Coevolutionary clines across selection mosaics

Abstract. Much of the dynamics of coevolution may be driven by the interplay between geographic variation in reciprocal selection (selection mosaics) and the homogenizing action of gene flow. We develop a genetic model of geographically structured coevolution in which gene flow links coevolving communities that may differ in both the direction and magnitude of reciprocal selection. The results show that geographically structured coevolution may lead to allele-frequency clines within both interacting species when fitnesses are spatially uniform or spatially heterogeneous. Furthermore, the results show that the behavior and shape of clines differ dramatically among different types of coevolutionary interaction. Antagonistic interactions produce dynamic clines that change shape rapidly through time, producing shifting patterns of local adaptation and maladaptation. Unlike antagonistic interactions, mutualisms generate stable equilibrium patterns that lead to fixed spatial patterns of adaptation. Interactions that vary between mutualism and antagonism produce both equilibrium and dynamic clines. Furthermore, the results demonstrate that these interactions may allow mutualisms to persist throughout the geographic range of an interaction, despite pockets of locally antagonistic selection. In all cases, the coevolved spatial patterns of allele frequencies are sensitive to the relative contributions of gene flow, selection, and overall habitat size, indicating that the appropriate scale for studies of geographically structured coevolution depends on the relative contributions of each of these factors.     

CONSUMER–RESOURCE INTERACTIONS AND THE EVOLUTION OF MIGRATION

Theoretical studies have demonstrated that selection will favor increased migration when fitnesses vary both temporally and spatially, but it is far from clear how pervasive those theoretical conditions are in nature. Although consumer–resource interactions are omnipresent in nature and can generate spatial and temporal variation, it is unknown even in theory whether these dynamics favor the evolution of migration. We develop a mathematical model to address whether and how migration evolves when variability in fitness is determined at least in part by consumer–resource coevolutionary interactions. Our analyses show that such interactions can drive the evolution of migration in the resource, consumer, or both species and thus supplies a general explanation for the pervasiveness of migration. Over short time scales, we show the direction of change in migration rate is determined primarily by the state of local adaptation of the species involved: rates increase when a species is locally maladapted and decrease when locally adapted. Our results reveal that long‐term evolutionary trends in migration rates can differ dramatically depending on the strength or weakness of interspecific interactions and suggest an explanation for the evolutionary divergence of migration rates among interacting species.     

Parasite local adaptation in a geographic mosaic

A central prediction of the geographic mosaic theory of coevolution is that coevolving interspecific interactions will show varying degrees of local maladaptation. According to the theory, much of this local maladaptation is driven by selection mosaics and spatially intermingled coevolutionary hot and cold spots, rather than a simple balance between gene flow and selection. Here I develop a genetic model of host-parasite coevolution that is sufficiently general to incorporate selection mosaics, coevolutionary hot and cold spots, and a diverse array of genetic systems of infection/resistance. Results from this model show that the selection mosaics experienced by the interacting species are an important determinant of the sign and magnitude of local maladaptation. In some cases, this effect may be stronger than a previously described effect of relative rates of parasite and host gene flow. These results provide the first theoretical evidence that selection mosaics and coevolutionary hot and cold spots per se determine the magnitude and sign of local maladaptation. At the same time, however, these results demonstrate that coevolution in a geographic mosaic can lead to virtually any pattern of local adaptation or local maladaptation. Consequently, empirical studies that describe only patterns of local adaptation or maladaptation do not provide evidence either for or against the theory.     

Detecting small-scale genotype-environment interactions in apomictic dandelion (Taraxacum officinale) populations

Studies of genotype × environment interactions (G × E) and local adaptation provide critical tests of natural selection’s ability to counter opposing forces such as gene flow. Such studies may be greatly facilitated in asexual species, given the possibility for experimental replication at the level of true genotypes (rather than populations) and the possibility of using molecular markers to assess genotype–environment associations in the field (neither of which is possible for most sexual species). Here, we tested for G × E in asexual dandelions (Taraxacum officinale) by subjecting six genotypes to experimental drought, mown and benign (control) conditions and subsequently using microsatellites to assess genotype–environment associations in the field. We found strong G × E, with genotypes that performed poorly under benign conditions showing the highest performance under stressful conditions (drought or mown). Our six focal genotypes comprise > 80% of plants in local populations. The most common genotype in the field showed its highest relative performance under mown conditions (the most common habitat in our study area), and almost all plants of this genotype in the field were found growing in mowed lawns. Genotypes performing best under benign experimental conditions were found most frequently in unmown conditions in the field. These results are strongly indicative of local adaptation at a very small scale, with unmown microsites of only a few square metres typically embedded within larger mown lawns. By studying an asexual species, we were able to map genotypes with known ecological characteristics to environments with high spatial precision.     

