This dataset was developed by Chris Stermer (CDFG - RAP Program). No original metadata were located, but the following is an abstract from a document describing the product: We conducted field surveys for Willow Flycatchers (Empidonax traillii brewsterii) in 1997 and 1998, from June 15 through July 31, within the McCloud Flats region of Siskiyou County, California. A Geographic Information System (GIS) was used to predict potentially suitable habitat to survey prior to field visits. We used a GIS to model willow flycatcher habitat within our study area from remotely sensed data and digitally mapped data layers. Spatially explicit data used in our predictions included a vegetation map (a vegetation classification derived from Landsat 5 Thematic Mapper imagery), a Digital Elevation Model (DEM), a slope gradient model, and a stream layer. Seventy-seven Willow Flycatcher territories were found during our surveys. Nine of the territories were located within a large montane meadow complex (Bigelow Meadows) known to have Willow Flycatchers, the remaining territories (68) were predicted using a GIS pattern analysis. We characterized vegetation within .07 ha circular plots centered on sixty-six territories located in 1997. Riparian thickets > 2 m in height was the most abundant vegetation type, making up 53% of the vegetation within the plots. Twenty-one percent of the vegetation was a composite of live green grasses and forbs. A pattern based habitat predictability model was developed using the 66 territories located in the 1997 field season as image training sites. The model integrated two environmental variables found to have predictive capability: (1) composition of vegetation classes, and; (2) slope gradient. An accuracy assessment indicated the model was 94% correct when predicting suitable habitat greater than 6 ac. We concluded that Landsat Thematic Mapper imagery, when applied in conjunction with other landscape data, was an effective technique to identify suitable Willow Flycatcher habitat for our study area. Currently, this technique is being used by the California Department of Fish and Game to identify habitat throughout Northern California. This dataset was modified on May 17, 2005 by Eric Haney of CDFG - Information Services branch. Modifications included addition of a Site_ID Field, and fields representing UTM Northing and Easting coordinates (using NAD83 Datum). These fields were added to assist in an effort to field validate the dataset. Note that not all UTM coordinates are located within habitat polygons. Depending on the irregular shape of the polygons, some of the utm coordinates are located outside the boundaries. These coordinates are only to be used for coarse navigational purposes. While there is no publication date planned, Region 1 staff are working to validate the model results.

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

Buffer of Recorded Chinook Distribution in King CountyWRIA 7 Snohomish Drainage - Snohomish County Planning And Development Services, Cartography Section Chinook distribution depicted on this map represent areas of existing use by any life stage. It should also be noted that the distribution areas depicted on this map may not be complete or accurate in all cases due to incomplete data, the changing nature of fish distribution and use, or error in collecting or documenting the information. Original Data Sources Chinook Salmon Source for Snohomish R., Skykomish R. and Snoqualmie R.: Snohomish River Basin Work Group, Fish Mapping Workshop, August 16, 1995, Everett, Washington. Source for Sauk R., Suiattle R., Swamp Ck., North Ck. and Little Bear Ck.: Washington State Department of Fish & Wildlife; 1:100,000 scale StreamNet Data Base, Updated 1997. Source for Stillaguamish River Basin: WDFW Stream Catalog, Spawner Survey Database (WDFW) and the Technical Advisory Group of the Washington Conservation Commission, September 1999. Presumed Chinook Distribution In addition to the known and presumed distribution shown on this map, presumed habitat shall also include any areas that meet the following minimum physical parameters necessary to support chinook salmon during any life history stage. Spawning and holding habitat constitutes all accessible rivers and streams with a channel width of greater than or equal to 10 feet and gradient of less than or equal to 5%. Rearing habitat constitutes all accessible contiguous surface waters downstream of spawning habitat including streams, lakes, ponds, side-channels, sloughs, fresh water wetlands, estuarine tidal marsh and the lower half mile of accessible types 1,2 or 3 direct tributaries (less than or equal to 5% gradient) to streams which meet the spawning criteria. WRIA 8 Cedar-Sammamish Drainage - King County Department of Natural Resources, Water and Land Resources Division This map depicts the known freshwater distribution of chinook salmon (Oncorhynchus tshawytscha) for Water Resource Inventory Area (WRIA) 8. The depicted limits of known freshwater distribution of chinook salmon are based upon the collective personal knowledge of participants in the WRIA 8 mapping project and data they gathered from published and unpublished databases. This map may underestimate or overestimate the actual distribution of chinook salmon Also, this map may inaccurately depict the location of water bodies. For example, some water bodies may be incorrectly located on this map, or may not be depicted on this map at all. All users of this map should seek the assistance of qualified professionals such as surveyors, hydrologists, or fishery biologists as needed to ensure that such users possess complete, precise, and up to date information on freshwater chinook salmon distribution and water body location. The information depicted on this map is current as of May 2000. This map may be revised at any time. Although the WRIA 8 Technical Committee intends to revise this map on an annual basis, the WRIA 8 Technical Committee cannot and does not guarantee that this map will be revised on an annual basis or at any other interval. NO EXPRESS OR IMPLIED WARRANTIES; NO WARRANTY OF MERCHANTABILITY; NO WARRANTY OF FITNESS FOR A PARTICULAR PURPOSE. There are not express or implied warranties for this map, the information it depicts, the data on which it is based, or any service furnished herein. There is no warranty of merchantability for this map's accuracy or its depiction of chinook salmon distribution or water body location. This map is not warranted as fit for a particular purpose. WRIA 9 Duwamish-Green Drainage - King County Department of Natural Resources, Water and Land Resources Division Credits: WRIA 9 Factors of Decline SubCommittee (King Cnty DNR, WLRD) History: This data was developed from two Fish Distribution Workshops held by the WRIA 9 Factors of Decline Subcommittee on May 18th, 1999 and May 24th, 2000. Participants in the workshops hailed from a range of affiliations both inside and outside King County ( list available here, scroll down). These workshops gathered existing fish distribution information for WRIA 9 and developed working draft maps and GIS data. This existing information came from published studies, as well as from personal observations and field data gathered from government and tribal agency officials, private consultants, non-profit representatives, and local and industry experts. The maps and GIS line data depict the best available information on the distribution of seven fish species: chinook (Oncorhynchus tshawytscha), coho (Oncorhynchus kisutch), sockeye (Oncorhynchus nerka), chum (Oncorhynchus keta), pink (Oncorhynchus gorbuscha), steelhead (Oncorhynchus mykiss), and cutthroat trout (Oncorhynchus clarki). The maps and GIS point data also contain limited information regarding species age, spawning observations, outplant locations, fish barriers, and habitat conditions. The maps were published (December 2000) in the "WRIA 9 Habitat Limiting Factors and Reconnaissance Assessment Report" (under "Fish Distribution Maps" in the Part V: Appendix section). The linework for the GIS line data depicting species distribution was pulled almost exclusively from the standard King County hydrography layer, WTRCRS. Some additional linework not appearing in WTRCRS was added to FISH9 as needed, but this was in less than 1% of the cases, and was on-screen digitized from the best available aerial photography. All attributes from WTRCRS, other than wtr.name, were dropped in FISH9 and the species presence information from the workshops added.WRIA 10 Puyallup-White Drainage - Northwest Indian Fisheries Commission This data was received from the NWIFC, with out detailed meta data. More details have been requested from John Kerwin, the Regional Technical Coordinator for WRIA 10. This section compiled on April 18, 2001.

