Light‐level geolocator tags use ambient light recordings to estimate the whereabouts of an individual over the time it carried the device. Over the past decade, these tags have emerged as an important tool and have been used extensively for tracking animal migrations, most commonly small birds. Analysing geolocator data can be daunting to new and experienced scientists alike. Over the past decades, several methods with fundamental differences in the analytical approach have been developed to cope with the various caveats and the often complicated data. Here, we explain the concepts behind the analyses of geolocator data and provide a practical guide for the common steps encompassing most analyses – annotation of twilights, calibration, estimating and refining locations, and extraction of movement patterns – describing good practices and common pitfalls for each step. We discuss criteria for deciding whether or not geolocators can answer proposed research questions, provide guidance in choosing an appropriate analysis method and introduce key features of the newest open‐source analysis tools. We provide advice for how to interpret and report results, highlighting parameters that should be reported in publications and included in data archiving. Finally, we introduce a comprehensive supplementary online manual that applies the concepts to several datasets, demonstrates the use of open‐source analysis tools with step‐by‐step instructions and code and details our recommendations for interpreting, reporting and archiving.

Ecological “big data”: Human activities are rapidly altering the natural world. Nowhere is this more evident, perhaps, than in the Arctic, yet this region remains one of the most remote and difficult to study. Researchers have increasingly relied on animal tracking data in these regions to understand individual species' responses, but if we want to understand larger-scale change, we need to integrate our understanding across species. Davidsonet al.introduce an open-source data archive that currently hosts more than 15 million location data points across 96 species and use it to show distinct climate change responses across species. Such ecological “big data” can lead to a wider understanding of change.Science, this issue p.712

For the first time, we use a small archival global positioning system (GPS) tag to identify and characterize non-breeding territories, quantify migratory connectivity and identify population boundaries of Ovenbirds (Seiurus aurocapilla), a small migratory songbird, captured at two widely separated breeding locations. We recovered 15 (31%) GPS tags with data and located the non-breeding territories of breeding Ovenbirds from Maryland and New Hampshire, USA (0.50 ± 0.15 ha, mean ± SE). All non-breeding territories had similar environmental attributes despite being distributed across parts of Florida, Cuba and Hispaniola. New Hampshire and Maryland breeding populations had non-overlapping non-breeding population boundaries that encompassed 114,803 and 169,233 km², respectively. Archival GPS tags provided unprecedented pinpoint locations and associated environmental information of tropical non-breeding territories. This technology is an important step forward in understanding seasonal interactions and ultimately population dynamics of populations throughout the annual cycle.

Technological advancements have spurred rapid growth in the study of migratory connectivity, the linkage of individuals and populations between seasons of the annual cycle. The strength of migratory connectivity is a measure of the co‐occurrence of populations throughout the annual cycle and can be represented by a correlation of the distances between individuals during one season and another (Mantel correlation, r_M). However, measurement of seasonal distributions most often involves incomplete sampling and use of technologies that vary in accuracy and precision. For these reasons, we expanded r_M to measure the strength of migratory connectivity (MC) with population‐specific transition probabilities that can be derived from many data types and uneven sampling. We explore the sensitivity of MC to possible real‐world variation in input parameters: transition probabilities, abundance among regions, spatial arrangement of regions, and sample sizes. We compare MC to r_M, present a series of resampling approaches for propagating uncertainty in input values into estimation of MC and r_M, and validate the method with bird tracking data. Migratory connectivity was negative when populations are further apart between seasons, positive when populations remain together between seasons, and zero when populations have no patterns in distribution between seasons. MC is most sensitive to transition probabilities and spatial arrangement of regions and performs better than r_M when sampling effort is not proportional to true abundance, and when the strength of migratory connectivity varies across the range of the species. Our estimators for MC and r_M performed well across several data types. We hope that these methods and the MigConnectivity r package will facilitate quantitative comparisons of migratory connectivity across studies, data types, and taxa to better understand the causes and consequences of the seasonal distributions of populations. Several study design recommendations emerge from our simulations: (1) incorporate abundance among regions when sampling is not proportional; (2) measure transition probabilities across as much of the range as logistically possible; (3) define study regions with either biological information about population delineation, or use discrete study locations as centroids of regions; and (4) estimate and report uncertainty from appropriate sources of sampling and process errors.

Insect migration is globally ubiquitous and can involve continental-scale movements and complex life histories. Apart from select species of migratory moths and butterflies, little is known about the structure of the annual cycle for migratory insects. Using stable-hydrogen isotope analysis of 852 wing samples from eight countries spanning 140 years, combined with 21 years of citizen science data, we determined the full annual cycle of a large migratory dragonfly, the common green darner ( Anax junius ). We demonstrate that darners undertake complex long-distance annual migrations governed largely by temperature that involve at least three generations. In spring, the first generation makes a long-distance northbound movement (further than 650 km) from southern to northern range limits, lays eggs and dies. A second generation emerges and returns south (further than 680 km), where they lay eggs and die. Finally, a third resident generation emerges, reproducing locally and giving rise to the cohort that migrates north the following spring. Since migration timing and nymph development are highly dependent on temperature, continued climate change could lead to fundamental changes in the biology for this and similar migratory insects.