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

Abstract

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.

Capturing the facets of evolvability in a mechanistic framework

“…Evolvability research sheds new light on genomic architecture [2], the structure of regulatory networks [3,4], and many other features of biological systems (see Glossary). It has yielded surprising new insights, such as: adaptive evolution can proceed at a pace similar to ecological change, resulting in intricate and unexpected ecoevolutionary dynamics [5,6]; evolvability and robustness do not conflict but mutually reinforce each other [3,7,8]; and organisms with high evolvability can 'generalize' over environments [9,10]. Furthermore, evolvability research may add new perspectives to the formulation of a predictive theory of evolution (Box 1 and see Outstanding questions).…”

Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies

“…In Supplementary Material Section 3.2, we also consider an alternative implementation in which the pathogen reduces the value of each food item by a certain percentage. The parental population produced an offspring population (of the same size) as follows: to each offspring, a parent was assigned at random by a weighted lottery, with weights proportional to lifetime net energy (Netz et al, 2021;Gupte et al, 2021). This way, the expected number of offspring produced by a parent is proportional to the parent's lifetime net energy.…”

“…Individuals assign a suitability (see Netz et al, 2021;Gupte et al, 2021) to their current position and each of the five locations, using their inherited preferences for each of the cues:…”

“…Food items are distributed over a two-dimensional, continuous-space resource landscape with wrapped boundaries (a torus). Our model, similar to previous eco-evolutionary individual based models (Getz et al, 2015;Netz et al, 2021;Gupte et al, 2021), has two distinct timescales: (1) an ecological timescale comprising of T timesteps that make up one generation (T = 100 by default), and (2) an evolutionary timescale consisting of 5,000 generations (G). At the ecological timescale, individuals sense local counts of food items and competitors, move according to inherited movement strategies, and forage for food.…”

“…Our model individuals move in small, discrete steps of fixed size ( d M = 1 unit). Each step is chosen based on the individuals’ assessment of local environmental cues, and this assessment is made using evolved movement strategies (as in Netz et al, 2021; Gupte et al, 2021). First, individuals scan their current location, and five equally spaced points around their position, at a distance of 1 unit for three cues ( d S , see Supplementary Material Fig.…”