Metapopulation Allee effects, habitat destruction, and extinction in metacommunities.

Previous metapopulation models developed to examine consequences of habitat destruction and metapopulation Allee effects are biologically plausible for only small degrees of habitat destruction. For larger, realistic amounts of habitat destruction, those models fail to capture a metapopulation Allee effect. We here present a new model that allows biologically meaningful metapopulation Allee effects at all feasible levels of habitat destruction. When applied to metacommunities of competitive species that face habitat destruction, this new model shows that metapopulation Allee effects may drastically alter predictions about the fates of the competitors compared to when Allee effects are ignored. In particular, the number of extinctions increase, the times to those extinctions decrease, and the order in which the extinctions occur can change dramatically.

Revisiting Darwin's conundrum reveals a twist on the relationship between phylogenetic distance and invasibility.

A key goal of invasion biology is to identify the factors that favor species invasions. One potential indicator of invasiveness is the phylogenetic distance between a nonnative species and species in the recipient community. However, predicting invasiveness using phylogenetic information relies on an untested assumption: that both biotic resistance and facilitation weaken with increasing phylogenetic distance. We test the validity of this key assumption using a mathematical model in which a novel species is introduced into communities with varying ecological and phylogenetic relationships. Contrary to what is generally assumed, we find that biotic resistance and facilitation can either weaken or intensify with phylogenetic distance, depending on the mode of interspecific interactions (phenotype matching or phenotype differences) and the resulting evolutionary trajectory of the recipient community. Thus, we demonstrate that considering the mechanisms that drive phenotypic divergence between native and nonnative species can provide critical insight into the relationship between phylogenetic distance and invasibility.

The Genetics of Phenotypic Plasticity. XIV. Coevolution.

Plastic changes in organisms' phenotypes can result from either abiotic or biotic effectors. Biotic effectors create the potential for a coevolutionary dynamic. Through the use of individual-based simulations, we examined the coevolutionary dynamic of two species that are phenotypically plastic. We explored two modes of biotic and abiotic interactions: ecological interactions that determine the form of natural selection and developmental interactions that determine phenotypes. Overall, coevolution had a larger effect on the evolution of phenotypic plasticity than plasticity had on the outcome of coevolution. Effects on the evolution of plasticity were greater when the fitness-maximizing coevolutionary outcomes were antagonistic between the species pair (predator-prey interactions) than when those outcomes were augmenting (competitive or mutualistic). Overall, evolution in the context of biotic interactions reduced selection for plasticity even when trait development was responding to just the abiotic environment. Thus, the evolution of phenotypic plasticity must always be interpreted in the full context of a species' ecology. Our results show how the merging of two theory domains--coevolution and phenotypic plasticity--can deepen our understanding of both and point to new empirical research.

Long-distance gene flow and adaptation of forest trees to rapid climate change.

Forest trees are the dominant species in many parts of the world and predicting how they might respond to climate change is a vital global concern. Trees are capable of long-distance gene flow, which can promote adaptive evolution in novel environments by increasing genetic variation for fitness. It is unclear, however, if this can compensate for maladaptive effects of gene flow and for the long-generation times of trees. We critically review data on the extent of long-distance gene flow and summarise theory that allows us to predict evolutionary responses of trees to climate change. Estimates of long-distance gene flow based both on direct observations and on genetic methods provide evidence that genes can move over spatial scales larger than habitat shifts predicted under climate change within one generation. Both theoretical and empirical data suggest that the positive effects of gene flow on adaptation may dominate in many instances. The balance of positive to negative consequences of gene flow may, however, differ for leading edge, core and rear sections of forest distributions. We propose future experimental and theoretical research that would better integrate dispersal biology with evolutionary quantitative genetics and improve predictions of tree responses to climate change.

Biotic interactions, rapid evolution, and the establishment of introduced species.

The biotic environment can pose a challenge to introduced species; however, it is not known how rapid evolution in introduced and resident species influences the probability that the introduced species will become established. Here, we analyze the establishment phase of invasion with eco-evolutionary models of introduced species involved in predator-prey, mutualistic, or competitive interactions with a resident species. We find that, depending on the strength of the biotic interaction, establishment is impossible, guaranteed, or, in a narrow range, determined by genetic variation. Over this narrow range, rapid evolution of the introduced species always favors establishment, whereas resident evolution may either inhibit or facilitate establishment, depending on the interaction type. Coevolution can also either increase or decrease the chance of establishment, depending on the initial genotype frequencies as well as the interaction type. Our results suggest that the conditions under which genetic variation influences establishment success are limited, but they highlight the importance of considering the resident community's evolutionary response to introduced species as a component of its invasibility.

Conserving and promoting evenness: organic farming and fire-based wildland management as case studies.

