Plants transmit ecologically relevant messages to neighbouring plants through chemical cues. For instance, insect herbivory triggers the production of herbivore‐induced plant volatiles (HIPVs), which can enhance neighbouring plant defences. HIPVs are emitted from directly damaged plant tissues and from systemic, nondamaged tissues. Although volatile‐mediated interplant interactions have been observed both above‐ and belowground, it remains unknown whether belowground herbivory induces systemic HIPVs aboveground that influence neighbouring plants. To explore how belowground herbivory affects interplant interactions aboveground, we characterised systemic HIPVs from squash induced by belowground striped cucumber beetle (Acalymma vittatum) larval herbivory. We exposed squash ‘receiver plants’ to systemic HIPVs or volatiles from nondamaged plants. We then measured herbivore resistance by challenging ‘receiver plants’ with aboveground‐feeding herbivores: adult beetles (A. vittatum) or squash bugs (Anasa tristis). We discovered belowground‐damaged plants emitted more (E)‐β‐ocimene, a key volatile from the systemic HIPV blend, than nondamaged controls, and that exposure to systemic HIPVs enhanced neighbouring plant resistance to aboveground squash bugs, but not adult beetles. Further investigations into the mechanism of interplant interaction revealed β‐ocimene alone can elicit plant resistance against squash bugs. Overall, our findings reveal a novel form of volatile‐mediated interactions between plants spanning across aboveground–belowground plant systems.

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches.