Accurate representation of stream networks at various scales in a hydrogeologic system is integral to modeling groundwater-stream interactions at the continental scale. To assess the accurate representation of stream networks, the distance of a point on the land surface to the nearest stream (DS) has been calculated. DS was calculated from the 30-meter Multi Order Hydrologic Position (MOHP) raster datasets for 18 watersheds in the United States that have been prioritized for intensive monitoring and assessment by the U.S. Geological Survey. DS was calculated by multiplying the 30-meter MOHP Lateral Position (LP) datasets by the 30-meter MOHP Distance from Stream Divide (DSD) datasets for stream orders one through five. DS was calculated for the purposes of considering the spatial scale needed for accurate representation of groundwater-stream interaction at the continental scale for a grid with 1-kilometer cell spacing. The data are available as Comma-Separated Value formatted files.

This dataset contains home range size, habitat availability and selection ratio data, calculated from GPS data fixes collected from individual European nightjars, in four concurrent years (2015-2018). Home ranges are 95% areas of use, presented in hectares. Habitat availability data are presented as the percentage (%) of each habitat category (n = 6, pooled from 14 original habitat types) available to each individual within their 95% home range. Selection ratios are Manly Selection Ratios for 14 habitat types and express the extent to which each habitat type is used by each individual bird, compared to how much of it is available. Selection Ratios >1 express positive selection – i.e. used more than expected, given availability. Selection Ratios <1 express avoidance – i.e. used less than expected, given availability.

This file gives the results from tests on the latitudinal distribution of species ranges and study areas. For each taxon, a Wilcoxon test was conducted to determine if there was a significant difference in the latitudinal coverage of species ranges and study areas, with the p-value and the difference in means provided. Units are degrees latitude.

About this dataset

Context

The dataset tabulates the population of South Range by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of South Range across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.54% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the South Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of South Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range Population by Gender. You can refer the same here

Raw radio tracking data used to determine the precise distance to Venus (and improve knowledge of the Astronomical Unit) from the Galileo flyby on 10 February 1990.

This dataset contains the boundaries of the Public Land Survey System (PLSS) – Township Range Section boundaries, as well as the boundaries of the Ranchos and Landgrants that pre-dated the PLSS. In general these match the USGS topographic Quad Sheets from the US Geological Survey.Note – some boundaries may not match parcel boundaries where that is logical – we haven’t had the time to complete that movement.These are historic boundaries that still have impacts upon the names and geography of Los Angeles county today. For example, where does the name “Verdugo Mountains” come from – it comes from the Rancho established there.BackgroundPLSS (from wikipedia)The Public Land Survey System (PLSS) is the surveying method used historically over the largest fraction of the United States to survey and spatially identify land parcels before designation of eventual ownership, particularly for rural, wild or undeveloped land. It is sometimes referred to as the rectangular survey system (although non rectangular methods such as meandering can also be used)RanchosThe Spanish and, later, Mexican governments encouraged settlement of territory now known as California by the establishment of large land grants called ranchos, from which the English word ranch is derived. Land-grant titles (concessions) were government-issued, permanent, unencumbered property-ownership rights to land called ranchos.Why this dataset?This dataset was created in order to integrate the boundaries from two different datasets – a Rancho Boundary file created by Mike McDaniel of El Segundo, and a parcel-accurate Township Range Section file created by the LA County Department of Public Works. Thanks to both of those entities for creating those valuable source files. There are many sources of this data out there, but the rancho are holes in the PLSS datasets and the TRS is a hole in the rancho files. This combines both of those.Method of conflationTo merge these two datasets together, they were combined, and then any holes and overlaps were conflated to match the Rancho boundaries that were created by Mike McDaniel. When there were questions I used the USGS topographic quad sheets to verify numbering and naming. We have not (yet) attempted to snap the boundaires to parcels where they most likely should be.FieldsThese fields contains information about ranchos/landgrantsLANDGRANT - name of the land grantNAME_2 - secondary name of the land grantGRANTEE_P - the Grantee NamePATENTEE_ - Secondary Grantee NameGRANT_NO - Grant NumberGRANTED - Date grantedPATENT_DAT - Date patentedSURVEYOR - Survery NameSURVEY_DAT - Survey Month and DateCO - CountyTYPE - Rancho (Ro) or PuebloACRES_1 - Number of acres of the grantThe fields contain information about the PLSSSECTION - Section NumberMERIDIAN - The meridianTOWNSHIP - Township NumberRANGE - Range NumberNOTES - Notes that show changes in informationFeatType - IF this is a TRS or a Rancho

Here's a sample Production Bates Range for a Gap Analysis exercise via Python. It's a CSV with one column containing a range of numbers following the convention "D0000001, D0000002, .... D0099999."

