AndreaBozzo/ceres-open-data-index huggingface dataset:
Mule Deer Range - Region 2 [ds796]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This layer shows critical and non-critical winter and summer ranges, fall holding areas, fawning grounds and migration corridors for deer (Odocoileus hemionus) in CDFW Region 2, North Central Region, Ca. In 1990, CDFW Wildlife Biologists compiled these data from the CDFW Migratory Deer Herd Management Plans, biotelemetry studies, personal knowledge, and predicted use of habitats. These data were subsequently digitized onto USGS 15' quadrangle maps to produce this dataset.</SPAN></P></DIV></DIV></DIV>
Salmonid Restoration Validation in Ten Mile Creek, Mendocino County 2021-2022

Juvenile Coho Salmon abundance was measured for a before-after, control- impact (BACI) analysis of restoration effectiveness. Analyses evaluated the effectiveness of Phase I habitat restoration at increasing Coho Salmon abundance. These analyses include three years of pre-treatment and three years of post-treatment monitoring data for each monitoring metric. The resulting report includes Juvenile Reach Level Abundance Estimates, Pre- and Post-Treatment Comparison of Reach-Scale Abundance (T-tests), BACI Models to Test for Treatment Effects, and Production of juvenile Coho Salmon estimated for: (1) the watershed upstream of the upper trap, and (2) the watershed upstream of the lower trap. Seasonal patterns of juvenile Coho Salmon movement, growth, residence time, and survival within the project reach were compared across years to assess whether these variables were influenced by implementation of Phase I restoration. 

Data were collected as detailed in Stillwater Sciences (2023). Report available upon request.
Stillwater Sciences. 2023. South Fork Ten Mile River Coho Salmon Restoration Project: Phase 1 Validation Monitoring and Life History Characterization. Final Report. Prepared by Stillwater Sciences, Arcata, California for The Nature Conservancy, San Francisco, California.

This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000677. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.
Predicted Nearshore Benthic Substrates of California - R7 - CDFW [ds3091]

This dataset uses rugosity measurements collected during the California Seafloor Mapping Project (CSMP) as a proxy to classify benthic habitat as either Hard or Soft. While originally developed for MPA planning and MLPA implementation, this dataset provides a comprehensive estimate of benthic habitat for California waters. Two versions of this data are available:Statewide mosaic raster of all data resampled at 10m (shown here). Vector format for visualization and analysis. Substrate classifications are binned into depth ranges and 1 degree latitude/longitude grids.
Acoustic Bird Survey - Terrestrial Species Stressor Monitoring [ds2824]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Automated acoustic recorders were deployed at 331 sites across the Mojave Desert ecoregion and 263 sites across the Great Valley ecoregion between March and July of 2016 and 2017. At each survey location, an SM3-BAT bioacoustic recorder was deployed with both an SMM-A1 acoustic microphone and an SMM-U1 ultrasonic microphone (Wildlife Acoustics, Inc., Maynard, MA, USA, hereafter termed ARU). Automated recorders were cable locked to securely-placed T-posts, with microphones 2 meters above the ground, and were located within 10 meters of the site center at a relatively open spot not occupied by thick vegetation. The ARUs were programmed to record three 5-minute sessions each morning during the survey period, with the first session at 30 minutes before sunrise, the second at sunrise, and the third at 30 minutes after sunrise. The ARUs were also programmed to alternate between 7.5-minute audible acoustic recordings and 22.5-minute triggered, full-spectrum ultrasonic recordings from 30 minutes before sunset until 0400 the following morning, at which time the ultrasonic recordings ceased; the 7.5-minute acoustic recordings continued every 30 minutes until the 5-minute dawn chorus recordings began at 30 minutes before sunrise. Deployments lasted for seven consecutive days, on average. </SPAN></P><P><SPAN>For the avian acoustic analysis, all birds were surveyed on the acoustic channel from the three 5-minute dawn chorus recordings collected from three mornings during the survey period, preferably from consecutive days. Recordings were reviewed, and bird species identified by song or call. To aid in bird identification, spectrograms were visually examined using Raven Pro software (v. 1.5; Cornell Lab of Ornithology Bioacoustics Research Program, Ithaca, NY, USA). Multispecies hierarchical occupancy models were then used to estimate 1) the probability a given bird species occurred within the area sampled by an acoustic recorder during the survey period, 2) the richness of bird species, and 3) community and species-specific responses to different ecological covariates. For each sampling location, each 5-minute acoustic recording (n = 9) was treated as a repeat survey at that particular site. Occurrence and detection probabilities were assumed to differ between years and among species, and were assumed to be influenced by ecological covariates (e.g., crop cover, forest cover, water availability). </SPAN></P></DIV></DIV></DIV>
Acoustic Bat Survey - Terrestrial Species Stressor Monitoring [ds2825]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Automated acoustic recorders were deployed at 331 sites across the Mojave Desert ecoregion and 263 sites across the Great Valley ecoregion between March and July of 2016 and 2017. At each survey location, an SM3-BAT bioacoustic recorder was deployed with both an SMM-A1 acoustic microphone and an SMM-U1 ultrasonic microphone (Wildlife Acoustics, Inc., Maynard, MA, USA, hereafter termed ARU). Automated recorders were cable locked to securely-placed T-posts 2 meters above the ground, and were located within 10 meters of the site center at a relatively open spot not occupied by thick vegetation. The ARUs were programmed to record three 5-minute sessions each morning during the survey period, with the first session at 30 minutes before sunrise, the second at sunrise, and the third at 30 minutes after sunrise. The ARUs were also programmed to alternate between 7.5-minute audible acoustic recordings and 22.5-minute triggered, full-spectrum ultrasonic recordings from 30 minutes before sunset until 0400 the following morning, at which time the ultrasonic recordings ceased; the 7.5-minute acoustic recordings continued every 30 minutes until the 5-minute dawn chorus recordings began at 30 minutes before sunrise. Deployments lasted for seven consecutive days, on average. Following the survey, Kaleidoscope Pro version 4.3.2 with the KPro classifier (Wildlife Acoustics, Inc., Maynard, MA, USA) was used to auto-classify file recordings to the species level for bats. Using the probabilistic output from the classifier, files were filtered to retain only those determined to have a misidentification probability less than 0.05. All of the remaining files were then manually reviewed (i.e., examination of diagnostics and spectrograms) to confirm or reject the auto-classified species identification. The automated and post-validated datasets were analyzed using five occupancy modeling frameworks, which differed in how they did or did not address false positive detections.</SPAN></P></DIV></DIV></DIV>
Habitat Rapid Assessment Survey - 2016 - Terrestrial Species Stressor Monitoring [ds2827]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Vegetation surveys were conducted at all Great Valley and Mojave Desert study sites between May and July 2016. These surveys followed the California Native Plant Society/Department of Fish and Wildlife protocol for vegetation rapid assessment and were representative of either the lifeform for which the survey site was selected or the dominant vegetation type encountered on site, if the preassigned lifeform is not found. The rapid assessment sample was based not on a plot but on a single representative stand with 12-20 of the dominant or characteristic species and their cover values recorded. The CDFW Vegetation Classification and Mapping Program reviewed and confirmed or corrected all field assessments herein. </SPAN></P></DIV></DIV></DIV>
Habitat and Vegetation Assessment - 2017 - Terrestrial Species Stressor Monitoring [ds2828]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Vegetation surveys were conducted at each Great Valley and Mojave Desert study site between March and June 2017. The surveys followed the California Department of Fish and Wildlife Vegetation Reconnissance Field Protocol and were representative of either the lifeform for which the survey site was selected, or the dominant vegetation type encountered on site, if the preassigned lieform was not found. These reconnaissance vegetation surveys are based on a representative stand with a subset of dominant or characteristic plant species and their cover values recorded rather than a plot based survey. Field crews determined the primary alliance for the stand and identified the dominant species present, along with percent cover and phenology information. If there was any uncertainty in keying to alliance, a secondary alliance was also described, along with any details to aid in later determination. The CDFW Vegetation Classification and Mapping Program reviewed and confirmed or corrected all field assessments herein. </SPAN></P></DIV></DIV></DIV>
