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 anti-drive 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.

American Journal of BotanyVolume 109, Issue 10 p. 1519-1524 COMMENTARYOpen Access Phenotypic plasticity made simple, but not too simple Richard Gomulkiewicz, Corresponding Author Richard Gomulkiewicz [email protected] orcid.org/0000-0001-6346-9576 School of Biological Sciences, Washington State University, Pullman, Washington, 99164 USA Correspondence Richard Gomulkiewicz, School of Biological Sciences, PO Box 644236, Washington State University, Pullman, WA 99164, USA. Email: [email protected]Search for more papers by this authorJohn R. Stinchcombe, John R. Stinchcombe orcid.org/0000-0003-3349-2964 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S3B2 CanadaSearch for more papers by this author Richard Gomulkiewicz, Corresponding Author Richard Gomulkiewicz [email protected] orcid.org/0000-0001-6346-9576 School of Biological Sciences, Washington State University, Pullman, Washington, 99164 USA Correspondence Richard Gomulkiewicz, School of Biological Sciences, PO Box 644236, Washington State University, Pullman, WA 99164, USA. Email: [email protected]Search for more papers by this authorJohn R. Stinchcombe, John R. Stinchcombe orcid.org/0000-0003-3349-2964 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, M5S3B2 CanadaSearch for more papers by this author First published: 15 September 2022 https://doi.org/10.1002/ajb2.16068AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Phenotypic plasticity refers to environment-dependent trait expression (Dewitt and Scheiner, 2004).1 Knowledge of phenotypic plasticity is important in virtually all areas of basic and applied biology. Researchers in applied fields (such as agriculture, medicine, public health, wildlife management, and conservation biology) have a vested interest in knowing how traits are or will be expressed under specific conditions. Ecologists are interested in how the expression of traits in different environmental conditions and habitats might affect population and community dynamics. And evolutionary biologists are interested in how traits with environmentally-conditional expression have and will evolve. The widespread interest in phenotypic plasticity has made it a prominent focus of biological research. Phenotypic plasticity is an especially active research area in ecology and evolution with a brimming literature that has advanced the understanding of organismal variation, adaptation, and speciation (Sarkar, 2004; Pfennig, 2021). Most advances, especially recently, are based on highly simplified biological scenarios such as dichotomous environments or linear environmental gradients. Here we advocate a path for taking modern plasticity research in a far more biologically relevant direction. Phenotypic plasticity, like any trait, can be heritable and respond to any evolutionary force. What makes plasticity unique is that it manifests only in a variable environment and is thus automatically complex. The key to addressing plasticity's ineluctable complexity, we contend, is a simple but comprehensive conceptual framework that can be used to address questions about phenotypic plasticity (including connections among areas of development, behavior, genetics, ecology, and evolution) with far more depth and realism than current literature. The framework (Figure 1) involves four independent components: (1) patterns of plasticity; (2) environment encounters; (3) fitness consequences; and (4) inheritance. The first two components are needed to predict realized patterns of expression, the first three determine population dynamics, and all four contribute to evolution. Below, we describe each component in turn, highlighting key concepts and practices that enable researchers to enrich the understanding of phenotypic plasticity and its evolution in nature. While none of these four components is new, we have not seen them presented together in a systematic way, as here. We contend that widespread use of this structured quartet of concepts would drive modern studies of phenotypic plasticity in a much more productive, profound, connected, and comprehensible direction. Figure 1Open in figure viewerPowerPoint The four fundamental elements of phenotypic plasticity and their roles in determining patterns of phenotypic expression realized in nature, ecology (population or community dynamics), and evolution. PATTERNS OF PLASTICITY The most complete and universal description of environment-dependent phenotypic expression, i.e., phenotypic plasticity, is the reaction norm (Woltereck, 1909; Johannsen, 1911; Schmalhausen, 1949), which refers to the set of phenotypes a genotype expresses in different environments. "Environments" can be quantitative or qualitative, simple or multicomponent, discrete or continuous, physical or biotic (including social), external or internal to an organism. They can encompass ancestral environments if phenotypic expression is impacted by trans-generational (epigenetic) effects (Bonduriansky, 2021) or internal environments (e.g., age, metabolic rate, body condition). All reaction norms can be described as either a multivariate trait—an ordered