The total dispersal kernel: a review and future directions.

Liba Pejchar,Onja H Razafindratsima,Jenny Zambrano,Manette E Sandor,Eugene W Schupp,Bette Loiselle,James M Bullock,Gesine Pufal,W Christopher Strickland,Robert Stephen Cantrell,Katriona Shea,Noelle G Beckman,Haldre S Rogers,Florian Hartig,Jeremy S Johnson,Claire Aslan,Damaris Zurell

doi:10.1093/aobpla/plz042

Abstract

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.

Highlights

Dispersal is a central demographic process with implications for population persistence, spatial spread, gene flow and community dynamics (Nathan and Muller-Landau 2000; Levin et al 2003; Levine and Murrell 2003)
We find that significant empirical challenges exist, as many studies do not account for all dispersal vectors, inadequately measure or estimate long-distance dispersal resulting from multiple vectors and/or neglect spatial heterogeneity and context dependence
We propose that better estimations of dispersal will result from improved assessment of the contributions of multiple dispersal vectors to the total dispersal kernel (TDK), advances in combining different methodological approaches, improved approaches to model rare long-distance dispersal (LDD) events, use of traits and trait databases to identify functional types, studies focusing on the role of heterogeneous landscapes in dispersal and an increased focus on SDE (Table 1)

Summary

Introduction

Dispersal is a central demographic process with implications for population persistence, spatial spread, gene flow and community dynamics (Nathan and Muller-Landau 2000; Levin et al 2003; Levine and Murrell 2003). We propose that better estimations of dispersal will result from improved assessment of the contributions of multiple dispersal vectors to the TDK, advances in combining different methodological approaches, improved approaches to model rare LDD events, use of traits and trait databases to identify functional types, studies focusing on the role of heterogeneous landscapes in dispersal and an increased focus on SDE (Table 1). Similar problems arise in many areas of ecology, and typical solutions are to either (i) group plant or animal species a priori into functional groups according to their traits or phylogenies (Aslan et al 2019), or (ii) fit hierarchical statistical models, where species that are close according to their phylogenies or traits are assumed to have similar properties (Mokany et al 2014) These solutions should work well (with the known limitations) for the problem of characterizing total seed dispersal kernels.We recommend to first fit the dispersal kernels of the biotic and abiotic vectors independently, if possible. Models can accelerate knowledge gain about important processes, closing the modelling cycle (Fig. 2; Jeltsch et al 2013)

Conclusions and Outlook

Findings

Literature Cited