.4 (2018-2020). These data sets are now publicly available through the eAtlas data portal: https://doi.org/10.25909/y1yk-9w85 . In making this data publicly available for management, the authors and data custodians request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood.

Methods: The data were collected using a variety of survey methods to describe and monitor seagrass sites and meadows. For intertidal sites/meadows, these include walking, observations from helicopters in low hover, and observations from hovercraft when intertidal banks were exposed. For subtidal sites/meadows, methods included free diving, scuba diving, video transects from towed cameras attached to a sled with/without a sled net, video drops with filmed quadrats, trawl and net samples, and van Veen grab samples. These methods were selected and tailored by the data custodians to the location, habitat surveyed, and technology available. Important site and method descriptions and contextual information is contained in the original trip reports and publications for each data set provided in Table 1 of Carter et al. (2022).

Geographic Information System (GIS) Mapping data for historic records (1980s) were transcribed from original logged and mapped data based on coastal topography, dead reckoning fixes and RADAR estimations. More recent data (1990’s onwards) is GPS located. All spatial data were converted to shapefiles with the same coordinate system (GDA 1994 Geoscience Australia Lambert), then compiled into a single point shapefile and a single polygon shapefile (seagrass meadows) using ArcMap (ArcGIS version 10.8 Redlands, CA: Environmental Systems Research Institute, ESRI). Some early spatial data was offset by several hundred metres and where this occurred data was repositioned to match the current coastline projection. The satellite base map used throughout this report is courtesy ESRI 2022.

Seagrass Site Layer: This layer contains information on data collected at assessment sites, and includes: 1. Temporal survey details – Survey month and year; 2. Spatial position - Latitude/longitude; 3. Survey name; 4. Depth for each subtidal site is m below MSL Depth and was extracted from the Australian Bathymetry and Topography Grid, June 2009 (Whiteway 2009). This approach was taken due to inconsistencies in depth recordings among data sets, e.g., converted to depth below mean sea level, direct readings from depth sounder with no conversion, or no depth recorded. Depth for intertidal sites was recorded as 0 m MSL, with an intertidal site defined as one surveyed by helicopter, walking, or hovercraft when banks were exposed during low tide;
5. Seagrass information including presence/absence of seagrass, and whether individual species were present/absent at a site; 6. Dominant sediment - Sediment type in the original data sets were based on grain size analysis or deck descriptions. For consistency, in this compilation we include only the most dominant sediment type (mud, sand, shell, rock, rubble), removed descriptors such as “fine”, “very fine”, “coarse”, etc., and replaced redundant terms, e.g. “mud” and “silt” are termed “mud”; 7. Survey methods – In this compilation we have updated and standardised the terms used to describe survey methods from the original reports; and
8. Data custodians.

Seagrass Meadow Layer: Polygons in the meadow layer are drawn from extent data collected during some surveys. Not all surveys collected meadow extent data (e.g., Torres Strait lobster surveys). The seagrass meadow layer is a composite of all the spatial polygon data we could access where meadow boundaries were mapped as part of the survey. All spatial layers were compiled into a single spatial layer using the ArcToolbox ‘merge’ function in ArcMap. Where the same meadow was surveyed multiple times as part of a long-term monitoring program, the overlapping polygons were compiled into a single polygon using the ‘merge’ function in ArcMap. Because meadows surveyed more than once were merged, there were some cases where adjacent polygons overlap each other.

Meadow Data Includes: 1. Temporal survey details – Survey month and year, or a list of survey dates for meadows repeatedly sampled; 2. Survey methods; 3. Meadow persistence – Classified into three categories: a. Unknown – Unknown persistence as the meadow was surveyed less than five times; b. Enduring – Seagrass is present in the meadow ≥90% of the surveys; c. Transitory – Seagrass is present in the meadow <90% of the surveys; 4. Meadow depth – Classified into three categories: a. Intertidal – Meadow was mapped on an exposed bank during low tide, e.g. Karumba monitoring meadow; b. Subtidal – Meadow remains completely submerged during spring low tides, e.g. Dugong Sanctuary meadow; c. Intertidal-Subtidal – Meadow includes sections that expose during low tide and sections that remain completely submerged, e.g. meadows adjacent to the Thursday Island shipping channel; 5. Dominant species of the meadow based on the most recent survey; 6. Presence or absence of individual seagrass species in a meadow; 7. Meadow density categories – Seagrass meadows were classified as light, moderate, dense, variable or unknown based on the consistency of mean above-ground biomass of the dominant species among all surveys, or percent cover of all species combined (see Table 2 in Carter et al. 2022). For example, a Halophila ovalis dominated meadow would be classed as “light” if the mean meadow biomass was always <1 gram dry weight m-2 (g DW m-2) among years, “variable” if mean meadow biomass ranged from <1 - >5 g DW m-2, and “dense” if mean meadow biomass was always >5 g DW m-2 among years. For meadows with density assessments based on both percent cover (generally from older surveys) and biomass, we assessed density categories based on the biomass data as this made the assessment comparable to a greater number of meadows, and comparable to the most recent data. Meadows with only one year of data were assigned a density category based on that year but no assessment of variability could be made and these are classified as “unknown”; 8. The minimum and maximum annual mean above-ground biomass measured in g DW m-2 (+ standard error if available) for each meadow is included for meadows with >1 year of biomass data. For meadows that were only surveyed once the mean meadow biomass (+ standard error if available) is presented as the minimum and maximum biomass of the meadow. “-9999” represents meadows where no above-ground biomass data was collected.; 9. The minimum and maximum annual mean percent cover is included for each meadow with >1 year of percent cover data. For meadows that were only surveyed once the mean meadow percent cover is presented as the minimum and maximum percent cover of the meadow. Older surveys (e.g., 1986 Gulf of Carpentaria surveys) used percent cover rather than biomass. For some surveys percent cover was estimated as discrete categories or ‘data binning’ (e.g., <10% - >50%). “-9999” represents meadows where no percent cover data was collected; 10. Meadow area survey details – The minimum, maximum and total area (hectares; ha) for each meadow: a. Total area - Total area of each meadow was estimated in the GDA 1994 Geoscience Australia Lambert projection using the ‘calculate geometry’ function in ArcMap. For meadows that were mapped multiple times, meadow area represents the merged maximum extent for

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf of Mexico Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 ofthe final reportprovides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on theNOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on theMarine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster.Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among