A comprehensive landscape genomics approach for seed sourcing strategies in landscapes under varying degrees of habitat disturbance

As most ecosystems around the world are threatened by anthropogenic degradation and climate change, there is an increasing urgency to implement restoration strategies aiming at ensuring ecosystem self‐sustainability and resilience. An initial step towards that goal relies on selecting the most suitable seed sources for a successful revegetation, which can be extremely challenging in highly degraded landscapes. The most common seed sourcing strategy is to select local seeds because it is assumed that plants experience strong adaptations to their natal sites. An alternative strategy is the selection of climate‐adapted genotypes to future conditions. While considering future climatic projections is important to account for spatial shifts in climate to inform assisted gene flow and translocations, to restore highly degraded landscapes we need a comprehensive approach that first accounts for species adaptations to current at‐site environmental conditions. In this issue of Molecular Ecology Resources, Carvalho et al. present a novel landscape genomics framework to identify the most appropriate seed sourcing strategy for moderately and highly degraded sites by integrating genotype, phenotype and environmental data in a spatially explicit context for two native plant species with potential to help restore iron‐rich Amazonian savannas. This framework is amenable to be applicable and adapted to a broad range of restoration initiatives, as the dichotomy between focusing on the current or future climatic conditions should depend on the goals and environmental circumstances of each restoration site.     

Testing the effect of restoration‐focused silviculture on oak regeneration and groundlayer plant communities in urban–exurban oak woodlands

Throughout their global range, oak‐dominated ecosystems have undergone state changes in stand structure and composition. Land managers face an especially acute challenge in restoring oak ecosystems and promoting oak regeneration in urban–exurban areas, where high‐intensity silvicultural treatments are often not feasible. To investigate low‐intensity management alternatives which could be widely applied in urban–exurban forests, a large‐scale adaptive management experiment was implemented in Lake County, IL, in 2012. Five canopy manipulation treatments of varying intensity, timing, and spatial aggregation were replicated across three study areas and oak seedlings were under‐planted into treatment units following management. Responses of understory light environment, shrub and groundlayer plant communities, and survival and growth of underplanted oak seedlings were evaluated. Understory light availability, canopy openness, total groundlayer plant cover, and groundlayer species diversity all differed among treatments. However, although understory light availability was significantly increased by canopy manipulation, groundlayer communities and oak seedling survival and growth did not differ among treatments. High overall seedling survival rates suggest current conditions are amenable to oak regeneration, but long‐term monitoring will be needed to assess the potential for seedlings to transition to the sapling and canopy layers. Early results demonstrate that canopy‐focused silvicultural treatments can affect the understory light environment and, to some degree, groundlayer plant communities. However, underplanting of oak seedlings paired with subcanopy thinning may be sufficient to restore an oak seedling layer, and (when necessary or preferred) canopy manipulation could potentially be deferred until later in the restoration timeline to promote oak recruitment.     

Science,Uncertainty, and Values in Ecological Restoration: A Case Study in Structured Decision‐Making and Adaptive Management

This article demonstrates a structured and collaborative approach to decision‐making in the context of adaptive management experiments, using a case study involving the restoration of a hydrological regime in a regulated river in western Canada. It provides a framework based on principles of decision analysis for structuring difficult multi‐attribute decisions and building the trust and technical capacity needed to implement them. Participants included ecologists and fisheries biologists, government regulators, electric utility employees, and representatives of aboriginal communities. The case study demonstrates a values‐based approach to implementing adaptive management that addresses some of the long‐standing difficulties associated with integrating adaptive management into restoration decisions. It highlights practical methods for incorporating participants' values concerned with learning, cultural quality, and stewardship as part of developing a decision‐making and monitoring framework for restoration initiatives. It also provides an example of how to implement principles of meaningful consultation in a restoration context, with emphasis on ensuring that all voices and concerns are heard and meaningfully incorporated. Participants have adopted the framework as a model to guide future collaborative decision‐making processes involving Aboriginal communities, regulatory agencies, and other parties.     

Quantifying the effects of migration and mutation on adaptation and demography in spatially heterogeneous environments

How do mutation and gene flow influence population persistence, niche expansion and local adaptation in spatially heterogeneous environments? In this article, we analyse a demographic and evolutionary model of adaptation to an environment containing two habitats in equal frequencies, and we bridge the gap between different theoretical frameworks. Qualitatively, our model yields four qualitative types of outcomes: (i) global extinction of the population, (ii) adaptation to one habitat only, but also adaptation to both habitats with, (iii) specialized phenotypes or (iv) with generalized phenotypes, and we determine the conditions under which each equilibrium is reached. We derive new analytical approximations for the local densities and the distributions of traits in each habitat under a migration–selection–mutation balance, compute the equilibrium values of the means, variances and asymmetries of the local distributions of phenotypes, and contrast the effects of migration and mutation on the evolutionary outcome. We then check our analytical results by solving our model numerically, and also assess their robustness in the presence of demographic stochasticity. Although increased migration results in a decrease in local adaptation, mutation in our model does not influence the values of the local mean traits. Yet, both migration and mutation can have dramatic effects on population size and even lead to metapopulation extinction when selection is strong. Niche expansion, the ability for the population to adapt to both habitats, can also be prevented by small migration rates and a reduced evolutionary potential characterized by rare mutation events of small effects; however, niche expansion is otherwise the most likely outcome. Although our results are derived under the assumption of clonal reproduction, we finally show and discuss the links between our model and previous quantitative genetics models.     