This script can be run against a variable/column on a document production index to identify document sequence gaps, which can be helpful to determine missing documents in a set or to diagnose a technical issue during data processing or exchange phases.

More broadly, this code can be updated to apply over any sequential data range (dates, student ID, serial number, item number, etc.), to show any gaps or available digits.

This dataset contains global tidal variables in form of GeoTIFF raster layers generated by Vestbo et al (2018). The raster layers were generated using the Finite Element Solution oceanographic model (FES2012), provided by Noveltis, Legos and CLS Space Oceanography Division and distributed by AVISO+ (http://www.aviso.altimetry.fr). FES2012 includes overall 32 tidal constituents distributed on 1/16° grids (amplitude and phase), corresponding to 3.75 arc-minutes.

The dataset contains the following five raster layers, plus the algorithm for calling the FES program (written in C).

(1) Annual average cycle amplitude in cm. (2) Maximum annual cycle amplitude in cm. (3) Annual standard deviation of cycle amplitude in cm. (4) Annual average duration of tidal cycles in hours. (5) Annual number of cycles.

A detailed description of the data generation procedure is provided in the original paper (Vestbo et al 2018). References: Vestbo S, Obst M, Quevedo-Fernandez F, Intanai I, Funch P (2018). Present and Potential Future Distributions of Asian Horseshoe Crabs Determine Areas for Conservation. Frontiers in Marine Science. doi: 10.3389/fmars.2018.00164 https://www.frontiersin.org/articles/10.3389/fmars.2018.00164/abstract

The dataset contains the following five raster layers, plus the algorithm for calling the FES program (written in C): (1) Annual average cycle amplitude in cm (2) Maximum annual cycle amplitude in cm (3) Annual standard deviation of cycle amplitude in cm (4) Annual average duration of tidal cycles in hours (5) Annual number of cycles

Although many studies have focused on the effects of the environment and area on local patterns of species richness, few studies have demonstrated how to reconcile the availability of more niches with smaller habitat areas in heterogeneous localities. Here, the environmental range that a species prefers was defined as a niche; a space was defined as an available space if the environmental range of the space matches a niche; and the metric “environmental range per unit space (ERUS)” was presented to describe the heterogeneity of localities. Because the spaces with stressful environmental ranges, outside the niche, increased with increasing heterogeneity, available spaces did not continue to indefinitely increase, but the proportion of available spaces in spaces was low at high heterogeneous localities. Consequently, the probability of species occurring in their respective available spaces was unimodal. Due to the presence of large spaces in homogenous localities and more spaces in hetero...

About this dataset

Context

The dataset tabulates the population of Grass Range by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Grass Range across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 52.63% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Grass Range is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Grass Range total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Grass Range Population by Gender. You can refer the same here

This file gives the results from tests on the temperature distribution of species ranges and study areas. For each taxon, a Wilcoxon test was conducted to determine if there was a significant difference in the temperature coverage of species ranges and study areas, with the p-value and the difference in means provided. Units are degrees celcius.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in South Range: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 38(14.29%) households where the householder is under 25 years old, 72(27.07%) households with a householder aged between 25 and 44 years, 80(30.08%) households with a householder aged between 45 and 64 years, and 76(28.57%) households where the householder is over 65 years old.
• In South Range, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for South Range median household income by age. You can refer the same here

Summary:

Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.

Description:

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.

For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the foundational research used to support the ASTM F3442/F3442M-20 well clear criteria of 2000 feet laterally and 250 feet AGL vertically.

The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the v1.3 uncorrelated encounter models and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”

Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.

The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.

It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.

For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands open dataset. Aircraft type was identified as part of the em-processing-opensky workflow.

• All: No filter, all observations that satisfied encounter conditions
• nearRunway: Aircraft within or at 2 nautical miles of a runway
• awayRunway: Observations more than 2 nautical miles from a runway
• glider: Observations when aircraft type is a glider
• fwme: Observations when aircraft type is a fixed-wing multi-engine
• fwse: Observations when aircraft type is a fixed-wing single engine
• rotorcraft: Observations when aircraft type is a rotorcraft

License

This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).