Healthy ecosystems include many species (high richness) with similar abundances (high evenness). Thus, both aspects of biodiversity are worthy of conservation. Simultaneously conserving richness and evenness might be difficult, however, if, for example, the restoration of previously absent species to low densities brings a cost in reduced evenness. Using meta-analysis, we searched for benefits to biodiversity following adoption of two common land-management schemes: the implementation of organic practices by farmers and of controlled burning by natural-land managers. We used rarefaction to eliminate sampling bias in all of our estimates of richness and evenness. Both conservation practices significantly increased evenness and overall abundance across taxonomic classifications (arthropods, birds, non-bird vertebrates, plants, soil organisms). Evenness and richness varied independently, leading to no richness-evenness correlation and no significant overall change in richness. Demonstrating the importance of rarefaction, analyses of raw data that did not receive rarefaction indicated misleadingly strong benefits of organic agriculture and burning for richness while underestimating true gains in evenness. Both organic farming and burning favored species that were not numerically dominant, re-balancing communities as uncommon species gained individuals. Our results support the assertion that richness and evenness capture separate facets of biodiversity, each needing individual attention during conservation.

The sterile insect technique is protected from evolution of mate discrimination.

Background: The sterile insect technique (SIT) has been used to suppress and even extinguish pest insect populations. The method involves releasing artificially reared insects (usually males) that, when mating with wild individuals, sterilize the broods. If administered on a large enough scale, the sterility can collapse the population. Precedents from other forms of population suppression, especially chemicals, raise the possibility of resistance evolving against the SIT. Here, we consider resistance in the form of evolution of female discrimination to avoid mating with sterile males. Is resistance evolution expected? Methods: We offer mathematical models to consider the dynamics of this process. Most of our models assume a constant-release protocol, in which the same density of males is released every generation, regardless of wild male density. A few models instead assume proportional release, in which sterile releases are adjusted to be a constant proportion of wild males. Results: We generally find that the evolution of female discrimination, although favored by selection, will often be too slow to halt population collapse when a constant-release implementation of the SIT is applied appropriately and continually. The accelerating efficacy of sterile males in dominating matings as the population collapses works equally against discriminating females as against non-discriminating females, and rare genes for discrimination are too slow to ascend to prevent the loss of females that discriminate. Even when migration from source populations sustains the treated population, continued application of the SIT can prevent evolution of discrimination. However, periodic premature cessation of the SIT does allow discrimination to evolve. Likewise, use of a 'proportional-release' protocol is also prone to escape from extinction if discriminating genotypes exist in the population, even if those genotypes are initially rare. Overall, the SIT is robust against the evolution of mate discrimination provided care is taken to avoid some basic pitfalls. The models here provide insight for designing programs to avoid those pitfalls.

Manipulation of Vector Host Preference by Pathogens: Implications for Virus Spread and Disease Management.

Some plant pathogens manipulate the behavior and performance of their vectors, potentially enhancing pathogen spread. The implications are evolutionary and epidemiological but also economic for pathogens that cause disease in crops. Here we explore with models the effects of vector manipulation on crop yield loss to disease and on the economic returns for vector suppression. We use two frameworks, one that simulates the proportional occurrence of the pathogen in the vector population with the option to eliminate vectors by a single insecticidal treatment, and one that includes vector population dynamics and the potential for multiple insecticidal sprays in a season to suppress vectors. We parameterize the models with published data on vector manipulation, crop yields as affected by the age of the plant at infection, commodity prices and costs of vector control for three pathosystems. Using the first framework, maximum returns for treating vectors are greater with vector manipulation than without it by approximately US$10 per acre (US$24.7/ha) in peas infected by Pea enation mosaic virus and Bean leaf roll virus, and approximately US$50 per acre (US$124/ha) for potatoes infected by Potato leaf roll virus. Using the second framework, maximum returns for controlling the psyllid vectors of Candidatus Liberibacter solanacearum are 50% greater (approximately US$400/acre, US$988/ha) but additional returns for multiple weekly sprays diminish more with vector manipulation than without it. These results suggest that the economics of vector manipulation can be substantial and provide a framework that can inform management decisions.

Gene drive escape from resistance depends on mechanism and ecology.

Gene drives can potentially be used to suppress pest populations, and the advent of CRISPR technology has made it feasible to engineer them in many species, especially insects. What remains largely unknown for implementations is whether antidrive resistance will evolve to block the population suppression. An especially serious threat to some kinds of drive is mutations in the CRISPR cleavage sequence that block the action of CRISPR, but designs have been proposed to avoid this type of resistance. Various types of resistance at loci away from the cleavage site remain a possibility, which is the focus here. It is known that modest-effect suppression drives can essentially "outrun" unlinked resistance even when that resistance is present from the start. We demonstrate here how the risk of evolving (unlinked) resistance can be further reduced without compromising overall suppression by introducing multiple suppression drives or by designing drives with specific ecological effects. However, we show that even modest-effect suppression drives remain vulnerable to the evolution of extreme levels of inbreeding, which halt the spread of the drive without actually interfering with its mechanism. The landscape of resistance evolution against suppression drives is therefore complex, but avenues exist for enhancing gene drive success.

