The evolution of density‐dependent dispersal in a noisy spatial population model
The evolution of density‐dependent dispersal in a noisy spatial population model
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噪声空间种群模型中密度相关扩散的演化
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
I. Scheuring
中科院分区：
文献类型：
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作者：
Á. Kun;I. Scheuring
众所周知，扩散在许多不同的生态情况下是有利的，例如，在种群生活在空间和时间异质性栖息地的地方，在当地灾难中幸存下来。然而，关键的问题，什么样的分散策略是最佳的，在特定的情况下，仍然没有答案。本文研究了耦合映象格子模型中种群动力学受外界环境噪声扰动的密度依赖扩散的演化。我们使用了一个非常灵活的扩散函数，使进化选择几乎所有可能类型的单调密度依赖的扩散函数。我们把扩散函数的参数看作是连续变化的表型性状。通过数值模拟研究了进化稳定扩散策略。我们指出，无论分散的成本和环境噪声的强度，这种策略会导致一个非常弱的分散低于阈值密度，和扩散率增加加速的方式超过这个阈值。降低扩散成本会增加种群密度分布的偏度，而增加环境噪声会导致该分布更明显的双峰性。在环境噪声的正时间自相关的情况下，在阈值以下没有分散，并且在阈值以下只有低分散，另一方面，在负自相关的情况下，几乎所有个体分散在阈值以上。我们发现我们的结果是在良好的一致性与经验观察。
It is well-known that dispersal is advantageous in many different ecological situations, e.g. to survive local catastrophes where populations live in spatially and temporally heterogeneous habitats. However, the key question, what kind of dispersal strategy is optimal in a particular situation, has remained unanswered. We studied the evolution of density-dependent dispersal in a coupled map lattice model, where the population dynamics are perturbed by external environmental noise. We used a very flexible dispersal function to enable evolution to select from practically all possible types of monotonous density-dependent dispersal functions. We treated the parameters of the dispersal function as continuously changing phenotypic traits. The evolutionary stable dispersal strategies were investigated by numerical simulations. We pointed out that irrespective of the cost of dispersal and the strength of environmental noise, this strategy leads to a very weak dispersal below a threshold density, and dispersal rate increases in an accelerating manner above this threshold. Decreasing the cost of dispersal increases the skewness of the population density distribution, while increasing the environmental noise causes more pronounced bimodality in this distribution. In case of positive temporal autocorrelation of the environmental noise, there is no dispersal below the threshold, and only low dispersal below it, on the other hand with negative autocorrelation practically all individual disperses above the threshold. We found our results to be in good concordance with empirical observations.