Synthetic populations for regions of the World (SPW) | Alabama

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (usa_140002904_data_v_0_9.zip)

Derived data files

Validation and measures files

Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here. This dataset contains boundaries for all 2010 Census Urbanized Areas (UAs) in the State of Florida with 2021 population density estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021). BEBR provides 2021 population estimates for counties in Florida. However, UA boundaries may not coincide with the jurisdictional boundaries of counties and UAs often spread into several counties. To estimate the population for an UA, first the ratio of the subject UA that is contained within a county (or sub-area) to the area of the entire county was determined. That ratio was multiplied by the estimated county population to obtain the population for that sub-area. The population for the entire UA is the sum of all sub-area populations estimated from the counties they are located within. For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” In 2010, the US Census Bureau identified two types of urban areas—UAs and Urban Clusters (UCs). UAs have a population of 50,000 or more people. Note: Pensacola, FL--AL Urbanized Area is located in two states: Florida (Escambia County and Santa Rosa County) and Alabama (Baldwin County). 2021 population of Baldwin County, AL used for this estimation is from the US Census annual population estimates (2020-2021). All other Urbanized Areas are located entirely within the state of Florida. Please see the Data Dictionary for more information on data fields. Data Sources:FDOT FTO 2020 and 2021 Population Estimates by Urbanized Area and CountyUS Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2021 Date of Publication: October 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

GPWv4 is a gridded data product that depicts global population data from the 2010 round of Population and Housing Censuses. The Population Density, 2015 layer represents persons per square kilometer for year 2015. Data SummaryGPWv4 is constructed from national or subnational input areal units of varying resolutions. The native grid cell size is 30 arc-seconds, or ~1 km at the equator. Separate grids are available for population count, population density, estimated land area, and data quality indicators; which include the water mask represented in this service. Population estimates are derived by extrapolating the raw census counts to estimates for the 2010 target year. The development of GPWv4 builds upon previous versions of the data set (Tobler et al., 1997; Deichmann et al., 2001; Balk et al., 2006).The full GPWv4 data collection will consist of population estimates for the years 2000, 2005, 2010, 2015, and 2020, and will include grids for estimates of total population, age, sex, and urban/rural status. However, this release consists only of total population estimates for the year 2015. This data is being released now to allow users access to the population grids.Recommended CitationCenter for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed DAY MONTH YEAR

The Atlas of the Biosphere is a product of the Center for Sustainability and the Global Environment (SAGE), part of the Gaylord Nelson Institute for Environmental Studies at the University of Wisconsin - Madison. The goal is to provide more information about the environment, and human interactions with the environment, than any other source.

The Atlas provides maps of an ever-growing number of environmental variables, under the following categories:

Human Impacts (Humans and the environment from a socio-economic perspective; i.e., Population, Life Expectancy, Literacy Rates);

Land Use (How humans are using the land; i.e., Croplands, Pastures, Urban Lands);

Ecosystems (The natural ecosystems of the world; i.e., Potential Vegetation, Temperature, Soil Texture); and

Water Resources (Water in the biosphere; i.e., Runoff, Precipitation, Lakes and Wetlands).

Map coverages are global and regional in spatial extent. Users can download map images (jpg) and data (a GIS grid of the data in ESRI ArcView Format), and can view metadata online.