The influence of interspecific interactions on species range expansion rates

Ongoing and predicted global change makes understanding and predicting species' range shifts an urgent scientific priority. Here, we provide a synthetic perspective on the so far poorly understood effects of interspecific interactions on range expansion rates. We present theoretical foundations for how interspecific interactions may modulate range expansion rates, consider examples from empirical studies of biological invasions and natural range expansions as well as process‐based simulations, and discuss how interspecific interactions can be more broadly represented in process‐based, spatiotemporally explicit range forecasts. Theory tells us that interspecific interactions affect expansion rates via alteration of local population growth rates and spatial displacement rates, but also via effects on other demographic parameters. The best empirical evidence for interspecific effects on expansion rates comes from studies of biological invasions. Notably, invasion studies indicate that competitive dominance and release from specialized enemies can enhance expansion rates. Studies of natural range expansions especially point to the potential for competition from resident species to reduce expansion rates. Overall, it is clear that interspecific interactions may have important consequences for range dynamics, but also that their effects have received too little attention to robustly generalize on their importance. We then discuss how interspecific interactions effects can be more widely incorporated in dynamic modeling of range expansions. Importantly, models must describe spatiotemporal variation in both local population dynamics and dispersal. Finally, we derive the following guidelines for when it is particularly important to explicitly represent interspecific interactions in dynamic range expansion forecasts: if most interacting species show correlated spatial or temporal trends in their effects on the target species, if the number of interacting species is low, and if the abundance of one or more strongly interacting species is not closely linked to the abundance of the target species.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Identifying coevolving loci using interspecific genetic correlations

Evaluating the importance of coevolution for a wide range of evolutionary questions, such as the role parasites play in the evolution of sexual reproduction, requires that we understand the genetic basis of coevolutionary interactions. Despite its importance, little progress has been made identifying the genetic basis of coevolution, largely because we lack tools designed specifically for this purpose. Instead, coevolutionary studies are often forced to re‐purpose single species techniques. Here, we propose a novel approach for identifying the genes mediating locally adapted coevolutionary interactions that relies on spatial correlations between genetic marker frequencies in the interacting species. Using individual‐based multi‐locus simulations, we quantify the performance of our approach across a range of coevolutionary genetic models. Our results show that when one species is strongly locally adapted to the other and a sufficient number of populations can be sampled, our approach accurately identifies functionally coupled host and parasite genes. Although not a panacea, the approach we outline here could help to focus the search for coevolving genes in a wide variety of well‐studied systems for which substantial local adaptation has been demonstrated.     

Local biotic adaptation of trees and shrubs to plant neighbors

Natural selection as a result of plant–plant interactions can lead to local biotic adaptation. This may occur where species frequently interact and compete intensely for resources limiting growth, survival, and reproduction. Selection is demonstrated by comparing a genotype interacting with con‐ or hetero‐specific sympatric neighbor genotypes with a shared site‐level history (derived from the same source location), to the same genotype interacting with foreign neighbor genotypes (from different sources). Better genotype performance in sympatric than allopatric neighborhoods provides evidence of local biotic adaptation. This pattern might be explained by selection to avoid competition by shifting resource niches (differentiation) or by interactions benefitting one or more members (facilitation). We tested for local biotic adaptation among two riparian trees, Populus fremontii and Salix gooddingii, and the shrub Salix exigua by transplanting replicated genotypes from multiple source locations to a 17 000 tree common garden with sympatric and allopatric treatments along the Colorado River in California. Three major patterns were observed: 1) across species, 62 of 88 genotypes grew faster with sympatric neighbors than allopatric neighbors; 2) these growth rates, on an individual tree basis, were 44, 15 and 33% higher in sympatric than allopatric treatments for P. fremontii, S. exigua and S. gooddingii, respectively, and; 3) survivorship was higher in sympatric treatments for P. fremontii and S. exigua. These results support the view that fitness of foundation species supporting diverse communities and dominating ecosystem processes is determined by adaptive interactions among multiple plant species with the outcome that performance depends on the genetic identity of plant neighbors. The occurrence of evolution in a plant‐community context for trees and shrubs builds on ecological evolutionary research that has demonstrated co‐evolution among herbaceous taxa, and evolution of native species during exotic plants invasion, and taken together, refutes the concept that plant communities are always random associations.     

