Ecological impacts of unsustainable sand mining: Urgent lessons learned from a critically endangered freshwater cetacean
Cite this dataset
Han, Yi et al. (2022). Ecological impacts of unsustainable sand mining: Urgent lessons learned from a critically endangered freshwater cetacean [Dataset]. Dryad. https://doi.org/10.5061/dryad.gqnk98srw
Sand production, tripled in the last two decades, is an emerging concern for global biodiversity. However, the paucity of sand mining data worldwide prevents understanding the extent of sand mining impacts and how it affects wildlife populations and ecosystems, which is critical for timely mitigation and conservation actions. Integrating remote sensing and field surveys over fourteen years, we investigated mining impacts on the critically endangered Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) in Dongting Lake, China. We found that sand mining presented a consistent, widespread disturbance in Dongting Lake. The porpoise strongly avoided mining sites, especially the areas subjected to higher mining intensity. The extensive sand mining significantly contracted the porpoise’s range and restricted their habitat use in the lake. Water traffic for sand transportation further blocked the species’ river-lake movements, affecting the population connectivity. In addition, mining-induced loss of nearshore habitats, a critical foraging and nursery ground for the porpoise, occurred in nearly 70% of the water channels of our study region. Our findings provide the first empirical evidence on the impacts of unregulated sand extractions on species distribution. Our spatiotemporally explicit approach and findings can support regulation and conservation, yielding broader implications for sustainable sand mining worldwide.
This dataset contains (1) sand mining information from 2006–2019 in Dongting Lake, (2) data of porpoise distributions from nine range-wide surveys from 2006–2019, (3) water traffic information at the outlet channel of Dongting Lake from 2001–2019, (4) data of the lake morphology from 2006–2019, (5) codes for all data analyses.
Sand dredgers and sand barges were identified using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper-plus (ETM+), and Landsat 8 Operational Land Imager (OLI) orthorectified top-of-atmosphere (TOA) reflectance images. The identification process was performed on Google Earth Engine. Locations and acquired dates of the identified sand dredgers/barges were extracted and transferred to a geographical information system for further analysis (GIS, ArcMap 10.3).
The porpoise distribution data was compiled from nine lake-wide surveys conducted from 2006 to 2019. Sighting records with geographic information were used for analysis in this study.
ArcGIS or similar softwares are required to open raster data (TIFF files) and vector data (shapefiles). Codes for data analysis were written in R.
Strategic Priority Research Program of the Chinese Academy of Sciences, Award: XDA23040403
National Key Programme of Research and Development of Ministry of Science and Technology of China, Award: 2016YFC0503200
National Natural Science Foundation of China, Award: 31500456
National Natural Science Foundation of China, Award: 31430080
National Natural Science Foundation of China, Award: 31801988
Ocean Park Conservation Foundation, Hong Kong, Award: AW02_1819