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

The 2016 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

This dataset is a shapefile representing the proportion of threatened endemic species (both plants and animals) in 247 countries along with associated environmental and socioeconomic drivers. The geographic coordinate system is World Geodetic System 1984 (EPSG: 4326). Information on a total of 65,125 endemic species including 27,294 globally threatened endemic species (55% threatened plant species, 45% threatened animal species) was extracted from the IUCN Red List. The categories of threatened species used in the analyses included vulnerable (VU), endangered (EN), critically endangered (CR), extinct in the wild (EW) and globally extinct (EX). We calculated the proportion of globally threatened endemic species among the total number of assessed endemic species per country (Chamberlain et al., 2020). Associated environmental socioeconomic regional correlates included: 1) Cropland: The proportion of each country covered by crops (including food, fibre and fodder crops and pasture grasses) was determined based on a FAO global map with a resolution of 5 arc-minutes (von Velthuizen et al., 2007); 2) HANPP: The proportion of net primary production appropriated by humans (HANPP) by harvesting or burning biomass and by converting natural ecosystems to managed lands with lower productivity was derived for the year 2010 from Krausmann et al. (2013); 3) Delta HANPP: We also computed the increase in HANPP over the period 1962-2010 (Krausmann et al., 2013); 4) per area GDP: The per area gross domestic product (GDP, in international $) was obtained by calculating the median value over each country of all 5 arcmin cells of a recently gridded GDP dataset (Kummu et al., 2018); 5) Human Footprint (HFP): The global terrestrial human footprint (HFP) is an index integrating the influence of built environments, population density, electric infrastructure, croplands, pasture lands, roads, railways, and navigable waterways on the environment based on remotely-sensed and bottom-up survey information (Venter et al., 2016). We extracted from a 1 km resolution HFP map the median value over each country in 2009; 6) Delta HFP: We also calculated the increase in median HFP over the period 1993-2009 (Venter et al., 2016); 7) Invasive alien plants: The richness of invasive alien vascular plant species recorded in each country was compiled by Essl et al. (2019); 8) Invasive alien animals: The richness of invasive alien animal species was derived from the Global Register of Introduced and Invasive Species database (http://griis.org/ accessed on 27-6-2018); 9) Delta temperature: Based on decadal climate maps produced by the IPCC over the last century with a 0.5° resolution, we calculated the median of the change in annual mean temperature (in °C) between 1901-1910 and 1981-1990 (Mitchell & Jones, 2005); 10) Delta rainfall: The same for annual precipitation (in mm); 11) Velocity temperature: We also calculated the median velocity of climate change based on the formula from Hamann et al. (2015) to evaluate the distance (in °) over which a species must migrate over the surface of the earth to maintain constant temperature conditions; 12) Velocity rainfall: The same for precipitation; 13) Roadless areas: The median area of a roadless fragment (in km²) was calculated from the global map of roadless areas published by Ibisch et al. (2016); 14) Wilderness areas: The proportion of wildlands (categories ‘wild woodlands' and ‘wild treeless and barren lands') was calculated from the anthropogenic biome map of Ellis et al. (2010); 15) Protected areas: The proportion of protected areas was estimated from the IUCN's shapefile of World Database on Protected Areas (https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas); 16) Conservation spending: The mean annual conservation spending of each country (in international $) was taken from Waldron et al. (2017) to quantify investment to mitigate biodiversity loss; 17) Completeness of biodiversity information: We used data on the estimated percentage completeness of species records in GBIF, as assessed through comparison with independent estimates of native richness. Inventory effort indices available for vertebrates (Meyer et al., 2015) and vascular plants (Meyer et al., 2016) were merged into a single metric based upon an average weighted by estimated native species richness.

Carrying Capacity EstimationsThe carrying capacity for individuals in this dataset is based on estimated population density (people per km²) for forager societies, as derived from Kavanagh et al. Using a fitted piecewise structural equation model, the authors estimated global population density at a 0.5° × 0.5° resolution.The corresponding raster dataset used in our simulation model is provided in this repository. This raster file is a spatial object in .asc format, where each cell value represents the estimated population density (people per km²) for that location. The file is named:4K_popD_raster_Dec.ascKavanagh, P. H. et al. Hindcasting global population densities reveals forces enabling the origin of agriculture. Nature human behaviour 2, 478 (2018).Language Diversity Data for North AmericaThe empirical richness pattern of language diversity in North America, as used in our simulation, is based on established literature and previously published language range maps.The final dataset is provided as a shapefile, containing:The name of each languageThe corresponding Glottolog identification codeThis shapefile can be found in this repository under the name:NAM_Continent.shpHaynie, H. J. & Gavin, M. C. Modern language range mapping for the study of language diversity. (2019).