list or vector—over discrete environments (Via and Lande, 1985) or as a function-valued trait—a curve or surface—over continuous environments (Stinchcombe et al., 2012; Kingsolver et al., 2015). Standard multivariate methods can be used for estimation, modeling, and inference; unobserved components of reaction norms can be imputed or interpolated; Gomulkiewicz et al. (2018) describes a number of function-valued methods, most of which require no information about genetics or relatedness. Why do we encourage use of reaction norms to describe environment-dependent phenotypic expression over metrics expressly designed to quantify plasticity, especially given the simplicity and intuitive appeal of the latter? Though plasticity measures are easy to conjure, no single quantity pertains to all situations, particularly when there are more than two environments. Consequently, no scale exists to compare the plasticities of different genotypes, not even one that preserves rank orders. For example, Figure 2 depicts the reaction norms expressed by genotypes G1 and G2 over three environments (E1, E2, E3). Were plasticity measured as phenotypic variance over environments, as is common, genotype G1 would rank as more plastic than genotype G2. However, were plasticity measured by the range of phenotypic responses—also commonplace—the ranking would be reversed. Finally, measuring plasticity as a mean difference between environments—also very common—requires specifying one environment as the reference point, and results in at least as many plasticities as there are pairs of environments (e.g., E1-E2, E1-E3, and E2-E3, and all the reverse orders). Absent an order-preserving scale, comparative statements like "this genotype is more plastic than that one" become effectively meaningless over realistically complex environments. Nonetheless, countless studies (uncritically) assume plasticity can be rank-ordered, likely because most consider just two environments or only linear reaction norms. Figure 2Open in figure viewerPowerPoint Counterexample proving that there is no universal rank-preserving metric of phenotypic plasticity over more than two environments. Shown are hypothetical reaction norms for two genotypes (G1, G2) over three environments (E1, E2, E3). If plasticity is measured by overall variation, genotype G1 is more plastic than G2. However, were plasticity measured by a genotype's maximal between-environment difference in expression, genotype G2 ranks above G1. It can be highly tempting to fit reaction norms using linear functions (an approach that one of us has used himself), i.e., if there are two experimental environments, a plasticity metric such as a mean difference is mathematically equivalent to a slope, which seems like it would characterize a reaction norm. Likewise, if only linear functions are used, the slopes and intercepts appear to characterize the reaction norm. While intuitively and analytically appealing, these scenarios (two environments, linear reaction norms) are in fact special situations in which plasticities can be ordered consistently (by, say, variance or slope) but not always (e.g., when using nonlinear transformations of pairs of phenotypic values; Wang et al., 2022). Thus, one should be skeptical that conclusions from studies confined to two environments or linear reaction norms extend to more realistic scenarios. Focus on these special cases perpetuates a situation in which a general understanding and synthesis remains beyond our grasp despite an accumulation of plasticity studies. If organisms typically experience more than two types of environments or if it is common for reaction norms to be non-linear, studies ignoring these realities are analogous to taking out-of-focus pictures with a camera, i.e., simply snapping more out-of-focus photos is not going to improve the quality of the image just as doing more oversimplified studies will not sharpen our picture of plasticity. Reaction norms encompassing multiple environments and potentially non-linear changes in phenotypes have, unlike plasticity metrics, a standard representation depending on the environment of interest (see above). Reaction norms can also be used to calculate any plasticity measure, which makes them superior for studying any aspect of plasticity. The reverse is not true, i.e., a particular value of a plasticity metric such as a mean difference, range, or variance will almost always correspond to multiple reaction norms. In other words, the reaction norm, and not the (human-invented) metric, captures the biology. Importantly, even in the event that reaction norms are linear, nothing is lost by adopting the reaction norm framework to study plasticity over either discrete or continuous environments. When population variation is described in terms of reaction norms, those that lack plasticity are not unique, but instead are merely part of a (multivariate) distribution. Indeed, the evolution and consequences of aplastic reaction norms involve the exact same mechanisms as plastic ones (Sultan, 2015). Plasticity per se is too nonspecific of a concept and it lacks a universal measure to address anything but rudimentary questions about its evolution. In