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Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

Forward stagewise estimation is a revived slow-brewing approach for model building that is particularly attractive in dealing with complex data structures for both its computational efficiency and its intrinsic connections with penalized estimation. Under the framework of generalized estimating equations, we study general stagewise estimation approaches that can handle clustered data and non-Gaussian/non-linear models in the presence of prior variable grouping structure. As the grouping structure is often not ideal in that even the important groups may contain irrelevant variables, the key is to simultaneously conduct group selection and within-group variable selection, that is, bi-level selection. We propose two approaches to address the challenge. The first is a bi-level stagewise estimating equations (BiSEE) approach, which is shown to correspond to the sparse group lasso penalized regression. The second is a hierarchical stagewise estimating equations (HiSEE) approach to handle more general hierarchical grouping structure, in which each stagewise estimation step itself is executed as a hierarchical selection process based on the grouping structure. Simulation studies show that BiSEE and HiSEE yield competitive model selection and predictive performance compared to existing approaches. We apply the proposed approaches to study the association between the suicide-related hospitalization rates of the 15-19 age group and the characteristics of the school districts in the State of Connecticut.

The directed acyclic graph (DAG) is a powerful tool to model the interactions of high-dimensional variables. While estimating edge directions in a DAG often requires interventional data, one can estimate the skeleton of a DAG (i.e., an undirected graph formed by removing the direction of each edge in a DAG) using observational data. In real data analyses, the samples of the high-dimensional variables may be collected from a mixture of multiple populations. Each population has its own DAG while the DAGs across populations may have significant overlap. In this article, we propose a two-step approach to jointly estimate the DAG skeletons of multiple populations while the population origin of each sample may or may not be labeled. In particular, our method allows a probabilistic soft label for each sample, which can be easily computed and often leads to more accurate skeleton estimation than hard labels. Compared with separate estimation of skeletons for each population, our method is more accurate and robust to labeling errors. We study the estimation consistency for our method, and demonstrate its performance using simulation studies in different settings. Finally, we apply our method to analyze gene expression data from breast cancer patients of multiple cancer subtypes.

The persistence and dynamics of populations largely depend on the way they are configured and integrated into space and the ensuing eco-evolutionary dynamics. * We manipulated spatial and temporal variation in patch size in replicated experimental metapopulations of the herbivore mite Tetranychus urticae and followed evolutionary dynamics over approximately 30 generations. * A significant divergence in life-history traits, physiological endpoints and gene expression was recorded in the spatially and spatiotemporally variable metapopulation, but also a remarkable convergence relative to the stable reference metapopulation in traits related to size and fecundity and in its transcriptional regulation. * The observed evolutionary dynamics are tightly linked to demographic changes, more specifically frequent episodes of resource shortage that increased the reproductive performance of mites on tomato, a challenging host plant. This points towards a general, adaptive stress response in stable spatial variable and spatiotemporal variable metapopulations that pre-adapts a herbivore arthropod to novel environmental stressors

1. There is relatively little information on how evolutionary processes that alter genetic variation in a focal species may affect interactions with other species, impact the structure or function of the community and ecosystem, and affect evolutionary feedbacks among interacting species through time. 2. Because evolution can occur at ecological time-scales, it is important to understand how major selective events, such as climatic changes, can impact the community of interacting species and ecosystem processes by changing intraspecific genetic variation. 3. The evidence linking genetic variation and evolution to community change and feedbacks has arisen from several different approaches whose results have not been synthesized into one conceptual framework, and whose commonalities may not be fully understood. 4. This review synthesizes several different experimental approaches on how evolution may impact communities and ecosystems and focuses on five main issues: (i) the genetic basis to communities and ecosystems; (ii) the community and ecosystem consequences of among-population genetic differentiation; (iii) the role of local adaptation and co-evolution; (iv) the effects of transgenerational feedbacks and the eco-evo dynamic and; (v) the integration of community and ecosystem genetics and multi-level selection. 5. Evolution can alter intraspecific genetic variation to affect indirect genetic effects and feedbacks. Future studies should investigate how communities and ecosystems are affected when evolution causes the strength of feedbacks to change.

