The data stream contains information on the number of students per year of course and age range. Equal education
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This map displays protection-weighted range-size rarity of species in the lower 48 United States that are protected by the Endangered Species Act and/or considered to be in danger of extinction. It is part of the Map of Biodiversity Importance (MoBI) data collection, a series of maps that identify areas of high importance for protecting species from extinction in the contiguous United States.Building on habitat suitability models for 2,493 of the nation’s most imperiled species, and information on range size and degree of protection derived from those models, the MoBI project provides a series of maps that can help inform conservation efforts. This map depicts summed protection-weighted range-size rarity for Critically Imperiled (categorized by NatureServe as “G1”), Imperiled (“G2”), and ESA-listed (i.e., species listed as Endangered or Threatened under the Endangered Species Act) species in the following groups:Vertebrates (birds, mammals, amphibians, reptiles, freshwater fishes; 333 species) Freshwater invertebrates (mussels and crayfishes; 234 species) Pollinators (bumblebees, solitary bees, butterflies, and skippers; 80 species) Vascular plants (1,957 species)High values identify areas where unprotected, restricted-range species are likely to occur. These areas are of interest to conservationists due to both the restricted range sizes and need for protection from threats such as habitat loss.Habitat models for most species were generated using the random forest algorithm. Data to train the models came from the NatureServe Network (e.g. state Natural Heritage Programs) supplemented by data from Global Biodiversity Information Facility, and other publicly available sources of population and locality data. Environmental predictors used for the modeling include representations of terrain, climate, land cover, soils, and hydrology. The modeling resolution for terrestrial species was either 30-m (most species) or 330-m (some wide-ranging species). Models for aquatic species used the medium resolution National Hydrography Dataset (NHD) as the modeling unit. For species not amenable to random forest modeling, habitat maps were derived by buffering locality data and/or building simple deductive models based on habitat information. NatureServe converted habitat maps to a 330-m raster to provide a consistent unit of aggregation and avoid revealing the precise location of sensitive species. Range-size rarity for each species in the inverse of the total area mapped as habitat (using the 330-m raster). Protection-weighted range-size rarity (PWRSR) maps combine information on both range-size rarity and the degree to which habitat for the species in protected. Protected habitat was defined as that occurring within protected areas managed for biodiversity (i.e., Gap Status 1 and 2 lands in the USGS Protected Areas Database; PAD-US 4.0). Each species was assigned a PWRSR score equal to the product of range-size rarity and the percent of habitat that is unprotected. The PWRSR raster sums these scores for all species with habitat that overlaps a cell.These data layers are intended to identify areas of high potential value for on-the-ground biodiversity protection efforts. As a synthesis of predictive models, they cannot guarantee either the presence or absence of imperiled species at a given location. For site-specific decision-making, these data should be used in conjunction with field surveys and/or documented occurrence data, such as is available from the NatureServe Network.For more information, see:Hamilton, H., Smyth, R.L., Young, B.E., Howard, T.G., Tracey, C., Breyer, S., Cameron, D.R., Chazal, A., Conley, A.K., Frye, C. and Schloss, C. (2022), Increasing taxonomic diversity and spatial resolution clarifies opportunities for protecting imperiled species in the U.S.. Ecological Applications. Accepted Author Manuscript e2534. https://doi.org/10.1002/eap.2534Note that the above citation is based on the MoBI 2020 product and does not reflect the most current information. Please contact NatureServe for more information.This data supersedes the MoBI 2020 data which can be found here. A summary of changes between MoBI 2020 and 2024:Species included: MoBI 2024 includes 2,493 species, compared to 2,216 in MoBI 2024. Due to a combination of taxonomic updates and global rank/ESA status changes, 177 species from the 2020 product were removed while 454 species were added to this 2020 product. All taxonomic groups included in MoBI 2020 are included in the 2024 product, with the addition of several solitary bee genera.Scale changes: We increased the resolution from 990-m to 330-m for all MoBI products. Due to this resolution increase, we recommend caution conducting direct comparisons between the MoBI 2020 and MoBI 2024 products.To download data as a layer package, navigate here.
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Range Resources reported $344.57M in Cash and Equivalent for its fiscal quarter ending in March of 2025. Data for Range Resources | RRC - Cash And Equivalent including historical, tables and charts were last updated by Trading Economics this last July in 2025.
OSU_SnowCourse Summary: Manual snow course observations were collected over WY 2012-2014 from four paired forest-open sites chosen to span a broad elevation range. Study sites were located in the upper McKenzie (McK) River watershed, approximately 100 km east of Corvallis, Oregon, on the western slope of the Cascade Range and in the Middle Fork Willamette (MFW) watershed, located to the south of the McKenzie. The sites were designated based on elevation, with a range of 1110-1480 m. Distributed snow depth and snow water equivalent (SWE) observations were collected via monthly manual snow courses from 1 November through 1 April and bi-weekly thereafter. Snow courses spanned 500 m of forested terrain and 500 m of adjacent open terrain. Snow depth observations were collected approximately every 10 m and SWE was measured every 100 m along the snow courses with a federal snow sampler. These data are raw observations and have not been quality controlled in any way. Distance along the transect was estimated in the field. OSU_SnowDepth Summary: 10-minute snow depth observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meterological stations were located in the approximate center of each forest or open snow course transect. These data have undergone basic quality control. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN flags for missing data to NA, and added site attributes such as site name and cover. We replaced positive values with NA, since snow depth values in raw data are negative (i.e., flipped, with some correction to use the height of the sensor as zero). Thus, positive snow depth values in the raw data equal negative snow depth values. Second, the sign of the data was switched to make them positive. Then, the smooth.m (MATLAB) function was used to roughly smooth the data, with a moving window of 50 points. Third, outliers were removed. All values higher than the smoothed values +10, were replaced with NA. In some cases, further single point outliers were removed. OSU_Met Summary: Raw, 10-minute meteorological observations collected at OSU met stations in the upper McKenzie River Watershed and the Middle Fork Willamette Watershed during Water Years 2012-2014. Each meterological tower was deployed to represent either a forested or an open area at a particular site, and generally the locations were paired, with a meterological station deployed in the forest and in the open area at a single site. These data were collected in conjunction with manual snow course observations, and the meteorological stations were located in the approximate center of each forest or open snow course transect. These stations were deployed to collect numerous meteorological variables, of which snow depth and wind speed are included here. These data are raw datalogger output and have not been quality controlled in any way. See manufacturer specifications for individual instruments to determine sensor accuracy. This file was compiled from individual raw data files (named "RawData.txt" within each site and year directory) provided by OSU, along with metadata of site attributes. We converted the Excel-based timestamp (seconds since origin) to a date, changed the NaN and 7999 flags for missing data to NA, and added site attributes such as site name and cover. OSU_Location Summary: Location Metadata for manual snow course observations and meteorological sensors. These data are compiled from GPS data for which the horizontal accuracy is unknown, and from processed hemispherical photographs. They have not been quality controlled in any way.
