Climate change and lithium mining influence flamingo abundance in the Lithium Triangle

The development of technologies to slow climate change has been identified as a global imperative. Nonetheless, such ‘green’ technologies can potentially have negative impacts on biodiversity. We explored how climate change and the mining of lithium for green technologies influence surface water availability, primary productivity, and the abundance of three threatened and economically important flamingo species in the ‘Lithium Triangle’ of the Chilean Andes. We combined climate and primary productivity data with remotely sensed measures of surface water levels and a 30-year dataset on flamingo abundance using structural equation modeling. We found that, regionally, flamingo abundance fluctuated dramatically from year-to-year in response to variation in surface water levels and primary productivity but did not exhibit any temporal trends. Locally, in the Salar de Atacama — where lithium mining is focused — we found that mining was negatively correlated with the abundance of two of the three flamingo species. These results suggest continued increases in lithium mining and declines in surface water could soon have dramatic effects on flamingo abundance across their range. Efforts to slow the expansion of mining and the impacts of climate change are therefore urgently needed to benefit local biodiversity and the local human economy that depends on it.

Methods

Data on regional flamingo abundance came from comprehensive, simultaneous surveys of flamingos across the five salares during the breeding (Jan) and nonbreeding seasons (Jul). These surveys were carried out by the Corporación Nacional Forestal de Chile (CONAF).
Data on local flamigo abundance came from quarterly surveys (Jan/Feb, Apr/May, Jul/Aug, and Oct/Nov) at seven lagunas performed by CONAF in agreement with the Sociedad Química y Minera.
Data on surface water change came from Landsat 5 Thematic Mapper (1984–2011) and Landsat 8 Operational Land Imager (2013–2018) satellite imagery.
Data on normalized difference vegetation index (NDVI) came from the Google Earth Engine Platform.
Data on climatic variables (precipitation, minimum and maximum temperature, runoff, and potential evapotranspiration) were extracted using the Google Earth Engine Platform. We extracted these variables monthly for each watershed from the TerraClimate dataset, which has a ~4-km (1/24th degree) spatial resolution (see www.climatologylab.org/terraclimate.html). Watershed boundaries were constructed from the HydroBASINS dataset.
Data on human population size came from the Global Human Settlement Layer for 1975, 1990, 2000, and 2015.
Data on mining pond area were calculated from satellite imagery. To calculate changes in mining pond area, we digitized the maximum pond extent for each year of our surface water area analysis and summed the areas for that year. Although pond area is not a direct measure of the water used for lithium extraction, it is a good measure of relative change in potential water use.