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Data for: Transboundary conservation hotspots in China and potential impacts of the Belt and Road Initiative

Cite this dataset

Shi, Kaichong et al. (2022). Data for: Transboundary conservation hotspots in China and potential impacts of the Belt and Road Initiative [Dataset]. Dryad. https://doi.org/10.5061/dryad.573n5tb9x

Abstract

Aim: Biodiversity hotspots often span international borders, thus conservation efforts must as well. China is one of the most biodiverse countries and the length of its international land borders is the longest in the world; thus, there is a strong need for transboundary conservation. We identify China’s transboundary conservation hotspots and analyze the potential effects of the Belt and Road Initiative (BRI) on them to provide recommendations for conservation actions.

Location: China, Asia

Methods: We compiled a species list of terrestrial vertebrates that span China’s borders. Using their distribution, we extracted the top 30% of the area with the highest richness value weighted by Red List category and considered these transboundary hotspots for conservation priority. Then we analyzed protected area (PA) coverage and connectivity to identify conservation gaps. To measure the potential impact of the BRI, we counted the species whose distribution range is traversed by the BRI and calculated the aggregation index, proportion of natural land, and night light index along its routes.

Results: We identified 1,964 terrestrial vertebrate species living in the border region. We identified four transboundary hotspots and found insufficient PA coverage and low connectivity in three of them. The BRI routes intersected all four hotspots and traversed 82.4% (1,619/1,964) of the transboundary species, half of which (918) are sensitive to the potential risks brought by the BRI. Night light index increased generally along the BRI. However, the proportion of natural land and the aggregation index near the BRI showed different trends in hotspots.

Main conclusions: There is an urgent need for conservation action in China’s transboundary region. The BRI should put biodiversity conservation at the core of its development strategy. Furthermore, we suggest using the planned BRI as a platform for dialogue and consultation, knowledge and data sharing, and joint planning to promote transboundary conservation.

Methods

Data summary:

This is the dataset used in the Diversity and Distributions contribution article "Transboundary conservation hotspots in China and potential impacts of the Belt and Road Initiative". The dataset includes heat maps of the transboundary distribution of terrestrial vertebrates in China drawn by the authors, as well as selected hotspots in the top 30% by value. In addition, a rasterized 0-1 protected area layer for the study area is provided for research reproduction.

The heatmap and hotspots of transboundary species distribution were created as follows:

We compiled a list of transboundary terrestrial vertebrates in China from the International Union for Conservation of Nature (IUCN) Red List database (https://www.iucnredlist.org/). We downloaded data of all species of mammals, birds, amphibians and reptiles from the database and filtered those living in terrestrial ecosystems. We then filtered these species based on their geographic ranges, to retain species living both in China and other neighboring countries. Furthermore, we filtered the species by their distribution codes and retained those with codes of “Extant”, “Possibly Extant”, “Native”, and for birds we excluded “Passage”. The retained species were classified as transboundary terrestrial vertebrates in China.

We downloaded distribution maps of transboundary species from the IUCN Red List (IUCN, 2021) and BirdLife International and the Handbook of the Birds of the World (BirdLife International, 2018). We then refined the distribution range (R 4.1.0, terra package)(Hijmans, 2022) for each species according to its suitable habitat types (i.e., land cover types) and elevation range, which were obtained from the IUCN Red List. Land cover data were obtained from (Jung et al., 2020), which is consistent with the IUCN habitat classification, and elevation data were obtained from WorldClim (https://worldclim.org/) (Fick and Hijmans, 2017). All raster layers were rescaled to a spatial resolution of 1 km and were under spatial reference coordinate system of WGS1984. We created 10 km, 50 km and 100 km buffer zones on both sides of China's border as border region (made in ArcGIS 10.2.2). We used this border region to crop the distribution maps of transboundary terrestrial species in China. Within the border region, each specie has a distribution layer with a value of 0 or 1 in each 1-km2 cell, where 1 represents presence and 0 represents absence. All species were then weighted by their Red List category, assuming Least Concern (LC) as 1, Near Threatened (NT) as 2, Vulnerable (VU) as 3, Endangered (EN) as 4 and Critically Endangered (CR) as 5 (Balaguru et al., 2006). We valued DD as 3 because DD species are often considered potentially at risk of extinction (Jaric et al., 2016). However, excluding the 65 DD species did not affect the main results. The weighted distribution layers were stacked to obtain a weighted-richness map. Finally, we extracted the top 30% of cells with highest values in the weighted-richness map as conservation hotspots. The 30% was chosen as the threshold because according to the 2030 action target 3 of the 15th meeting of the Conference of the Parties to the Convention on Biological Diversity (COP15)(Convention on Biological Diversity, 2020), it is necessary to protect 30% of land and sea globally by 2030.

The raster layer of protected area was created as follows:

We obtained map layers of PAs in China’s neighboring countries from the World Database on Protected Areas (UNEP-WCMC, 2017) and supplemented China’s PAs from Yang et al. (Yang et al., 2018). For some PAs which are point data in the WDPA dataset, we constructed circles around the points with areas equal to the sizes listed in the attribute table. We rasterized this map and reassignment the value to 0(without PAs) and 1(with PAs). Finally, we used border regions to crop the raster map.

References

  1. Balaguru, B., Britto, S. J., Nagamurugan, N., Natarajan, D. and Soosairaj, S. (2006) 'Identifying conservation priority zones for effective management of tropical forests in Eastern Ghats of India', Biodiversity and Conservation, 15(4), pp. 1529-1543.
  2. BirdLife International (2018) 'BirdLife International and handbook of the birds of the world (2018) Bird species distribution maps of the world. Version 2018.1. Available at http://datazone.birdlife.org/.'.
  3. Convention on Biological Diversity (2020) 'Update of the zero draft of the post‐2020 global biodiversity framework'.
  4. Fick, S. E. and Hijmans, R. J. (2017) 'WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas', International Journal of Climatology, 37(12), pp. 4302-4315.
  5. Hijmans, R. J. (2022) 'terra: Spatial Data Analysis'.
  6. IUCN (2021) 'The IUCN Red List of Threatened Species. 2021-3. https://www.iucnredlist.org. Downloaded on 20 june 2022.'.
  7. Jaric, I., Courchamp, F., Gessner, J. and Roberts, D. L. (2016) 'Potentially threatened: a Data Deficient flag for conservation management', Biodiversity and Conservation, 25(10), pp. 1995-2000.
  8. Jung, M., Dahal, P. R., Butchart, S. H. M., Donald, P. F., De Lamo, X., Lesiv, M., Kapos, V., Rondinini, C. and Visconti, P. (2020) 'A global map of terrestrial habitat types', Scientific Data, 7(1), pp. 256.
  9. UNEP-WCMC (2017) 'World Database on Protected Areas User Manual 1.5. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/WDPA_Manual'.
  10. Yang, L., Chen, M. H., Challender, D. W. S., Waterman, C., Zhang, C., Huo, Z. M., Liu, H. W. and Luan, X. F. (2018) 'Historical data for conservation: reconstructing range changes of Chinese pangolin (Manis pentadactyla) in eastern China (1970-2016)', Proceedings of the Royal Society B-Biological Sciences, 285(1885).

Usage notes

R 4.2.0, package include "sf","terra". Or alternative Qgis 3.22, ArcGIS 10.6.

Funding

National Natural Science Foundation of China, Award: 31822049