Hiding in the background: community-level patterns in invertebrate herbivory across the tundra biome

Invertebrate herbivores depend on external temperature for growth and metabolism. Continued warming in tundra ecosystems is proposed to result in increased invertebrate herbivory. However, empirical data about how current levels of invertebrate herbivory vary across the Arctic is limited and generally restricted to a single host plant or a small group of species, so predicting future change remains challenging. We investigated large-scale patterns of invertebrate herbivory across the tundra biome at the community level and explored how these patterns are related to long-term climatic conditions and year-of-sampling weather, habitat characteristics, and aboveground biomass production. Utilizing a standardized protocol, we collected samples from 92 plots nested within 20 tundra sites during summer 2015. We estimated the community-weighted biomass lost based on the total leaf area consumed by invertebrates for the most common plant species within each plot. Overall, invertebrate herbivory was prevalent at low intensities across the tundra, with estimates averaging 0.94% and ranging between 0.02 and 5.69% of plant biomass. Our results suggest that mid-summer temperature influences the intensity of invertebrate herbivory at the community level, consistent with the hypothesis that climate warming should increase plant losses to invertebrates in the tundra. However, most of the observed variation in herbivory was associated with other site level characteristics, indicating that other local ecological factors also play an important role. More details about the local drivers of invertebrate herbivory are necessary to predict the consequences for rapidly changing tundra ecosystems.KeywordsBackground herbivory Biomass loss Climate change Community-weighted average Invertebrate Insects Tundra </div

Removal of Resuscitation Artefacts from Ventricular Fibrillation ECG Signals Using Kalman

Removing cardiopulmonary resuscitation (CPR) related artefacts from human ventricular fibrillation (VF) ECG signals would provide the possibility to continuously detect rhythm changes and estimate the probability of defibrillation success. This would avoid ”hands-off ” analysis times which diminish the cardiac perfusion and thus deteriorate the chance for a successful defibrillation attempt. Our approach consists in representing the CPR-corrupted signal by a seasonal state-space model. This allows for a stochastically changing shape of the periodic signal and also copes with time-dependent periods. The residuals of the Kalman estimation can be identified with the CPRfiltered ECG signal. Preliminary results using only a small pool of human VF and animal asystole CPR data show that the seasonal model is not as effective as models using reference signals, but it might be useful in combination with them. 1