contrast, reaction norms have no such limitations. We thus recommend that plasticity be employed only as a category label and, in particular, it should not be quantified. Reaction norms are the proper quantitative platform to study environment-dependent phenotypic expression. ENVIRONMENTAL ENCOUNTERS Plasticity itself can only be expressed if genotypes are exposed to more than one environment, and realized patterns of plasticity in any setting, natural or not, depends as much on the reaction norm as the frequencies of environmental exposures. Indeed, the distribution of environmental encounters is as crucial to the evolutionary and ecological consequences of plasticity as the reaction norm itself (Gomulkiewicz and Kirkpatrick, 1992). Yet studies rarely consider or attempt to measure environmental distributions that species encounter in nature (Arnold and Peterson, 2002). There are innumerable ways populations experience environmental variability. "Fine" and "coarse" grained scales of environmental variation can be encountered through time or across space. Different distributions of exposure generally lead to different realized patterns of phenotypic expression and fitnesses (see section below), even for a genetically uniform population. To predict these realizations, one needs both a description/estimate of reaction norms found in a population and a description/estimate of the distribution of environments encountered (Figure 1). Studies of phenotypic plasticity oftentimes assume—usually implicitly—that environments are encountered equally often. In an experimental context, the equal replication of different treatments differs—dramatically—from the natural distribution of these environments. If, say, an organism or genotype encounters an environment 50% of the time in an experiment (i.e., one with two treatments), but only 10% of the time in the wild, such a balanced design would disproportionately overweigh that component of the reaction norm and underweigh others compared to nature. Although using balanced experiments2 or assuming a uniform distribution of environments in theoretical studies greatly simplifies comparisons of different patterns of plasticity, such comparisons will not represent nature if environments are encountered at all unevenly in the wild. Empirical estimates of environmental encounter frequencies are the ultimate means to test this speculation, which suggests a straightforward research agenda, i.e., measure the frequencies of environments an organism actually encounters. Fortunately, many environmental variables (CO2, temperature, salinity, humidity, freezing days, precipitation, etc.) can be measured remotely with data loggers, ibuttons, and other instruments. Other, more biotic environments (e.g., competitor or mutualist densities) will require old-fashioned ecological field work. Moreover, documented patterns of environmental encounters will enable biologists to assess the proportionate importance of different environments for the evolutionary and ecological causes and consequences3 of phenotypic plasticity (e.g., Kingsolver et al., 2001; Kingsolver and Buckley, 2017). A number of theories that invoke plasticity, such as plasticity-led evolution, genetic assimilation, the Baldwin effect, and "buying time" for persistence (Crispo, 2007; Diamond and Martin, 2021) imagine a single, abrupt change from an ancestral environment to a novel one. If the novel environment is constant, as is usually implied, the only possible role for plasticity is phenotypic expression in the novel condition. This is the only moment one reaction norm could be favored directly over another. Post-shift, the novel environment becomes the "new normal." Consequently, any subsequent evolution of plasticity must be non-adaptive (see section below). Were the novel environment truly unprecedented in the history of the species then, akin to a new mutation, the phenotype expressed could be adaptive or nonadaptive in the new setting (Ghalambor et al., 2007). Early models of phenotypic plasticity assumed passive environmental encounters (Via and Lande, 1985; Gomulkiewicz and Kirkpatrick, 1992; Gavrilets and Scheiner, 1993), but recent ones consider organisms that actively determine encounters either through habitat choice/preferences or by changing their local environment directly (niche construction; e.g., Sultan, 2015; Scheiner et al., 2021). Yet other models consider "internal" environments like age or individual condition itself (e.g., Matthey-Doret et al., 2020). With habitat-dependent dispersal (Edelaar and Bolnick, 2012), migration itself is a plastic trait that determines environmental encounters, potentially resulting in different exposures for different genotypes. Clearly more work is needed to understand how dynamic distributions of encounters might influence the expression and evolution of reaction norms… and vice versa. FITNESS CONSEQUENCES Trait expression can affect an organism's fitness in environments it encounters; individual fitnesses collectively determine population dynamics; and if the trait's expression is heritable, evolution. These truisms apply to plastic and non-plastic traits alike. Since plasticity manifests only in a variable