Matsuda and Abrams (Theor Popul Biol 45(1):76-91, 1994) initiated the exploration of self-extinction in species through evolution, focusing on the advantageous position of mutants near the extinction boundary in a prey-predator system with evolving foraging traits. Previous models lacked theoretical investigation into the long-term effects of harvesting. In our model, we introduce constant-effort prey and predator harvesting, along with individual logistic growth of predators. The model reveals two distinct evolutionary outcomes: (i) Evolutionary suicide, marked by a saddle-node bifurcation, where prey extinction results from the invasion of a lower forager mutant; and (ii) Evolutionary reversal, characterized by a subcritical Hopf bifurcation, leading to cyclic prey evolution. Employing an innovative approach based on Gröbner basis computation, we identify various bifurcation manifolds, including fold, transcritical, cusp, Hopf, and Bogdanov-Takens bifurcations. These contrasting scenarios emerge from variations in harvesting parameters while keeping other factors constant, rendering the model an intriguing subject of study.

AbstractMutation rates vary widely along genomes and across inheritance systems. This suggests that complex traits-resulting from the contributions of multiple determinants-might be composite in terms of the underlying mutation rates. Here we investigate through mathematical modeling whether such a heterogeneity may drive changes in a trait\'s architecture, especially in fluctuating environments, where phenotypic instability can be beneficial. We first identify a convexity principle related to the shape of the trait\'s fitness function, setting conditions under which composite architectures should be adaptive or, conversely and more commonly, should be selected against. Simulations reveal, however, that applying this principle to realistic evolving populations requires taking into account pervasive epistatic interactions that take place in the system. Indeed, the fate of a mutation affecting the architecture depends on the (epi)genetic background, which itself depends on the current architecture in the population. We tackle this problem by borrowing the adaptive dynamics framework from evolutionary ecology-where it is routinely used to deal with such resident/mutant dependencies-and find that the principle excluding composite architectures generally prevails. Yet the predicted evolutionary trajectories will typically depend on the initial architecture, possibly resulting in historical contingencies. Finally, by relaxing the large population size assumption, we unexpectedly find that not only the strength of selection on a trait\'s architecture but also its direction depend on population size, revealing a new occurrence of the recently identified phenomenon coined \"sign inversion.\"

The textbook conceptualization of phenotype creation, \"genotype (G) + environment (E) + genotype & environment interactions (GE) ↦ phenotype (Ph)\", is modeled with open quantum systems theory (OQST) or more generally with adaptive dynamics theory (ADT). The model is quantum-like, i.e., it is not about quantum physical processes in biosystems. Generally such modeling is about applications of the quantum formalism and methodology outside of physics. Macroscopic biosystems, in our case genotypes and phenotypes, are treated as information processors which functioning matches the laws of quantum information theory. Phenotypes are the outputs of the E-adaptation processes described by the quantum master equation, Gorini-Kossakowski-Sudarshan-Lindblad equation (GKSL). Its stationary states correspond to phenotypes. We highlight the class of GKSL dynamics characterized by the camel-like graphs of (von Neumann) entropy: in the process of E-adaptation phenotype\'s state entropy (disorder) first increases and then falls down - a stable and well-ordered phenotype is created. Traits, an organism\'s phenotypic characteristics, are modeled within the quantum measurement theory, as generally unsharp observables given by positive operator valued measures (POVMs. This paper is also a review on the methods and mathematical apparatus of quantum information biology.

Understanding the coevolutionary dynamics of hosts and their parasites remains a major focus of much theoretical literature. Despite empirical evidence supporting the presence of sterility-mortality tolerance trade-offs in hosts and recovery-transmission trade-offs in parasites, none of the current models have explored the potential outcomes when both trade-offs are considered within a coevolutionary framework. In this study, we consider a model where the host evolves sterility tolerance at the cost of increased mortality and the parasite evolves higher transmission rate at the cost of increased recovery rate (reduced infection duration), and use adaptive dynamics to predict the coevolutionary outcomes under such trade-off assumptions. We particularly aim to understand how our coevolutionary dynamics compare with single species evolutionary models. We find that evolutionary branching in the host can drive the parasite population to branch, but that cycles in the population dynamics can prevent the coexisting strains from reaching their extremes. We also find that varying crowding does not impact the recovery rate when only the parasite evolves, yet coevolution reduces recovery as crowding intensifies. We conclude by discussing how different host and parasite trade-offs shape coevolutionary outcomes, underscoring the pivotal role of trade-offs in coevolution.

Ecological theory suggests that a host organism\'s internal spatial structure can promote the persistence of mutualistic microbes by allowing for the turnover of tissue occupied by non-beneficial or cheating microbes. This type of regulation, whereby a host preferentially rewards tissue occupied by beneficial members of its microbiome but sanctions tissue occupied by non-beneficial cheaters, is expected to generate a competition-extinction trade-off by allowing beneficial microbes to experience a lower extinction rate than competitively dominant cheaters. Using an adaptive dynamics approach, we demonstrate that although ecologically stable, microbial regulation via sanctioning is not stable in any evolutionary sense, as each individual host will be under pressure to reduce the costs incurred from cheater suppression in order to maximize its own fitness at the expense of the rest of the host population. However, increasing the diversity of non-beneficial cheaters in the host population metamicrobiome can lead to an increase in the relative fitness of hosts that actively sanction non-performing tissue, thus facilitating the evolutionary emergence and persistence of such strategies in host-microbial systems. These counter-intuitive results demonstrate how diversity at multiple levels of biological organization and spatiotemporal scales can interact to facilitate the establishment and maintenance of mutualistic relationships.

