The distribution and abundance of plants across the world depends in part on their ability to move, which is commonly characterized by a dispersal kernel. For seeds, the total dispersal kernel (TDK) describes the combined influence of all primary, secondary and higher-order dispersal vectors on the overall dispersal kernel for a plant individual, population, species or community. Understanding the role of each vector within the TDK, and their combined influence on the TDK, is critically important for being able to predict plant responses to a changing biotic or abiotic environment. In addition, fully characterizing the TDK by including all vectors may affect predictions of population spread. Here, we review existing research on the TDK and discuss advances in empirical, conceptual modelling and statistical approaches that will facilitate broader application. The concept is simple, but few examples of well-characterized TDKs exist. We find that significant empirical challenges exist, as many studies do not account for all dispersal vectors (e.g. gravity, higher-order dispersal vectors), inadequately measure or estimate long-distance dispersal resulting from multiple vectors and/or neglect spatial heterogeneity and context dependence. Existing mathematical and conceptual modelling approaches and statistical methods allow fitting individual dispersal kernels and combining them to form a TDK; these will perform best if robust prior information is available. We recommend a modelling cycle to parameterize TDKs, where empirical data inform models, which in turn inform additional data collection. Finally, we recommend that the TDK concept be extended to account for not only where seeds land, but also how that location affects the likelihood of establishing and producing a reproductive adult, i.e. the total effective dispersal kernel.

中文翻译：

---

总分散核：回顾和未来方向。

世界各地植物的分布和丰度部分取决于它们的移动能力，这通常以分散核为特征。对于种子，总分散内核 (TDK) 描述了所有初级、次级和高级分散载体对植物个体、种群、物种或群落的整体分散内核的综合影响。了解 TDK 中每个载体的作用，以及它们对 TDK 的综合影响，对于预测植物对不断变化的生物或非生物环境的反应至关重要。此外，通过包含所有向量来完全表征 TDK 可能会影响对人口分布的预测。在这里，我们回顾了关于 TDK 的现有研究并讨论了实证研究的进展，概念建模和统计方法将促进更广泛的应用。这个概念很简单，但很少有典型的 TDK 示例。我们发现存在重大的经验挑战，因为许多研究没有考虑所有的扩散向量（例如重力、高阶扩散向量），没有充分测量或估计由多个向量引起的长距离扩散和/或忽略空间异质性和上下文相关性. 现有的数学和概念建模方法和统计方法允许拟合单个分散内核并将它们组合以形成 TDK；如果有可靠的先验信息，这些将表现最佳。我们建议使用建模周期来参数化 TDK，其中经验数据为模型提供信息，而模型又为其他数据收集提供信息。最后，