Identifying environmental characteristics that limit species’ distributions is important for contemporary conservation and inferring responses to future environmental change. The Tasmanian native hen is an island-endemic flightless rail and a survivor of a prehistoric extirpation event. Little is known about the regional-scale environmental characteristics influencing the distribution of native hens, or how their future distribution might be impacted by environmental shifts (e.g., climate change). Using a combination of local fieldwork and species distribution modelling, we assess environmental factors shaping the contemporary distribution of the native hen, and project future distribution changes under predicted climate change. We find 37.2% of Tasmania is currently suitable for the native hens, owing to low summer precipitation, low elevation, human-modified vegetation, and urban areas. Moreover, in unsuitable regions, urban areas can create ‘oases’ of habitat, able to support populations with high breeding activity by providing resources and buffering against environmental constraints. Under climate change predictions, native hens were predicted to lose only 5% of their occupied range by 2055. We conclude that the species is resilient to climate change and benefits overall from anthropogenic landscape modifications. As such, this constitutes a rare example of a flightless rail to have adapted to human activity. Methods Local-scale factors measurements (fieldwork) We selected geographically distant populations presenting different rainfall profiles during the late-autumn to spring period, April-November 2019, as rainfall is an important factor for native hens’ survival and reproduction (Ridpath, 1972a; Lévêque, 2022): ‘East’ (wukaluwikiwayna/Maria Island National park; 42°34'51"S 148°03'56"E), ‘North’ (Narawntapu National park; 41°08'53"S 146°36'52"E), and ‘West’ (adjacent to the town of Zeehan [712 inhabitants]; 41°53'03"S 145°19'56"E). The period April-November corresponds to the six-month period preceding the middle point of the breeding season, generally used for native hens’ surveys (Goldizen et al., 1998; Lévêque, 2022). All three populations were surveyed between the 10th and the 22nd of November 2019 (late spring, in the middle point of the breeding season) to determine population structure (total number of groups, group composition [number of adults and young], and breeding activity). Each population was monitored over two to five days, depending on habitat complexity and extent of the population area, until all native hens in the area had been surveyed, i.e., when the territories’ structure was found identical at least four times for populations with no previous data (‘North’ and ‘West’), and at least two times in well-known populations (‘East’; Lévêque, 2022), over two different half-day. To align with methods used by Lévêque (2022), we used territory mapping (Bibby et al., 2000; Gibbons & Gregory, 2006) as native-hens maintain year-round territories, and population sizes were measurable with our survey methodology. Territory mapping consists of establishing the location of birds over a number of visits to obtain distinct clusters representing each territory. Boundaries are determined by vocal disputes between neighbours, which are frequent in native hens. During each survey, a minimum of two observers conducted repeated group identification, based on location, neighbours’ location, and number of individuals per group (from two to five individuals per group in this study). The number of individuals and their age category (fledgling, juvenile, or adult) were recorded per territory. The total pasture area surveyed per population, and the total pasture area occupied by native hens were: North population: 2.0 km2 (1.3 km2 occupied); West population: 1.5 km2 (0.7 km2 occupied); East population: 1.5 km2 (0.6 km2 occupied). We measured environmental characteristics in the native-hens’ territory following methods established by Goldizen et al. (1998) to obtain quantitative measures of i) protection cover, ii) water availability, and iii) food availability; these parameters are important for native hen reproduction (Goldizen et al., 1998).

Protection cover was determined as the length (m) of the interface between dense patches of bushes and pasture, used by native hens for hiding and protecting chicks against predators (Lévêque, 2022). It is an important parameter for breeding success (Goldizen et al., 1998). We measured the total protection cover available to native hens in each population using satellite data from Google Maps (www.google.com/maps, accessed on 09/12/2019). For measures of food availability (grass) on territories, we selected random transects of a total length of 1 m across all territories (East: n = 15, North: n = 26, West: n = 22). Measurements of vegetation characteristics were measured and recorded every 2 cm along each transect, including the percentage of i) total vegetation cover, ii) green vegetation, iii) vegetation cover that was grass, iv) vegetation cover that was moss, and v) the grass height (average length of grass blades). The same observer (LL) recorded all measures. Water availability on territories was recorded as territories that had access to water (running or stagnant) at the time the surveys were undertaken. Rainfall data was collected from the Bureau of Meteorology (B.O.M.; www.bom.gov.au/climate/data) at the three population sites: North population at Port Sorell (Narawntapu National Park – 4km away from the population site), West population at Zeehan (West Coast Pioneers Museum), East population at Maria Island (Darlington). Rainfall was reported as the amount of rainwater that had accumulated i) during the six months prior to breeding season midpoint (31/10/2019); following Goldizen et al. (1998)) and ii) during summer [December-February]. Information on recent droughts (on a 3- to 11-month period prior to 31/10/2019) was assessed using values on rainfall percentile deficiency (below the 10th percentile) from B.O.M. (http://www.bom.gov.au/climate/drought/#tabs=Rainfall-tracker). The 6-, 7-, and 12- month-periods were not accessible. B.O.M. defines the category ‘Serious deficiency’ as rainfall that “lies above the lowest five percent of recorded rainfall but below the lowest ten percent (decile range 1) for the period in question”, and ‘severe deficiency’ as “rainfall is among the lowest five percent for the period in question”.