This raster portrays the distribution of sagebrush within the geographic extent of the sagebrush biome in the United States. It was created for the Western Association of Fish and Wildlife Agency’s (WAFWA) Sagebrush Conservation Strategy publication as a visual for the schematic figures and to calculate summary statistics. This distribution incorporates the most recently available sagebrush cover mapping (Xian et al. 2015, Rigge et al. 2019) and classified LANDFIRE EVT (Department of Ecosystem Science, University of Wyoming 2016). Both datasets were rigorously evaluated and extensive ground measurements taken to evaluate accuracy by the respective authors. We created a combined binary sagebrush distribution by classifying the Rigge et al. (2019) product to a binary form where sagebrush cover was greater than 5%, which is equal to the root mean squared error of the analysis (RMSE = 5.09). The Rigge et al. (2019) raster is not complete across the sagebrush biome, so we filled in the areas of NoData with the 'Sagebrush-dominated Ecological Systems' pixels from binary sagebrush raster (Department of Ecosystem Science, University of Wyoming 2016) to create a continuous raster across the sagebrush biome. The input layers are informative to conditions circa the beginning of 2015.
The project lead for the collection of this data in California was Terri Weist. She, along with Danielle Walsh, Shelly Blair, and other personnel, captured 30 adult female mule deer from July 2012 to November 2014, equipping the deer with Iridium satellite collars manufactured by Lotek. The data was collected from the interstate Carson River herd, where a portion of the population spends the summer months in the Sierra range of California and the winter months in western Nevada. An additional 57 deer were collared in Nevada and provided by Cody Schroeder of the Nevada Department of Wildlife. Summer range is mostly within Alpine County, California, but also extends into El Dorado County and Mono County. Winter range is confined to the California-Nevada border area in Alpine County, CA. and Douglas County, NV. GPS location data was collected between February 2012 to July 2019. Between 2 and 12 location fixes were recorded per day, with a maximum of a fix taken every 2 hours during migration sequences. 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 and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 45 deer, including location, date, time, and average location error as inputs in Migration Mapper. Due to the large study area and a concentration of deer movement east of Lake Tahoe in the Carson Range, the population was split into two distinct sub-herds. Twenty deer contributing 52 migration sequences were used in the modeling analysis for the Carson Range. Twenty-five deer contributing 58 migration sequences were used from the rest of the population surrounding the Carson Valley. 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. Winter range analyses were based on data from 48 individual deer and 92 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 2 deer (10% of the sample), and greater than or equal to 4 deer (20% of the sample) from the Carson Range dataset and greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 5 deer (20% of the sample) from the Carson Valley dataset 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. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.
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Joshua tree is a visually distinctive plant found in California''s Mojave Desert and adjacent areas. The unique silhouette and tall stature of Joshua tree relative to typical surrounding vegetation make it one of the most recognizable native plants of California deserts. There are two species of Joshua tree in California, western Joshua Tree (Yucca brevifolia) and eastern Joshua tree (Yucca jaegeriana). Eastern Joshua tree (Yucca brevifolia ssp. jaegeriana) distribution is represented in the data incidentally, but the primary purpose of this dataset is to illustrate the distribution of western Joshua tree. Western Joshua tree is distributed in discontinuous populations in the Mojave Desert and in a portion of the Great Basin Desert. Western Joshua tree is often noted as being abundant near the borders of the Mojave Desert in transition zones. No attempt was made to map Joshua tree distribution outside of California, and therefore the data are limited to geographic areas within California. CDFW possesses vegetation maps that cover a large portion of the California deserts where Joshua tree generally occurs. CDFWs Vegetation Classification and Mapping Program (VegCAMP) uses a combination of aerial imagery and fieldwork to delineate polygons with similar vegetation and to categorize the polygons into vegetation types. In 2013, an effort was made to create a vegetation map that covers a large portion of the California deserts. The vegetation data from this project includes percent absolute cover of Joshua tree and in some instances only Joshua tree presence and absence data. Western Joshua tree and eastern Joshua tree were lumped together as one species in these vegetation maps. A rigorous accuracy assessment of Joshua tree woodland vegetation alliance was performed using field collected data and it was determined to be mapped with approximately 95 percent accuracy. This means that approximately 95 percent of field-verified, polygons mapped as Joshua tree woodland alliance were mapped correctly. While Joshua tree woodland alliance requires even cover of Joshua tree at greater than or equal to 1 percent, the vegetation dataset has polygons recorded with less than 1 percent cover of Joshua tree as well as simple presence and absence data. The CDFW used Joshua tree polygons from vegetation mapping combined with additional point data from other sources including herbarium records, Calflora, and iNaturalist to create the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree. CDFW reviewed publicly available point observations that appeared to be geographic outliers to ensure that incorrectly mapped and erroneous observations did not substantially expand the presumed range of the species. In a limited region, hand digitized points were used where obvious Joshua tree occurrences that had not been mapped elsewhere were present on aerial photographs. Creating a range map with incomplete presence data can sometimes be misleading because the absence of data does not necessarily mean the absence of the species. Some of the observations used to produce the range map may also be old, particularly if they are based on herbarium records, and trees may no longer be present in some locations. Additionally, different buffer distances around data points can yield wildly different results for occupied areas. To create the the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree, CDFW buffered presence locations, but did not use a specific buffer value, and instead used the data described above in a geographic information system exercise to extend the range polygons to closely follow known occurrence boundaries while eliminating small gaps between them.
