AndreaBozzo/ceres-open-data-index huggingface dataset:
Mule Deer Migration Corridors - Downieville-Nevada City - 2018-2020 [ds2973]

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (11 adult females) from the Downieville-Nevada City herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2021. GPS fixes were between 11-14 hours. The Downieville-Nevada City herd migrates from winter ranges in the western foothills of the Sierra Nevada range north and east of Nevada City to high altitude terrain near Rattlesnake Mountain north of Interstate 80 and Jackson Meadows Reservoir. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 8 migrating deer, including 19 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 14.11 days and 32.18 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 8 individual deer and 10 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.
Mule Deer Migration Corridors - Grizzly Flat - 2018-2021 [ds2974]

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (6 adult females) from the Grizzly Flat herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2021. GPS fixes were between 11-13 hours. The Grizzly Flat herd migrates from winter ranges in the western foothills of the Sierra Nevada range near the Grizzly Flats eastward to higher altitude terrain in El Dorado national Forest, staying south of Interstate 50. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 5 migrating deer, including 16 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 11.63 days and 43.10 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 5 individual deer and 8 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.
Mule Deer Migration Corridors - Salt Springs - 2018-2020 [ds2975]

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (8 adult females) from the Salt Springs herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2020. GPS fixes were between 11-14 hours. The Salt Springs herd migrates from winter ranges in the western foothills of the Sierra Nevada range to high altitude terrain near Lower Bear River Reservoir and Salt Springs Reservoir. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 6 migrating deer, including 13 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 18.53 days and 27.44 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 6 individual deer and 8 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.
Mule Deer Migration Corridors - Kern River - 2020-2022 [ds2977]

The project leads for the collection of this data were Julie Garcia and Evan King. Mule deer (32 adult females) from the Kern River herd were captured and equipped with Lotek LiteTrack Iridium collars, transmitting data from 2020-2022. GPS fixes were set for 2-hour intervals. The Kern River herd migrates from winter ranges in Sequoia National Forest north of Johnsondale and east of Slate Mountain northward to the area around Redrocks Meadows and along the Kern Canyon ridgeline to Sequoia National Park. Due to a high percentage of poor fixes, likely due to highly variable topographic terrain, between 2-18% of GPS locations per deer, or 5.78% of the entire dataset, were fixed in 2-dimensional space and removed to ensure locational accuracy. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors and stopovers. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 27 migrating deer, including 75 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 15.76 days and 31.51 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Separate models using Brownian bridge movement models (BMMM) and fixed motion variances (FMV) of 1000 were produced per migration sequence. Due to high variances, Only FMV models were retained. Corridors were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 28 individual deer and 59 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to3 deer (10% of the sample), and greater than or equal to 6 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.
Mule Deer Migration Corridors - Manache - 2020-2022 [ds2978]

The project leads for the collection of this data were Julie Garcia and Evan King. Mule deer (47 adult females) from the Manache herd were captured and equipped with Lotek LiteTrack Iridium collars, transmitting data from 2020-2022. GPS fixes were set for 2-hour intervals. The Manache herd migrates from winter ranges just west of Route 395 on the steep slopes and valleys of the Sierra Nevada range near Dunmovin and Haiwee eastward to some of the higher altitude terrain in the continental USA in Inyo and Sequoia National Forests. Due to a high percentage of poor fixes, likely due to highly variable topographic terrain, between 2-18% of GPS locations per deer, or 5.78% of the entire dataset, were fixed in 2-dimensional space and removed to ensure locational accuracy. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors and stopovers. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 38 migrating deer, including 114 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 9.34 days and 13.65 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Separate models using Brownian bridge movement models (BMMM) and fixed motion variances (FMV) of 1000 were produced per migration sequence. Due to high variances, Only FMV models were retained. Corridors were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 40 individual deer and 88 wintering sequences using an FMV of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to 4 deer (10% of the sample), and greater than or equal to 8 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.
Elk Home Range - Bald Hills - 2017-2021 [ds2979]

The project lead for the collection of this data was Carrington Hilson. Elk (9 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Bald Hills herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd's home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 9 elk, including 18 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to a high variance parameter (greater than 8000), one home range sequence was run with a fixed motion variance of 1000. Large water bodies were clipped from the final output. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.
Elk Home Range - Big Lagoon - 2017-2021 [ds2980]

The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Big Lagoon herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 elk, including 6 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to a high variance parameter (greater than 8000), one home range sequence was run with a fixed motion variance of 1000. Large water bodies were clipped from the final output. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.
California Recreational Fisheries Survey, Catch Per Unit Angler for Salmon - R7 - 2004-2024 - CDFW [ds3184]

The data is reported in Catch Per Unit Angler (CPUA) which is a simple calculation of the number of fish caught divided by the number of anglers targeting the fishery type within a particular 1' block in a given period. CRFS Catch records for all caught species were aggregated into 5-year bins from 2004-2024 as well as the entire period. All catch of these species were summed and attributed to the blocks (1' x 1' area) which were recorded during the survey interview. If multiple blocks were recorded, then the catch data was divided across all blocks indicated. Catch data is further divided into 'kept' and 'released' fish which is used to calculate a CPUA for only kept fish and a CPUA for both kept and released fish. The total number of anglers targeting any species in each block was calculated for each time frame. An angler represents a single person fishing in a block on a single day. The data is limited to blocks that had 3 or more reported fishing trips over the entire time period. Effort under the 'All' trip type is an aggregation of all anglers surveyed. This includes anglers from specific trip types such as highly migratory, salmon, bottomfish, coastal migratory, inshore, other anadromous, invertebrates and anything. Raw data from 2004 - 2015 was extracted from the Pacific States Marine Fisheries Commission (PSMFC) legacy RecFIN database. Data from 2016 - 2024 was extracted from the CDFW's CRFS data system. The data is divided into reported catch (includes reported kept, reported released alive, and reported released dead), observed kept catch, effort and location tables. Attributes: * Block Box: 1' grid where the data was reported. * Catch: The species or the name of the species group whose individual catch records were aggregated. * Trip Type: Trip type category that includes the primary target species for the angler that was aggregated. This layer is an aggregation of all angler effort. * All_04_09: CPUA for both kept and released catch for 2004-2009. * All_10_15: CPUA for both kept and released catch for 2010-2015. * All_16_20: CPUA for both kept and released catch for 2016-2020. * All_21_24: CPUA for both kept and released catch for 2021-2024. * Kept_04_09: CPUA for only kept catch for 2004-2009. * Kept_10_15: CPUA for only kept catch for 2010-2015. * Kept_16_20: CPUA for only kept catch for 2016-2020. * Kept_21_24: CPUA for only kept catch for 2021-2024. * CPUA_All: CPUA for both kept and released catch for the entire 2004-2024 period. * CPUA_Kept: CPUA for only kept catch for the entire 2004-2024 period. This field is used as the source of the symbology for this layer. Gray blocks represent where effort occurred, but no catch was reported. * Samples: The number of individual survey samples that reported fishing in block for the entire period.
Recent Stocked Waters - California [ds778]

Each feature in this dataset represents a release water that has been stocked by the California Department of Fish and Wildlife. Release water is defined as a surface water where fish stocking took place. A single release water may be a moving water such as a portion of a stream or river, or a non-moving water such as a lake, reservoir, or pond. The locations are generalized, and do not represent specific locations of stocking. Note that there may be multiple fish planting sites on a designated water during a stocking event but only one is represented for each stocked water.
Elk Home Range - Camp Lincoln - 2017-2021 [ds2981]

The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Camp Lincoln herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 elk, including 4 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.