Dead again

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

Bibliographic Data

ID15548014
AuthorsAndreas P Wion (0000-0002-0701-2843, Colorado State University, corresponding author), David D Breshears (0000-0001-6601-0058, University of Arizona), Charles J W Carroll (0000-0003-3254-0282, Colorado State University), Neil S Cobb (0000-0002-6155-9444, Northern Arizona University), Sarah J Hart (0000-0003-0974-3209, Colorado State University), Darin J Law (0000-0002-0903-4210, University of Arizona), Nashelly Meneses (Northern Arizona University), Miranda D Redmond (0000-0002-4657-7943, Colorado State University)
Year2022
Volume17
Issue7
Pages074031-074031
Publication date2022-06-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac7968
OpenAlexW4283020339
LanguageEN
References cited58

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 thresholds derived from drought-metrics have been proposed to predict mortality of 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 as a natural experiment. We compared the ability of existing mortality thresholds derived from four drought metrics (the Forest Drought Severity Index (FDSI), the Standardized Precipitation Evapotranspiration Index, and raw values of precipitation (PPT) and vapor pressure deficit, calculated using 4 km PRISM data) to predict areas of P. edulis die-off following an extreme drought in 2018 across the southwestern US. 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 using sensitivity, specificity, and the area under the curve (AUC). Overall, existing mortality 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. The simple PPT threshold had the highest AUC score (71%) as well as fair sensitivity and specificity, but the FDSI had the greatest sensitivity to die-off (85.9%). We highlight that empirically derived climate thresholds may be useful forecasting tools to identify vulnerable areas to drought induced die-off, allowing for targeted responses to future droughts and improved management of at-risk areas

Biology · Climate change · Climatology · Evapotranspiration · Geography · Juniper · Meteorology · Physical geography · Precipitation · Woodland · Environmental Science · Fire effects on ecosystems · Plant Water Relations and Carbon Dynamics · Tree-ring climate responses · Ecology · Forestry · Geology

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