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Brazil Direct Investment: Liabilities: Equity Capital: Inflow: Transactions Lower than or Equal to US$10 Million data was reported at 513.920 USD mn in May 2019. This records an increase from the previous number of 508.066 USD mn for Apr 2019. Brazil Direct Investment: Liabilities: Equity Capital: Inflow: Transactions Lower than or Equal to US$10 Million data is updated monthly, averaging 665.797 USD mn from Mar 2014 (Median) to May 2019, with 63 observations. The data reached an all-time high of 1.358 USD bn in Dec 2015 and a record low of 479.425 USD mn in Mar 2019. Brazil Direct Investment: Liabilities: Equity Capital: Inflow: Transactions Lower than or Equal to US$10 Million data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Investment – Table BR.OA017: Direct Investment: Liabilities: Equity Capital: Inflow: by Value Ranges.
ABSTRACT - Relative and absolute elevations of the Sierra Nevada and adjacent Basin and Range province, timing of their differentiation, and location, amount, and timing of strike-slip movement between them are controversial. The provincial boundary near Reno developed in two stages. (1) At ca. 12 Ma, the (greater than or equal to) 700 km2 Verdi-Boca sedimentary basin formed across what was to become the boundary, probably as a result of a small-magnitude but regional extensional episode that affected much of the western Basin and Range. (2) At 3 Ma, the basin was complexly faulted and folded during a larger magnitude extensional episode that established the modern Sierran structural and topographic boundary in this area. The boundary is really a transition zone with a western edge along the Donner Pass, California, fault zone, which is farther west than previously placed. Both episodes appear to have resulted from east-west extension only, which suggests that northwest motion of the Sierra Nevada relative to the Basin and Range shown by geodetic data began after 3 Ma or was taken up farther east.
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Analysis of ‘Mule Deer Migration Corridors - Carson River - 2012-2019 [ds2888]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4547b7bc-6a60-4349-b710-a168883fd834 on 26 January 2022.
--- Dataset description provided by original source is as follows ---
The project lead for the collection of this data in California was Terri Weist. She, along with Danielle Walsh, Shelly Blair, and other personnel, captured 30 adult female mule deer from July 2012 to November 2014, equipping the deer with Iridium satellite collars manufactured by Lotek. The data was collected from the interstate Carson River herd, where a portion of the population spends the summer months in the Sierra range of California and the winter months in western Nevada. An additional 57 deer were collared in Nevada and provided by Cody Schroeder of the Nevada Department of Wildlife. Summer range is mostly within Alpine County, California, but also extends into El Dorado County and Mono County. Winter range is confined to the California-Nevada border area in Alpine County, CA. and Douglas County, NV. GPS location data was collected between February 2012 to July 2019. Between 2 and 12 location fixes were recorded per day, with a maximum of a fix taken every 2 hours during migration sequences. 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 and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 45 deer, including location, date, time, and average location error as inputs in Migration Mapper. Due to the large study area and a concentration of deer movement east of Lake Tahoe in the Carson Range, the population was split into two distinct sub-herds. Twenty deer contributing 52 migration sequences were used in the modeling analysis for the Carson Range. Twenty-five deer contributing 58 migration sequences were used from the rest of the population surrounding the Carson Valley. 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. Winter range analyses were based on data from 48 individual deer and 92 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 2 deer (10% of the sample), and greater than or equal to 4 deer (20% of the sample) from the Carson Range dataset and greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 5 deer (20% of the sample) from the Carson Valley dataset 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. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.
