Applying population and community ecology theory to advance understanding of belowground biogeochemistry

Approaches to quantifying and predicting soil biogeochemical cycles mostly consider microbial biomass and community composition as products of the abiotic environment. Current numerical approaches then primarily emphasise the importance of microbe–environment interactions and physiology as controls on biogeochemical cycles. Decidedly less attention has been paid to understanding control exerted by community dynamics and biotic interactions. Yet a rich literature of theoretical and empirical contributions highlights the importance of considering how variation in microbial population ecology, especially biotic interactions, is related to variation in key biogeochemical processes like soil carbon formation. We demonstrate how a population and community ecology perspective can be used to (1) understand the impact of microbial communities on biogeochemical cycles and (2) reframe current theory and models to include more detailed microbial ecology. Through a series of simulations we illustrate how density dependence and key biotic interactions, such as competition and predation, can determine the degree to which microbes regulate soil biogeochemical cycles. The ecological perspective and model simulations we present lay the foundation for developing empirical research and complementary models that explore the diversity of ecological mechanisms that operate in microbial communities to regulate biogeochemical processes.     

Developmental processes regulate craniofacial variation in disease and evolution

Variation in development mediates phenotypic differences observed in evolution and disease. Although the mechanisms underlying phenotypic variation are still largely unknown, recent research suggests that variation in developmental processes may play a key role. Developmental processes mediate genotype–phenotype relationships and consequently play an important role regulating phenotypes. In this review, we provide an example of how shared and interacting developmental processes may explain convergence of phenotypes in spliceosomopathies and ribosomopathies. These data also suggest a shared pathway to disease treatment. We then discuss three major mechanisms that contribute to variation in developmental processes: genetic background (gene–gene interactions), gene–environment interactions, and developmental stochasticity. Finally, we comment on evolutionary alterations to developmental processes, and the evolution of disease buffering mechanisms.     

Inferring the potentially complex genetic architectures of adaptation,sexual dimorphism and genotype by environment interactions by partitioning of mean phenotypes

Genetic architecture fundamentally affects the way that traits evolve. However, the mapping of genotype to phenotype includes complex interactions with the environment or even the sex of an organism that can modulate the expressed phenotype. Line‐cross analysis is a powerful quantitative genetics method to infer genetic architecture by analysing the mean phenotype value of two diverged strains and a series of subsequent crosses and backcrosses. However, it has been difficult to account for complex interactions with the environment or sex within this framework. We have developed extensions to line‐cross analysis that allow for gene by environment and gene by sex interactions. Using extensive simulation studies and reanalysis of empirical data, we show that our approach can account for both unintended environmental variation when crosses cannot be reared in a common garden and can be used to test for the presence of gene by environment or gene by sex interactions. In analyses that fail to account for environmental variation between crosses, we find that line‐cross analysis has low power and high false‐positive rates. However, we illustrate that accounting for environmental variation allows for the inference of adaptive divergence, and that accounting for sex differences in phenotypes allows practitioners to infer the genetic architecture of sexual dimorphism.     

How do species interactions affect species distribution models?

One of the most promising recent advances in biogeography has been the increased interest and understanding of species distribution models – estimates of the probability that a species is present given environmental data. Unfortunately, such analyses ignore many aspects of ecology, and so are difficult to interpret. In particular, we know that species interactions have a profound influence on distributions, but it is not usually possible to incorporate this knowledge into species distribution models. What is needed is a rigorous understanding of how unmeasured biotic interactions affect the inferences generated by species distribution models. To fill this gap, we develop a general mathematical approach that uses probability theory to determine how unmeasured biotic interactions affect inferences from species distribution models. Using this approach, we reanalyze one of the most important classes of mechanistic models of competition: models of consumer resource dynamics. We determine how measurements of one aspect of the environment – a single environmental variable – can be used to estimate the probability that an environment is suitable with species distribution models. We show that species distribution models, which ignore numerous facets of consumer resource dynamics such as the presence of a competitor or the dynamics of depletable resources, can furnish useful predictions for the probability that an environment is suitable in some circumstances. These results provide a rigorous link between complex mechanistic models of species interactions and species distribution models. In so doing they demonstrate that unmeasured biotic interactions can have strong and counterintuitive consequences on species distribution models.     

