Technological advancements in the past 20 years or so have spurred rapid growth in the study of migratory connectivity (the linkage of individuals and populations between seasons of the annual cycle). A new article in Methods in Ecology and Evolution provides methods to help make quantitative comparisons of migratory connectivity across studies, data types, and taxa to better understand the causes and consequences of the seasonal distributions of populations.
The seasonal long-distance migration of all kinds of animals – from whales to dragonflies to amphibians to birds – is as astonishing a feat as it is mysterious and this is an especially exciting time to study migratory animals. In the past 20 years, rapidly advancing technologies – from tracking devices, to stable isotopes in tissues, to genomics and analytical techniques for the analysis of ring re-encounter databases – mean that it’s now possible to follow many animals throughout the year and solve many of the mysteries of migration.
What is Migratory Connectivity?
One of the many important things we’re now able to measure is migratory connectivity, the connections of migratory individuals and populations between seasons. There are really two components of migratory connectivity:
Linking the geography of where individuals and populations occur between seasons.
The extent, or strength, of co-occurrence of individuals and populations between seasons.
This month’s issue contains one Applications article and two Open Access articles, all of which are freely available.
– POPART: An integrated software package that provides a comprehensive implementation of haplotype network methods, phylogeographic visualisation tools and standard statistical tests, together with publication-ready figure production. The package also provides a platform for the implementation and distribution of new network-based methods.
Michalis Vardakis et al. provide this month’s first Open Access article. In ‘Discrete choice modelling of natal dispersal: ‘Choosing’ where to breed from a finite set of available areas‘ the authors show how the dispersal discrete choice model can be used for analysing natal dispersal data in patchy environments given that the natal and the breeding area of the disperser are observed. This model can be used for any species or system that uses some form of discrete breeding location or a certain degree of discretization can be applied.