--- Original source retains full ownership of the source dataset ---
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Species across the planet are shifting their ranges to track suitable climate conditions in response to climate change. Given that protected areas have higher quality habitat and often harbor higher levels of biodiversity compared to unprotected lands, it is often assumed that protected areas can serve as steppingstones for species undergoing climate-induced range shifts. However, there are several factors that may impede successful range shifts among protected areas, including the distance that must be travelled, unfavorable human land uses and climate conditions along potential movement routes, and lack of analogous climates. Through a species-agnostic lens, we evaluate these factors across the global terrestrial protected area network as measures of climate connectivity, which is defined as the ability of a landscape to facilitate or impede climate-induced movement. We found that over half of protected land areas and two-thirds of the number of protected units across the globe are at risk of climate connectivity failure, casting doubt on whether many species can successfully undergo climate-induced range shifts among protected areas. Consequently, protected areas are unlikely to serve as steppingstones for a large number of species under a warming climate. As species disappear from protected areas without commensurate immigration of species suited to the emerging climate (due to climate connectivity failure), many protected areas may be left with a depauperate suite of species under climate change. Our findings are highly relevant given recent pledges to conserve 30% of the planet by 2030 (30x30), underscore the need for innovative land management strategies that allow for species range shifts, and suggest that assisted colonization may be necessary to promote species that are adapted to the emerging climate. Methods Identifying climate analogs We followed the methods of Abatzoglou et al. (2020) and Parks et al. (2022) to characterize climate and identify backward and forward climate analogs. The specific climate variables we used were average minimum temperature of the coldest month (Tmin), average maximum temperature of the warmest month (Tmax), annual actual evapotranspiration (AET), and annual climate water deficit (CWD). AET and CWD concurrently account for evaporative demand and availability of water (N. L. Stephenson, 1990). These four variables provide complementary information pertinent to ecological systems and collectively capture the major climatic constraints on species distributions and ecological processes across a range of taxa (Dobrowski et al., 2021; Lutz et al., 2010; Parker & Abatzoglou, 2016; N. Stephenson, 1998; C. M. Williams et al., 2015). Monthly data acquired from TerraClimate (Abatzoglou et al., 2018) were used to produce these annual summaries from 1961-1990 (resolution = ~4km), which were then averaged over the same time period to represent reference period climate normals. The reference time period (1961–1990) is meant to represent climate conditions and climate niches prior to the bulk of recent warming. Future climate conditions were also computed from TerraClimate (available from www.climatologylab.org/terraclimate.html) and correspond to a 2°C increase above pre-industrial levels that are likely to manifest by mid-21st century without immediate and massive changes in global climate policies (Friedlingstein et al., 2014). As with the reference period climate, we summarized the four +2°C climate metrics annually and over a 30-year time period to represent future climate normals. All analyses in this study were conducted in the R statistical platform (R Core Team, 2020). We identified backwards and forwards analogs by estimating the climatic dissimilarity between each protected focal pixel (resolution = ~4km to match gridded climate data) and all protected pixels within a 500-km radius using a standardized Mahalanobis distance (Mahony et al., 2017). We chose the 500-km search radius as it encompasses an upper range of dispersal for some terrestrial animals and plants (Chen et al., 2011) when assuming 2°C warming by the mid-21st century; this search radius has also been used in previous studies (Bellard et al., 2014; Parks et al., 2022; J. W. Williams et al., 2007). The Mahalanobis distance metric synthesized the four climate variables (i.e. Tmin, Tmax, AET, and CWD; fig. 2a) by measuring distance in multivariate space away from a centroid using principal components analysis of standardized anomalies. Mahalanobis distance scales multivariate mean climate conditions between a pixel and those within the search radius by the focal pixel’s covariance and magnitude of interannual climate variability (ICV) across the four metrics. For backwards analogs, we characterized +2°C ICV and reference period climate normals to calculate climatic dissimilarity; for forward analogs, we used reference period ICV and +2°C climatic normals to calculate climatic dissimilarity. We standardized Mahalanobis distance to account for data dimensionality by calculating a multivariate z-score (σd) based on a Chi distribution (Mahony et al., 2017). σd represents the climate similarity between each focal pixel and its candidate backward and forward analogs (i.e. all other protected terrestrial pixels within 500 km), and we considered any protected pixels with σd ≤ 0.5 as climate analogs (fig. 2b) (following Parks et al., 2022). We were unable to calculate Mahalanobis distance when there was no ICV for any one of the four variables, and as a consequence, these areas are omitted from all analyses; this affects, for example, a relatively small tropical area in South America (CWD=0 each year) and areas perennially covered by snow (CWD=0 each year; e.g. most of Greenland). We focused our analyses on protected areas as defined by the World Database on Protected Areas (WDPA) (IUCN & UNEP-WCMC, 2019) and included protected areas classified as IUCN (International Union of Conservation for Nature) Management Categories I-VI, except those identified as ‘proposed’, ‘marine’, or otherwise aquatic (e.g. wetland, riverine, endorheic). A large number of protected areas, however, were not assigned an IUCN category in the WDPA (identified as ‘Not Reported’, ‘Not Assigned’, or ‘Not Applicable’) but are likely to have reasonably high levels of protection (e.g. Kruger National Park in South Africa). We included these additional protected areas if the level of human modification was similar or less than that observed within IUCN category I-VI protected areas. To do so, we measured mean land-use intensity within each IUCN category I-VI protected area using the Human Modification Gradient (HMG) raster dataset (Kennedy et al., 2019) and calculated the 80th percentile of the resulting distribution. Any unassigned protected areas with a mean HMG less than or equal to this identified threshold were included in our study (following Dobrowski et al., 2021). We then converted this vector-based polygon dataset to raster format (resolution = ~4km to match gridded climate data; n=1,063,748 pixels). It is well-recognized that the WDPA contains a large number of duplicate and overlapping polygons (Palfrey et al., 2022; Vimal et al., 2021). Although this does not affect summaries across the globe or for individual countries (described below), it provides a challenge when trying to summarize by individual protected areas (due to double-counting). Consequently, we ‘cleaned’ the WDPA prior to summarizing the climate connectivity metrics for individual protected areas by removing polygons that exhibited ≥ 90% overlap with another; this resulted in 29,752 individual protected areas (available in the Electronic Supplemental Material). Least-cost path modelling Following Dobrowski and Parks (2016) and Carroll et al. (2018), we used least-cost path modelling (Adriaensen et al. 2003) to build potential climate-induced movement routes between each protected focal pixel and its backward and forward analogs. The least-cost models were parameterized with resistance surfaces based on climate dissimilarity and the human modification gradient (HMG) (Kennedy et al., 2019). For backward analog modelling, we characterized climatic dissimilarity (i.e. climatic resistance) using two intermediate surfaces, the first being the Mahalanobis distance between each focal pixel (using +2°C ICV) and all other pixels using reference period climate normals (fig. 2c) and the second being the Mahalanobis distance (using +2°C ICV) and all other pixels using +2°C climate normals (fig. 2d). These two surfaces provide a proxy for climate similarity designed to capture transient changes between the reference period and +2°C climate; these were then averaged to characterize the overall climatic resistance across time and space (fig. 2d). For forward analog modelling, the process is similar except we used reference period ICV when characterizing climatic resistance (fig. 2a-2d). We then multiplied the climatic resistance (fig. 2d) by HMG (fig. 2e) to create the final resistance surface for least-cost path modeling (cf. Parks et al., 2020). Prior to this step, we rescaled HMG from its native range (0–1) to 1–25 to correspond with the range of Mahalanobis distance values and thereby grant comparable weights to climatic resistance and HMG resistance (~95% of all Mahalanobis distance values are below 25 within a 500km radius). Open water was given a resistance=25 so that paths would avoid water when possible. Least-cost path modelling was achieved using the gdistance package (van Etten, 2017); paths represent the least accumulated cost across the final resistance surface (fig. 2f) between each focal pixel and analog (fig. 2g). Because paths were rarely straight lines, some were longer than the 500km that we established as a search radius. We removed these longer paths to abide by the biologically informed upper dispersal
Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features, and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific, limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM _location, and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site _location shapefile using the site field. Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animal’s home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data. Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use.
