Towards quantifying the mass extinction debt of the Anthropocene

To make sense of our present biodiversity crises, the modern rate of species extinctions is commonly compared to a benchmark, or ‘background,’ rate derived from the fossil record. These estimates are critical for bounding the scale of modern diversity loss, but are yet to fully account for the fundamental structure of extinction rates through time. Namely, a substantial fraction of extinctions within the fossil record occurs within relatively short-lived extinction pulses, and not during intervals characterized by background rates of extinction. Accordingly, it is more appropriate to compare the modern event to these pulses than to the long-term average rate. Unfortunately, neither the duration of extinction pulses in the geological record nor the ultimate magnitude of the extinction pulse today is resolved, making assessments of their relative sizes difficult. In addition, the common metric used to compare current and past extinction rates does not correct for large differences in observation duration. Here, we propose a new predictive metric that may be used to ascertain the ultimate extent of the ongoing extinction threat, building on the observation that extinction magnitude in the marine fossil record is correlated to the magnitude of sedimentary turnover. Thus, we propose that the ultimate number of species destined for extinction today can be predicted by way of a quantitative appraisal of humanity's modification of ecosystems as recorded in sediments—that is, by comparing our future rock record with that of the past. The ubiquity of habitat disruption worldwide suggests that a profound mass extinction debt exists today, but one that might yet be averted by preserving and restoring ecosystems and their geological traces.

Wastewater Analysis of SARS-CoV-2 as a Predictive Metric of Positivity Rate for a Major Metropolis

Wastewater monitoring for SARS-CoV-2 has been suggested as an epidemiological indicator of community infection dynamics and disease prevalence. We report wastewater viral RNA levels of SARS-CoV-2 in a major metropolis serving over 3.6 million people geographically spread over 39 distinct sampling sites. Viral RNA levels were followed weekly for 22 weeks, both before, during, and after a major surge in cases, and simultaneously by two independent laboratories. We found SARS-CoV-2 RNA wastewater levels were a strong predictive indicator of trends in the nasal positivity rate two-weeks in advance. Furthermore, wastewater viral RNA loads demonstrated robust tracking of positivity rate for populations served by individual treatment plants, findings which were used in real-time to make public health interventions, including deployment of testing and education strike teams.

Metric Structure and Rhyme Predictability Modulate Speech Intensity During Child-Directed and Read-Alone Productions of Children’s Literature

Temporal and phonological predictability in children’s literature may support early literacy acquisition. Realization of predictive structure in caregiver prosody could guide children’s attention during shared reading, thereby supporting reading subskill development. However, little is known about how predictive structure is realized prosodically during child-directed reading. We investigated whether speakers use word intensity to signal predictive metric and rhyme structure in child-directed and read-alone productions of The Cat in the Hat (Dr. Seuss, 1957), by modeling maximum intensity (dB) of monosyllabic words as a function of metric strength, rhyme predictability, and a set of control parameters. In the control model, intensity increased with lower lexical frequency, capitalization, first mention, and likelihood of a syntactic boundary. Metric structure predicted word intensity beyond these control factors in a hierarchical manner: words aligned with beat one in a 6/8 metric structure were produced with highest intensity, words aligned with beat four were produced with intermediate intensity, and words aligned with all other beats were produced with the lowest intensity. Additionally, phonologically predictable rhyme targets were reduced in intensity. The effects of meter and rhyme were not moderated by the presence of a child audience. These results demonstrate that predictability along multiple dimensions is encoded during reading of poetic children’s literature, and that metric structure is realized hierarchically in word intensity. Further, the manner by which predictability is encoded in word intensity differs from that previously reported for word duration in this corpus (Breen, 2018), demonstrating that intensity and duration present nonidentical prosodic information channels.

Statistical Discriminability Estimation for Pattern Classification Based on Neural Incremental Attribute Learning

Feature ordering is a significant data preprocessing method in Incremental Attribute Learning (IAL), a novel machine learning approach which gradually trains features according to a given order. Previous research has shown that, similar to feature selection, feature ordering is also important based on each feature's discrimination ability, and should be sorted in a descending order of their discrimination ability. However, such an ordering is crucial for the performance of IAL. As the number of feature dimensions in IAL is increasing, feature discrimination ability also should be calculated in the corresponding incremental way. Based on Single Discriminability (SD), where only the feature discrimination ability is computed, a new filter statistical feature discrimination ability predictive metric, called the Accumulative Discriminability (AD), is designed for the dynamical feature discrimination ability estimation. Moreover, a criterion that summarizes all the produced values of AD is employed with a GA (Genetic Algorithm)-based approach to obtain the optimum feature ordering for classification problems based on neural networks by means of IAL. Compared with the feature ordering obtained by other approaches, the method proposed in this paper exhibits better performance in the final classification results. Such a phenomenon indicates that, (i) the feature discrimination ability should be incrementally estimated in IAL, and (ii) the feature ordering derived by AD and its corresponding approaches are applicable with IAL.