Recent Megafires Provide a Tipping Point for Desertification of Conifer Ecosystems

Recent megafires and gigafires are contributing to the desertification of conifer forest ecosystems due to their size and severity. Megafires have been increasing in their frequency in the past two decades of the 21st century. They are classed as such because of being 40,469 to 404,694 ha in size, having high complexity, resisting suppression, and producing desertification due to erosion and vegetation type conversion. Increasingly, gigafires (>404,694 ha) are impacting coniferous forest ecosystems. These were once thought of as only pre-20th century phenomena when fire suppression was in its infancy. Climate change is an insidious inciting factor in large wildfire occurrences. Fire seasons are longer, drier, hotter, and windier due to changes in basic meteorology. Conifer forests have accumulated high fuel loads in the 20th and 21st centuries. Ignition sources in conifer forests have increased as well due to human activities, economic development, and population demographics. Natural ignitions from lightning are increasing as a result of greater severe thunderstorm activity. Drought has predisposed these forests to easy fire ignition and spread. Wildfires are more likely to produce vegetation shifts from conifers to scrublands or grasslands, especially when wildfires occur with higher frequency and severity. Severe erosion after megafires has the collateral damage of reducing conifer resilience and sustainability.

A macromutation eliminates colour patterning in captive butterflies

Captive populations often harbor variation that is not present in the wild due to artificial selection. Recent efforts to map this variation have provided insights into the genetic and molecular basis of variation. Heliconius butterflies display a large array of pattern variants in the wild and the genetic basis of these patterns has been well-described. Here we sought to identify the genetic basis of an unusual pattern variant that is instead found in captivity, the ivory mutant, in which all scales on both the wings and body become white or yellow. Using a combination of autozygosity mapping and coverage analysis from 37 captive individuals, we identify a 78kb deletion at the cortex wing patterning locus as the ivory mutation. This deletion is undetected among 458 wild Heliconius genomes samples, and its dosage explains both homozygous and heterozygous ivory phenotypes found in captivity. The deletion spans a large 5' region of the cortex gene that includes a facultative 5' UTR exon detected in larval wing disk transcriptomes. CRISPR mutagenesis of this exon replicates the wing phenotypes from coding knock-outs of cortex, consistent with a functional role of ivory-deleted elements in establishing scale color fate. Population demographics reveal that the stock giving rise to the ivory mutant has a mixed origin from across the wild range of H. melpomene, and supports a scenario where the ivory mutation occurred after the introduction of cortex haplotypes from Ecuador. Homozygotes for the ivory deletion are inviable, joining 40 other examples of allelic variants that provide heterozygous advantage in animal populations under artificial selection by fanciers and breeders. Finally, our results highlight the promise of autozygosity and association mapping for identifying the genetic basis of aberrant mutations in captive insect populations.

COVID-19: Identifying countries with indicators of success in responding to the outbreak

Background: In designing responses to the COVID-19 pandemic, it is critical to understand what has already worked well. We aimed to identify countries with emerging success stories from whom policymakers might draw important lessons. Methods: We developed a process to first include countries with large enough populations that results were unlikely to be due to chance, that had sufficient cases for response mechanisms to be tested, and that shared the necessary publicly available data. Within these countries, we looked at indicators suggesting success in terms of detecting disease, containing the outbreak, and treating those who were unwell. To support comparability, we measured indicators per capita (per million) and across time. We then used the indicators to identify three countries with emerging success stories to include some diversity in global region, population demographics and form of government. Results: We identified 66 countries that met our inclusion criteria on 18th May 2020. Several of these countries had indicators of success against the set indicators at different times in the outbreak. Vietnam had high levels of testing and successful containment with no deaths reported. South Korea had high levels of testing early in the outbreak, supporting containment. Germany had high levels of sustained testing and slower increases in cases and deaths than seen in other comparable settings. Conclusions: At the time of our assessment, Vietnam and South Korea were able to contain the outbreak of COVID-19 and avoid the exponential growth in cases seen elsewhere. Germany had more cases and deaths, but was nevertheless able to contain and mitigate the outbreak. Despite the many limitations to the data currently available, looking at comparative data can help identify countries from whom we can draw lessons, so that countries can inform and adapt their strategies for success in response to COVID-19.