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Aim: Gene flow from central to edge populations is thought to limit population growth at range edges by constraining local adaptation. In this study, we explore the thesis that range edges can differ in their dynamics and be either “hard” (e.g. a river) or “soft” (e.g. ecological gradients). We hypothesize that soft edge populations will have smaller effective population sizes than central populations and that gene flow will be greater from the center to the edge than vice versa. Conversely, we hypothesize that hard edge populations should have similar effective population sizes to central populations and that gene flow will be equal between the two. Location: Kentucky, West Virginia, and Virginia, USA. Taxon: Plethodon kentucki (Caudata: Plethodontidae). Methods: We evaluated landscape suitability using an ecological niche model, then we compared gene flow and effective population sizes between edge and central populations and quantified gene flow between populations. Finally, we characterized landscape genetic variation, testing for isolation by distance and isolation by environment. Results: We found continuously decreasing habitat quality along soft edges, with hard edges more variable. Additionally, we found that soft edges had lower effective population sizes than central populations and that gene flow was greater from the center of the range to the soft edges than the reverse. In hard edges, by contrast, we found effective population sizes in edge populations were similar to central populations, with relatively equal gene flow in both directions. Main conclusions: Understanding why species have range limits is central to investigations of the structure of biodiversity, yet the evolutionary dynamics of range edges remain poorly understood. We show that within a single species with a small range, the evolutionary dynamics operating at range boundaries may depend on the nature of the boundary. Methods We gathered blood samples and tail tips from 39 individuals in 31 localities of Plethodon kentucki. Plethodon glutinosus, a morphologically cryptic relative, occurs sympatrically, so a known P. glutinosus sample was included to identify species. Genomic DNA was extracted using Qiagen DNeasy Blood and Tissue Kits (Qiagen Corp., Valencia CA) following the manufacturer’s protocol. We quantified genetic variation using single nucleotide polymorphisms (SNPs). To obtain SNPs, we used double-digest restriction-site associated DNA sequencing (ddRAD) (Peterson et al., 2012), including a protocol that has been optimized for use in salamanders (Jones and Weisrock, 2018). Briefly, we double-digested extracted DNA using equal amounts of the restriction enzymes EcoRI and SphI (New England BioLabs). DNA fragments were indexed for each individual and pooled for size selection of 420 bp +/- 10% on a Pippin Prep (Sage Science, Beverly MA). The resulting libraries were amplified by PCR for 12 cycles with Phusion high-fidelity DNA polymerase (New England Biolabs, Ipswich MA) and cleaned with Dynabeads (ThermoFisher, Waltham MA) and AMPure XP beads (Beckman Coulter, Inc., Brea CA). They were sequenced with 150 bp paired-end reads using a 1% PhiX DNA spike-in on an Illumina (Illumina, San Diego CA) HiSeq 2500 at Novogene. To filter the raw sequencing data, we first checked the quality of reads with fastQC v. 0.11.9 (Andrews, 2010). Then, we demultiplexed raw sequences in Stacks v. 2.61 (Catchen et al., 2011; Catchen et al. 2013) following Rochette and Catchen (2017). We built a custom pipeline based on Hime et al. (2019) (Appendix A1-10). We demultiplexed the raw, stitched reads by individual with the process_radtags function, allowing for one mismatch between observed and expected barcodes. We retained reads with both restriction enzyme cut sites and had a mean Phred quality score greater than 20 over 45 bp sliding-window intervals (Hime et al., 2019). We excluded two individuals with fewer than 900,000 reads (DH_64627 and SRK_2997). We optimized parameters by testing M 1-12 using the R80 method following Rochette and Catchen (2017) and found M12 to retain the greatest number of reads. We then assembled the sequences de novo. We used ustacks to build loci within individuals, cstacks to assemble a catalog of loci across individuals, sstacks to match samples to catalog, tsv2bam to convert files, gstacks to genotype individuals, and populations to compute summary statistics and export files. We then further excluded two individuals with > 95% missing data (EFW_0006 and SRK_3200), resulting in a final sample of 35 individuals from 31 localities. When allowing 10% missing data per SNP, we obtained 30,155 SNPs, and when we did not allow any missing data, we retained 6,803 SNPs. For other methods, see the Materials and Methods section of Watts et al. 2024.
