Identification of migratory bird flyways in North America using community detection on biological networks.

Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 yr. However, previous flyway characterizations are not easily updated with current bird movement data and fail to provide assessments of the importance of specific geographical regions to the identification of flyways. Here, we developed a network model of migratory movement for four waterfowl species, Mallard (Anas platyrhnchos), Northern Pintail (A. acuta), American Green-winged Teal (A. carolinensis), and Canada Goose (Branta canadensis), in North America, using bird band and recovery data. We then identified migratory flyways using a community detection algorithm and characterized the importance of smaller geographic regions in identifying flyways using a novel metric, the consolidation factor. We identified four main flyways for Mallards, Northern Pintails, and American Green-winged Teal, with the flyway identification in Canada Geese exhibiting higher complexity. For Mallards, flyways were relatively consistent through time. However, consolidation factors revealed that for Mallards and Green-winged Teal, the presumptive Mississippi flyway was potentially a zone of high mixing between other flyways. Our results demonstrate that the network approach provides a robust method for flyway identification that is widely applicable given the relatively minimal data requirements and is easily updated with future movement data to reflect changes in flyway definitions and management goals.

Population structure and covariate analysis based on pairwise microsatellite allele matching frequencies.

We describe a general model for pairwise microsatellite allele matching probabilities. The model can be used for analysis of population substructure, and is particularly focused on relating genetic correlation to measurable covariates. The approach is intended for cases when the existence of subpopulations is uncertain and a priori assignment of samples to hypothesized subpopulations is difficult. Such a situation arises, for example, with western Arctic bowhead whales, where genetic samples are available only from a possibly mixed migratory assemblage. We estimate genetic structure associated with spatial, temporal, or other variables that may confound the detection of population structure. In the bowhead case, the model permits detection of genetic patterns associated with a temporally pulsed multi-population assemblage in the annual migration. Hypothesis tests for population substructure and for covariate effects can be carried out using permutation methods. Simulated and real examples illustrate the effectiveness and reliability of the approach and enable comparisons with other familiar approaches. Analysis of the bowhead data finds no evidence for two temporally pulsed subpopulations using the best available data, although a significant pattern found by other researchers using preliminary data is also confirmed here. Code in the R language is available from www.stat.colostate.edu/~geof/gammmp.html.