Species Distribution Modelling Data preparation We collected presence-point data for native hens across Tasmania from the Atlas of Living Australia (ALA: www.ala.org.au; accessed 19 February 2021). We additionally included data from BirdLife Tasmania, the Department of Primary Industries, Water and Environment (DPIPWE) reports, and our personal observations, resulting in a total of 23,923 occurrences. Our study area included the Tasmanian mainland and nearby islands, however a large area from the south-west of Tasmania was removed where native hen distribution is not well documented, however, they are thought to be rare or absent in this region due to large proportion of button grass vegetation creating unsuitable habitat (Fig. S2). All subsequent analyses were undertaken in Program R v4.0.4 (R Core Team, 2021). Duplicates were removed by converting presence points into grid presences at 1 km2 resolution and retaining one native hen observation per grid (n = 2447 grid points after this step). Occurrences were visually inspected for any potential errors/outliers from outside Tasmania and Tasmanian islands: this removed seven false occurrences on King and Flinders islands and two observations in freshwater inland lakes (Lake Crescent and Great Lake). As true-absence records were mostly unavailable, we generated pseudoabsences for sites where other land-bird species had been recorded (indicating observation effort at that point), but without native hen detections (Hanberry et al., 2012; Amin et al., 2021; Barlow et al., 2021). Native hens are large-bodied, ground-dwelling, active in the day, and have a loud, distinct call, all of which accounts for a high detectability, if present at a location. We extracted these data from ALA, with 780,499 possible observations on the Tasmanian mainland and all nearby islands. We then excluded all grid cells with a native hen presence and removed any records within 3 km of native hen records: this value was chosen because it is the dispersal distance under which a native hen can naturally move outside of its territory (Ridpath, 1972a). This process resulted in 3,222 pseudoabsence grid points. Citizen-science datasets offer unique opportunities to study a species distribution using ‘crowd-sourced’ effort, however, they tend to be access-biased and have non-random, clustered observations, leading to overrepresentation of certain regions and biases towards some environmental conditions (usually near urban areas; Steen et al., 2021). One way to reduce spatial autocorrelation is to selectively de-cluster occurrences in biased areas using a pre-defined (minimum linear) Nearest Minimum-neighbour Distance NMD (Pearson et al. 2007). As un-urbanised, sparsely populated areas have the least spatial point clustering (and hence spatial bias), the average number of observations in low human densities areas provides the threshold number of records that can be used to tune and select the optimal NMD (Amin et al., 2021). Therefore, we subdivided our data on a grid of 25 km2 cells to be relevant to the metric of human density and used the median of population density index (excluding cells < 1 human/km2) to define thresholds for low and high density. Population density was extracted from the ‘2011 Census of Population and Housing across Australia’ (bit.ly/3bth7W9). ‘Low density’ was defined as < 6 people/km2 and ‘High density’ as

Scottish Marine and Freshwater Science Vol 8 No 25 For a previous Scottish Government funded project (MMSS/001/11), seal telemetry data were combined with haul-out specific population data to generate usage maps for both grey and harbour seals around the UK at a spatial resolution of 5 x 5 km (Jones et al., 2013). These maps provided estimates of seal abundance (and associated confidence intervals) aggregated between 1988 and 2012, thus taking into account changes in population size through time. For Jones et al. (2015), maps were generated which were scaled to the estimated population size in 2013, resulting in the most up-to-date understanding of current seal usage at that time. The Department of Business and Industrial Strategy (BEIS) funded a large deployment of tags on grey seals in the southern North Sea and subsequently commissioned an updated North Sea usage map reflecting the estimated grey seal population size in 2015 (Jones and Russell, 2016). At the request of Scottish Government, usage maps have been generated for Orkney on a finer spatial resolution (0.6 x 0.6 km; Jones et al., 2016; Jones et al., 2017b). Changes A requirement to update the UK-wide usage maps was identified. As part of this, three updates were proposed: (1) inclusion of additional telemetry data (up to 2016); (2) improvement in how count data are incorporated into the usage map framework (Jones et al., 2016; Jones et al., 2017a) and updating count data resulting in estimates of usage scaled to the estimated population size in 2015; and (3) clustering of haul-out sites to increase the proportion of sites for which there are associated telemetry data (Jones and Russell, 2016). Data are also available, with a DOI of 10.7489/2029-1.

