Steven Hamblin

Finding the evolutionarily stable learning rule for frequency-dependent foraging

The cognitive mechanisms by which an organism comes to employ an optimal response to a situation are of great interest in behavioural ecology, but the basis and form of these mechanisms have been little studied. One approach employs learning rules, which are mathematical expressions that calculate the value of the behavioural alternatives in an organisms repertoire based on past and present rewards to those alternatives. Previous work on these learning rules has examined the performance of rules to determine whether they can achieve evolutionarily stable optimums. However, not only has this work tested rules in isolation, but the parameter values chosen to test them have been few and chosen arbitrarily. Moreover, the environments in which the rules have been evaluated are unchanging, a condition that does not favour learning. In this study we extend simulation work on three learning rules (relative payoff sum, linear operator and perfect memory). We use a genetic algorithm to both estimate the optimal parameter values for each rule and place the rules in competition with each other in a foraging game with a changing environment. Our results confirm earlier findings that the relative payoff sum is an ES learning rule. However, the results go much further because they show that the form of the learning rule that qualifies as evolutionarily stable combines near inextinguishable producing with highly responsive scrounging. The relative payoff sum may provide a single rule that can account for the way an animals ecology can come to affect its specific set of learning sensitivities.

On the practical usage of genetic algorithms in ecology and evolution

genetic algorithms, but none is required to read this article. 2. I review the basics of genetic algorithm methodology and provide suggestions on problems that may or may not benefit from genetic algorithm methods. The genetic operators (selection, replacement, mutation, crossover) and their rate parameters (mutation rate, etc.) are a source of potential confusion and to ease their selection, I present recommendations informed by historical usage and best practices, concluding with potential pitfalls. 3. Good reasons for employing genetic algorithms include: solving optimisation problems beyond the reach of analytical techniques, relaxing model assumptions, evolving behaviour in individual-based models, and simulating co-evolutionary processes. However, genetic algorithms are not always the correct technique to use. Simpler methods may sometimes outperform genetic algorithms, and certain problem features may cause trouble. 4. Genetic algorithms strike a balance between exploration of the search space and exploitation of good solutions, driven by the choice of genetic operators and rate parameters. I outline a basic set of parameter values and operator choices for genetic algorithm models as a starting point and provide advice on how to modify them to explore the exploitation/exploration trade-off. Finally, I provide general advice on analysis and reporting of these models. 5. There are a number of potential issues that can surprise unwary users, such as rate parameter values that lead to pathological outcomes; I suggest ways to detect and correct these problems. Also, contrary to popular usage, genetic algorithms can find solutions to game theory problems but cannot guarantee their stability. Finally, while genetic algorithms offer great power and flexibility by drawing inspiration from evolutionary processes, they are (usually) not a faithful model of genetics or evolution.

Viral mutation rates: modelling the roles of within-host viral dynamics and the trade-off between replication fidelity and speed

Many viruses, particularly RNA viruses, mutate at a very high rate per genome per replication. One possible explanation is that high mutation rates are selected to meet the challenge of fluctuating environments, including the host immune response. Alternatively, recent studies argue that viruses evolve under a trade-off between replication speed and fidelity such that fast replication is selected, and, along with it, high mutation rates. Here, in addition to these factors, we consider the role of viral life-history properties: namely, the within-host dynamics of viruses resulting from their interaction with the host. We develop mathematical models incorporating factors occurring within and between hosts, including deleterious and advantageous mutations, host death owing to virulence and clearance of viruses by the host. Beneficial mutations confer both a within-host and a transmission advantage. First, we find that advantageous mutations have only a weak effect on the optimal genomic mutation rate. Second, viral life-history properties have a large effect on the mutation rate. Third, when the speed–fidelity trade-off is included, there can be two locally optimal mutation rates. Our analysis provides a way to consider how life-history properties combine with biochemical trade-offs to shape mutation rates.

Taking the Operant Paradigm into the Field: Associative Learning in Wild Great Tits

Associative learning is essential for resource acquisition, predator avoidance and reproduction in a wide diversity of species, and is therefore a key target for evolutionary and comparative cognition research. Automated operant devices can greatly enhance the study of associative learning and yet their use has been mainly restricted to laboratory conditions. We developed a portable, weatherproof, battery-operated operant device and conducted the first fully automated colour-associative learning experiment using free-ranging individuals in the wild. We used the device to run a colour discrimination task in a monitored population of tits (Paridae). Over two winter months, 80 individuals from four species recorded a total of 5,128 trials. Great tits (Parus major) were more likely than other species to visit the devices and engage in trials, but there were no sex or personality biases in the sample of great tits landing at the devices and registering key pecks. Juveniles were more likely than adults to visit the devices and to register trials. Individuals that were successful at solving a novel technical problem in captivity (lever-pulling) learned faster than non-solvers when at the operant devices in the wild, suggesting cross-contextual consistency in learning performance in very different tasks. There was no significant effect of personality or sex on learning rate, but juveniles’ choice accuracy tended to improve at a faster rate than adults. We discuss how customisable automated operant devices, such as the one described here, could prove to be a powerful tool in evolutionary ecology studies of cognitive traits, especially among inquisitive species such as great tits.

