When confronted with an adaptive challenge, such as extreme temperature, closely related species frequently evolve similar phenotypes using the same genes. Although such repeated evolution is thought to be less likely in highly polygenic traits and distantly related species, this has not been tested at the genome scale. We performed a population genomic study of convergent local adaptation among two distantly related species, lodgepole pine and interior spruce. We identified a suite of 47 genes, enriched for duplicated genes, with variants associated with spatial variation in temperature or cold hardiness in both species, providing evidence of convergent local adaptation despite 140 million years of separate evolution. These results show that adaptation to climate can be genetically constrained, with certain key genes playing nonredundant roles.
Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past 2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome.
A magical new poster has arrived for Disney's upcoming live adaptation of Beauty and the Beast. It's a rendering of the classic enchanted rose, the one Beast keeps safe in a glass case. In the new adaptation, Downton Abbey star Dan Stevens will play the Beast, while Emma Watson plays Belle. The film, directed by Oscar winner Bill Condon, also stars Luke Evans, Kevin Kline, Josh Gad, Ewan McGregor, Stanley Tucci, Ian McKellen, Emma Thompson, Gugu Mbatha-Raw and more. SEE ALSO: 'Beauty and the Beast' teaser claws past'Star Wars: The Force Awakens' record Beauty and the Beast hits theaters on March 17, 2017.
This work provides a framework for addressing the problem of supervised domain adaptation with deep models. The main idea is to exploit adversarial learning to learn an embedded subspace that simultaneously maximizes the confusion between two domains while semantically aligning their embedding. The supervised setting becomes attractive especially when there are only a few target data samples that need to be labeled. In this few-shot learning scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by carefully designing a training scheme whereby the typical binary adversarial discriminator is augmented to distinguish between four different classes, it is possible to effectively address the supervised adaptation problem.
Human societies will transform to address climate change and other stressors. How they choose to transform will depend on what societal values they prioritize. Managed retreat can play a powerful role in expanding the range of possible futures that transformation could achieve and in articulating the values that shape those futures. Consideration of retreat raises tensions about what losses are unacceptable and what aspects of societies are maintained, purposefully altered, or allowed to change unaided. Here we integrate research on retreat, transformational adaptation, climate damages and losses, and design and decision support to chart a roadmap for strategic, managed retreat. At its core, this roadmap requires a fundamental reconceptualization of what it means for retreat to be strategic and managed. The questions raised are relevant to adaptation science and societies far beyond the remit of retreat. Evolving social norms, technologies, and economies will create futures that fundamentally differ from our world today. Climate change constrains the range of possible futures and affects the level of transformation societies will experience.