Pretty Darn Good Control: When are Approximate Solutions Better than Approximate Models.

Existing methods for optimal control struggle to deal with the complexity commonly encountered in real-world systems, including dimensionality, process error, model bias and data heterogeneity. Instead of tackling these system complexities directly, researchers have typically sought to simplify models to fit optimal control methods. But when is the optimal solution to an approximate, stylized model better than an approximate solution to a more accurate model? While this question has largely gone unanswered owing to the difficulty of finding even approximate solutions for complex models, recent algorithmic and computational advances in deep reinforcement learning (DRL) might finally allow us to address these questions. DRL methods have to date been applied primarily in the context of games or robotic mechanics, which operate under precisely known rules. Here, we demonstrate the ability for DRL algorithms using deep neural networks to successfully approximate solutions (the "policy function" or control rule) in a non-linear three-variable model for a fishery without knowing or ever attempting to infer a model for the process itself. We find that the reinforcement learning agent discovers a policy that outperforms both constant escapement and constant mortality policies-the standard family of policies considered in fishery management. This DRL policy has the shape of a constant escapement policy whose escapement values depend on the stock sizes of other species in the model.

Multi-locus genomic signatures of local adaptation to snow across the landscape in California populations of a willow leaf beetle.

Organisms living in mountains contend with extreme climatic conditions, including short growing seasons and long winters with extensive snow cover. Anthropogenic climate change is driving unprecedented, rapid warming of montane regions across the globe, resulting in reduced winter snowpack. Loss of snow as a thermal buffer may have serious consequences for animals overwintering in soil, yet little is known about how variability in snowpack acts as a selective agent in montane ecosystems. Here, we examine genomic variation in California populations of the leaf beetle Chrysomela aeneicollis, an emerging natural model system for understanding how organisms respond to climate change. We used a genotype-environment association approach to identify genomic signatures of local adaptation to microclimate in populations from three montane regions with variable snowpack and a coastal region with no snow. We found that both winter-associated environmental variation and geographical distance contribute to overall genomic variation across the landscape. We identified non-synonymous variation in novel candidate loci associated with cytoskeletal function, ion transport and membrane stability, cellular processes associated with cold tolerance in other insects. These findings provide intriguing evidence that variation in snowpack imposes selective gradients in montane ecosystems.

Tracking an invasion front with environmental DNA.

Data from environmental DNA (eDNA) may revolutionize environmental monitoring and management, providing increased detection sensitivity at reduced cost and survey effort. However, eDNA data are rarely used in decision-making contexts, mainly due to uncertainty around (1) data interpretation and (2) whether and how molecular tools dovetail with existing management efforts. We address these challenges by jointly modeling eDNA detection via qPCR and traditional trap data to estimate the density of invasive European green crab (Carcinus maenas), a species for which, historically, baited traps have been used for both detection and control. Our analytical framework simultaneously quantifies uncertainty in both detection methods and provides a robust way of integrating different data streams into management processes. Moreover, the joint model makes clear the marginal information benefit of adding eDNA (or any other) additional data type to an existing monitoring program, offering a path to optimizing sampling efforts for species of management interest. Here, we document green crab eDNA beyond the previously known invasion front and find that the value of eDNA data dramatically increases with low population densities and low traditional sampling effort, as is often the case at leading-edge locations. We also highlight the detection limits of the molecular assay used in this study, as well as scenarios under which eDNA sampling is unlikely to improve existing management efforts.

Characterizing the culturable surface microbiomes of diverse marine animals.

Biofilm-forming bacteria have the potential to contribute to the health, physiology, behavior and ecology of the host and serve as its first line of defense against adverse conditions in the environment. While metabarcoding and metagenomic information furthers our understanding of microbiome composition, fewer studies use cultured samples to study the diverse interactions among the host and its microbiome, as cultured representatives are often lacking. This study examines the surface microbiomes cultured from three shallow-water coral species and two whale species. These unique marine animals place strong selective pressures on their microbial symbionts and contain members under similar environmental and anthropogenic stress. We developed an intense cultivation procedure, utilizing a suite of culture conditions targeting a rich assortment of biofilm-forming microorganisms. We identified 592 microbial isolates contained within 15 bacterial orders representing 50 bacterial genera, and two fungal species. Culturable bacteria from coral and whale samples paralleled taxonomic groups identified in culture-independent surveys, including 29% of all bacterial genera identified in the Megaptera novaeangliae skin microbiome through culture-independent methods. This microbial repository provides raw material and biological input for more nuanced studies which can explore how members of the microbiome both shape their micro-niche and impact host fitness.