Environmental Cue Integration and Phenology in a Changing World.

Many organisms use environmental cues to time events in their annual cycle, such as reproduction and migration, with the appropriate timing of such events impacting survival and reproduction. As the climate changes, evolved mechanisms of cue use may facilitate or limit the capacity of organisms to adjust phenology accordingly, and organisms often integrate multiple cues to fine-tune the timing of annual events. Yet, our understanding of how suites of cues are integrated to generate observed patterns of seasonal timing remains nascent. We present an overarching framework to describe variation in the process of cue integration in the context of seasonal timing. This framework incorporates both cue dependency and cue interaction. We then summarize how existing empirical findings across a range of vertebrate species and life cycle events fit into this framework. Finally, we use a theoretical model to explore how variation in modes of cue integration may impact the ability of organisms to adjust phenology adaptively in the face of climate change. Such a theoretical approach can facilitate the exploration of complex scenarios that present challenges to study in vivo but capture important complexity of the natural world.

Evolution in stage-structured populations.

For many organisms, stage is a better predictor of demographic rates than age. Yet no general theoretical framework exists for understanding or predicting evolution in stage-structured populations. Here, we provide a general modeling approach that can be used to predict evolution and demography of stage-structured populations. This advances our ability to understand evolution in stage-structured populations to a level previously available only for populations structured by age. We use this framework to provide the first rigorous proof that Lande's theorem, which relates adaptive evolution to population growth, applies to stage-classified populations, assuming only normality and that evolution is slow relative to population dynamics. We extend this theorem to allow for different means or variances among stages. Our next major result is the formulation of Price's theorem, a fundamental law of evolution, for stage-structured populations. In addition, we use data from Trillium grandiflorum to demonstrate how our models can be applied to a real-world population and thereby show their practical potential to generate accurate projections of evolutionary and population dynamics. Finally, we use our framework to compare rates of evolution in age- versus stage-structured populations, which shows how our methods can yield biological insights about evolution in stage-structured populations.

Evading resistance to gene drives.

Gene drives offer the possibility of altering and even suppressing wild populations of countless plant and animal species, and CRISPR technology now provides the technical feasibility of engineering them. However, population-suppression gene drives are prone to select resistance, should it arise. Here, we develop mathematical and computational models to identify conditions under which suppression drives will evade resistance, even if resistance is present initially. Previous models assumed resistance is allelic to the drive. We relax this assumption and show that linkage between the resistance and drive loci is critical to the evolution of resistance and that evolution of resistance requires (negative) linkage disequilibrium between the two loci. When the two loci are unlinked or only partially so, a suppression drive that causes limited inviability can evolve to fixation while causing only a minor increase in resistance frequency. Once fixed, the drive allele no longer selects resistance. Our analyses suggest that among gene drives that cause moderate suppression, toxin-antidote systems are less apt to select for resistance than homing drives. Single drives of moderate effect might cause only moderate population suppression, but multiple drives (perhaps delivered sequentially) would allow arbitrary levels of suppression. The most favorable case for evolution of resistance appears to be with suppression homing drives in which resistance is dominant and fully suppresses transmission distortion; partial suppression by resistance heterozygotes or recessive resistance are less prone to resistance evolution. Given that it is now possible to engineer CRISPR-based gene drives capable of circumventing allelic resistance, this design may allow for the engineering of suppression gene drives that are effectively resistance-proof.

When is correlation coevolution?

Studying the correlation between traits of interacting species has long been a popular approach for identifying putative cases of coevolution. More recently, such approaches have been used as a means to evaluate support for the geographic mosaic theory of coevolution. Here we examine the utility of these approaches, using mathematical and computational models to predict the correlation that evolves between traits of interacting species for a broad range of interaction types. Our results reveal that coevolution is neither a necessary nor a sufficient condition for the evolution of spatially correlated traits between two species. Specifically, our results show that coevolutionary selection fails to consistently generate statistically significant correlations and, conversely, that non-coevolutionary processes can readily cause statistically significant correlations to evolve. In addition, our results demonstrate that studies of trait correlations per se cannot be used as evidence either for or against a geographic mosaic process. Taken together, our results suggest that understanding the coevolutionary process in natural populations will require detailed mechanistic studies conducted in multiple populations or the use of more sophisticated statistical approaches that better use information contained in existing data sets.

