The CWC data repository brings together information on wildlife corridors in India and makes it available to all, from professionals to the general public. This comprehensive, spatially explicit, and dynamic web database can be used for assistance in policy making, research, and conservation decisions. The data portal comprises an interactive geographical map displaying the locations of wildlife corridors across India. Users can navigate to select a corridor and extract information through downloadable corridor profiles - a report that documents all available information about the wildlife corridor. Users can also have a quick view that gives a glimpse of the status of a corridor through eight principal indicators:


  1. Area of natural habitat
    2.  What percentage of the corridor area is natural habitat?
    To calculate this indicator, land use categories from a 100m LULC layer from Copernicus Global Land Service (2019) are reclassified into two separate classes - Natural land use class (open forests, closed forests, shrubs, herbaceous vegetation, permanent water, moss and lichen, bare and sparse vegetation, snow and ice, herbaceous wetlands), and Modified land use class (cropland, built-up). The total percentage of area under the Natural land use class is then calculated using the open-source R package landscapemetrics (Hesselbarth et al. 2019) (R Core Team, 2017)

  2. What percentage of the corridor area has legal protection?
    3. What percentage of the corridor area has legal protection?
    To calculate this indicator, government maps from department records are assessed to identify the extent of corridor area that is granted varying levels of protection under the forest department

  3. Threatened species richness
    4.  What is the extent of threatened species presence in the corridor?
    To calculate this indicator, a layer representing threatened species richness was developed (Arpit Deomurari, WWF-India). This layer is based on IUCN 1km resolution range maps for threatened species including amphibians, birds, mammals, crabs, fish, plants, molluscs, odonata, and reptiles.

  4. Average human population
    5.  What is the density of humans residing in the corridor?
    To calculate this indicator a 1km resolution dataset obtained from the Worldpop repository (https://www.worldpop.org/project/categories?id=18) (2020) is utilised

  5. Human Modification Index Score
    6.  To what extent has the corridor landscape been modified by human activity?
    To calculate this indicator, the human modification of terrestrial systems dataset from the Socioeconomic Data and Applications Center, NASA (2016) is used. The information in the dataset is based on the modelling of different anthropogenic stressors and their estimated impacts in a region (Kennedy et al. 2019)

  6. Landscape Complexity Index
    7.  What is the extent of different land uses in the corridor?
    To calculate this indicator, a 100m LULC layer from the ESA Copernicus Global Land Service repository (2019) is utilised. The structural complexity of the spatial pattern in each corridor is then calculated using the open source R package landscapemetrics (Hesselbarth et al. 2019) in R (R Core Team, 2017)

  7. Land Use Change Index
    8.  How has land use changed in the corridor?
    (Source - currently unavailable)

  8. Natural Habitat Fragmentation Index
    9.  To what extent is the corridor area fragmented?
    This indicator is calculated using a 100m LULC layer from the ESA Copernicus Global Land Service repository (2019). Given the total area of natural habitat in the corridor area, the open source R package landscapemetrics is used to calculate clumpiness- a measure of the degree to which natural habitat is aggregated in a region (Hesselbarth et al. 2019) in R (R Core Team, 2017)

For viewing the map, users can choose background and overlay layers from multiple options


Background layers:

LULC layer This background layer provides an overview of the dynamic classification of land cover in a particular region. This 100m global land cover layer has been adapted from the ESA Copernicus Global Land Service (2019)
Open street maps This default background layer provides ground route data and has been adapted under the Open Database License
Human Modification Index This background layer provides a measure of the extent of human modification a region has undergone based on a set of anthropogenic stressors and their impacts. This 1km resolution layer has been adapted from the Socioeconomic Data and Applications Center, NASA (2016)
Population Density This background layer provides a colour coded visual of human population density across India from a scale of 0 people/km2 to 1000 people/km2

Overlay layers: Protected Areas, National Highways, State Highways, Railway lines


References:
1 Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Herold, M., Fritz, S. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe 2020.
2 Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K., & Nowosad, J. (2019). landscapemetrics: An open-source R tool to calculate landscape metrics. Ecography, 42(10), 1648-1657.
3 Team, R. C. (2017). R - A language and environment for statistical computing. Versión 3.4. 3, Vienna, Austria, R Foundation for Statistical Computing.
4 WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674
5 Kennedy, C. M., J. R. Oakleaf, D. M. Theobald, S. Baruch-Mordo, and J. Kiesecker. 2020. Global Human Modification of Terrestrial Systems. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/edbc-3z60.
6 Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., & Kiesecker, J. (2019). Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology, 25(3), 811-826.
7 OpenStreetMap. (2022). https://openstreetmap.org.
8 IUCN. (2019). The IUCN Red List of Threatened Species. IUCN Red List of Threatened Species. https://www.iucnredlist.org/resources/spatial-data-download