Extinction is a key issue in the assessment of global biodiversity. However, many extinction rate measures do not account for species that went extinct before they could be discovered. The highly developed island city-state of Singapore has one of the best-documented tropical floras in the world. This allowed us to estimate the total rate of floristic extinctions in Singapore since 1822 after accounting for sampling effort and crypto extinctions by collating herbaria records. Our database comprised 34,224 specimens from 2076 native species, of which 464 species (22%) were considered nationally extinct. We assumed that undiscovered species had the same annual per-species extinction rates as discovered species and that no undiscovered species remained extant. With classical and Bayesian algorithms, we estimated that 304 (95% confidence interval, 213-414) and 412 (95% credible interval, 313-534) additional species went extinct before they could be discovered, respectively; corresponding total extinction rate estimates were 32% and 35% (range 30-38%). We detected violations of our 2 assumptions that could cause our extinction estimates, particularly the absolute numbers, to be biased downward. Thus, our estimates should be treated as lower bounds. Our results illustrate the possible magnitudes of plant extirpations that can be expected in the tropics as development continues.

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Reliable detection and prediction of neural activity and behavior requires a user model of brain activity that dynamically adapts based on known time-dependent physiological processes, as well as unknown traits of the user. We have applied wireless electroencephalography (EEG) sensors, edge devices with feedback capability, and cloud-assisted data acquisition to realtime and longitudinal brain monitoring and alerting. Toward a user model of brain function, we collected neural and behavioral data from humans in the field. The data replicate previous findings that were obtained under tight laboratory control, suggesting that the methods that we describe will be useful for user modeling of human brain activity under more natural conditions. Specifically, we report that frontal cortex oscillations reorganized with age. Focusing on time-varying aspects of behavior, we then found that performance on memory-intensive cognitive tasks declined during the day. Next, we examined interactions between neural activity and behavioral performance. We report that neural activity and performance co-varied and that this co-variation depended on the cognitive task in ways that were, again, consistent with previous laboratory studies. Lastly, we report the foundations of an adaptive model based on this system that will enable dynamic personalization tailored to each user.