The effect of COVID-19 on self-reported safety incidents in aviation: An examination of the heterogeneous effects using causal machine learning.

Introduction: Disruptions to aviation operations occur daily on a micro-level with negligible impacts beyond the inconvenience of rebooking and changing aircrew schedules. The unprecedented disruption in global aviation due to COVID-19 highlighted a need to evaluate emergent safety issues rapidly.

Method: This paper uses causal machine learning to examine the heterogeneous effects of COVID-19 on reported aircraft incursions/excursions.

Temperature responses vary between riffle beetles from contrasting aquatic environments.

Organisms living in environmentally stable ecosystems are hypothesized to exhibit narrow environmental tolerance ranges; however, previous experiments testing this prediction with invertebrates in spring habitats are equivocal. Here we examined the effects of elevated temperatures on four riffle beetle species (family: Elmidae) native to central and west Texas, USA. Two of these, Heterelmis comalensis and Heterelmis cf.