Point Count Survey - Terrestrial Species Stressor Monitoring [ds2829]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Avian point count surveys were conducted at 184 Great Valley study sites, and 107 Mojave Desert sites, between March and June 2017. Whenever possible, three independent point count surveys of five minutes each were conducted over the course of the month-long survey period at each site. To enable relatively accurate distance measurements, observers would mentally mark 50 meters in each cardinal direction using a rangefinder. Observer qualifications determined the complexity of the survey being conducted, with a focal survey recording data for only 25 pre-designated species; for comprehensive surveys, data for all bird species was collected. During survey site establishment visits, a 15-minute quiet period was taken at the point count center prior to starting the survey; subsequent visits only required a 5-minute quiet period. During the quiet period, the observer recorded general information about the survey location, weather, etc. During the point count, observers recorded the name of each species using six letter codes. Individuals of each species were tallied within the three different distance categories: &lt; 50 meters, &gt; 50 meters, and flyover. Birds seen at multiple distances were recorded only at the closest distance. Observers also recorded the confidence level of their observations and the type of detection (aural and/or visual). When individuals were detected at different confidence levels, the observer created distinct entries of the species and separated them by confidence. If an unknown species was detected, the observation was labelled as UNK, numbered, recorded with the technician’s best guess to species ID, and marked at low confidence (e.g. UNK1 – HOUFIN).</SPAN></P></DIV></DIV></DIV>
Quadrat Survey - Great Valley - 2016 - Terrestrial Species Stressor Monitoring [ds2830]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>A total of 36 visual encounter quadrat surveys were conducted at a subset of study sites in the Great Valley between April and July 2016. A time-constrained search of 1 hour was conducted within a one-hectare quadrat, which was positioned to overlap survey site center and to be representative of the lifeform associated with the survey site. Waypoints were established at the four corners of a 100m by 100m square, and a single observer systematically surveyed by visually searching for any surface-active reptiles, amphibians, or other terrestrial vertebrates, while also checking under cover objects, crevices, and within burrows using a hand mirror. For each encounter, the observer noted time, substrate, and behavior such as basking, foraging, courtship/reproduction, territoriality, and fleeing. As with transect surveys, all wildlife sign (tracks, scat, burrows, nests) were noted.</SPAN></P></DIV></DIV></DIV>
Site Establishment Survey - Terrestrial Species Stressor Monitoring [ds2831]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The TSM study areas were the USDA-defined Great Valley (GV) and Mojave Desert (MD) ecoregions, truncated to California state boundaries. A grid of hexagons adapted from the USDA Forest Inventory and Analysis program, each having an approximate radius of 2,600 meters, was used as the sampling frame. Initially, a spatially-balanced, stratified random sampling approach was used to identify hexagons to be included in the study. Vegetation maps from a variety of sources were used to calculate the total cover of key lifeforms within each ecoregion. These lifeforms were determined based not only on distinct categories of vegetation, but also on habitats or features known or thought to be important to wildlife. A spatially-balanced random sample was drawn for the Mojave Desert ecoregion, while site selection in the Great Valley was more opportunistic based on the greater proportion of private land ownership.To select discrete survey locations within the hexagons, a finer-scale grid of approximately 2,400 points spaced 100 meters apart was created within each selected hexagon; for parcels that did not encompass an entire hexagon, the 100-meter grid was limited to the area within the parcel boundary. Generally, two survey points located 1,000-2,000 meters apart were selected in each hexagon. Initial points were identified by assigning random numbers to all of the grid points in each hexagon, and then selecting the lowest numbered points that met other constraints, including stratified sampling goals and land access restrictions. On rare occasions, more than two sites were located within a given hexagon, but the preferred practice was to avoid duplication or monitoring in adjacent hexagons. Study sites were not repeated between the two years, so that the entire monitoring effort comprised unique locations.</SPAN></P></DIV></DIV></DIV>
Time Constrained Search - 2017 - Terrestrial Species Stressor Monitoring [ds2832]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Time constrained surveys for reptiles and amphibians were conducted at 184 Great Valley study sites, and 107 Mojave Desert sites, between March and June 2017. Whenever possible, three independent surveys of 30 minutes each were conducted over the course of the month-long survey period at each site. Time constrained searches were typically conducted after completion of avian point count surveys. Once the study site lifeform was determined, the observer began their search within the stand, but could move throughout the lifeform. In narrow riparian areas, observers could search the edges of these habitats in addition to the main lifeform. Observers were free to move about anywhere within the lifeform, making sure to check any cover object that was safe to inspect; this included rock crevices, burrows, downed logs, etc. Moved cover objects were to be returned exactly as they were found. When an observation was made, observers recorded the species (six-letter code if known, otherwise full common or scientific names), time of observation, age class (hatchling, juvenile, adult) if possible, confidence level, and a GPS location. Each subsequent TCS was conducted with the same amount of intensity as previous searches.</SPAN></P></DIV></DIV></DIV>
Transect Survey - Mojave Desert - 2017 - Terrestrial Species Stressor Monitoring [ds2834]

