The total dispersal kernel: a review and future directions
The total dispersal kernel: a review and future directions
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总分散核：回顾和未来方向
DOI：
10.1093/aobpla/plz042
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发表时间：
2019
期刊：
影响因子：
2.9
通讯作者：
Loiselle, Bette
中科院分区：
文献类型：
--
作者：
Rogers, Haldre S;Beckman, Noelle G;Hartig, Florian;Johnson, Jeremy S;Pufal, Gesine;Shea, Katriona;Zurell, Damaris;Bullock, James M;Cantrell, Robert Stephen;Loiselle, Bette
植物在世界各地的分布和丰富程度部分取决于它们的移动能力，这通常以扩散核为特征。对于种子，总扩散核（TDK）描述了所有初级、次级和高阶扩散载体对植物个体、种群、物种或群落的总扩散核的综合影响。了解TDK中每个载体的作用及其对TDK的综合影响，对于能够预测植物对变化的生物或非生物环境的反应至关重要。此外，通过包括所有矢量来充分描述TDK可能会影响人口传播的预测。在这里，我们回顾现有的研究TDK和讨论的经验，概念建模和统计方法，将促进更广泛的应用进展。这个概念很简单，但很少有充分表征TDK的例子。我们发现，存在重大的经验挑战，因为许多研究没有考虑到所有的扩散矢量（例如重力，高阶扩散矢量），不充分的测量或估计长距离扩散导致多个矢量和/或忽视空间异质性和上下文依赖。现有的数学和概念建模方法和统计方法允许拟合单个扩散核，并将它们组合起来形成TDK;如果有可靠的先验信息，这些方法的效果最好。我们建议一个建模周期来参数化TDK，其中经验数据为模型提供信息，这反过来又为额外的数据收集提供信息。最后，我们建议，TDK的概念进行扩展，不仅考虑到种子的土地，但也如何该位置影响建立和产生一个生殖成人的可能性，即总的有效传播内核。
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.