This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.

MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.

As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.

Distribution Statement

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2021 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.

This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.

This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of

MethodsStudy area: Our initial study area included the entire globe. We began with a seamless grid of cells with a resolution of 0.5 degrees (i.e., ~50 km at the equator). Next, we created polylines representing coastlines using SRTM (Shuttle Radar Topographic Mission) v4.1 global digital elevation model data at a resolution of 250 m (Reuter et al. 2007). We used these coastline polylines to identify and retain cells that intersected the coast. We excluded 192,227 cells that did not intersect the coast. To avoid cells with minimal potential coastal wetland habitat, we used the coastline data to remove an additional 1,056 coastal cells that contained less than or equal to 5% coverage of land. We also removed 176 cells which did not have suitable climate data; most of these cells were removed because they either did not have minimum air temperature data or they had unrealistic low or high minimum air temperature data relative to their neighboring cells. Collectively, these steps produced a grid (hereafter, study grid) that contained a total of 4,908 cells at a resolution of 0.5 degrees. Biogeographic zone and range limit assignmentsFor biogeographic zone and range limit-specific analyses, we assigned various identification codes to each study grid cell. Biogeographic zone assignments included either Atlantic East Pacific (AEP) or Indo West Pacific (IWP) (sensu Duke et al. 1998). Range limits, defined as areas where mangroves abruptly become absent from coastlines, were assigned individually using a combination of climate data, mangrove presence data, and descriptions in the literature. We conducted a literature review to develop hypotheses regarding the climatic and non-climatic factors that control each range limit (Table 1). We created polygons for 14 focal range limits (Fig. 2), and used these polygons to assign study grid cells to a particular range limit. All range limits spanned a mangrove presence-absence transition. For range limits that were expected to be controlled, at least in part, by winter temperatures, we created polygons that spanned the cold-to-hot transition zone. Where possible, this zone extended from a minimum temperature of -20 °C (cold) up to a maximum temperature of 20 °C (hot). However, due to various constraints, most of these transitions covered smaller temperature gradients. For range limits that were expected to be controlled, at least in part, by precipitation, we created polygons that spanned the wet-to-dry transition zone, as determined via the mean annual precipitation data.Climate dataPrior studies in North America have identified the importance of using air temperature extremes in mangrove distribution and abundance models (Osland et al. 2013, Cavanaugh et al. 2014). For all cells within the study grid, we sought to identify the absolute coldest daily air temperature that occurred across a recent multi-decadal period. Although monthly-based mean minimum air temperature data are readily available, daily minimum air temperature data have historically been more difficult to obtain at the global scale (Donat et al. 2013). Due to the absence of a consistent and seamless global dataset of daily air temperature minima, we used a combination of three different gridded daily minimum air temperature data sources. For cells in the United States, we used 2.5-arcminute resolution data created by the PRISM Climate Group (Oregon State University; http://prism.oregonstate.edu) (Daly et al. 2008), for the period extending from 1981-2010. For all continental cells outside of the United States (i.e., coastal cell connected to large bodies of land on all continents except for the United States), we used 1-degree resolution data created by Sheffield et al. (2006), for the same time period. For most islands, we used 0.5-degree resolution data created by Maurer et al. (2009), for the period extending from 1971-2000. From these three data sources, we created a minimum temperature (MINT) data set for the study grid cells to represent the absolute coldest air temperature that occurred across a recent three to four decade period, depending upon the source. For each study grid cell, we also obtained 30-second resolution mean annual precipitation (MAP) data from the WorldClim Global Climate Data (Hijmans et al. 2005), for the period extending from 1950-2000. We also obtained 5-arcminute resolution global gridded mean annual sea surface temperature data from a dataset produced by UNEP-WCMC (2015), for the period extending from 2009-2013. In addition to the gridded climate data, we obtained station-based air temperature data. For 13 of the 14 focal range limits, we identified a proximate station with a long-term record of daily air temperatures. For each of these stations, we obtained daily minimum air temperature data for the 30-year period extending from 1981-2010. From these data, we calculated: (1) the absolute coldest temperature during the 30-year record (MINT); (2) the annual minimum temperature (i.e., the coldest temperature of each year); and (3) annual mean winter minimum temperature (i.e., the mean of the daily minima for the coldest quarter of each year). Mangrove dataTo determine mangrove presence, we used two global mangrove distribution data sources (Spalding et al. 2010, Giri et al. 2011), and assigned a binary code to each study grid cell denoting presence or absence. For most of the world, mangrove presence was assigned to a cell only when both of these sources deemed that mangroves were present. For Myanmar, however, the two mangrove distribution sources were not in agreement, and the Giri et al. (2011) data were deemed more reliable and used to assign mangrove presence for those cells. The two sources were also not in agreement for the coasts of Gabon, Congo, and the Cabinda Province of Angola, and the Spalding et al. (2011) data were deemed more reliable and used to assign mangrove presence for those cells. To determine mangrove species richness within each cell, we used data produced by Polidoro et al. (2010). For each cell where mangroves were deemed to be present, we used the sum of the species-specific mangrove distributional range data to determine the total number of mangrove species potentially present within a cell. To determine mangrove abundance within each cell, we used the 30-m resolution global mangrove distribution data produced by Giri et al. (2011).