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Visual Encounter Transect Surveys of reptiles, amphibians, and other vertebrate wildlife taxa were conducted at a subset of study sites in the Mojave Desert in 2017. A total of 52 transect surveys at 17 study sites were conducted between March and June 2017. For each survey, a single observer walked two 200-meter transects and recorded counts and distances of all wildlife observed. The two transects are laid out as a cross intersecting at a right angle, with this intersection occurring at survey site center. The exact orientation of the cross and the sinuosity of transects was left to the discretion of the surveyor(s) to optimize sampling of the site-designated lifeform and to facilitate ease of travel (e.g., barriers or steep slopes). In the case of a riparian lifeform site, one transect was aligned along or near the stream course, and the second along the gradient extending to either side of the stream course. In the case of a guzzler or wetland, transects were aligned along approximately perpendicular sides. A laser rangefinder was used to accurately measure the perpendicular distance from the transect to the location of each wildlife group at the instance of its initial observation. These data were used for distance sampling methods to estimate abundance, and for comparing 2016 and 2017 data. Whenever possible, these transect surveys overlapped spatially with Time Constrained Searches at the same location.</SPAN></P></DIV></DIV></DIV>
Rail Lines BNSF, UPRR

<div style='text-align:Left;'><div><div><p>The objective is to delineate potential crude by rail routes across California, focusing on a subset of the Caltrans "California Rail Network" dataset. Specifically, this subset comprises Union Pacific Railroad (UPRR) and Burlington Northern Santa Fe (BNSF) routes, the primary carriers for crude by rail in the state. For the original dataset, please refer to the <a target='_blank' href='https://gisdata-caltrans.opendata.arcgis.com:443/datasets/2ac93358aca84aa7b547b29a42d5ff52' rel='nofollow ugc noopener noreferrer'><u>Caltrans GIS Data Hub</u></a> web page.</p><p>The purpose of the data is to depict the rail network by utilizing available reference data and information from the California Region Timetable 20. Within the Rail feature class, alignment details for passenger and freight railroad lines are included, encompassing commuter rail and heavy rail. This feature class facilitates the selection of various rail alignments in California, covering passenger, commuter, recreational, freight, and shortline rail, excluding BART.</p></div></div></div>
Meadowfoam Monitoring Plan - North Table Mountain Ecological Reserve, Butte County

Butte County meadowfoam (Limnanthes floccosa ssp. californica) is an annual plant that is designated as endangered under the California Endangered Species Act and federal Endangered Species Act. Butte County meadowfoam is only found in a narrow 28-mile strip along the eastern Sacramento Valley in Butte County, and a population of Butte County meadowfoam is located at the California Department of Fish and Wildlife (CDFW) North Table Mountain Ecological Reserve (Reserve). Populations of Butte County meadowfoam have been subject to historic grazing practices. Grazing took place at the Reserve for many years prior to it becoming an ecological reserve, and CDFW has continued this practice to manage vegetation. The monitoring consists of three primary parts: (1) a census of Butte County meadowfoam within monitoring plots; (2) monitoring photographs; and (3) general written documentation of grazing impacts, if available. The census data is intended to to represent the total number of Butte County meadowfoam plants at the reserve, subpopulations may exist that we did not locate, and if so, our dataset does not include them. The monitoring photograph files are named with a unique identifier (e.g. "Al") followed by a date in the format YYYYMMDD.

This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000047. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.
Mountain Lion Habitat Suitability - Summer - CDFW [ds2916]

Habitat suitability model for mountain lion based on a summer resource selection function as described in Dellinger et al. 2020. Using Mountain Lion Habitat Selection in Management. Journal of Wildlife Management 84(2): 359-371. https://wildlife.onlinelibrary.wiley.com/doi/full/10.1002/jwmg.21798
Essential Connectivity Areas - California Essential Habitat Connectivity (CEHC) [ds620]

The California Department of Transportation (Caltrans) and California Department of Fish and Game (CDFG) commissioned the California Essential Habitat Connectivity Project because a functional network of connected wildlands is essential to the continued support of Californias diverse natural communities in the face of human development and climate change. The Essential Connectivity Map depicts large, relatively natural habitat blocks that support native biodiversity (Natural Landscape Blocks) and areas essential for ecological connectivity between them (Essential Connectivity Areas). This coarse-scale map was based primarily on the concept of ecological integrity, rather than the needs of particular species. Essential Connectivity Areas are placeholder polygons that can inform land-planning efforts, but that should eventually be replaced by more detailed Linkage Designs, developed at finer resolution based on the needs of particular species and ecological processes. It is important to recognize that even areas outside of Natural Landscape Blocks and Essential Connectivity Areas support important ecological values that should not be "written off" as lacking conservation value. Furthermore, because the Essential Habitat Connectivity Map was created at the statewide scale, based on available statewide data layers, and ignored Natural Landscape Blocks smaller than 2,000 acres squared, it has errors of omission that should be addressed at regional and local scales.
Solar RESOLVE Regions

Data provided by CPUC. <span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>Solar RESOLVE Regions (Draft CPUC layer) used for energy planning and modeling.</span>