Strategies for performing genotype–phenotype association studies in nonhuman primates

Anthropoid primate models offer opportunities to study genetic influence on alcohol consumption and alcohol-related intermediate phenotypes in socially and behaviorally complex animal models that are closely related to humans, and in which functionally equivalent or orthologous genetic variants exist. This review will discuss the methods commonly used for performing candidate gene-based studies in rhesus macaques in order to model how functional genetic variation moderates risk for human psychiatric disorders. Various in silico and in vitro approaches to identifying functional genetic variants for performance of these studies will be discussed. Next, I will provide examples of how this approach can be used for performing candidate gene-based studies and for examining gene by environment interactions. Finally, these approaches will then be placed in the context of how function-guided studies can inform us of genetic variants that may be under selection across species, demonstrating how functional genetic variants that may have conferred selective advantage at some point in the evolutionary history of humans could increase risk for addictive disorders in modern society.     

The third party

Abstract. Spatial and temporal variation in interactions among plants, other species and the abiotic environment create context‐dependency in vegetation pattern. We argue that we can enhance understanding of context‐dependency by being more explicit about the kinds of direct interactions that occur among more than two living and non‐living entities (i.e., third through nth parties) and formalizing how their combinations create context‐dependency using simple conceptual models. This general approach can be translated into field studies of context‐dependency in communities by combining: progressive sampling of local variation in vegetation pattern that encompasses variation in combinations of direct interactions; spatial and temporal measures of these direct interactions; locally parameterized versions of the conceptual models; and appropriately scaled experiments.     

Local adaptation of a parasite to solar radiation impacts disease transmission potential,spore yield,and host fecundity*

Environmentally transmitted parasites spend time in the abiotic environment, where they are subjected to a variety of stressors. Learning how they face this challenge is essential if we are to understand how host–parasite interactions may vary across environmental gradients. We used a zooplankton–bacteria host–parasite system where availability of sunlight (solar radiation) influences disease dynamics to look for evidence of parasite local adaptation to sunlight exposure. We also examined how variation in sunlight tolerance among parasite strains impacted host reproduction. Parasite strains collected from clearer lakes (with greater sunlight penetration) were most tolerant of the negative impacts of sunlight exposure, suggesting local adaptation to sunlight conditions. This adaptation came with both a cost and a benefit for parasites: parasite strains from clearer lakes produced relatively fewer transmission stages (spores) but these strains were more infective. After experimental sunlight exposure, the most sunlight-tolerant parasite strains reduced host fecundity just as much as spores that were never exposed to sunlight. Sunlight availability varies greatly among lakes around the world. Our results suggest that the selective pressure sunlight exposure exerts on parasites may impact both parasite and host fitness, potentially driving variation in disease epidemics and host population dynamics across sunlight availability gradients.     

Temperature checks the Red Queen? Resistance and virulence in a fluctuating environment

Numerous studies have revealed genetic variation in resistance and susceptibility in host–parasite interactions and therefore the potential for frequency‐dependent selection (Red Queen dynamics). Few studies, if any, have considered the abiotic environment as a mediating factor in these interactions. Using the pea aphid, Acyrthosiphon pisum, and its fungal pathogen, Erynia neoaphidis, as a model host–parasite system, we demonstrate how temperature can mediate the expression of genotypic variation for susceptibility and virulence. Whilst previous studies have revealed among‐clone variation in aphid resistance to this pathogen, we show that resistance rankings derived from assessments at one temperature, are not conserved across differing temperature regimes. We suggest that variation in environmental temperature, through its nonlinear impact on parasite virulence and host defence, may contribute to the general lack of evidence for frequency‐dependent selection in field systems.     

A unified GMDR method for detecting gene–gene interactions in family and unrelated samples with application to nicotine dependence

Gene–gene and gene–environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene–gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring’s samples together as compared with individual analysis strategies and the Fisher’s combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.     

Recent evolutionary history predicts population but not ecosystem‐level patterns

In the face of rapid anthropogenic environmental change, it is increasingly important to understand how ecological and evolutionary interactions affect the persistence of natural populations. Augmented gene flow has emerged as a potentially effective management strategy to counteract negative consequences of genetic drift and inbreeding depression in small and isolated populations. However, questions remain about the long‐term impacts of augmented gene flow and whether changes in individual and population fitness are reflected in ecosystem structure, potentiating eco‐evolutionary feedbacks. In this study, we used Trinidadian guppies (Poecilia reticulata) in experimental outdoor mesocosms to assess how populations with different recent evolutionary histories responded to a scenario of severe population size reduction followed by expansion in a high‐quality environment. We also investigated how variation in evolutionary history of the focal species affected ecosystem dynamics. We found that evolutionary history (i.e., gene flow vs. no gene flow) consistently predicted variation in individual growth. In addition, gene flow led to faster population growth in populations from one of the two drainages, but did not have measurable impacts on the ecosystem variables we measured: zooplankton density, algal growth, and decomposition rates. Our results suggest that benefits of gene flow may be long‐term and environment‐dependent. Although small in replication and duration, our study highlights the importance of eco‐evolutionary interactions in determining population persistence and sets the stage for future work in this area.     