Two different great basin perimeter files were intersected and dissolved using ArcGIS 10.2.2 to create the outer perimeter of the great basin for use modeling long-term wildfire effects on sage-grouse population growth, and development of sage-grouse concentration areas based on modeled habitat quality, lek density, and population abundance (Coates et al. 2015). These two perimeter files included a 1:1,000,000 map of hydrographic areas in the Great Basin) (Buto 2009), and vegetation characteristics (Karl et al. 2001). The resulting Modified Great Basin Extent represented a combination of hydrographic and floristic features best suited for the defining the spatial extent of the analyses. To ensure moving window analyses of habitat and fire areas covered the entire extent, the extent was then buffered by 15.8 km. The extent also encompasses large parts of sage-grouse management zones III (Southern Great Basin), IV (Snake River Plain), and V (Northern Great Basin). A small portion (< 5%) of management zone II (Wyoming Basins) is also encompassed. REFERENCES Buto, S.G., 2009, Digital Representation of 1:1,000,000-scale Hydrographic Areas of the Great Basin: U.S. Geological Survey Data Series 457, 5 p .http://pubs.usgs.gov/ds/457 Coates, P.S., Ricca, M.A., Prochazka, B.G., Doherty, K. D., Brooks, M.L., Casazza, M.L. 2015. Long-term effects of wildfire on greater sage-grouse - integrating population and ecosystem concepts for management in Great Basin. http://pubs.er.usgs.gov/publication/ofr20151165 Karl, M., Durtsche, B.M., Morgan, K. 2001. Great Basin Restoration Initiative Area. http://sagemap.wr.usgs.gov/SearchData.aspx

This temperature and precipitation dataset in csv format complements 13 other datasets as part of a study that compared ancient settlement patterns with modern environmental conditions in the Jazira region of Syria.

This study examined settlement distribution and density patterns over the past five millennia using archaeological survey reports and French 1930s 1:200,000 scale maps to locate and map archaeological sites. An archaeological site dataset was created and compared to and modelled with soil, geology, terrain (contour), surface and subsurface hydrology and normal and dry year precipitation pattern datasets; there are also three spreadsheet datasets providing 1963 precipitation and temperature readings collected at three locations in the region. The environmental datasets were created to account for ancient and modern population subsistence activities, which comprise barley and wheat farming and livestock grazing.

These environmental datasets were subsequently modelled with the archaeological site dataset, as well as, land use and population density datasets for the Jazira region. Ancient trade routes were also mapped and factored into the model, and a comparison was made to ascertain if there was a correlation between ancient and modern settlement patterns and environmental conditions; the latter influencing subsistence activities.

These temperature and precipitation data were transferred to and processed in a spreadsheet to generate bar graph to display evapotranspiration (ETP) rate for al-Hasakah, Syria. Purpose was to show differences in evapotranspiration rates between the south, middle and north of the Jazira; higher ETP rates occur in the south near Dayr az-Zawr, Syria.

Satellites provide direct observations of fire activities (e.g., burned area, fire radiative power) (Andela et al., 2017; Giglio et al., 2006; Luo et al., 2024), but their temporal coverages are limited because most satellite data were available only after 1980s (Chuvieco et al., 2019). Fire modules in dynamic global vegetation models (DGVMs) are able to simulate long-term burned area and the interactions with vegetation dynamics based on climate conditions and soil properties (Sitch et al., 2015; Sitch et al., 2024), but the spatial resolution at the global scale is usually coarse due to the coarse resolution of the input meteorological forcing data, and most models failed in capturing the global trends of burned area (Andela et al., 2017; Hantson et al., 2020). The processes included and the parameterizations of fire processes are widely different across fire models, resulting in a large range of simulated burned area at both the regional and global scales (Hantson et al., 2020). Considering the limitations of satellite observations and fire models, a spatiotemporally consistent burned area dataset over the 20th century trained from present-day observations, is essential for fire modelling and can serve as publicly available benchmark for fire ecology and carbon cycle studies.

