Abstract
Many organism behaviors are innate or instinctual and have been “hard-coded” through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a simulated population of organisms. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the derivation of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.
Highlights
The fundamental evolutionary events are stochastic birth and death events, and the most successful organisms that emerge under these dynamics are not always those predicted by fitness-based approaches
We show that the environmental structure and the population dynamics of the simulated model select for particular phenotypes, even though birth rates depend only on spatial location and not the phenotype
We show that we can understand the origins of weak selection pressures using a minimal mean-field model, in which we can derive the growth rate as a function of phenotype, which acts as an effective fitness function, when organism density is low (A minimal mean-field model explains variability in competitive phenotypes)
Summary
Foraging for resources—shelter, food, etc.—is one of the fundamental problems an organism must solve in order to survive and reproduce, ensuring the continuity of its species [1]. Perhaps the most well-known candidate principle is “survival of the fittest:” species with adaptations that give them any edge for outcompeting other species in their environments are those that go on to proliferate. In order to render this problem well-posed for quantitative modeling, a common approach is to define a “fitness function,” a function of a population’s phenotypic parameters whose output is a proxy for that phenotypic configuration to survive and reproduce in its environment. Often the fitness function is modeled as the fitness of a single representative agent, rather than an entire population This idea has given rise to modeling evolution as an optimization process that maximizes an organism’s fitness [4], i.e., selects for the phenotype parameters that maximize the fitness function. Fitness functions can range from relatively straightforward and concrete quantities, such as an organism’s rate of producing offspring or the time it takes to find shelter or food [5–8], or they can be relatively abstract, such as the amount of information an organism’s sensory organs convey to its nervous system [9–12]—the idea being that the more efficiently an organism’s nervous system can process information it acquires about its environment, the more successful it will be at finding food, shelter, mates, etc
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