The maintenance of genetic diversity under host–parasite coevolution in finite,structured populations

As a corollary to the Red Queen hypothesis, host–parasite coevolution has been hypothesized to maintain genetic variation in both species. Recent theoretical work, however, suggests that reciprocal natural selection alone is insufficient to maintain variation at individual loci. As highlighted by our brief review of the theoretical literature, models of host–parasite coevolution often vary along multiple axes (e.g. inclusion of ecological feedbacks or abiotic selection mosaics), complicating a comprehensive understanding of the effects of interacting evolutionary processes on diversity. Here we develop a series of comparable models to explore the effect of interactions between spatial structures and antagonistic coevolution on genetic diversity. Using a matching alleles model in finite populations connected by migration, we find that, in contrast to panmictic populations, coevolution in a spatially structured environment can maintain genetic variation relative to neutral expectations with migration alone. These results demonstrate that geographic structure is essential for understanding the effect of coevolution on biological diversity.     

Comparing species interaction networks along environmental gradients

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co‐variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant–hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis‐based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.     

Genetic and environmental effects on complex traits in mice

The interaction between genotype and environment is recognized as an important source of experimental variation when complex traits are measured in the mouse, but the magnitude of that interaction has not often been measured. From a study of 2448 genetically heterogeneous mice, we report the heritability of 88 complex traits that include models of human disease (asthma, type 2 diabetes mellitus, obesity, and anxiety) as well as immunological, biochemical, and hematological phenotypes. We show that environmental and physiological covariates are involved in an unexpectedly large number of significant interactions with genetic background. The 15 covariates we examined have a significant effect on behavioral and physiological tests, although they rarely explain >10% of the variation. We found that interaction effects are more frequent and larger than the main effects: half of the interactions explained >20% of the variance and in nine cases exceeded 50%. Our results indicate that assays of gene function using mouse models should take into account interactions between gene and environment.     

On the roles of landscape heterogeneity and environmental variation in determining population genomic structure in a dendritic system

Dispersal and natural selection are key evolutionary processes shaping the distribution of phenotypic and genetic diversity. For species inhabiting complex spatial environments however, it is unclear how the balance between gene flow and selection may be influenced by landscape heterogeneity and environmental variation. Here, we evaluated the effects of dendritic landscape structure and the selective forces of hydroclimatic variation on population genomic parameters for the Murray River rainbowfish, Melanotaenia fluviatilis across the Murray–Darling Basin, Australia. We genotyped 249 rainbowfish at 17,503 high‐quality SNP loci and integrated these with models of network connectivity and high‐resolution environmental data within a riverscape genomics framework. We tested competing models of gene flow before using multivariate genotype–environment association (GEA) analysis to test for signals of adaptive divergence associated with hydroclimatic variation. Patterns of neutral genetic variation were consistent with expectations based on the stream hierarchy model and M. fluviatilis’ moderate dispersal ability. Models incorporating dendritic network structure suggested that landscape heterogeneity is a more important factor determining connectivity and gene flow than waterway distance. Extending these results, we also introduce a novel approach to controlling for the unique effects of dendritic network structure in GEA analyses of populations of aquatic species. We identified 146 candidate loci potentially underlying a polygenic adaptive response to seasonal fluctuations in stream flow and variation in the relative timing of temperature and precipitation extremes. Our findings underscore an emerging predominant role for seasonal variation in hydroclimatic conditions driving local adaptation and are relevant for informing proactive conservation management.     

