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Dead Again: Predictions of repeat tree die-off under hotter droughts confirm mortality thresholds for a dryland conifer species

Citation

Wion, Andreas (2022), Dead Again: Predictions of repeat tree die-off under hotter droughts confirm mortality thresholds for a dryland conifer species, Dryad, Dataset, https://doi.org/10.5061/dryad.tht76hf15

Abstract

Tree die-off, driven by extreme drought and exacerbated by a warming climate, is occurring rapidly across every wooded continent - threatening carbon sinks and other ecosystem services provided by forests and woodlands. Forecasting the spatial patterns of tree die-off in response to drought is a priority for the management and conservation of forested ecosystems under projected future hotter and drier climates. Several drought-related metrics have recently been proposed to predict the mortality threshold (i.e., tipping point) for Pinus edulis, a model tree species in many studies of drought-induced tree die-off. To improve future capacity to forecast tree mortality, we used a severe drought in 2018 across the southwestern United States as a natural experiment. We compared the ability of published mortality thresholds derived from four drought metrics (the Forest Drought Severity Index, the Standardized Precipitation Evapotranspiration Index, and raw values of precipitation and vapor pressure deficit) to predict areas of P. edulis die-off following extreme drought. Using aerial detection surveys of tree mortality in combination with gridded climate data, we calculated the agreement between these four proposed thresholds and the presence and absence of regional-scale tree die-off. Overall, such thresholds tended to over predict the spatial extent of tree die-off across the landscape, yet some retain moderate skill in discriminating between areas that experienced and did not experience tree die-off. Area under the curve (AUC) of these thresholds ranged from 0.51 to 0.71, sensitivity (true-positive rate) ranged from 0.54 to 0.86, and specificity (true-negative rates) ranged from 0.16 to 0.73. We highlight that empirically derived climate thresholds may be a useful forecasting tool to identify vulnerable areas to drought induced die-off, allowing for targeted responses to future droughts and improved management of at-risk areas.

Methods

Data from USDA Aerial Detection Surveys (Region 2, 3, and 4) for 2019 and PRISM Climate Data, processed in R using the raster package.

Funding