environment, this too is required for plasticity itself to evolve adaptively. While this is a seemingly obvious point, many studies that consider adaptive phenotypic plasticity refer only to its evolution in a single environment, such as a novel one (see section above). Plasticity can evolve in a single environment but only non-adaptively via indirect selection due to associated "plasticity costs", as a correlated response, or by random genetic drift. Not only can expression of a phenotype change in response to a change in environment but the fitness consequences of a particular expressed phenotype may also vary from one environment to the next. The realized fitness of an individual in a given environment or set of environments must reflect both considerations (e.g., Chevin et al., 2010) as well as any constitutive or environment-specific costs paid to enable plastic expression. In addition, the environment that determines trait expression during a "sensitive period" can, because of developmental or other delays, differ from the environment that determines fitness. The relevant measure of fitness will depend on an organism's life history and how that relates to environmental variability. For example, an individual could experience multiple environments within its lifetime (e.g., daily thermal variation). Individual fitness would integrate over these fine-grained distributions (e.g., Kingsolver et al., 2007). At the other, coarse-grained extreme, an individual experiences a single environment in its lifetime but its descendants could develop in different environments because of in situ temporal change or dispersal. The fitness consequences of plasticity for both demography and adaptive evolution must then reflect these among-generational changes. Many studies over continuous environments assume optimizing selection such that the optimum phenotype changes linearly (e.g., Chevin et al., 2010). This assumption is mathematically convenient with a bonus feature, viz., the optimal reaction norm is necessarily linear. Consequently, studies often consider only linear reaction norms, which lends itself to the further, conceptual perk that slope directly reflects plasticity. In reality, neither linearity assumption is empirically justified. Future studies should consider nonlinear versions of optimizing selection and distributions that include nonlinear reaction norms. Finally, plastic phenotypes may in fact have no differential effect on fitness, that is, different reaction norms may have equivalent consequences for total fitness. In these cases, phenotypic plasticity is a neutral trait and its evolution is best understood in terms of non-adaptive evolutionary processes including random genetic drift (Lande, 1976; Kimura, 1983). INHERITANCE Like any trait, the heritable basis of a reaction norm could range from a major gene to many loci of individually small effect; it can be inherited in organisms that are asexual, sexual, self-fertile, self-incompatible, diploid, polyploid, or even via non-Mendelian mechanisms (extra-nuclear or transgenerational epigenetic; Auge et al., 2017). Any responses to selection (i.e., adaptation) can be described using standard population and quantitative genetics, as can other evolutionary processes that might affect their evolution such as mutation, recombination, and random genetic drift (e.g., Charlesworth and Charlesworth, 2010). Describing the spatial structure of genetic variation is of particular importance for species whose local populations encounter coarse-grained environmental variation via migration. Many explicit multi-locus models of phenotypic plasticity posit the existence of "plastic" and "non-plastic" gene expression profiles across environments. While convenient, these gene classes are neither biologically necessary nor justified. Indeed, two genes with opposite reaction norms would additively produce an aplastic phenotype (Figure 3). A better approach for future studies is to consider gene-level reaction norms—a generalization of "mutation reaction norm" (Ogbunugafor, 2022)—that, when combined, produces overall reaction norms, whether plastic or not (Figure 3). Conceivably, gene-level reaction norms could prove valuable for detailed prediction of evolutionary responses to selection (see earlier section on Fitness consequences) or for describing the expected course of random genetic drift in study systems where reaction norm variation depends on just a few segregating genes or genotypes. Figure 3Open in figure viewerPowerPoint Gene expression profiles (allelic reaction norms) and resulting phenotypic reaction norms. Left panel: Additive effects of four alleles (A, B, C, D) in each of three environments (E1, E2, E3). Note that allele D has the same effect in all environments, i.e., D is not plastic. Right panel: Phenotypic reaction norms of three diploid genotypes with different combinations of alleles shown in the left panel. The phenotype expressed in each environment is determined by adding the allelic effects. Note that diploid genotype AC is not plastic even though both alleles are individually plastic whereas genotype AD is plastic despite allele D being aplastic. Studies often emphasize