Emergence of universality in the transmission dynamics of COVID-19

The complexities involved in modelling the transmission dynamics of COVID-19 has been a roadblock in achieving predictability in the spread and containment of the disease. In addition to understanding the modes of transmission, the effectiveness of the mitigation methods also needs to be built into any effective model for making such predictions. We show that such complexities can be circumvented by appealing to scaling principles which lead to the emergence of universality in the transmission dynamics of the disease. The ensuing data collapse renders the transmission dynamics largely independent of geopolitical variations, the effectiveness of various mitigation strategies, population demographics, etc. We propose a simple two-parameter model—the Blue Sky model—and show that one class of transmission dynamics can be explained by a solution that lives at the edge of a blue sky bifurcation. In addition, the data collapse leads to an enhanced degree of predictability in the disease spread for several geographical scales which can also be realized in a model-independent manner as we show using a deep neural network. The methodology adopted in this work can potentially be applied to the transmission of other infectious diseases and new universality classes may be found. The predictability in transmission dynamics and the simplicity of our methodology can help in building policies for exit strategies and mitigation methods during a pandemic.

Batch Effects are Causal Effects: Applications in Human Connectomics

Batch effects, undesirable sources of variance across multiple experiments, present a substantial hurdle for scientific and clinical discoveries. Specifically, the presence of batch effects can create both spurious discoveries and hide veridical signals, contributing to the ongoing reproducibility crisis. Typical approaches to dealing with batch effects conceptualize ‘batches’ as an associational effect, rather than a causal effect, despite the fact that the sources of variance that comprise the batch – potentially including experimental design and population demographics – causally impact downstream inferences. We therefore cast batch effects as a causal problem rather than an associational problem. This reformulation enables us to make explicit the assumptions and limitations of existing approaches for dealing with batch effects. We therefore develop causal batch effect strategies—CausalDcorr for discovery of batch effects and CausalComBat for mitigating batch effects – which build upon existing statistical associational methods by incorporating modern causal inference techniques. We apply these strategies to a large mega-study of human connectomes assembled by the Consortium for Reliability and Reproducibility, consisting of 24 batches including over 1700 individuals to illustrate that existing approaches create more spurious discoveries (false positives) and miss more veridical signals (true positives) than our proposed approaches. Our work therefore introduces a conceptual framing, as well as open source code, for combining multiple distinct datasets to increase confidence in claims of scientific and clinical discoveries.

Accurate Epigenetic Aging in Bottlenose Dolphins (Tursiops truncatus), an Essential Step in the Conservation of at-Risk Dolphins

Epigenetics, specifically DNA methylation, allows for the estimation of animal age from blood or remotely sampled skin. This multi-tissue epigenetic age estimation clock uses 110 longitudinal samples from 34 Navy bottlenose dolphins (Tursiops truncatus), identifying 195 cytosine-phosphate-guanine sites associated with chronological aging via cross-validation with one individual left out in each fold (R2 = 0.95). With a median absolute error of 2.5 years, this clock improves age estimation capacity in wild dolphins, helping conservation efforts and enabling a better understanding of population demographics.

COVID-19 Due to Wild-Type SARS-CoV-2 More Prevalent in Adolescents and Youth than in Older Adults Based on 19 US States in Fall 2020 Prior to Vaccine Availability

PURPOSE: In a prior study, we examined data from six US states during Summer 2020, and found that prevalence of COVID-19 for adolescents and youth was significantly greater than for older adults (p<.00001) as was a prevalence-related measure: Number of cases observed ÷ Number of cases expected (p<.005). We now extended our study to more states in Fall 2020 to confirm the prevalence relationships we found previously. Vaccines were still not available as of Fall 2020. Presumably, the SARS-CoV-2 strain circulating at the time was the wild-type lineage since no variants were reported in the US until the end of December 2020. METHODS: We examined data from 19 U.S. states experiencing surges in cases to determine prevalence of COVID-19, and a prevalence-related measure: [Number of cases observed in a given age group] ÷ [Number of cases expected in the age group based on population demographics]. RESULTS: In 16 of the 19 states, we found that: (1) prevalence of COVID-19 for adolescents and youth was significantly greater than for older adults (p-values ranged from p<0.00001 to p = 0.0175; (2) the ratio of cases observed to cases expected was significantly greater in adolescents and youth than in older adults (p-values ranging from p< 0.00001 to p = 0.004). CONCLUSIONS: Our results are consistent with our previous study in Summer 2020. The finding of lower prevalence in older adults cannot be attributed to access to vaccination since our data are from Fall 2020 when vaccinations were not yet available. Our findings with the SARS-CoV-2 wild-type strain are consistent with the findings currently being reported in the UK for the delta variant. In both studies, prevalence in adolescents and youth exceeded that in older adults. The UK findings are more pronounced perhaps because that study transpired following months of vaccinations of older adults whereas ours occurred before vaccinations were available.