Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York, United States of America.
Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York, United States of America.
PLoS Comput Biol. 2022 Mar 14;18(3):e1009934. doi: 10.1371/journal.pcbi.1009934. eCollection 2022 Mar.
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.
许多生物行为是先天的或本能的，并且已经通过进化“硬编码”。目前，理解这些行为的方法将进化建模为一个优化问题，其中生物体的特征被假设为优化代表进化适应性的目标函数。在这里，我们使用机械的生死动力学方法来研究模拟生物种群中先天行为策略的进化。具体来说，我们进行了基于主体的随机模拟和均值场分析，研究了在随机环境中探索并相互竞争以找到资源丰富的位置的生物体。我们发现，当生物体密度较低时，均值场模型允许我们推导出一个有效的目标函数，预测最具竞争力的表型如何取决于高资源位点稀缺性与这些位点为生物体提供的出生率增加之间的探索-利用权衡。然而，增加生物体密度会改变最具竞争力的行为策略，并排除了定义良好的目标函数的推导。此外，存在一个密度范围，在该范围中，许多表型可以长时间共存以进行进化。