The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'. Since then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.

The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 119. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

The Australian Antarctic Data Centre's Casey Station GIS data were originally mapped from Aerial photography (January 4 1994). Refer to the metadata record 'Casey Station GIS Dataset'. Since then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, the locations of other features have been obtained from a variety of sources. The data are included in the data available for download from the provided URLs. The data conforms to the SCAR Feature Catalogue which includes data quality information. See the provided URL. Data described by this metadata record has Dataset_id = 17. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands.

Update May 2015 - This dataset has been rename from "Brattstrand Bluff penguin GIS dataset" to "Islands NE of Brattstrand Bluff penguin GIS dataset" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown.

Dave Gardner was on Heard Island in January and February 2000 as part of the 2000 ANARE. Opportunistic use was made of the the differential gps system to take accurate locations of 16 points identified from the 1987 aerial photography, so that they could be used as reference points for merging the photographs into an accurate photo mosaic.

Around the station and to the NE it was easy to identify features from the photographs with confidence. To the west of the station the topography and features of the azorella wallows had changed significant and it was not possible to identify features with confidence.

Temperature is a measure of the kinetic energy of an object. Kinetic energy is the energy an object has due to motion. The temperature map shows the measure of temperatures for different bedrock types in Ireland. Bedrock is the solid rock at or below the land surface. As there are many bedrock types in Ireland, it is not possible to take a measurement from all of them so the points on the map correspond to the temperature of the rocks in that specific area.Geologists record thermal properties of rocks. Temperature below the surface is recorded from a borehole (a deep narrow round hole drilled in the ground).To create this dataset, existing data was collected from previous projects. The data was combined in an excel spreadsheet. The data was then digitised and mapped as points of the locations of each borehole measurement. This Temperature Map is to the scale 1:100,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 1km.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The temperature data is shown as points. Each point contains the temperature values for the rock type at a specific depth in the borehole, a Geothermal Temperature Unique ID, a Geothermal ID, Index Number, Borehole Name, Borehole ID, X Easting (ITM), Y Northing (ITM), Elevation (height above sea level) of the surface of the borehole in meters, Recorded surface air temperature on day of temperature logging, Estimated average ground temperature, True vertical depth below sea level in meters calculated using the following formula: TVDSS = Elevation – Depth, Temperature gradient recorded, Temperature gradient calculated, Type of data collection observation whether the Data was Corrected and Data Source.This dataset may have some limitations, as there is a lack of available data. There is also no data from many bedrock types in Ireland.