DOI: 10.5061/dryad.zgmsbccd5
title: Climate change and lithium mining influence flamingo abundance in the Lithium Triangle

description: 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.
description: 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.
description: Images for 2002 were missing from the record so surface water
 area and NDVI could not be determined for that year. For the regional
 multrigroup SEM we had to remove year 1985-1987 due to convergence issues
 - most likely due to limited flamingo abundance data, from Salar de
 Atacama mainly. We removed rows where response or predictors are
 NA.