A weather surveillance radar view of Alaskan avian migration

The first documentation of the Nearctic-Paleotropical migratory route of the Arctic Warbler

The Arctic Warbler (Phylloscopus borealis) is a cryptically plumed songbird with an uncommon Nearctic-Paleotropical migratory strategy. Using light-level geolocators, we provide the first documentation of the migratory routes and wintering locations of two territorial adult male Arctic Warblers from Denali National Park and Preserve, Alaska. After accounting for position estimation uncertainties and biases, we found that both individuals departed their breeding grounds in early September, stopped over in southeastern Russia and China during autumn migration, then wintered in the Philippines and the island of Palau. Our documentation of Arctic Warbler wintering on Palau suggests that additional study is needed to document their wintering range. Our study provides hitherto unknown information on stopover and wintering locations for Arctic Warblers and indicates that this species may migrate further overwater than previously thought.

Estimating the movements of terrestrial animal populations using broad-scale occurrence data

As human and automated sensor networks collect increasingly massive volumes of animal observations, new opportunities have arisen to use these data to infer or track species movements. Sources of broad scale occurrence datasets include crowdsourced databases, such as eBird and iNaturalist, weather surveillance radars, and passive automated sensors including acoustic monitoring units and camera trap networks. Such data resources represent static observations, typically at the species level, at a given location. Nonetheless, by combining multiple observations across many locations and times it is possible to infer spatially continuous population-level movements. Population-level movement characterizes the aggregated movement of individuals comprising a population, such as range contractions, expansions, climate tracking, or migration, that can result from physical, behavioral, or demographic processes. A desire to model population movements from such forms of occurrence data has led to an evolving field that has created new analytical and statistical approaches that can account for spatial and temporal sampling bias in the observations. The insights generated from the growth of population-level movement research can complement the insights from focal tracking studies, and elucidate mechanisms driving changes in population distributions at potentially larger spatial and temporal scales. This review will summarize current broad-scale occurrence datasets, discuss the latest approaches for utilizing them in population-level movement analyses, and highlight studies where such analyses have provided ecological insights. We outline the conceptual approaches and common methodological steps to infer movements from spatially distributed occurrence data that currently exist for terrestrial animals, though similar approaches may be applicable to plants, freshwater, or marine organisms.