Adaptation in the Alleyways: Candidate Genes Under Potential Selection in Urban Coyotes

In the context of evolutionary time, cities are an extremely recent development. Although our understanding of how urbanization alters ecosystems is well developed, empirical work examining the consequences of urbanization on adaptive evolution remains limited. To facilitate future work, we offer candidate genes for one of the most prominent urban carnivores across North America. The coyote (Canis latrans) is a highly adaptable carnivore distributed throughout urban and nonurban regions in North America. As such, the coyote can serve as a blueprint for understanding the various pathways by which urbanization can influence the genomes of wildlife via comparisons along urban-rural gradients, as well as between metropolitan areas. Given the close evolutionary relationship between coyotes and domestic dogs, we leverage the well-annotated dog genome and highly conserved mammalian genes from model species to outline how urbanization may alter coyote genotypes and shape coyote phenotypes. We identify variables that may alter selection pressure for urban coyotes and offer suggestions of candidate genes to explore. Specifically, we focus on pathways related to diet, health, behavior, cognition, and reproduction. In a rapidly urbanizing world, understanding how species cope and adapt to anthropogenic change can facilitate the persistence of, and coexistence with, these species.

The evolutionary consequences of learning under competition

Learning is a taxonomically widespread process by which animals change their behavioural responses to stimuli as a result of experience. In this way, it plays a crucial role in the development of individual behaviour and underpins substantial phenotypic variation within populations. Nevertheless, the impact of learning in social contexts on evolutionary change is not well understood. Here, we develop game theoretical models of competition for resources in small groups (e.g. producer-scrounger and hawk-dove games) in which actions are controlled by reinforcement learning and show that biases in the subjective valuation of different actions readily evolve. Moreover, in many cases, the convergence stable levels of bias exist at fitness minima and therefore lead to disruptive selection on learning rules and, potentially, to the evolution of genetic polymorphisms. Thus, we show how reinforcement learning in social contexts can be a driver of evolutionary diversification. In addition, we consider the evolution of ability in our games, showing that learning can also drive disruptive selection on the ability to perform a task.

A neural network model for the evolution of learning in changing environments

Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a clear effect on the evolved learning strategy: a shorter lifespan or a broader resource distribution lead to fewer learning episodes and larger learning rates. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.

Animal cognition in an urbanised world

Explaining how animals respond to an increasingly urbanised world is a major challenge for evolutionary biologists. Urban environments often present animals with novel problems that differ from those encountered in their evolutionary past. To navigate these rapidly changing habitats successfully, animals may need to adjust their behaviour flexibly over relatively short timescales. These behavioural changes, in turn, may be facilitated by an ability to acquire, store and process information from the environment. The question of how cognitive abilities allow animals to avoid threats and exploit resources (or constrain their ability to do so) is attracting increasing research interest, with a growing number of studies investigating cognitive and behavioural differences between urban-dwelling animals and their non-urban counterparts. In this review we consider why such differences might arise, focusing on the informational challenges faced by animals living in urban environments, and how different cognitive abilities can assist in overcoming these challenges. We focus largely on birds, as avian taxa have been the subject of most research to date, but discuss work in other species where relevant. We also address the potential consequences of cognitive variation at the individual and species level. For instance, do urban environments select for, or influence the development of, particular cognitive abilities? Are individuals or species with particular cognitive phenotypes more likely to become established in urban habitats? How do other factors, such as social behaviour and individual personality, interact with cognition to influence behaviour in urban environments? The aim of this review is to synthesise current knowledge and identify key avenues for future research, in order to improve our understanding of the ecological and evolutionary consequences of urbanisation.

How learning can change the course of evolution

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome.

Unpredictable environments enhance inhibitory control in pheasants

The ability to control impulsive actions is an important executive function that is central to the self-regulation of behaviours and, in humans, can have important implications for mental and physical health. One key factor that promotes individual differences in inhibitory control (IC) is the predictability of environmental information experienced during development (i.e. reliability of resources and social trust). However, environmental predictability can also influence motivational and other cognitive abilities, which may therefore confound interpretations of the mechanisms underlying IC. We investigated the role of environmental predictability, food motivation and cognition on IC. We reared pheasant chicks, Phasianus colchicus, under standardised conditions, in which birds experienced environments that differed in their spatial predictability. We systematically manipulated spatial predictability during their first 8 weeks of life, by either moving partitions daily to random locations (unpredictable environment) or leaving them in fixed locations (predictable environment). We assessed motivation by presenting pheasants with two different foraging tasks that measured their dietary breadth and persistence to acquire inaccessible food rewards, as well as recording their latencies to acquire a freely available baseline worm positioned adjacent to each test apparatus, their body condition (mass/tarsus³) and sex. We assessed cognitive performance by presenting each bird with an 80-trial binary colour discrimination task. IC was assessed using a transparent detour apparatus, which required subjects to inhibit prepotent attempts to directly acquire a visible reward through the barrier and instead detour around a barrier. We found greater capacities for IC in pheasants that were reared in spatially unpredictable environments compared to those reared in predictable environments. While IC was unrelated to individual differences in cognitive performance on the colour discrimination task or motivational measures, we found that environmental predictability had differential effects on sex. Males reared in an unpredictable environment, and all females regardless of their rearing environment, were less persistent than males reared in a predictable environment. Our findings, therefore, suggest that an individual’s developmental experience can influence their performance on IC tasks.

Enriching behavioral ecology with reinforcement learning methods

This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning.