Crown dieback and mortality of urban trees linked to heatwaves during extreme drought

Cities have been described as 'heat islands' and 'dry islands' due to hotter, drier air in urban areas, relative to the surrounding landscape. As climate change intensifies, the health of urban trees will be increasingly impacted. Here, we posed the question: Is it possible to predict urban tree species mortality using (1) species climate envelopes and (2) plant functional traits? To answer these, we tracked patterns of crown dieback and recovery for 23 common urban tree and shrub species in Sydney, Australia during the record-breaking austral 2019-2020 summer. We identified 10 heat-tolerant species including five native and five exotic species, which represent climate-resilient options for urban plantings that are likely to continue to thrive for decades. Thirteen species were considered vulnerable to adverse conditions due to their mortality, poor health leading to tree removal, and/or extensive crown dieback. Crown dieback increased with increasing precipitation of the driest month of species climate of origin, suggesting that species from dry climates may be better suited for urban forests in future climates. We effectively grouped species according to their drought strategy (i.e., tolerance versus avoidance) using a simple trait-based framework that was directly linked with species mortality. The seven most climate-vulnerable species used a drought-avoidance strategy, having low wood density and high turgor loss points along with large, thin leaves with low heat tolerance. Overall, plant functional traits were better than species climate envelopes at explaining crown dieback. Recovery after stress required two mild, wet years for most species, resulting in prolonged loss of cooling benefits as well as economic losses due to replacement of dead/damaged trees. Hotter, longer, and more frequent heatwaves will require selection of more climate-resilient species in urban forests, and our results suggest that future research should focus on plant thermal traits to improve prediction models and species selection.