Ba3Sr4O7 is Caswellsilverite-like structured and crystallizes in the orthorhombic Immm space group. The structure is three-dimensional. there are two inequivalent Ba2+ sites. In the first Ba2+ site, Ba2+ is bonded to six O2- atoms to form BaO6 octahedra that share corners with two equivalent SrO6 octahedra, corners with four BaO6 octahedra, edges with three BaO6 octahedra, and edges with nine SrO6 octahedra. The corner-sharing octahedra tilt angles range from 1–6°. There are a spread of Ba–O bond distances ranging from 2.69–2.81 Å. In the second Ba2+ site, Ba2+ is bonded to six O2- atoms to form BaO6 octahedra that share corners with six BaO6 octahedra, edges with four BaO6 octahedra, and edges with eight equivalent SrO6 octahedra. The corner-sharing octahedra tilt angles range from 0–1°. There are two shorter (2.70 Å) and four longer (2.74 Å) Ba–O bond lengths. There are two inequivalent Sr2+ sites. In the first Sr2+ site, Sr2+ is bonded to six O2- atoms to form SrO6 octahedra that share corners with two equivalent BaO6 octahedra, corners with four SrO6 octahedra, edges with five equivalent BaO6 octahedra, and edges with seven SrO6 octahedra. The corner-sharing octahedra tilt angles range from 2–8°. There are a spread of Sr–O bond distances ranging from 2.53–2.76 Å. In the second Sr2+ site, Sr2+ is bonded to six O2- atoms to form SrO6 octahedra that share corners with six SrO6 octahedra, edges with four SrO6 octahedra, and edges with eight BaO6 octahedra. The corner-sharing octahedra tilt angles range from 0–2°. There are two shorter (2.64 Å) and four longer (2.70 Å) Sr–O bond lengths. There are four inequivalent O2- sites. In the first O2- site, O2- is bonded to two equivalent Ba2+ and four Sr2+ atoms to form a mixture of corner and edge-sharing OBa2Sr4 octahedra. The corner-sharing octahedra tilt angles range from 1–5°. In the second O2- site, O2- is bonded to two equivalent Ba2+ and four equivalent Sr2+ atoms to form a mixture of corner and edge-sharing OBa2Sr4 octahedra. The corner-sharing octahedra tilt angles range from 0–1°. In the third O2- site, O2- is bonded to two equivalent Ba2+ and four equivalent Sr2+ atoms to form a mixture of corner and edge-sharing OBa2Sr4 octahedra. The corner-sharing octahedra tilt angles range from 2–8°. In the fourth O2- site, O2- is bonded to four Ba2+ and two equivalent Sr2+ atoms to form a mixture of corner and edge-sharing OBa4Sr2 octahedra. The corner-sharing octahedra tilt angles range from 0–2°.
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At Sugarloaf we monitored female tortoises (range 184–289 mm midline carapace length [CL]) weekly using radio telemetry in 1991–1993 and 1997–2005 as part of a reproductive ecology study (Averill-Murray 2002; Averill-Murray et al. 2018). During the 11 years of radio-tracking, all burrows used by both telemetered and opportunistically encountered tortoises were marked with numbered aluminum tags (n = 522). We use the term “burrow” specifically to refer to a subsurface cavity >1/2 the tortoise’s length either formed by erosion or excavated by a tortoise or another animal (Burge 1978). We categorized burrow types as “rock” (cover provided by rocks or boulders, including large boulder piles in which the tortoise could not be visualized), “soil” (cover provided by soil or vegetation with no substantial rock component), and “packrat” (freestanding white-throated woodrat Neotomoa albigula nest independent of other shelter types; packrat nests inside rock shelters were categorized as “rock”). We did not individually mark “pallets” (shallow, scraped out areas 220 mm CL) in the analysis, and we excluded observations between the first and last dates of hibernation each year, estimating the date that each tortoise terminated hibernation as the last day the tortoise was observed inside or < 10 m from its hibernaculum. We calculated variograms, fit movement models, and estimated home ranges with area-corrected, optimally weighted, autocorrelated kernel density estimation (wAKDEC) using the ctmm package in R. We estimated total home ranges across the entire study period using perturbative Hybrid REML (pHREML) wAKDEC conditional on the selected underlying movement model for each tortoise. We estimated core areas as the area encompassed by the 50% AKDE isopleth, the proportion of the total (95%) home range area contained by the 50% core area (PA), and the intensity of core area use (ICU = core area isopleth/[50% core area/95% AKDE area]). We used the ‘meta’ function in ctmm to calculate population-level home range and core area estimates that account for estimation uncertainty, as well as overall effect sizes between sites and sex-specific effect sizes within and between sites. We calculated the number of burrows available to each tortoise and estimated burrow densities by clipping vectors of the mapped burrows to the 50% AKDE core areas, the 95% AKDE home ranges, and the 50–95% non-core areas in QGIS 3.34.1. Location data are not included because Gopherus morafkai is a protected species. Inquiries for these data should be made to the Arizona Game and Fish Department.We compared home ranges estimated in this study with 1) MCP estimates for the same individuals for up to four years at FMR reported by Riedle et al. (2008) and 2) AKDE estimates based on up to 10 years of data for the same individuals at Sugarloaf reported by Averill-Murray et al. (2020). To compare MCP and AKDE estimates at FMR, we fit a logistic regression of the ratio of MCP to AKDE estimates against effective sample size (N ̂area) using the quantreg package in R. A good fit of the data to a logistic curve would indicate that MCP estimates underestimate AKDE estimates at low N ̂area. We conducted a repeated-measures ANOVA of the paired estimates from each tortoise using the rstatix package in R. A significant test result would indicate that home-range estimates from the 4-year subset of data are not equivalent to estimates from the full dataset.We hypothesized that adult AKDE home ranges were equivalent between sexes and sites and that ranges were not affected by the number of available burrows (loge transformed). We used the R package metafor in mixed-effects, within-study meta-analyses to explicitly account for the variation (heterogeneity) among the true effects arising from estimated uncertainty in home-range size within individual tortoises. We conducted a permutation test to validate the robustness of the final model and to adjust inferences accordingly. We compared density of the total number of burrows within AKDE home ranges between sites and sexes with generalized least squares regression (GLS) using R package nlme. We repeated the meta-analysis for 50% core areas and 50–95% non-core areas and followed by assessing burrow density versus sex, site, and home range portion with repeated-measures ANOVA. We also compared the relationship of ICU to the number of burrows within core areas and between sites and sexes with a generalized linear model and Gamma distribution with a logarithmic link function in R package glmmTMB.We used GLS in nlme to evaluate the hypotheses that the number of burrows used by individual tortoises did not differ between sites, sexes, or relative to the number of burrows available within their 95% AKDE home ranges, 50% core areas, and 50–95% non-core areas.