genotype-by-environment interaction ("G × E"), as it is necessary for plasticity to evolve (Saltz et al., 2018). The absence of G × E (parallel reaction norms) implies absence of genetic variation in plasticity. However, the absence of G × E does not imply the absence of plasticity per se, nor does the presence of G × E ensure the evolution of plastic genotypes. Consequently, G × E is necessary but not sufficient for plasticity to evolve. Although estimates of G × E variances can sometimes reveal how much fitness variation across environments is maintained by rank changes versus changes in variance (Vaidya and Stinchcombe, 2020) it is unknown if those inferences apply to other phenotypic measures of G × E variation. Regardless, one can always use a reaction norm approach to dissect root causes of G × E variation if not necessarily the reverse (Saltz et al., 2018). CONCLUSIONS We urge that future studies of phenotypic plasticity organize around our four-component framework of reaction norms, environmental encounters, fitness consequences, and inheritance (Figure 1). Box 1 lists some best practices and compelling future research directions suggested by our framework. A complete understanding of phenotypic plasticity requires all four components (Via et al., 1995; Sultan, 2021) and, while we might pine for studies that consider the full foursome, this emphatically does not imply that studies must include all of the components to make valuable contributions. Rather, a key advantage of our framework is to provide a simple, but not too simple conceptual "wrapper" for investigations that address one or more of the components we describe, collectively providing a clear and consistent context for how each study contributes to our holistic understanding of phenotypic plasticity and its evolution. Box 1.. Future Advances and Best Practices We strongly encourage future studies of phenotypic plasticity to involve the following advances and best conceptual practices: Treat plasticity as a category, not a quantity; use reaction norms to study plasticity instead. Consider reaction norms over more than two environments. Don't limit studies of plasticity over continuous environments to linear reaction norms or linear gene expression profiles. Resist the temptation to fit reaction norms using only linear functions; embrace non-linearity. Non-plastic reaction norms are nothing special, biologically speaking. Though they are distinctly easy to describe, they are a priori no more important biologically than any other reaction norm shape. Give greater attention to the distribution of environmental encounters (including ancestral) and examine the implications of organism-mediated encounters (niche construction; habitat choice). Avoid automatically assuming that plasticity is adaptive, particularly in novel environments. Indeed, we need a "neutral theory" of plasticity evolution to enable more rigorous analyses and inferences of adaptive plasticity patterns in nature. Don't stop with detection of G × E interactions when studying the evolution of phenotypic plasticity. Use reaction norms to unpack causes of G × E variation. ACKNOWLEDGMENTS We thank Jeremiah Busch, Carl Schlichting, and an anonymous reviewer for helpful feedback that improved this commentary as well as numerous colleagues for discussions about plasticity over the years. Our work was supported by the U.S. National Science Foundation (grant number DEB-2217559 to R.G.) and an NSERC Discovery grant (to J.R.S.). REFERENCES Arnold, S. J., and C. R. Peterson. 2002. A model for optimal reaction norms: The case of the pregnant garter snake and her temperature-sensitive embryos. American Naturalist 160: 306– 316. Auge, G. A., L. D. Leverett, B. R. Edwards, and K. Donohue. 2017. Adjusting phenotypes via within- and across-generational plasticity. New Phytologist 216: 343– 349. Bonduriansky, R. 2021. Plasticity across generations. In D. W. Pfennig [ed.], Phenotypic plasticity and evolution: Causes, consequences, and controversies, 327– 348. CRC Press, Boca Raton, Florida, USA. 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Verhandlungen der Deutschen Zoologischen Gesellschaft 19: 110– 173. 1 Plasticity typically refers to the consistent expression of phenotypes in different environments. Traits that change unpredictably in different environments are usually said to be 'noisy' rather than plastic. Various terms are used to describe traits with the same phenotype in all environments, including 'aplastic', 'non-plastic', 'fixed', 'constant', 'canalized', and 'environmentally insensitive'. 2 The issue of balance is additional to the artificiality of the experimentally-controlled environmental conditions themselves. 3 It is crucial to distinguish ecological from evolutionary effects since, for example, an extreme environment could easily cause all genotypes to have the same, albeit low absolute fitness. This would completely preclude natural selection (because of the lack of variation in relative fitness) but the prospect of extinction—an ecological outcome—could be catastrophically permanent even were the extreme condition rare. Volume109, Issue10October 2022Pages 1519-1524 FiguresReferencesRelatedInformation

