Seed dispersals deal with complex systems through which the data collected using advanced seed tracking facilities pose challenges to conventional approaches, such as empirical and deterministic models. The use of stochastic models in current seed dispersal studies is encouraged. This review describes three existing stochastic models: the birth–death process (BDP), a 2 dimensional ($2\mathrm{D}$) symmetric random walks and a $2\mathrm{D}$ intermittent walks. The three models possess Markovian property, which make them flexible for studying natural phenomena. Only a few of applications in ecology are found in seed dispersals. The review illustrates how the models are to be used in seed dispersals context. Using the nonlinear BDP, we formulate the individual-based models for two competing plant species while the cover time model is formulated by the symmetric and intermittent random walks. We also show that these three stochastic models can be formulated using the Gillespie algorithm. The full cover time obtained by the symmetric random walks can approximate the Gumbel distribution pattern as the other searching strategies do. We suggest that the applications of these models in seed dispersals may lead to understanding of many complex systems, such as the seed removal experiments and behaviour of foraging agents, among others.

种子传播涉及复杂的系统，通过这些系统，使用高级种子跟踪工具收集的数据对常规方法（例如经验模型和确定性模型）构成挑战。鼓励在当前种子传播研究中使用随机模型。这篇评论描述了三种现有的随机模型：出生-死亡过程（BDP），二维（$2\mathrm{d}$）对称随机游走和 $2\mathrm{d}$间歇性散步。这三个模型具有马尔可夫性质，这使得它们可以灵活地研究自然现象。种子散布仅在生态学中有少数应用。该评论说明了如何在种子传播背景下使用这些模型。使用非线性BDP，我们为两种竞争植物物种建立了基于个体的模型，而覆盖时间模型则由对称和间歇性随机游走建立。我们还表明，可以使用Gillespie算法来制定这三个随机模型。对称随机游走所获得的全部覆盖时间可以像其他搜索策略那样近似于Gumbel分布模式。我们建议这些模型在种子传播中的应用可能会导致对许多复杂系统的理解，