Anisotropic adaptive finite element method for modelling blood flow

In this study, we present an adaptive anisotropic finite element method (FEM) and demonstrate how computational efficiency can be increased when applying the method to the simulation of blood flow in the cardiovascular system. We use the SUPG formulation for the transient 3D incompressible Navier–Stokes equations which are discretised by linear finite elements for both the pressure and the velocity field.

Given the pulsatile nature of the flow in blood vessels we have pursued adaptivity based on the average flow over a cardiac cycle. Error indicators are derived to define an anisotropic mesh metric field. Mesh modification algorithms are used to anisotropically adapt the mesh according to the desired size field. We demonstrate the efficiency of the method by first applying it to pulsatile flow in a straight cylindrical vessel and then to a porcine aorta with a stenosis bypassed by a graft. We demonstrate that the use of an anisotropic adaptive FEM can result in an order of magnitude reduction in computing time with no loss of accuracy compared to analyses obtained with uniform meshes.     

Stress-Induced Mutagenesis in Bacteria

ABSTRACT

Bacteria spend their lives buffeted by changing environmental conditions. To adapt to and survive these stresses, bacteria have global response systems that result in sweeping changes in gene expression and cellular metabolism. These responses are controlled by master regulators, which include: alternative sigma factors, such as RpoS and RpoH; small molecule effectors, such as ppGpp; gene repressors such as LexA; and, inorganic molecules, such as polyphosphate. The response pathways extensively overlap and are induced to various extents by the same environmental stresses. These stresses include nutritional deprivation, DNA damage, temperature shift, and exposure to antibiotics. All of these global stress responses include functions that can increase genetic variability. In particular, up-regulation and activation of error-prone DNA polymerases, down-regulation of error-correcting enzymes, and movement of mobile genetic elements are common features of several stress responses. The result is that under a variety of stressful conditions, bacteria are induced for genetic change. This transient mutator state may be important for adaptive evolution.     

Proteomics advances for precision therapy in ovarian cancer

ABSTRACT

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior.

Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with ‘ovarian cancer’ including ‘proteomics’, ‘proteogenomic’, ‘reverse-phase protein array’, ‘mass spectrometry’, and ‘adaptive response’, were used to search PubMed.

Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.     

Climate change and the optimal flowering time of annual plants in seasonal environments

Long‐term phenology monitoring has documented numerous examples of changing flowering dates during the last century. A pivotal question is whether these phenological responses are adaptive or not under directionally changing climatic conditions. We use a classic dynamic growth model for annual plants, based on optimal control theory, to find the fitness‐maximizing flowering time, defined as the switching time from vegetative to reproductive growth. In a typical scenario of global warming, with advanced growing season and increased productivity, optimal flowering time advances less than the start of the growing season. Interestingly, increased temporal spread in production over the season may either advance or delay the optimal flowering time depending on overall productivity or season length. We identify situations where large phenological changes are necessary for flowering time to remain optimal. Such changes also indicate changed selection pressures. In other situations, the model predicts advanced phenology on a calendar scale, but no selection for early flowering in relation to the start of the season. We also show that the optimum is more sensitive to increased productivity when productivity is low than when productivity is high. All our results are derived using a general, graphical method to calculate the optimal flowering time applicable for a large range of shapes of the seasonal production curve. The model can thus explain apparent maladaptation in phenological responses in a multitude of scenarios of climate change. We conclude that taking energy allocation trade‐offs and appropriate time scales into account is critical when interpreting phenological patterns.     

Variation in hippocampal glial cell numbers in food‐caching birds from different climates

Enhancements to memory are associated with enhanced neural structures that support those capabilities. A great deal of work has examined this relationship in the context of natural variation in spatial memory capability and hippocampal (Hp) structure. Most studies have focused on volumetric and neuron measures, but have seldom examined the role of glial cells. Once considered involved only in supportive functions associated with neurons, the importance of glial cells in cognitive processes, including memory, is gaining more attention. Building upon our previous study on the relationship between the brain, memory, and environmental severity in food‐caching birds, we compared the total number of Hp glial cells in wild‐sampled and in lab‐reared (common garden) black‐capped chickadees (Poecile atricapillus) originating from two different environmental extremes. We found that birds from more harsh climate tended to have significantly more Hp glial cells than those from more mild climate and that lab‐reared chickadees had significantly fewer Hp glial cells compared to the wild‐sampled birds. These results suggest that population differences in glial numbers may be controlled, at least in part, by heritable mechanisms, but glial numbers appear to be additionally regulated by an individual's environment. The pattern of Hp glial cell abundance among our treatment groups closely followed that of the Hp volume, suggesting that Hp glial cell number may be associated with the Hp volume. Unlike Hp neurons, however, the number of Hp glial cells may be, at least in part, affected by an individual's experiences and environment. © 2013 Wiley Periodicals, Inc. Develop Neurobiol 73: 480–485, 2013