The Effect of Exploration on the Use of Producer-Scrounger Tactics

Individuals foraging in groups can use two different tactics for obtaining food resources. Individuals can either search for food sources themselves (producing) or they can join food discoveries of others (scrounging). In this study we use a genetic algorithm in a spatially explicit producer-scrounger game to explore how individuals compromise between exploration (an important axis of animal personality) and scrounging and how characteristics of the environment affect this compromise. Agents varied in exploration and scrounging and a genetic algorithm searched for the optimal combination of exploration and scrounging. The foraging environments featured different levels of patch richness, predation and patch density. Our simulations show that under conditions of low patch densities slow exploring scroungers were favored whereas high patch density favored fast exploring individuals that either produced (at low patch richness) or scrounged (at high patch richness). In high predation environments fast exploring individuals were selected for but only at low to intermediate patch densities. Predation did not affect scrounging behavior. We did not find a divergence of exploration ‘types’ within a given environment, but there was a general association between exploration and scrounging across different environments: high rates of scrounging were observed over nearly the full spectrum of exploration values, whereas high rates of producing were only observed at high exploration values, suggesting that cases in which slow explorers start producing should be rare. Our results indicate that the spatial arrangement of food resources can affect the optimal social attraction rules between agents, the optimality of foraging tactic and the interaction between both.

Viral niche construction alters hosts and ecosystems at multiple scales

The classical picture of viruses focuses on pathogenic viruses damaging the hosts cells and physiology and host-pathogen immune coevolution. However, a broader picture of viruses is emerging that includes weakly pathogenic, commensal, or even mutualistic viruses and includes gross behavioural manipulations and viral effects on ecosystems. In this paper, we argue for niche construction as a unifying perspective on viral evolution. As obligate intracellular parasites, viruses are always modifying their environment, and these modifications drive evolutionary feedback between the virus and its environment across multiple scales from cells to ecosystems. We argue that niche construction will provide new insights into viral evolution, and that virology is a powerful source of empirical tests for niche construction.

Does cheating pay? Re-examining the evolution of deception in a conventional signalling game

The study of reliability, or ‘honesty’, in communication between individuals with conflicting interests has been a major focus of game theoretical modelling in evolutionary biology. It has been proposed that mixed populations of honest and deceptive signallers can be evolutionarily stable in a model of conventional, or ‘minimal cost’, signals of competitive ability, and evolutionary simulations have been presented to support this hypothesis. However, we find that these results are questionable on both theoretical and methodological grounds. Here, we examine the theoretical issues raised by this model and examine the proposed ‘cheating’ strategy through the use of a genetic algorithm. Our evolutionary simulations do not support the hypothesis that deception can be evolutionarily stable in this game. Intuition and common sense have it that animals communicate using ambiguous threat displays that have an underlying probabilistic mixed strategy type of mechanism, but there remains no working game theoretical model of such a communication system.

Behavioural manipulation of insect hosts by Baculoviridae as a process of niche construction

BackgroundNiche construction has received increasing attention in recent years as a vital force in evolution and examples of niche construction have been identified in a wide variety of taxa, but viruses are conspicuously absent. In this study we explore how niche construction can lead to viruses engineering their hosts (including behavioural manipulation) with feedback on selective pressures for viral transmission and virulence. To illustrate this concept we focus on Baculoviridae, a family of invertebrate viruses that have evolved to modify the feeding behaviour of their lepidopteran hosts and liquefy their cadavers as part of the course of infection.ResultsWe present a mathematical model showing how niche construction leads to feedback from the behavioural manipulation to the liquefaction of the host, linking the evolution of both of these traits, and show how this association arises from the action of niche construction. Model results show that niche construction is plausible in this system and delineates the conditions under which niche construction will occur. Niche construction in this system is also shown to be sensitive to parameter values that reflect ecological forces.ConclusionsOur model demonstrates that niche construction can be a potent force in viral evolution and can lead to the acquisition and maintenance of the behavioural manipulation and liquefaction traits in Baculoviridae via the niche constructing effects on the host. These results show the potential for niche construction theory to provide new insights into viral evolution.

The effects of linkage on comparative estimators of selection

BackgroundA major goal of molecular evolution is to determine how natural selection has shaped the evolution of a gene. One approach taken by methods such as KA/KS and the McDonald-Kreitman (MK) test is to compare the frequency of non-synonymous and synonymous changes. These methods, however, rely on the assumption that a change in frequency of one mutation will not affect changes in frequency of other mutations.ResultsWe demonstrate that linkage between sites can bias measures of selection based on synonymous and non-synonymous changes. Using forward simulation of a Wright-Fisher process, we show that hitch-hiking of deleterious mutations with advantageous mutations can lead to overestimation of the number of adaptive substitutions, while background selection and clonal interference can distort the site frequency spectrum to obscure the signal for positive selection. We present three diagnostics for detecting these effects of linked selection and apply them to the human influenza (H3N2) hemagglutinin gene.ConclusionVarious forms of linked selection have characteristic effects on MK-type statistics. The extent of background selection, hitch-hiking and clonal interference can be evaluated using the diagnostic statistics presented here. The diagnostics can also be used to determine how well we expect the MK statistics to perform and whether one form of the statistic may be preferable to another.