biostudies-literatureAspin TWHNatural Environment Research Council230-244https://www.ebi.ac.uk/biostudies/studies/S-EPMC7379955biostudies-literatureUnknown25(1)Functional traits are increasingly being used to predict extinction risks and range shifts under long-term climate change scenarios, but have rarely been used to study vulnerability to extreme climatic events, such as supraseasonal droughts. In streams, drought intensification can cross thresholds of habitat loss, where marginal changes in environmental conditions trigger disproportionate biotic responses. However, these thresholds have been studied only from a structural perspective, and the existence of functional nonlinearity remains unknown. We explored trends in invertebrate community functional traits along a gradient of drought intensity, simulated over 18 months, using mesocosms analogous to lowland headwater streams. We modelled the responses of 16 traits based on a priori predictGlobal change biologyExtreme drought pushes stream invertebrate communities over functional thresholds.PMC73799551547331NE/J023736/1NE/J02256X/1Woodward GMatthews TJLedger MEAspin TWHKhamis KO'Callaghan MJMilner AMTrimmer MfalseExtreme drought pushes stream invertebrate communities over functional thresholds.Functional traits are increasingly being used to predict extinction risks and range shifts under long-term climate change scenarios, but have rarely been used to study vulnerability to extreme climatic events, such as supraseasonal droughts. In streams, drought intensification can cross thresholds of habitat loss, where marginal changes in environmental conditions trigger disproportionate biotic responses. However, these thresholds have been studied only from a structural perspective, and the existence of functional nonlinearity remains unknown. We explored trends in invertebrate community functional traits along a gradient of drought intensity, simulated over 18 months, using mesocosms analogous to lowland headwater streams. We modelled the responses of 16 traits based on a priori predict2019-01-01T00:00:00Z2019 Jan2025-04-05T13:44:32.557Z2025-04-05T13:44:32.557ZS-EPMC73799553034609810.1111/gcb.14495