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

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. 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 used 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 regularly evolved in our individual-based simulations. However, in line with previous studies, the evolution of learning was less likely in relatively constant environments (where genetic adaptation alone can lead to efficient foraging) or in the case of short-lived organisms (that cannot afford to spend much of their lifetime on exploration). Once learning did evolve, the characteristics of the learning strategy (the duration of the learning period and the learning rate) and the average performance after learning were 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 had a strong effect on the evolved learning strategy. Interestingly, a longer learning period did not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we showed that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.Author Summary The ability to learn from experience is an important adaptation. However, it is still unclear how learning is shaped by natural selection. Here, we present a novel way of modelling the evolution of learning using small neural networks and a simple, biology-inspired learning mechanism. Computer simulations reveal that efficient learning readily evolves in this model. However, the evolution of learning is less likely in relatively constant environments (where evolved inborn preferences can guide animal behaviour) and in short-lived organisms (that cannot afford to spend much of their lifetime on learning). If learning does evolve, the evolved learning strategy is strongly affected by the lifespan and environmental richness but surprisingly little by the rate and degree of environmental change. In summary, we show that a simple and biologically plausible mechanism can help understand the evolution of learning and the structure of the evolved learning strategies.Competing Interest StatementThe authors have declared no competing interest

Modelling the evolution of learning

The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we present a novel way of modeling learning using small neural networks and a simple, biology-inspired learning algorithm. We used 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 regularly evolved in our individual-based simulations. However, the evolution of learning was less likely in relatively constant environments(where learning is less important) or in case of short-lived agents (that cannot afford to spend much of their lifetime on exploration). Once learning did evolve, the characteristics of the learning strategy and the average performance after learning were surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, agent lifespan had a strong effect on the evolved learning strategy.Interestingly, a longer learning period did not always lead to a better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, our study shows that even a relatively simple learning mechanism can lead to efficient adaptation

Ideal free distribution of unequal competitors:Spatial assortment and evolutionary diversification of competitive ability

Ideal free distribution theory attempts to predict the distribution of well-informed (‘ideal’) and unconstrained (‘free’) foragers in space based on adaptive individual decisions. When individuals differ in competitive ability, a whole array of equilibrium distributions is possible, and it is unclear which of these distributions are most likely. In the first part of our study, we show that strong competitors have an intrinsically stronger preference for highly productive habitat patches than poor competitors. This leads to an equilibrium distribution where the average competitive ability on a patch is strongly correlated with the productivity of the patch. In the second part of our study, we consider what happens if differences in competitive ability are heritable and, hence, subject to natural selection. Under constant environmental conditions, selection eliminates such differences: a single strategy prevails that optimally balances the costs and benefits associated with competitive ability. If the productivity of patches changes during the lifetime of individuals, the spatial assortment of competitors of equal competitive ability gives poor competitors a systematic advantage in times of environmental change, while good competitors benefit from equilibrium conditions. Using evolutionary individual-based simulations, we demonstrate that environmental change may then lead to the diversification of competitive ability

Complex eco-evolutionary dynamics induced by the coevolution of predator–prey movement strategies

The coevolution of predators and prey has been the subject of much empirical and theoretical research that produced intriguing insights into the interplay of ecology and evolution. To allow for mathematical analysis, models of predator–prey coevolution are often coarse-grained, focussing on population-level processes and largely neglecting individual-level behaviour. As selection is acting on individual-level properties, we here present a more mechanistic approach: an individual-based simulation model for the coevolution of predators and prey on a fine-grained resource landscape, where features relevant for ecology (like changes in local densities) and evolution (like differences in survival and reproduction) emerge naturally from interactions between individuals. Our focus is on predator–prey movement behaviour, and we present a new method for implementing evolving movement strategies in an efficient and intuitively appealing manner. Throughout their lifetime, predators and prey make repeated movement decisions on the basis of their movement strategies. Over the generations, the movement strategies evolve, as individuals that successfully survive and reproduce leave their strategy to more descendants. We show that the movement strategies in our model evolve rapidly, thereby inducing characteristic spatial patterns like spiral waves and static spots. Transitions between these patterns occur frequently, induced by antagonistic coevolution rather than by external events. Regularly, evolution leads to the emergence and stable coexistence of qualitatively different movement strategies within the same population. Although the strategy space of our model is continuous, we often observe the evolution of discrete movement types. We argue that rapid evolution, coexistent movement types, and phase shifts between different ecological regimes are not a peculiarity of our model but a result of more realistic assumptions on eco-evolutionary feedbacks and the number of evolutionary degrees of freedom

Effects of season length and uniparental care efficacy on the evolution of parental care

Parental care patterns differ enormously among and even within species. This is exemplified by Chinese penduline tits Remiz consobrinus, where biparental care, female-only care, male-only care and biparental desertion all occur in the same population; moreover, the distribution of these care patterns differs systematically between populations. The eco-evolutionary determinants of this diversity are largely unknown. We developed an individual-based model that allows us to investigate the effects of season length and offspring needs (expressed by the efficacy with which a clutch can be raised by a single parent) on the evolution of parental care patterns. The model is largely conceptual, aiming at general conclusions. However, to keep the model realistic, its set-up and the choice of parameters are motivated by field studies on Chinese penduline tits. Exploring a wide range of parameters, we investigate how parental care patterns are affected by season length and offspring needs and whether and under what conditions diverse parental care patterns can stably coexist. We report five main findings. First, under a broad range of conditions, different care patterns (e.g. male care and biparental care) coexist at equilibrium. Second, for the same parameters, alternative evolutionary equilibria are possible; this can explain differences in care patterns across populations. Third, rapid evolutionary transitions can occur between alternative equilibria; this can explain the often-reported evolutionary lability of parental care patterns. Fourth, season length has a strong but nonmonotonic effect on the evolved care patterns. Fifth, when uniparental care efficacy is low, biparental care tends to evolve; however, in many scenarios uniparental care is still common at equilibrium. In addition, our study sheds new light on Trivers' hypothesis that the sex with the highest prezygotic investment is predestined to invest more postzygotically as well. Our study highlights that diversity in parental care can readily evolve and it shows that even in the absence of environmental change parental care patterns can be evolutionary labile. In the presence of directional environmental change, systematic shifts in care patterns are to be expected.</p