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Connected PE Range for Public STP (Confined only to IWK)
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Context:
The ESA river discharge Climate Change Initiative (CCI) project is a precursor study. It aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data. It aims to provide a proof-of-concept for the feasibility for a potential River Discharge ECV product to meet the requirements for the Global Climate Observing System. This project covers precursor activities towards the production of data products that address the GCOS-defined requirements for the River Discharge ECV.
Data description :
Just as in-situ stage measurements can be used to gauge river discharge, altimetry-derived water surface elevation (WSE) can serve as an alternative means of estimating river discharge when discharge time series data is available. Several methodologies have been documented for deriving discharge time series from multimission altimetry observations and supplementary data (Biancamaria et al., 2024). At least two approaches will be used, depending on the available in situ discharge and altimetry water surface elevation (WSE) time series:
⋅ Method 1: The preferred approach relies on the altimetry water surface elevation time series and in situ discharge time series to create a rating curve (RC) characterized by a power relationship between these two variables following a Bayesian approach (Rantz et al., 1982). However, this method necessitates a significant overlap period between discharge data and radar altimetry measurements (e.g., Biancamaria et al., 2011; Papa et al., 2012), or it requires the assumption that the rating curve remains valid and consistent when discharge data is only available prior to the altimetry observation period.
⋅ Method 2: The final option, in cases where there is no temporal overlap between in-situ or simulated discharge and water surface elevation data, assumes that the validity and stability of the rating curve persist across the various time periods covered by the two datasets. Both of these time periods should be sufficiently long to encompass a wide range of events. With this assumption, Tourian et al. (2013, 2017) introduced a method for calculating the rating curve, not based on the time series of discharge and water surface elevation, but on the distribution of their quantiles. This method has been adopted by a limited number of recent studies (e.g., Belloni et al., 2021). However, it’s important to note that this methodology naturally introduces higher errors when compared to the preferred approach. For this reason, this methodology will be validated over some stations with various hydrological dynamics and satisfying previous methods (overlap period exists between WSE and Q).
Approaches to derive Rating Curve (RC) :
Bayesian Approach :
The Bayesian method is a robust statistical approach used for constructing a rating curve, frequently applied in the field of hydrology when the goal is to estimate unknown parameters from observed data, while taking into consideration the associated uncertainty in these estimates.
According to this, the estimation of the rating curve using the Bayesian method involves several steps:
The initial step entails defining a probabilistic model that describes the relationship between observed data and the parameters we aim to estimate. In many hydrological applications, the relationship between discharge data (Q) and water surface elevation data (WSE) is often expressed as a power function:
Q = a⋅(WSE-z0)b
Here, a, z0 and b are the parameters of the rating curve. a, is a scaling coefficient governing the magnitude of the Q-WSE relationship, b, characterizes the nature of this relationship, and z0, represents the height of the free surface above the reference point, corresponding to the river bottom's altitude. The power relationship is especially pertinent due to its consistency with numerous hydrodynamic phenomena. The exponent b within the equation allows for the representation of distinctive flow characteristics, including factors like roughness and channel geometry. Moreover, it offers adaptability in modelling to accommodate variations in flow characteristics, whether they are turbulent or laminar. This relationship, despite its mathematical simplicity, facilitates the fine-tuning of model adjustments in accordance with observed data (Chow, 1959).
The second step involves the use of prior normal distributions, reflecting our prior knowledge about these parameters. These distributions can either be informative or uninformative, depending on our level of knowledge. The limits and ranges for a, z0 and b can vary depending on the specific context of the study, the dataset used, and the characteristics of the river or channel being analysed.
Coefficient “a”: adjustment parameter for the rating curve representing the scaling factor for discharge. Its value can significantly fluctuate based on various factors such as the characteristics of the river or channel, hydraulic conditions, and other influencing factors. Consequently, "a" must be non-negative and constrained within a sensible range specific to the system under study. Following the Manning equation, “a” must be equal to W/n*S1/2 (Chow et al., 1988) where W is the river’s width (m), n the Manning’s roughness coefficient and S the slope (m/m). Given the considerable variability in river width and slope across different stations, a feasible range for this coefficient can be considered as:
a ∈ [0; 3000]
Coefficient “b”: adjustment parameter representing the exponent of the rating curve and indicating the hydraulic condition of the study site. Like "a," this value must comply with physical constraints and cannot be negative. Following the Manning equation, “b” must be equal to 5/3 for reference hydraulic condition (Rantz et al., 1982). To accommodate the variability in system characteristics across sites, the following range values can be considered for this coefficient:
b ∈ [0; 5]
Coefficient “z0”: offset or the elevation at which discharge begins. It should be within the range of elevations relevant to your study. For this reason, the value cannot exceed the minimum value of water surface elevation (WSE) and the range value need to consider of the variability in term of water depth over the sites. A feasible range for this coefficient can be considered as:
z0 ∈ [min(WSE)-30; min(WSE)]
The final step involves parameter estimation. The posterior distribution of the parameters yields probabilistic estimates of the rating curve parameters in the form of mean values (optimal values) and credibility intervals (95th percentiles). This accounts for the uncertainty associated with these parameters and is achieved through Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution. Two commonly employed MCMC algorithms are "NUTS" (No-U-Turn Sampler) and "Metropolis-Hastings." The Metropolis-Hasting sampler "MH" algorithm, which is relatively simple and efficient where a balance between exploration and exploitation is desired. This algorithm can be adapted to sample from discrete state spaces.
