Population and conservation threats on the vulnerable Sarus Crane (Grus antigone) in Nepal

The global decline in biodiversity is driven by habitat loss, fragmentation, over-exploitation, climate change, invasive species, pollution, and infrastructure development, especially outside protected areas where inadequate policies exacerbate risks. These threats have impacted the population of large wading bird species like the Sarus Crane (Grus antigone), inhabiting agricultural-wetland areas. Despite predicted increases in built-up areas and declines in agriculture and wetlands areas, there are few studies on the globally vulnerable Sarus Crane in Nepal, focusing on its population status and conservation threats. We assessed the pre-breeding population status of the Sarus Crane in ten 10 known districts of Nepal from April to June 2023. Overall, we recorded 690 Sarus Cranes in five districts of Nepal, with 685 individuals and 11 roosting sites reported in Lumbini Province alone. No Sarus Cranes were observed in the other five districts during this period. Wetland areas and distance..., , The data is in .csv (MS-DOS) format of Excel and can be analyzed using generalized linear modelling in any statistical software like R program, PAST, or SPSS., # Population and conservation threats on the vulnerable Sarus Crane (*Grus antigone*) in Nepal

Contact Information

* Name: Dr. Hari Prasad Sharma
* Email: [[e-mail]](mailto:[e-mail])

## File/Folder Details

* Description: a comma-delimited file containing information about individual counts of Sarus crane in Lumbini Province, Nepal. The file is prepared for analysis in the R program but can be reformated for use in other statistical software.
* Format(s): .csv
* Data name: \"Data.csv\"
* Data structure: 93rows*7columns

This is a dataset containing information on the Sarus crane presence/absence across 5km*5km grids in Lumbini province, Nepal. The dataset contains information on 93 observations for the Sarus crane along with the values of different variables for each observation and nonobservation locations. The dataset can be analyzed using generalized linear modelling in any statistical software.

Variables:

* SN: Serial number corresponding to each survey ...