These three data sets, produced by WWF and RESOLVE, show the location of current tiger habitat and priority areas for habitat conservation.*Tiger Conservation Landscapes: *Tiger Conservation Landscapes (TCLs) are large blocks of contiguous or connected area of suitable tiger habitat that that can support at least five adult tigers and where tiger presence has been confirmed in the past 10 years. The data set was created by mapping tiger distribution, determined by land cover type, forest extent, and prey base, against a human influence index. Areas of high human influence that overlapped with suitable habitat were not considered tiger habitat.*Tx2 Tiger Conservation Landscapes: *This data set displays 29 Tx2 Tiger Conservation Landscapes (Tx2 TCLs), defined areas that could double the wild tiger population through proper conservation and management by 2020.*Terai Arc Landscape corridors: *This data set displays 9 forest corridors on the Nepalese side of the Terai Arc Landscape (TAL). Corridors are defined as existing forests connecting current Royal Bengal tiger meta-populations in Nepal and India.

2020 TIGER FilesTopologically Integrated Geographic Encoding and Referencing (TIGER) files are a product of the U.S. Census Bureau. These files include vector data on features such as transportation and hydrography, landmarks, Congressional Districts, and census blocks and tracts.Full technical documentation for TIGER/Line® Shapefiles can be found here.2020 Redistricting DataPublic Law (P.L.) 94-171, enacted by Congress in December 1975, requires the Census Bureau to provide states the opportunity to identify the small area geography for which they need data in order to conduct legislative redistricting. The law also requires the U.S. Census Bureau to furnish tabulations of population to each state, including for those small areas the states have identified, within one year of Census day.Since the first Census Redistricting Data Program, conducted as part of the 1980 census, the U.S. Census Bureau has included summaries for the major race groups specified by the Statistical Programs and Standards Office of the U.S. Office of Management and Budget (OMB) in Directive 15 (as issued in 1977 and revised in 1997). Originally, the tabulation groups included White, Black, American Indian/Alaska Native, and Asian/Pacific Islander, plus “some other race.” These race data were also cross-tabulated by Hispanic/Non-Hispanic origin. At the request of the state legislatures and the Department of Justice, for the 1990 Census Redistricting Data Program, voting age (18 years old and over) was added to the cross-tabulation of race and Hispanic origin. For the 2000 Census, these categories were revised to the current categories used today.To view the full technical documentation for the 2020 Census Redistricting Data, please click here.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. 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 2,645 Urban Areas (UAs) in this data release with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

1.Prioritising conservation of source populations within landscapes is proposed as a strategy for recovering tigers globally. We studied population dynamics of tigers in Corbett National Park (CNP) in Indian Terai, which harbours the largest and highest density tiger population in any protected area of the world. Through population viability models we demonstrate the importance of CNP in tiger recovery of western Terai.

2.We camera trapped 521 km2 of CNP using open population capture‐mark‐recapture framework between 2010‐2015 to estimate annual abundance, spatially explicit density, survival, recruitment, temporary movements, sex ratio and proportion of females breeding. We model metapopulation persistence with and without Corbett as a source within western Terai landscape at different levels of poaching and habitat connectivity.

3.In six years we recorded 6202 photo‐captures of 307 individual tigers. Annual tiger abundance and density were stable at 120 (SE 19) and 14 (SE 3) per 100 ...

Today, most wild tigers live in small, isolated Protected Areas within human dominated landscapes in the Indian subcontinent. Future survival of tigers depends on increasing local population size, as well as maintaining connectivity between populations. While significant conservation effort has been invested in increasing tiger population size, few initiatives have focused on landscape-level connectivity and on understanding the effect different landscape elements have on maintaining connectivity. We combined individual-based genetic and landscape ecology approaches to address this issue in six protected areas with varying tiger densities and separation in the Central Indian tiger landscape. We non-invasively sampled 55 tigers from different protected areas within this landscape. Maximum-likelihood and Bayesian genetic assignment tests indicate long-range tiger dispersal (on the order of 650 km) between protected areas. Further geo-spatial analyses revealed that tiger connectivity was affected by landscape elements such as human settlements, road density and host-population tiger density, but not by distance between populations. Our results elucidate the importance of landscape and habitat viability outside and between protected areas and provide a quantitative approach to test functionality of tiger corridors. We suggest future management strategies aim to minimize urban expansion between protected areas to maximize tiger connectivity. Achieving this goal in the context of ongoing urbanization and need to sustain current economic growth exerts enormous pressure on the remaining tiger habitats and emerges as a big challenge to conserve wild tigers in the Indian subcontinent.

This dataset contains human population density for the state of California and a small portion of western Nevada for the year 2000. The population density is based on US Census Bureau data and has a cell size of 990 meters.

The purpose of the dataset is to provide a consistent statewide human density GIS layer for display, analysis and modeling purposes.