Demographic and genetic constraints on evolution.

Populations unable to evolve to selectively favored states are constrained. Genetic constraints occur when additive genetic variance in selectively favored directions is absent (absolute constraints) or present but small (quantitative constraints). Quantitative--unlike absolute--constraints are presumed surmountable given time. This ignores that a population might become extinct before reaching the favored state, in which case demography effectively converts a quantitative into an absolute constraint. Here, we derive criteria for predicting when such conversions occur. We model the demography and evolution of populations subject to optimizing selection that experience either a single shift or a constant change in the optimum. In the single-shift case, we consider whether a population can evolve significantly without declining or else declines temporarily while avoiding low sizes consistent with high extinction risk. We analyze when populations in constantly changing environments evolve sufficiently to ensure long-term growth. From these, we derive formulas for critical levels of genetic variability that define demography-caused absolute constraints. The formulas depend on estimable properties of fitness, population size, or environmental change rates. Each extends to selection on multivariate traits. Our criteria define the nearly null space of a population's G matrix, the set of multivariate directions effectively inaccessible to it via adaptive evolution.

Source-sink dynamics of virulence evolution.

To understand the evolution of genetic diversity within species--bacterial and others--we must dissect the first steps of genetic adaptation to novel habitats, particularly habitats that are suboptimal for sustained growth where there is strong selection for adaptive changes. Here, we present the view that bacterial human pathogens represent an excellent model for understanding the molecular mechanisms of the adaptation of a species to alternative habitats. In particular, bacterial pathogens allow us to develop analytical methods to detect genetic adaptation using an evolutionary 'source-sink' model, with which the evolution of bacterial pathogens can be seen from the angle of continuous switching between permanent (source) and transient (sink) habitats. The source-sink model provides a conceptual framework for understanding the population dynamics and molecular mechanisms of virulence evolution.

Spatial structure undermines parasite suppression by gene drive cargo.

Gene drives may be used in two ways to curtail vectored diseases. Both involve engineering the drive to spread in the vector population. One approach uses the drive to directly depress vector numbers, possibly to extinction. The other approach leaves intact the vector population but suppresses the disease agent during its interaction with the vector. This second application may use a drive engineered to carry a genetic cargo that blocks the disease agent. An advantage of the second application is that it is far less likely to select vector resistance to block the drive, but the disease agent may instead evolve resistance to the inhibitory cargo. However, some gene drives are expected to spread so fast and attain such high coverage in the vector population that, if the disease agent can evolve resistance only gradually, disease eradication may be feasible. Here we use simple models to show that spatial structure in the vector population can greatly facilitate persistence and evolution of resistance by the disease agent. We suggest simple approaches to avoid some types of spatial structure, but others may be intrinsic to the populations being challenged and difficult to overcome.

Hypothesis testing in comparative and experimental studies of function-valued traits.

Many traits of evolutionary interest, when placed in their developmental, physiological, or environmental contexts, are function-valued. For instance, gene expression during development is typically a function of the age of an organism and physiological processes are often a function of environment. In comparative and experimental studies, a fundamental question is whether the function-valued trait of one group is different from another. To address this question, evolutionary biologists have several statistical methods available. These methods can be classified into one of two types: multivariate and functional. Multivariate methods, including univariate repeated-measures analysis of variance (ANOVA), treat each trait as a finite list of data. Functional methods, such as repeated-measures regression, view the data as a sample of points drawn from an underlying function. A key difference between multivariate and functional methods is that functional methods retain information about the ordering and spacing of a set of data values, information that is discarded by multivariate methods. In this study, we evaluated the importance of that discarded information in statistical analyses of function-valued traits. Our results indicate that functional methods tend to have substantially greater statistical power than multivariate approaches to detect differences in a function-valued trait between groups.

Neutral evolution of multiple quantitative characters: a genealogical approach.

The G matrix measures the components of phenotypic variation that are genetically heritable. The structure of G, that is, its principal components and their associated variances, determines, in part, the direction and speed of multivariate trait evolution. In this article we present a framework and results that give the structure of G under the assumption of neutrality. We suggest that a neutral expectation of the structure of G is important because it gives a null expectation for the structure of G from which the unique consequences of selection can be determined. We demonstrate how the processes of mutation, recombination, and drift shape the structure of G. Furthermore, we demonstrate how shared common ancestry between segregating alleles shapes the structure of G. Our results show that shared common ancestry, which manifests itself in the form of a gene genealogy, causes the structure of G to be nonuniform in that the variances associated with the principal components of G decline at an approximately exponential rate. Furthermore we show that the extent of the nonuniformity in the structure of G is enhanced with declines in mutation rates, recombination rates, and numbers of loci and is dependent on the pattern and modality of mutation.