Intrinsic and extrinsic causes of spatial variability across scales in a metacommunity

The relative importance of extrinsic and intrinsic causes of variability is among the oldest unresolved problems in ecology. However, the interaction between large-scale intrinsic variability in species abundance and environmental heterogeneity is still unknown. We use a metacommunity model with disturbance-recovery dynamics to resolve the interaction between scales of environmental heterogeneity, biotic processes and of intrinsic variability. We explain how population density increases with environmental variability only when its scale matches that of intrinsic patterns of abundance, through their ability to develop in heterogeneous environments. Succession dynamics reveals how the strength of local species interactions, through its control of intrinsic variability, can in turn control the scale of metapopulation response to environmental scales. Our results show that the environment and species density might fail to show any correlation despite their strong causal association. They more generally suggest that the spatial scale of ecological processes might not be sufficient to build a predictive framework for spatially heterogeneous habitats, including marine reserve networks.     

Detecting local adaptation in a natural plant-pathogen metapopulation: a laboratory vs. field transplant approach

Antagonistic coevolution between hosts and parasites in spatially structured populations can result in local adaptation of parasites. Traditionally parasite local adaptation has been investigated in field transplant experiments or in the laboratory under a constant environment. Despite the conceptual importance of local adaptation in studies of (co)evolution, to date no study has provided a comparative analysis of these two methods. Here, using information on pathogen population dynamics, I tested local adaptation of the specialist phytopathogen, Podosphaera plantaginis, to its host, Plantago lanceolata at three different spatial scales: sympatric host population, sympatric host metapopulation and allopatric host metapopulations. The experiment was carried out as a field transplant experiment with greenhouse-reared host plants from these three different origins introduced into four pathogen populations. In contrast to results of an earlier study performed with these same host and parasite populations under laboratory conditions, I did not find any evidence for parasite local adaptation. For interactions governed by strain-specific resistance, field studies may not be sensitive enough to detect mean parasite population virulence. Given that parasite transmission potential may be mediated by the abiotic environment and genotype-by-environment interactions, I suggest that relevant environmental variation should be incorporated into laboratory studies of parasite local adaptation.     

Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: linking theory to data

Understanding stability across ecological hierarchies is critical for landscape management in a changing world. Recent studies showed that synchrony among lower‐level components is key to scaling temporal stability across two hierarchical levels, whether spatial or organizational. But an extended framework that integrates both spatial scale and organizational level simultaneously is required to clarify the sources of ecosystem stability at large scales. However, such an extension is far from trivial when taking into account the spatial heterogeneities in real‐world ecosystems. In this paper, we develop a partitioning framework that bridges variability and synchrony measures across spatial scales and organizational levels in heterogeneous metacommunities. In this framework, metacommunity variability is expressed as the product of local‐scale population variability and two synchrony indices that capture the temporal coherence across species and space, respectively. We develop an R function ‘var.partition’ and apply it to five types of desert plant communities to illustrate our framework and test how diversity shapes synchrony and variability at different hierarchical levels. As the observation scale increased from local populations to metacommunities, the temporal variability of plant productivity was reduced mainly by factors that decreased species synchrony. Species synchrony decreased from local to regional scales, and spatial synchrony decreased from species to community levels. Local and regional species diversity were key factors that reduced species synchrony at the two scales. Moreover, beta diversity contributed to decreasing spatial synchrony among communities. We conclude that our new framework offers a valuable toolbox for future empirical studies to disentangle the mechanisms and pathways by which ecological factors influence stability at large scales.     

Physiological adaptation along environmental gradients and replicated hybrid zone structure in swordtails (Teleostei: Xiphophorus)

Local adaptation is often invoked to explain hybrid zone structure, but empirical evidence of this is generally rare. Hybrid zones between two poeciliid fishes, Xiphophorus birchmanni and X. malinche, occur in multiple tributaries with independent replication of upstream‐to‐downstream gradients in morphology and allele frequencies. Ecological niche modelling revealed that temperature is a central predictive factor in the spatial distribution of pure parental species and their hybrids and explains spatial and temporal variation in the frequency of neutral genetic markers in hybrid populations. Among populations of parentals and hybrids, both thermal tolerance and heat‐shock protein expression vary strongly, indicating that spatial and temporal structure is likely driven by adaptation to local thermal environments. Therefore, hybrid zone structure is strongly influenced by interspecific differences in physiological mechanisms for coping with the thermal environment.     