The state of California, and a very small portion of western Nevada, was divided into pixels with a cell size 0.98 km2, or 990 meters on each side. For each pixel, the US Census Bureau data was clipped, the total human population was calculated, and that population was divided by the area to get human density (people/km2) for each pixel.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. 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.

This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

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 county boundaries 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). Please see the Data Dictionary for more information on data fields. Data Sources:FDOT FTO 2020 and 2021 Population Estimates by 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

CENSUS_BLCKGRPS_TIGER00_POPDENS_IN contains populaton densities calculated for all Indiana blockgroups identified by the US Bureau of the Census. Data is from U.S. Department of Commerce, U.S. Census Bureau, Census 2000 Tiger Line Files and SF1 tables.

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

A world without tigers is hard to imagine, but red flags are being hoisted across the tiger’s range. In Indochina, widespread poaching of tigers and wildlife continues to create empty forests, and the development of the proposed transnational economic corridors in the region will further fragment Indochina’s remaining forests and create dispersal barriers. In Sumatra and Malaysia, vast oil palm and acacia plantations are predicted to result in complete conversion of some of the richest lowland rain forests on Earth, habitats that were populated by tigers only a few years ago. The increasing demand for tiger parts for folk medicines in China and Southeast Asia and for costume adornment among Tibet’s growing middle-class has intensified threats to tigers across the range.But large mammals, including tigers, have coexisted for centuries with dense human populations. The release of the 1997 Tiger Conservation Unit Analysis identified where tigers can live in the future. During the decade since, experiences from implementing field conservation projects have confirmed that the future of wildlife conservation in Asia depends on judicious land use planning—zoning—of human use areas, core wildlife habitat, buffer zones, and corridors in large conservation landscapes to restore the harmony that once existed in the wild land-village interface of rural Asia.Learn more about the analysis and resultsThe User's Guide that highlights the remaining tiger lands—the large landscapes of habitat, often anchored by protected areas that are global priorities for conservation.The Technical Assessment: Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015The fate of wild tigers. BioScience, Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007.

This shapefile describes the Census 2010 published population estimates by US County-equivalent boundaries for the United States.

The original Census 2010 County-equivalent shapefile with Selected Demographic and Economics Data was obtained from the US Census Bureau TIGER/Line data: http://www2.census.gov/geo/tiger/TIGER2010DP1/County_2010Census_DP1.zip

Other TIGER/Line Selected Demographic and Economics Data shapefiles can be found at the US Census Bureau TIGER/Line web portal: https://www.census.gov/geo/maps-data/data/tiger-data.html

The 2020 TIGER/Line Shapefiles contain current geographic extent and boundaries of both legal and statistical entities (which have no governmental standing) for the United States, the District of Columbia, Puerto Rico, and the Island areas. This vintage includes boundaries of governmental units that match the data from the surveys that use 2020 geography (e.g., 2020 Population Estimates and the 2020 American Community Survey). In addition to geographic boundaries, the 2020 TIGER/Line Shapefiles also include geographic feature shapefiles and relationship files. Feature shapefiles represent the point, line and polygon features in the MTDB (e.g., roads and rivers). Relationship files contain additional attribute information users can join to the shapefiles. Both the feature shapefiles and relationship files reflect updates made in the database through September 2020. To see how the geographic entities, relate to one another, please see our geographic hierarchy diagrams here.Census Urbanized Areashttps://www2.census.gov/geo/tiger/TIGER2020/UACCensus Urban/Rural Census Block Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php2020 TIGER/Line and Redistricting shapefiles:https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.htmlTechnical documentation:https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdfTIGERweb REST Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.htmlThe legal entities included in these shapefiles are:American Indian Off-Reservation Trust LandsAmerican Indian Reservations – FederalAmerican Indian Reservations – StateAmerican Indian Tribal Subdivisions (within legal American Indian areas)Alaska Native Regional CorporationsCongressional Districts – 116th CongressConsolidated CitiesCounties and Equivalent Entities (except census areas in Alaska)Estates (US Virgin Islands only)Hawaiian Home LandsIncorporated PlacesMinor Civil DivisionsSchool Districts – ElementarySchool Districts – SecondarySchool Districts – UnifiedStates and Equivalent EntitiesState Legislative Districts – UpperState Legislative Districts – LowerSubminor Civil Divisions (Subbarrios in Puerto Rico)The statistical entities included in these shapefiles are:Alaska Native Village Statistical AreasAmerican Indian/Alaska Native Statistical AreasAmerican Indian Tribal Subdivisions (within Oklahoma Tribal Statistical Areas)Block Groups3-5Census AreasCensus BlocksCensus County Divisions (Census Subareas in Alaska)Unorganized Territories (statistical county subdivisions)Census Designated Places (CDPs)Census TractsCombined New England City and Town AreasCombined Statistical AreasMetropolitan and Micropolitan Statistical Areas and related statistical areasMetropolitan DivisionsNew England City and Town AreasNew England City and Town Area DivisionsOklahoma Tribal Statistical AreasPublic Use Microdata Areas (PUMAs)State Designated Tribal Statistical AreasTribal Designated Statistical AreasUrban AreasZIP Code Tabulation Areas (ZCTAs)Shapefiles - Features:Address Range-FeatureAll Lines (called Edges)All RoadsArea HydrographyArea LandmarkCoastlineLinear HydrographyMilitary InstallationPoint LandmarkPrimary RoadsPrimary and Secondary RoadsTopological Faces (polygons with all geocodes)Relationship Files:Address Range-Feature NameAddress RangesFeature NamesTopological Faces – Area LandmarkTopological Faces – Area HydrographyTopological Faces – Military Installations