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.

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.

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.

That observed behaviours conflate information processing with resulting responses presents a challenge for understanding how social information contributes to adaptive behaviour in animals. Here, we develop a mathematical model that isolates how individuals assess their environment and use it to examine how social information impacts those assessments. We consider the influences of personal and social sampling efforts, how individuals combine these sources of information, group size, social sampling practices and types of environmental variation. Our analyses lead to predictive formulas that show that social information often improves but sometimes impairs environmental assessments and that the magnitude of improvement or diminishment increases with the extent of environmental variation. We also show that the weight an individual gives to social information affects both whether it improves or degrades its assessment as well as the size of that effect. Simulation results show that group size does not affect the influence of social information on average but does affect the variability of those influences.

ABSTRACT 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.

Animal mating displays provide some of nature’s most dramatic and curious spectacles. Ring doves ( Streptopelia risoria ) are a case in point (Fig. 1). According to Cheng (ref. 1, p. 2), “When a male ring dove courts a female, he starts with majestic bowing and cooing (bow coo) interspersed with strutting directed toward the female… At some point the male … stops cooing and turns to chasing, strutting, and bow cooing; this … causes the female to flee. The male then resumes nest cooing, and the ritual repeats itself.” Fig. 1. A pair of ring doves ( S. risoria ) crossing. Image courtesy of Todd Petit (photographer). What could explain such elaborate mating behavior? Evolutionary biologists have been puzzling over this question since Darwin (2, 3). Many striking examples of mating displays, including the large male antlers of red deer Cervus elaphas (4), the deafening calls of male common toads Bufo bufo (5), and the bright red male dewlaps of Carolina anole lizards Anolis carolinensis (6), have evolved by sexual selection, which is driven by an association between the trait display and mating success (7). Species like the ring doves, however, are pair bonded and effectively offer … [↵][1]1Email: gomulki{at}wsu.edu. [1]: #xref-corresp-1-1

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.

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.

Abstract Genotype‐by‐environment interaction (G × E), that is, genetic variation in phenotypic plasticity, is a central concept in ecology and evolutionary biology. G×E has wide‐ranging implications for trait development and for understanding how organisms will respond to environmental change. Although G × E has been extensively documented, its presence and magnitude vary dramatically across populations and traits. Despite this, we still know little about why G × E is so evident in some traits and populations, but minimal or absent in others. To encourage synthetic research in this area, we review diverse hypotheses for the underlying biological causes of variation in G × E. We extract common themes from these hypotheses to develop a more synthetic understanding of variation in G × E and suggest some important next steps.

Many populations are doomed to extinction, but little is known about how evolution contributes to their longevity. We address this by modeling an asexual population consisting of genotypes whose abundances change independently according to a system of continuous branching diffusions. Each genotype is characterized by its initial abundance, growth rate, and reproductive variance. The latter two components determine the genotype's “risk function” which describes its per capita probability of extinction at any time. We derive the probability distribution of extinction times for a polymorphic population, which can be expressed in terms of genotypic risk functions. We use this to explore how spontaneous mutation, abrupt environmental change, or population supplementation and removal affect the time to extinction. Results suggest that evolution based on new mutations does little to alter the time to extinction. Abrupt environmental changes that affect all genotypes can have more substantial impact, but, curiously, a beneficial change does more to extend the lifetime of thriving than threatened populations of the same initial abundance. Our results can be used to design policies that meet specific conservation goals or management strategies that speed the elimination of agricultural pests or human pathogens.

