Methods for estimating long-distance dispersal
Article first published online: 17 OCT 2003
Volume 103, Issue 2, pages 261–273, November 2003
How to Cite
Nathan, R., Perry, G., Cronin, J. T., Strand, A. E. and Cain, M. L. (2003), Methods for estimating long-distance dispersal. Oikos, 103: 261–273. doi: 10.1034/j.1600-0706.2003.12146.x
- Issue published online: 17 OCT 2003
- Article first published online: 17 OCT 2003
- Manuscript Accepted 1 October 2002
Long-distance dispersal (LDD) includes events in which propagules arrive, but do not necessarily establish, at a site far removed from their origin. Although important in a variety of ecological contexts, the system-specific nature of LDD makes “far removed” difficult to quantify, partly, but not exclusively, because of inherent uncertainty typically involved with the highly stochastic LDD processes. We critically review the main methods employed in studies of dispersal, in order to facilitate the evaluation of their pertinence to specific aspects of LDD research. Using a novel classification framework, we identify six main methodological groups: biogeographical; Eulerian and Lagrangian movement/redistributional; short-term and long-term genetic analyses; and modeling. We briefly discuss the strengths and weaknesses of the most promising methods available for estimation of LDD, illustrating them with examples from current studies.
The rarity of LDD events will continue to make collecting, analyzing, and interpreting the necessary data difficult, and a simple and comprehensive definition of LDD will remain elusive. However, considerable advances have been made in some methodological areas, such as miniaturization of tracking devices, elaboration of stable isotope and genetic analyses, and refinement of mechanistic models. Combinations of methods are increasingly used to provide improved insight on LDD from multiple angles. However, human activities substantially increase the variety of long-distance transport avenues, making the estimation of LDD even more challenging.