1. Despite conservation efforts, large mammals such as tigers and their main prey, gaur, banteng, and sambar, are highly threatened and declining across their entire range. The only large viable source population of tigers in mainland Southeast Asia occurs in Thailand's Western Forest Complex (WEFCOM), an approximately 19,000 km2 landscape of 17 contiguous protected areas.

2. We used an occupancy modeling framework, which accounts for imperfect detection, to identify the factors that affect tiger distribution at the approximate scale of a female tiger's home range, 64 km2, and site use at a scale of 1 km2 in WEFCOM. At the larger scale, we estimated the proportion of sites occupied by tigers; at the finer scale, we identified the key variables that influence site-use and developed a predictive distribution map. At both scales, we examined key ecological and anthropogenic factors that help explain distribution and preferred habitat use.

3. WEFCOM is virtually only "half full" of tigers, it occupied 37% or 5,858 km2 of the landscape which was largely influenced by the combined presence of all three large prey species; in contrast, site use was most strongly influenced by presence of sambar.

4. By modeling occupancy while accounting for imperfect probability of detection, we established reliable benchmark data on the distribution of tigers. This study also identified factors that limit tiger distributions; which managers can then target to expand tiger distribution in WEFCOM and guide recovery elsewhere in Southeast Asia.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. 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.

Tiger (Panthera tigris) survival, as apex predators in forest ecosystems, largely depends on abundant prey in healthy, intact forests. Because large herbivore prey are drivers of plant biomass, we reasoned that tiger distribution and density are probably also closely linked with forest carbon (C) stock, the management of which is critical for mitigating climate change. However, whether tigers exert top-down control of forest C stocks or are passive surrogate C indicators bottom-up is a salient unanswered question in conservation and management, particularly in trophic rewilding. Here, we compiled estimates of global tiger presence and density to test the top-down effects of tigers on forest C stocks and tiger-carbon relationships along a gradient from “empty forests†without tigers to “target state†ecosystems with tigers living at different abundances. Our results showed that tiger presence was associated with higher forest vegetation C stocks, lower C emissions, and higher C inputs gl..., , # Global tiger density linked with forest carbon stock, top-down and bottom-up

Access this dataset on Dryad DOI: 10.5061/dryad.cjsxksnhj

Description of the data and file structure

Each script starts with loading the required packages, reading in the data, and converting categorical variables to factors, and describes the analysis workflow. The main scripts are noted from 1 to 4.

Data files contain extracted carbon, biodiversity, and environmental values within an area of interest (rows) derived from corresponding raster data in ArcGIS with either the Zonal Statistics as Table tool or Sample tool, with Python code, variable descriptions, and source information provided below.

Data files and variables

File: anosim_and_adonis_input_data.txt

Description:Â For manuscript section, "Testing for top-down effects of tiger density on forest carbon stocks". These data are to be used together with file anosim_and_adonis_code.R. Data in all co...,

This hosted feature layer has been published in RI State Plane Feet NAD 83.Census Designated Places (CDP) delineated by the US Census Bureau to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. Most boundaries represented by this shapefile are as of January 1, 2013, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). Limited updates that occurred after January 1, 2013, such as newly incorporated places, are also included. The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

Genetic evidence of differential dispersal pattern in Asiatic wild dog: Comparing populations with different pack size and tiger densities

The files contain genotyping microsatellite dataset for the male and female dholes of Tadoba-Andhari Tiger Reserve and Nawegaon-Nagzira Tiger Reserve.

Description of the Data and file structure

The data is separated in four different sheets as M_TATR, F_TATR, M_NNTR and F_NNTR

The main sources of information for the species distribution are the habitat description and geographic range contained in the published FAO Catalogues of Species (more details at http://www.fao.org/fishery/fishfinder ). Terms used in the descriptive context of the FAO Catalogues were converted in standard depth, geographic and ecological regions and inserted into a Geographic Information System.