Aim: Species geographical range sizes play a crucial role in determining species vulnerability to extinction. Although several mechanisms affect range sizes, the number of biotic interactions and species climatic tolerance are often thought to play discernible roles, defining two dimensions of the Hutchinsonian niche. Yet, the relative importance of the trophic and the climatic niche for determining species range sizes is largely unknown.Location: Central and Northern EuropeTime period: PresentMajor taxa studied: Gall-inducing sawflies and their parasitoidsMethods: We use data documenting the spatial distributions and biotic interactions of 96 herbivore species, and their 125 parasitoids, across Europe and analyse the relationship between species range size and the climatic and trophic dimensions of the niche. We then compare the observed relationships with null expectations based on species occupancy to understand whether the relationships observed are an inevitable consequence of spec...

The dataset consists of two csv files one for home range and net displacement analysis for adult snakes with at least 20 locations collected during the study period and one for the analysis of movement metrics for all adult snakes in the study. The home range data contains the calculated 100 percent and 95 percent minimum convex polygons (MCP) and 95 percent adaptive local convex hull (a-LoCoH) home range estimates, 3 measures of net displacement from the release location of the snake as well as other pertinent information about individual snakes (year included in study, id, treatment group, site, snout to vent length (SVL)). The movement data contains calculations of the following movement metrics: sinuosity, start-to-end distance, and total distance traversed of seasonal movement paths as well as information about individual snakes described above.

This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

INSPIRE Priority Data Set (Compliant) - Species range

How abiotic and biotic factors constrain distribution limits at the harsh and benign edges of species ranges is hotly debated, partly because macroecological experiments testing the proximate causes of distribution limits are scarce. It has long been recognized—at least since Darwin’s On the Origin of Species—that a harsh climate strengthens competition and thus sets species range limits. Using thorough field manipulations along a large elevation gradient, we show the mechanisms by which temperature determines competition type, resulting in a transition from interference to exploitative competition from the lower to the upper elevation limits in burying beetles (Nicrophorus nepalensis). This transition is an example of Darwin’s classic hypothesis that benign climates favor direct competition for highly accessible resources while harsh climates result in competition through resources of high rivalry. We propose that identifying the properties of these key resources will provide a more predictive framework to understand the interplay between biotic and abiotic factors in determining geographic range limits.

Central Plains Experimental Range Study for Long-Term Agroecosystem Research in Nunn, Colorado The Central Plains Experimental Range (CPER) is a site with the The Long-Term Agroecosystem Research (LTAR) Network, which consists of 18 sites across the continental United States (US) sponsored by the US Department of Agriculture, Agricultural Research Service, universities and non-governmental organizations. LTAR scientists seek to determine ways to ensure sustainability and enhance food production (and quality) and ecosystem services at broad regional scales. They are conducting common experiments across the LTAR network to compare traditional production strategies (“business as usual or BAU) with aspirational strategies, which include novel technologies and collaborations with farmers and ranchers. Within- and cross-site network success towards achieving the desired outcomes of enhancing quality food production and reducing environmental impact requires that LTAR scientists and collaborators have well-timed access to various data. We are striving to create opportunities to package and share long-term legacy observations from each site, with new data and metadata in useable, well documented and consistent formats for them. Resources in this dataset:Resource Title: Nunn, CO Central Plains Experimental Range Study (CONUCPER) CSV data. File Name: CONUCPER_csv_data.zipResource Description: CSV format data on Experimental Units, Field Sites, Grazing Plants, Grazing, Persons, Treatments, Weather Daily, Weather Station.