Evolution of spatially structured host–parasite interactions

Spatial structure has dramatic effects on the demography and the evolution of species. A large variety of theoretical models have attempted to understand how local dispersal may shape the coevolution of interacting species such as host–parasite interactions. The lack of a unifying framework is a serious impediment for anyone willing to understand current theory. Here, we review previous theoretical studies in the light of a single epidemiological model that allows us to explore the effects of both host and parasite migration rates on the evolution and coevolution of various life‐history traits. We discuss the impact of local dispersal on parasite virulence, various host defence strategies and local adaptation. Our analysis shows that evolutionary and coevolutionary outcomes crucially depend on the details of the host–parasite life cycle and on which life‐history trait is involved in the interaction. We also discuss experimental studies that support the effects of spatial structure on the evolution of host–parasite interactions. This review highlights major similarities between some theoretical results, but it also reveals an important gap between evolutionary and coevolutionary models. We discuss possible ways to bridge this gap within a more unified framework that would reconcile spatial epidemiology, evolution and coevolution.     

Non-genetic inheritance and the patterns of antagonistic coevolution

ABSTRACT: BACKGROUND: Antagonistic species interactions can lead to coevolutionary genotype or phenotype frequency oscillations, with important implications for ecological and evolutionary processes. However, direct empirical evidence of such oscillations is rare. The rarity of observations is generally attributed to inherent difficulties of ecological and evolutionary long-term studies, to weak or absent interaction between species, or to the absence of negative frequency-dependence. RESULTS: Here, we show that another factor - non-genetic inheritance, mediated for example by epigenetic mechanisms - can completely eliminate oscillations even if only a small fraction of offspring are affected. We analytically derive the threshold value of this fraction at which the dynamics change from oscillatory to stable, and investigate how selection, mutation and generation times differences between the two species affect the threshold value. These results strongly suggest that the lack of phenotype frequency oscillations should not be attributed to the lack of strong interactions between antagonistic species. CONCLUSIONS: Given increasing evidence of non-genetic effects on the outcomes of antagonistic species interactions, we suggest that these effects should be incorporated into ecological and evolutionary models of interacting species.     

Model-specific tests on variance heterogeneity for detection of potentially interacting genetic loci

ABSTRACT: BACKGROUND: Trait variances among genotype groups at a locus are expected to differ in the presence of an interaction between this locus and another locus or environment. A simple maximum test on variance heterogeneity can thus be used to identify potentially interacting single nucleotide polymorphisms (SNPs). RESULTS: We propose a multiple contrast test for variance heterogeneity that compares the mean of Levene residuals for each genotype group with their average as an alternative to a global Levene test. We applied this test to a Bogalusa Heart Study dataset to screen for potentially interacting SNPs across the whole genome that influence a number of quantitative traits. A user-friendly implementation of this method is available in the R statistical software package multcomp. CONCLUSIONS: We show that the proposed multiple contrast test of model-specific variance heterogeneity can be used to test for potential interactions between SNPs and unknown alleles, loci or covariates and provide valuable additional information compared with traditional tests. Although the test is statistically valid for severely unbalanced designs, care is needed in interpreting the results at loci with low allele frequencies.     

Biotic interactions hold the key to understanding metacommunity organisation

Biotic interactions are fundamental drivers governing biodiversity locally, yet their effects on geographical variation in community composition (i.e. incidence-based) and community structure (i.e. abundance-based) at regional scales remain controversial. Ecologists have only recently started to integrate different types of biotic interactions into community assembly in a spatial context, a theme that merits further empirical quantification. Here, we applied partial correlation networks to infer the strength of spatial dependencies between pairs of organismal groups and mapped the imprints of biotic interactions on the assembly of pond metacommunities. To do this, we used a comprehensive empirical dataset from Mediterranean landscapes and adopted the perspective that community assembly is best represented as a network of interacting organismal groups. Our results revealed that the co-variation among the beta diversities of multiple organismal groups is primarily driven by biotic interactions and, to a lesser extent, by the abiotic environment. These results suggest that ignoring biotic interactions may undermine our understanding of assembly mechanisms in spatially extensive areas and decrease the accuracy and performance of predictive models. We further found strong spatial dependencies in our analyses which can be interpreted as functional relationships among several pairs of organismal groups (e.g. macrophytes–macroinvertebrates, fish–zooplankton). Perhaps more importantly, our results support the notion that biotic interactions make crucial contributions to the species sorting paradigm of metacommunity theory and raise the question of whether these biologically-driven signals have been equally underappreciated in other aquatic and terrestrial ecosystems. Although more research is still required to empirically capture the importance of biotic interactions across ecosystems and at different spatial resolutions and extents, our findings may allow decision makers to better foresee the main consequences of human-driven impacts on inland waters, particularly those associated with the addition or removal of key species.     