In this paper, we investigate the consequences of dormancy in the \'rare mutation\' and \'large population\' regime of stochastic adaptive dynamics. Starting from an individual-based micro-model, we first derive the Polymorphic Evolution Sequence of the population, based on a previous work by Baar and Bovier (2018). After passing to a second \'small mutations\' limit, we arrive at the Canonical Equation of Adaptive Dynamics, and state a corresponding criterion for evolutionary branching, extending a previous result of Champagnat and Méléard (2011). The criterion allows a quantitative and qualitative analysis of the effects of dormancy in the well-known model of Dieckmann and Doebeli (1999) for sympatric speciation. In fact, quite an intuitive picture emerges: Dormancy enlarges the parameter range for evolutionary branching, increases the carrying capacity and niche width of the post-branching sub-populations, and, depending on the model parameters, can either increase or decrease the \'speed of adaptation\' of populations. Finally, dormancy increases diversity by increasing the genetic distance between subpopulations.

AbstractDispersal emerges as an outcome of organismal traits and external forcings. However, it remains unclear how the emergent dispersal kernel evolves as a by-product of selection on the underlying traits. This question is particularly compelling in coastal marine systems, where dispersal is tied to development and reproduction and where directional currents bias larval dispersal downstream, causing selection for retention. We modeled the dynamics of a metapopulation along a finite coastline using an integral projection model and adaptive dynamics to understand how asymmetric coastal currents influence the evolution of larval (pelagic larval duration) and adult (spawning frequency) life history traits, which indirectly shape the evolution of marine dispersal kernels. Selection induced by alongshore currents favors the release of larvae over multiple time periods, allowing long pelagic larval durations and long-distance dispersal to be maintained in marine life cycles in situations where they were previously predicted to be selected against. Two evolutionarily stable strategies emerged: one with a long pelagic larval duration and many spawning events, resulting in a dispersal kernel with a larger mean and variance, and another with a short pelagic larval duration and few spawning events, resulting in a dispersal kernel with a smaller mean and variance. Our theory shows how coastal ocean flows are important agents of selection that can generate multiple, often co-occurring evolutionary outcomes for marine life history traits that affect dispersal.

AbstractAnisogamy has evolved in most sexually reproducing multicellular organisms allowing the definition of male and female sexes, producing small and large gametes. Anisogamy, as the initial sexual dimorphism, is a good starting point to understand the evolution of further sexual dimorphisms. For instance, it is generally accepted that anisogamy sets the stage for more intense mating competition in males than in females. We argue that this idea stems from a restrictive assumption on the conditions under which anisogamy evolved in the first place: the absence of sperm limitation (assuming that all female gametes are fertilized). Here, we relax this assumption and present a model that considers the coevolution of gamete size with a mating competition trait, starting in a population without dimorphism. We vary gamete density to produce different scenarios of gamete limitation. We show that while at high gamete density the evolution of anisogamy always results in male investment in competition, gamete limitation at intermediate gamete densities allows for either females or males to invest more into mating competition. Our results thus suggest that anisogamy does not always promote mating competition among males. The conditions under which anisogamy evolves matter, as does the competition trait.

Trophically transmitted parasites often infect an intermediate prey host and manipulate their behaviour to make predation more likely, thus facilitating parasite transmission to the definitive host. However, it is unclear when such a manipulation strategy should be expected to evolve. We develop the first evolutionary invasion model to explore the evolution of manipulation strategies that are in a trade-off with parasite production of free-living spores. We find that the size of the susceptible prey population together with the threat of predation drives manipulation evolution. We find that it is only when the susceptible prey population is large and the threat of predation is relatively small that selection favours manipulation strategies over spore production. We also confirm that the system exhibits cyclic population dynamics, and this can influence the qualitative direction of selection.

Evolutionary theory has typically focused on pairwise interactions, such as those between hosts and parasites, with relatively little work having been carried out on more complex interactions including hyperparasites: parasites of parasites. Hyperparasites are common in nature, with the chestnut blight fungus virus CHV-1 a well-known natural example, but also notably include the phages of important human bacterial diseases. We build a general modeling framework for the evolution of hyperparasites that highlights the central role that the ability of a hyperparasite to be transmitted with its parasite plays in their evolution. A key result is that hyperparasites which transmit with their parasite hosts (hitchhike) will be selected for lower virulence, trending towards hypermutualism or hypercommensalism. We examine the impact on the evolution of hyperparasite systems of a wide range of host and parasite traits showing, for example, that high parasite virulence selects for higher hyperparasite virulence resulting in reductions in parasite virulence when hyperparasitized. Furthermore, we show that acute parasite infection will also select for increased hyperparasite virulence. Our results have implications for hyperparasite research, both as biocontrol agents and for their role in shaping community ecology and evolution and moreover emphasize the importance of understanding evolution in the context of multitrophic interactions.