Comparing avian species richness estimates from structured and semi-structured citizen science data.

Citizen science, including structured and semi-structured forms, has become a powerful tool to collect biodiversity data. However, semi-structured citizen science data have been criticized for higher variability in quality, including less information to adjust for imperfect detection and uneven duration that bias the estimates of species richness. Species richness estimators may quantify bias in estimates. Here, we test the effectiveness of Chao1 estimator in eBird (semi-structured) by comparing it to averaged species richness in Breeding Bird Survey Taiwan, BBS (structured) and quantifying bias. We then fit a power function to compare bias while controlling for differences in count duration. The Chao1 estimator increased the species richness estimates of eBird data from 56 to 69% of the average observed BBS and from 47 to 59% of the average estimated BBS. Effects of incomplete short duration samples and variability in detectability skills of observers can lead to biased estimates. Using the Chao1 estimator improved estimates of species richness from semi-structured and structured data, but the strong effect of singleton species on bias, especially in short duration counts, should be evaluated in advance to reduce the uncertainty of estimation processes.

Comparison of Reusable Models in Pericardiocentesis Simulation Training.

INTRODUCTION: Pericardiocentesis is a potentially life-saving procedure. We compared two low-cost models-an agar-based model and a novel model, Centesys-in terms of ultrasound image quality and realism, effectiveness of the model, and learners' confidence and satisfaction after training. METHODS: In this pilot randomised 2x2 crossover trial stratified by physician seniority, participants were assigned to undergo pericardiocentesis training either with the agar-based or Centesys model first, followed by the other model. Participants were asked to rate their confidence in performing ultrasound-guided pericardiocentesis, clarity and realism of cardiac structures on ultrasound imaging, and satisfaction on a 7-point Likert scale before and after training with each model. RESULTS: Twenty participants with median postgraduate year of 4 (interquartile range [IQR] 3.75-6) years were recruited. Pre-training, participants rated themselves a median score of 2.5 (IQR 2-4) for level of confidence in performing pericardiocentesis, which improved to 5 (IQR 4-6) post-training with Centesys (P=0.007). Centesys was recognised to be more realistic in simulating cardiac anatomy on ultrasound (median 5 [IQR 4-5] versus 3.5 [IQR 3-4], P=0.002) than the agar-based model. There was greater satisfaction with Centesys (median 5 [IQR 5-6] versus 4 [IQR 3.75-4], P<0.001). All 20 participants achieved successful insertion of a pericardial drain into the simulated pericardial sac with Centesys. CONCLUSION: Centesys achieved greater learner satisfaction as compared to the agar-based model, and was an effective tool for teaching ultrasound-guided pericardiocentesis and drain insertion.