This study produced a global monthly 0.5°×0.5° burned area fraction (BAF) dataset from1901 to 2020 using machine learning models based on climate conditions, vegetation states, population density and land use data. We first divided the globe into 14 regions following the Global Fire Emission Dataset (GFED regions) (Giglio et al., 2006; van Der Werf et al., 2017) and conducted all steps described below in each GFED region individually. To better capture extreme fires, we first developed a classification model to distinguish grid cells with extreme and regular fires, using the 90th percentile of all burned area fractions within a region as the threshold to define extreme fires. We then trained separate regression models for grid cells categorized as having extreme or regular fires. The models were trained against the satellite-based burned area product (FireCCI51) during 2003-2020 excluding cropland fires, and then used to reconstruct the burned area from 1901 to 2020. In addition to validation against satellite observations that were not used for model training, we also compared our burned area predictions with charcoal records and other independent global and regional burned area datasets. In addition to the historical reconstructed burned area dataset based on the FireCCI51 mentioned above, we also produced two additional products of historical burned area with the same spatiotemporal resolution as the FireCCI51-based burned area reconstruction: 1) the GFED5-based data version, which is based on machine-learning models trained by the burned area from GFED5 which has much more fires than GFED4 (Chen et al., 2023) instead of FireCCI51, and 2) the FireCCI51-based data with burned area further calibrated using the relationship between statistic-based burned area (Mouillot and Field, 2005) and GDP (Bolt and Van Zanden, 2024) at the regional scale before 2000 (named as FireCCI51-GDP version).

This dataset can be used to benchmark historical simulations from fire modules in DGVMs, re-calculate historical fire emissions and estimate legacy effects of vegetation recovery after fires on terrestrial carbon sink. Though the temporal coverage of our product is long enough to support studies related to fire disturbance, carbon dynamics and climate change, more reliable explanatory data for model training and burned area data for validation would help further improve the accuracy of the reconstructed burned area product.

Description of variable names in netcdf files:
'lat' -- latitude of the center of grid cells
'lon' -- longitude of the center of grid cells
'ba' -- burned area fraction in 0.5°×0.5°grid cells
'ba_type' -- 0=no fire; 1=regular burned area; 2=extreme burned area. Note that regular & extreme BA is defined in each GFED region individually. Extreme BA refers to that burned area within grid cells surpassing the 90th percentile of all grid cells with burned area in each GFED region.

This dataset is currently for manuscript submission to the scientifc journal

Abstract

Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments.

Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (Lynx lynx), the wolf (Canis lupus), the brown bear (Ursus arctos) and the wolverine (Gulo gulo). The golden jackal (Canis aureus) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst et al. 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović et al. 2016; Lanszki et al. 2018; Fenton et al. 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst et al. 2015; Hatlauf et al. 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts.

These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes.

Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc et al. 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though.

Methods

The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016.

In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2

The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in:

1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years)

3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time)

5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas)

6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence)

7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present)

8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present)

Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8).

To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (Lynx lynx) in the Alps (Molinari-Jobin et al. 2012):

Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images);

Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and

Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified.

See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.

Usage Notes

The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.

References

Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3

Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. Biological Conservation, 199: 51–55.

Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346: 1517-1519.

Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. Mammalian Biology, 101: 619–630.

Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (Canis aureus) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7

Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (Canis aureus) and its legal status

The 2009 Human Footprint, 2018 Release provides a global map of the cumulative human pressure on the environment in 2009, at a spatial resolution of ~1 km. The human pressure is measured using eight variables including built-up environments, population density, electric power infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways. The data set is produced by Venter et.al., and is available in the Mollweide projection.

Large carnivore distribution maps for Europe 2017 – 2022/23

https://doi.org/10.5061/dryad.3xsj3txrc

Description of the data and file structure

The mapping approach generally follows the methods described in (Chapron et al. 2014) and (Kaczensky et al. 2013). It updates the published Species Online Layers 2012-2016 for brown bear, Eurasian lynx, wolf, golden jackal, and wolverine (Kaczensky et al. 2021; Ranc et al. 2022) for the period 2017-2022/23.

Large carnivore presence was mapped at a 10 x 10 km (ETRS89-LAEA Europe) grid scale. This grid is widely used for Habitat Directive reporting to the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2. The map encompasses the continental EU countries plus Switzerland and Norway, and the EU candidate / potential candidate countries in the Balkan region, in addition ...

Human population density in 2000, by terrestrial ecoregion.

We summarized human population density by ecoregion using the Gridded Population of the World database and projections for 2015 (CIESIN et al. 2005). The mean for each ecoregion was extracted using a zonal statistics algorithm.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3). Socioeconomic Data and Applications Center (SEDAC), Columbia University Palisades, New York. Available at http://sedac.ciesin.columbia.edu/gpw. Digital media.

United Nations Population Division (UNPD). 2007. Global population, largest urban agglomerations and cities of largest change. World Urbanization Prospects: The 2007 Revision Population Database. Available at http://esa.un.org/unup/index.asp.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560