On the stability of the Bayenv method in assessing human SNP-environment associations

Background

Phenotypic variation along environmental gradients has been documented among and within many species, and in some cases, genetic variation has been shown to be associated with these gradients. Bayenv is a relatively new method developed to detect patterns of polymorphisms associated with environmental gradients. Using a Bayesian Markov Chain Monte Carlo (MCMC) approach, Bayenv evaluates whether a linear model relating population allele frequencies to environmental variables is more probable than a null model based on observed frequencies of neutral markers. Although this method has been used to detect environmental adaptation in a number of species, including humans, plants, fish, and mosquitoes, stability between independent runs of this MCMC algorithm has not been characterized. In this paper, we explore the variability of results between runs and the factors contributing to it.

Results

Independent runs of the Bayenv program were carried out using genome-wide single-nucleotide polymorphism (SNP) data from samples from 60 worldwide human populations following previous applications of the Bayenv method. To assess factors contributing to the method's stability, we used varying numbers of MCMC iterations and also analyzed a second modified data set that excluded two Siberian populations with extreme climate variables. Between any two runs, correlations between Bayes factors and the overlap of SNPs in the empirical p value tails were surprisingly low. Enrichments of genic versus non-genic SNPs in the empirical tails were more robust than the empirical p values; however, the significance of the enrichments for some environmental variables still varied among runs, contradicting previously published conclusions. Runs with a greater number of MCMC iterations slightly reduced run-to-run variability, and excluding the Siberian populations did not have a large effect on the stability of the runs.

Conclusions

Because of high run-to-run variability, we advise against making conclusions about genome-wide patterns of adaptation based on only one run of the Bayenv algorithm and recommend caution in interpreting previous studies that have used only one run. Moving forward, we suggest carrying out multiple independent runs of Bayenv and averaging Bayes factors between runs to produce more stable and reliable results. With these modifications, future discoveries of environmental adaptation within species using the Bayenv method will be more accurate, interpretable, and easily compared between studies.     

A meta-analysis of factors affecting local adaptation between interacting species

Adaptive divergence among populations can result in local adaptation, whereby genotypes in native environments exhibit greater fitness than genotypes in novel environments. A body of theory has developed that predicts how different species traits, such as rates of gene flow and generation times, influence local adaptation in coevolutionary species interactions. We used a meta-analysis of local-adaptation studies across a broad range of host-parasite interactions to evaluate predictions about the effect of species traits on local adaptation. We also evaluated how experimental design influences the outcome of local adaptation experiments. In reciprocally designed experiments, the relative gene flow rate of hosts versus parasites was the strongest predictor of local adaptation, with significant parasite local adaptation only in the studies in which parasites had greater gene flow rates than their hosts. When nonreciprocal studies were included in analyses, species traits did not explain significant variation in local adaptation, although the overall level of local adaptation observed was lower in the nonreciprocal than in the reciprocal studies. This formal meta-analysis across a diversity of host-parasite systems lends insight into the role of both biology (species traits) and biologists (experimental design) in detecting local adaptation in coevolving species interactions.     

Beyond species: why ecological interaction networks vary through space and time

Community ecology is tasked with the considerable challenge of predicting the structure, and properties, of emerging ecosystems. It requires the ability to understand how and why species interact, as this will allow the development of mechanism‐based predictive models, and as such to better characterize how ecological mechanisms act locally on the existence of inter‐specific interactions. Here we argue that the current conceptualization of species interaction networks is ill‐suited for this task. Instead, we propose that future research must start to account for the intrinsic variability of species interactions, then scale up from here onto complex networks. This can be accomplished simply by recognizing that there exists intra‐specific variability, in traits or properties related to the establishment of species interactions. By shifting the scale towards population‐based processes, we show that this new approach will improve our predictive ability and mechanistic understanding of how species interact over large spatial or temporal scales. Synthesis Although species interactions are the backbone of ecological communities, we have little insights on how (and why) they vary through space and time. In this article, we build on existing empirical literature to show that the same species may happen to interact in different ways when their local abundances vary, their trait distribution changes, or when the environment affects either of these factors. We discuss how these findings can be integrated in existing frameworks for the analysis and simulation of species interactions.