Assessing changes in arthropod predator–prey interactions through DNA‐based gut content analysis—variable environment,stable diet

Analysing the structure and dynamics of biotic interaction networks and the processes shaping them is currently one of the key fields in ecology. In this paper, we develop a novel approach to gut content analysis, thereby deriving a new perspective on community interactions and their responses to environment. For this, we use an elevational gradient in the High Arctic, asking how the environment and species traits interact in shaping predator–prey interactions involving the wolf spider Pardosa glacialis. To characterize the community of potential prey available to this predator, we used pitfall trapping and vacuum sampling. To characterize the prey actually consumed, we applied molecular gut content analysis. Using joint species distribution models, we found elevation and vegetation mass to explain the most variance in the composition of the prey community locally available. However, such environmental variables had only a small effect on the prey community found in the spider's gut. These observations indicate that Pardosa exerts selective feeding on particular taxa irrespective of environmental constraints. By directly modelling the probability of predation based on gut content data, we found that neither trait matching in terms of predator and prey body size nor phylogenetic or environmental constraints modified interaction probability. Our results indicate that taxonomic identity may be more important for predator–prey interactions than environmental constraints or prey traits. The impact of environmental change on predator–prey interactions thus appears to be indirect and mediated by its imprint on the community of available prey.     

Sexual selection and population divergence I: The influence of socially flexible cuticular hydrocarbon expression in male field crickets (Teleogryllus oceanicus)

Debates about how coevolution of sexual traits and preferences might promote evolutionary diversification have permeated speciation research for over a century. Recent work demonstrates that the expression of such traits can be sensitive to variation in the social environment. Here, we examined social flexibility in a sexually selected male trait—cuticular hydrocarbon (CHC) profiles—in the field cricket Teleogryllus oceanicus and tested whether population genetic divergence predicts the extent or direction of social flexibility in allopatric populations. We manipulated male crickets’ social environments during rearing and then characterized CHC profiles. CHC signatures varied considerably across populations and also in response to the social environment, but our prediction that increased social flexibility would be selected in more recently founded populations exposed to fluctuating demographic environments was unsupported. Furthermore, models examining the influence of drift and selection failed to support a role of sexual selection in driving population divergence in CHC profiles. Variation in social environments might alter the dynamics of sexual selection, but our results align with theoretical predictions that the role social flexibility plays in modulating evolutionary divergence depends critically on whether responses to variation in the social environment are homogeneous across populations, or whether gene by social environment interactions occur.     

Using connectivity to identify climatic drivers of local adaptation

Understanding the climatic drivers of local adaptation is vital. Such knowledge is not only of theoretical interest but is critical to inform management actions under climate change, such as assisted translocation and targeted gene flow. Unfortunately, there are a vast number of potential trait–environment combinations, and simple relationships between trait and environment are ambiguous: representing either plastic or evolved variation. Here, we show that by incorporating connectivity as an index of gene flow, we can differentiate trait–environment relationships reflecting genetic variation vs. phenotypic plasticity. In this way, we rapidly shorten the list of trait–environment combinations that are of significance. Our analysis of an existing data set on geographic variation in a tropical lizard shows that we can effectively rank climatic variables by the strength of their role in local adaptation. The promise of our method is a rapid and general approach to identifying the environmental drivers of local adaptation.     

Interactive effects of in utero nutrition and genetic inheritance on cognition: New evidence using sibling comparisons

A large literature links early environments and later outcomes, such as cognition; however, little is known about the mechanisms. One potential mechanism is sensitivity to early environments that is moderated or amplified by the genotype. With this mechanism in mind, a complementary literature outside economics examines the interaction between genes and environments, but often problems of endogeneity and bias in estimation are uncorrected. A key issue in the literature is exploring environmental variation that is not exogenous, which is potentially problematic if there are gene–environment correlation or gene–gene interactions. Using sibling pairs with genetic data in the Wisconsin Longitudinal Study we extend a previous, and widely cited, gene–environment study that explores an interaction between the FADS2 gene, which is associated with the processing of essential fatty acids related to cognitive development, and early life nutrition in explaining later-life IQ. Our base OLS findings suggest that individuals with specific FADS2 variants gain roughly 0.15 standard deviations in IQ for each standard deviation increase in birth weight, our measure of the early nutrition environment; while, individuals with other variants of FADS2 do not have a statistically significant association with early nutrition, implying the genotype is influencing the effects of environmental exposure. When including family-level fixed effects, however, the magnitude of the gene–environment interaction is reduced by half and statistical significance dissipates, implying the interaction between FADS2 and early nutrition in explaining later life IQ may in part be due to unobserved, family-level factors. The example has wider implications for the practice of investigating gene–environment interactions when the environmental exposure is not exogenous and robustness to unobserved variation in the genome is not controlled for in the analysis.