Quantile approach :
The Quantile approach employs statistical modelling using quantile functions to create a rating curve, eliminating the necessity for overlapping measurements. This algorithmic method enables the estimation of river discharge using satellite altimetry, even in instances where there are no in situ measurements within the altimeter's timeframe. This approach has undergone application and validation in diverse river basins spanning different climatic zones, such as the Amazon, Brahmaputra, Danube, Niger, and Ob (Tourian et al., 2013).
Assuming a stationary flow behaviour and no modification in the river bathymetry both at the altimetry virtual station and at the in-situ gage, this approach ensures the utilization of historical in situ data in current applications. This method computes the quantile functions of the altimetry water surface elevation on one hand and of the discharge time series on the other hand. Then a scatter plot of these in-situ discharge quantiles versus altimetry water surface elevation quantiles is computed to establish the rating curve using the bayesian approach described previously.
File description :
Column name Description
basin-station Basin name in capital letters and Station name in capital letters separated by "_" and where spaces have been replaced by "-".
lon Longitude in decimal degrees [-180,180] with 4 decimals - corresponding to the insitu discharge station.
lat Latitude in decimal degrees [-90,90] with 4 decimals – corresponding to the insitu discharge station.
a Adjustment parameter for the rating curve representing the scaling factor for discharge. Number with 3 decimals.
b Adjustment parameter representing the exponent of the RC and indicating the hydraulic condition of the study site. Number with 3 decimals.
z0 Offset of the elevation at which discharge begins. Number with 3 decimals.
a_sd Standard deviation of the coefficient "a". Number with 3 decimals.
b_sd Standard deviation of the coefficient "b". Number with 3 decimals.
z0_sd Standard deviation of the coefficient "z0". Number with 3 decimals.
period Period used to compute the rating curve under the format %Y-%m-%d where the start and the end dates are separated by ":"
nb Number of overlap dates to compute the rating curve.
Methodology Methodology used to
The project leads for the collection of this data were Sara Holm with California Department of Fish and Wildlife and Mike Cox with Nevada Department of Wildlife. Carl Lackey and Cody Schroeder of the Nevada Department of Wildlife and Julie Garcia of California Department of Fish and Wildlife also contributed to the completion of the mapping and project. The Verdi-Truckee mule deer herd primarily winters south of Interstate 80 in the Carson Range along the California-Nevada border, although a portion of this herd also winters north and east of Verdi, Nevada on Peavine Mountain. Migration routes to summer range follow I-80 southwest along both sides of the Truckee River toward Martis Valley and Truckee, California. The summer range for this small herd (approximately 500 animals estimated in 2019) is located east the town of Truckee, California and includes portions of Juniper Flat, Martis Creek, and south of the Truckee River to the confluence of Gray Creek. Migration behavior and timing of migration is highly dependent on seasonal weather conditions and the depth of snow during late fall and early winter periods. Significant challenges to this deer herd include barriers to movement, such as Interstate 80 and associated vehicle collisions, as well as increased housing development on winter range in the Garson Road area and surrounding Verdi neighborhoods. Twenty-five adult female mule deer were captured and fitted with store-on-board GPS collars from 2009 to 2010, and additional deer were collared sporadically between 2012-2017. Between 2 and 22 location fixes were recorded per day. 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 and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data, including location, date, time, and average location error as inputs in Migration Mapper. Thirty-one deer contributing 91 migration sequences were used in the modeling analysis. 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. Winter range analyses were based on data from 31 individual deer and 74 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd would likely expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the sample), and greater than or equal to 6 deer (20% of the sample) from the Loyalton dataset 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.
The project leads for the collection of most of this data were Scott Hill and Henry Lomeli. Mule deer (38 adult females and 7 adult males) from the East Tehama herd were captured and equipped with store-onboard GPS collar (G2110B, Advanced Telemetry Systems) and iridium satellite GPS collars (Siritrack/Lotek), transmitting data from 2010-2018. The Eastern Tehama herd migrates from a lower elevation winter range in the eastern foothills of the Sacramento Valley to upper elevation summer ranges in the southern Cascade and northern Sierra Nevada mountains (Hill and Figura 2020). A small percentage of the herd are residents, particularly along the Sacramento River and in areas of irrigated agriculture. GPS locations were fixed between 1-13 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 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 and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 35 migrating deer, including 63 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. Collars placed on males malfunctioned, likely leading to irregular behavior, and were therefore not considered for this analysis. The average migration time and average migration distance for deer was 25.68 days and 59.96 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 and a fixed motion variance of 1000. Winter range analyses were based on data from 33 individual deer and 36 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. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, 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 7 deer (20% of the sample) representing migration corridors, medium use corridors, 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.
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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 2020-2021. 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 percent of GPS locations per deer 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 69 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 15.38 days and 32.13 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 of 1000 were produced per migration sequence and compared for the entire dataset, with best models being combined prior to population-level analyses (10 percent of sequences selected with BMMM). 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 27 individual deer and 60 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 to 3 deer (10 percent of the sample), and greater than or equal to 6 deer (20 percent 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.
The data stream contains information on the number of students per year of course and age range. Equal education