The replication and segregation of multi-copy mitochondrial DNA (mtDNA) are not under strict control of the nuclear DNA. Within-cell selection may thus favour variants with an intracellular selective advantage but a detrimental effect on cell fitness. High relatedness among the mtDNA variants of an individual is predicted to disfavour such deleterious selfish genetic elements, but experimental evidence for this hypothesis is scarce. We studied the effect of mtDNA relatedness on the opportunities for suppressive mtDNA variants in the fungus Neurospora carrying the mitochondrial mutator plasmid pKALILO. During growth, this plasmid integrates into the mitochondrial genome, generating suppressive mtDNA variants. These mtDNA variants gradually replace the wild-type mtDNA, ultimately culminating in growth arrest and death. We show that regular sequestration of mtDNA variation is required for effective selection against suppressive mtDNA variants. First, bottlenecks in the number of mtDNA copies from which a ‘Kalilo’ culture started significantly increased the maximum lifespan and variation in lifespan among cultures. Second, restrictions to somatic fusion among fungal individuals, either by using anastomosis-deficient mutants or by generating allotype diversity, prevented the accumulation of suppressive mtDNA variants. We discuss the implications of these results for the somatic accumulation of mitochondrial defects during ageing.

The concept of species-level heritability is widely contested. Because it is most likely to apply to emergent, species-level traits, one of the central discussions has focused on the potential heritability of geographic range size. However, a central argument against range-size heritability has been that it is not compatible with the observed shape of present-day species range-size distributions (SRDs), a claim that has never been tested. To assess this claim, we used forward simulation of range-size evolution in clades with varying degrees of range-size heritability, and compared the output of three different models to the range-size distribution of the South American avifauna. Although there were differences among the models, a moderate-to-high degree of range-size heritability consistently leads to SRDs that were similar to empirical data. These results suggest that range-size heritability can generate realistic SRDs, and may play an important role in shaping observed patterns of range sizes.

Many eukaryotes have obligate associations with microorganisms that are transmitted directly between generations. A model for heritable symbiosis is the association of aphids, a clade of sap-feeding insects, and Buchnera aphidicola , a gammaproteobacterium that colonized an aphid ancestor 150 million years ago and persists in almost all 5,000 aphid species. Symbiont acquisition enables evolutionary and ecological expansion; aphids are one of many insect groups that would not exist without heritable symbiosis. Receiving less attention are potential negative ramifications of symbiotic alliances. In the short run, symbionts impose metabolic costs. Over evolutionary time, hosts evolve dependence beyond the original benefits of the symbiosis. Symbiotic partners enter into an evolutionary spiral that leads to irreversible codependence and associated risks. Host adaptations to symbiosis (e.g., immune-system modification) may impose vulnerabilities. Symbiont genomes also continuously accumulate deleterious mutations, limiting their beneficial contributions and environmental tolerance. Finally, the fitness interests of obligate heritable symbionts are distinct from those of their hosts, leading to selfish tendencies. Thus, genes underlying the host–symbiont interface are predicted to follow a coevolutionary arms race, as observed for genes governing host–pathogen interactions. On the macroevolutionary scale, the rapid evolution of interacting symbiont and host genes is predicted to accelerate host speciation rates by generating genetic incompatibilities. However, degeneration of symbiont genomes may ultimately limit the ecological range of host species, potentially increasing extinction risk. Recent results for the aphid– Buchnera symbiosis and related systems illustrate that, whereas heritable symbiosis can expand ecological range and spur diversification, it also presents potential perils.

Maternal sex ratio distorters (MSDs) are selfish elements that enhance their transmission by biasing their host's sex allocation in favor of females. While previous models have predicted that the female‐biased populations resulting from sex ratio distortion can benefit from enhanced productivity, these models neglect Fisherian selection for nuclear suppressors, an unrealistic assumption in most systems. We used individual‐based computer simulation modeling to explore the intragenomic conflict between sex ratio distorters and their suppressors and explored the impacts of these dynamics on population‐level competition between species characterized by MSDs and those lacking them. The conflict between distorters and suppressors was capable of producing large cyclical fluctuations in the population sex ratio and reproductive rate. Despite fitness costs associated with the distorters and suppressors, MSD populations often exhibited enhanced productivity and outcompeted non‐MSD populations in single and multiple‐population competition simulations. Notably, the conflict itself is beneficial to the success of populations, as sex ratio oscillations limit the competitive deficits associated with prolonged periods of male rarity. Although intragenomic conflict has been historically viewed as deleterious to populations, our results suggest that distorter–suppressor conflict can provide population‐level advantages, potentially helping to explain the persistence of sex ratio distorters in a range of taxa. We used individual‐based computer simulation modeling to explore the intragenomic conflict between sex ratio distorters and their suppressors. We examined the impacts of these dynamics on population‐level competition between species characterized by MSDs and those lacking them. MSD populations often exhibited enhanced productivity and outcompeted non‐MSD populations, suggesting that distorter–suppressor conflict can provide population‐level advantages, potentially helping to explain the persistence of sex ratio distorters in a range of taxa.

Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.