We hypothesized that the ongoing naturalization of frost/shade tolerant Asian bamboos in North America could cause environmental consequences involving introduced bamboos, native rodents and ultimately humans. More specifically, we asked whether the eventual masting by an abundant leptomorphic ("running") bamboo within Pacific Northwest coniferous forests could produce a temporary surfeit of food capable of driving a population irruption of a common native seed predator, the deer mouse (Peromyscus maniculatus), a hantavirus carrier. Single-choice and cafeteria-style feeding trials were conducted for deer mice with seeds of two bamboo species (Bambusa distegia and Yushania brevipaniculata), wheat, Pinus ponderosa, and native mixed diets compared to rodent laboratory feed. Adult deer mice consumed bamboo seeds as readily as they consumed native seeds. In the cafeteria-style feeding trials, Y. brevipaniculata seeds were consumed at the same rate as native seeds but more frequently than wheat seeds or rodent laboratory feed. Females produced a median litter of 4 pups on a bamboo diet. Given the ability of deer mice to reproduce frequently whenever food is abundant, we employed our feeding trial results in a modified Rosenzweig-MacArthur consumer-resource model to project the population-level response of deer mice to a suddenly available/rapidly depleted supply of bamboo seeds. The simulations predict rodent population irruptions and declines similar to reported cycles involving Asian and South American rodents but unprecedented in deer mice. Following depletion of a mast seed supply, the incidence of Sin Nombre Virus (SNV) transmission to humans could subsequently rise with dispersal of the peridomestic deer mice into nearby human settlements seeking food.

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.

We hypothesized that the ongoing naturalization of frost/shade tolerant Asian bamboos in North America could cause environmental consequences involving introduced bamboos, native rodents and ultimately humans. More specifically, we asked whether the eventual masting by an abundant leptomorphic ("running") bamboo within Pacific Northwest coniferous forests could produce a temporary surfeit of food capable of driving a population irruption of a common native seed predator, the deer mouse (Peromyscus maniculatus), a hantavirus carrier. Single-choice and cafeteria-style feeding trials were conducted for deer mice with seeds of two bamboo species (Bambusa distegia and Yushania brevipaniculata), wheat, Pinus ponderosa, and native mixed diets compared to rodent laboratory feed. Adult deer mice consumed bamboo seeds as readily as they consumed native seeds. In the cafeteria-style feeding trials, Y. brevipaniculata seeds were consumed at the same rate as native seeds but more frequently than wheat seeds or rodent laboratory feed. Females produced a median litter of 4 pups on a bamboo diet. Given the ability of deer mice to reproduce frequently whenever food is abundant, we employed our feeding trial results in a modified Rosenzweig-MacArthur consumer-resource model to project the population-level response of deer mice to a suddenly available/rapidly depleted supply of bamboo seeds. The simulations predict rodent population irruptions and declines similar to reported cycles involving Asian and South American rodents but unprecedented in deer mice. Following depletion of a mast seed supply, the incidence of Sin Nombre Virus (SNV) transmission to humans could subsequently rise with dispersal of the peridomestic deer mice into nearby human settlements seeking food.

to sp so to W pr W an in so an lig D cr pe so th in 20 am ho al sp st al or no ity her abiotic or biotic effectors. Biotic effectors create the potential r a coevolutionary dynamic. Through the use of individual-based ulations, we examined the coevolutionary dynamic of two species at are phenotypically plastic. We explored two modes of biotic and iotic interactions: ecological interactions that determine the form natural selection and developmental interactions that determine enotypes. Overall, coevolution had a larger effect on the evolution phenotypic plasticity than plasticity had on the outcome of coolution. Effects on the evolution of plasticity were greater when the ness-maximizing coevolutionary outcomes were antagonistic beeen the species pair (predator-prey interactions) than when those tcomes were augmenting (competitive or mutualistic). Overall, olution in the context of biotic interactions reduced selection for asticity even when trait development was responding to just the iotic environment. Thus, the evolution of phenotypic plasticity ust always be interpreted in the full context of a species’ ecology. Our sults show how the merging of two theory domains—coevolution d phenotypic plasticity—can deepen our understanding of both and int to new empirical research.

This chapter is an exercise in curve-thinking for integrative biologists, as applied to phenotypic plasticity and developmental trajectories. The ideas and methods are quite general, but it uses two main case studies to illustrate curve-thinking: thermal performance curves of insects, and growth trajectories of plants. First, the chapter discusses various approaches to quantifying variation in curves, and their advantages and disadvantages. It then reviews ways of characterizing and visualizing phenotypic and genetic variation in curves using principal components analysis. The chapter describes and illustrates a recent statistical method that identifies simple, interpretable patterns of phenotypic and genetic variation in curves. It also briefly describes how patterns of genetic variation in curves may affect and constrain evolutionary changes in the mean curve for a population. The chapter concludes by discussing some challenges and promising directions for curve-thinking in integrative biology.

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.