pamlr: A toolbox for analysing animal behaviour using pressure, acceleration, temperature, magnetic or light data in R.

Light-level geolocators have revolutionised the study of animal behaviour. However, lacking spatial precision, their usage has been primary targeted towards the analysis of large-scale movements. Recent technological developments have allowed the integration of magnetometers and accelerometers into geolocator tags in addition to barometers and thermometers, offering new behavioural insights. Here, we introduce an R toolbox for identifying behavioural patterns from multisensor geolocator tags, with functions specifically designed for data visualisation, calibration, classification and error estimation. More specifically, the package allows for the flexible analysis of any combination of sensor data using k-means clustering, expectation maximisation binary clustering, hidden Markov models and changepoint analyses. Furthermore, the package integrates tailored algorithms for identifying periods of prolonged high activity (most commonly used for identifying migratory flapping flight), and pressure changes (most commonly used for identifying dive or flight events). Finally, we highlight some of the limitations, implications and opportunities of using these methods.

Les géolocalisateurs lumineux ont révolutionné l'étude du comportement animal. Toutefois, en raison de leur manque de précision spatiale, leur utilisation a été principalement dirigée vers l'analyse de mouvements à grandes échelles. Les développements technologiques récents ont permis l'intégration de magnétomètres et d'accéléromètres dans les balises de géolocalisation, en plus de baromètres et de thermomètres, permettant de nouvelles analyses du comportement animalier. Nous présentons ici notre R package pour l'identification de modèles comportementaux à partir de balises géolocalisatrices multisensoriels. Le package intègre des fonctions conçues spécifiquement pour la visualisation de données, la calibration des balises, la classification du comportement et l'estimation des erreurs d'analyses. Plus précisément, le package permet l'analyse flexible de n'importe quelle combinaison de capteurs de données en utilisant le k-means clustering, le expectation maximisation binary clustering, les hidden Markov models et les analyses changepoint. En outre, le package intègre des algorithmes adaptés pour identifier les périodes de haute activité prolongée (le plus souvent utilisé pour identifier le vol migratoire d'oiseaux), et les changements de pression (le plus souvent utilisé pour identifier des periodes où l'animal est en plongée ou au vol). Enfin, nous soulignons les limites, les implications et les opportunités d'utilisation de ces méthodes.

Linking migratory performance to breeding phenology and productivity in an Afro-Palearctic long-distance migrant.

Understanding the relationship between migratory performance and fitness is crucial for predicting population dynamics of migratory species. In this study, we used geolocators to explore migration performance (speed and duration of migratory movements, migratory timings) and its association with breeding phenology and productivity in an Afro-Palearctic insectivore, the European bee-eater (Merops apiaster), breeding in Iberian Peninsula. Bee-eaters migrated at higher travel speeds and had shorter travel duration in spring compared to autumn. Individuals that departed earlier or spent fewer days in-flight arrived earlier to the breeding areas. Our results show overall positive, but year-specific, linkages between arrival and laying dates. In one year, laying was earlier and productivity was higher, remaining constant throughout the season, while in the subsequent year productivity was lower and, importantly, declined with laying date. These results suggest that arriving earlier can be advantageous for bee-eaters, as in years when breeding conditions are favourable, early and late breeders produce high and similar number of fledglings, but when conditions are unfavourable only early breeders experience high productivity levels.

Light-level geolocator analyses: A user's guide.

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.

Miniaturized multi-sensor loggers provide new insight into year-round flight behaviour of small trans-Sahara avian migrants.

BACKGROUND: Over the past decade, the miniaturisation of animal borne tags such as geolocators and GPS-transmitters has revolutionized our knowledge of the whereabouts of migratory species. Novel light-weight multi-sensor loggers (1.4 g), which harbour sensors for measuring ambient light intensity, atmospheric pressure, temperature and acceleration, were fixed to two long-distance migrant bird species - eurasian hoopoe (Upupa epops) and great reed warbler (Acrocephalus arundinaceus). Using acceleration and atmospheric pressure data recorded every 5 and 30 min, respectively, we aimed at reconstructing individual diurnal and seasonal patterns of flight activity and flight altitude and thereby, at describing basic, yet hitherto unknown characteristics of migratory flight behaviour. Furthermore, we wanted to characterise the variability in these migration characteristics between individuals, species and migration periods. RESULTS: The flight duration from breeding to sub-Saharan African non-breeding sites and back was more variable within than between the species. Great reed warblers were airborne for a total of 252 flight hours and thus, only slightly longer than eurasian hoopoes with 232 h. With a few exceptions, both species migrated predominantly nocturnally - departure around dusk and landing before dawn. Mean flight altitudes were higher during pre- than during post-breeding migration (median 1100 to 1600 m a.s.l.) and flight above 3000 m occurred regularly with a few great reed warblers exceeding 6000 m a.s.l. (max. 6458 m a.s.l.). Individuals changed flight altitudes repeatedly during a flight bout, indicating a continuous search for (more) favourable flight conditions. CONCLUSIONS: We found high variation between individuals in the flight behaviour parameters measured - a variation that surprisingly even exceeded the variation between the species. More importantly, our results have shown that multi-sensor loggers have the potential to provide detailed insights into many fundamental aspects of individual behaviour in small aerial migrants. Combining the data recorded on the multiple sensors with, e.g., remote sensing data like weather and habitat quality on the spatial and temporal scale will be a great step forward to explore individual decisions during migration and their consequences.

Spatiotemporal Group Dynamics in a Long-Distance Migratory Bird.

Thousands of species migrate [1]. Though we have some understanding of where and when they travel, we still have very little insight into who migrates with whom and for how long. Group formation is pivotal in allowing individuals to interact, transfer information, and adapt to changing conditions [2]. Yet it is remarkably difficult to infer group membership in migrating animals without being able to directly observe them. Here, we use novel lightweight atmospheric pressure loggers to monitor group dynamics in a small migratory bird, the European bee-eater (Merops apiaster). We present the first evidence of a migratory bird flying together with non-kin of different ages and sexes at all stages of the life cycle. In fact, 49% stay together throughout the annual cycle, never separating longer than 5 days at a time despite the ∼14,000-km journey. Of those that separated for longer, 89% reunited within less than a month with individuals they had previously spent time with, having flown up to 5,000 km apart. These birds were not only using the same non-breeding sites, but also displayed coordinated foraging behaviors-these are unlikely to result from chance encounters in response to the same environmental conditions alone. Better understanding of migratory group dynamics, using the presented methods, could help improve our understanding of collective decision making during large-scale movements.

Setting conservation priorities for migratory networks under uncertainty.

Conserving migratory species requires protecting connected habitat along the pathways they travel. Despite recent improvements in tracking animal movements, migratory connectivity remains poorly resolved at a population level for the vast majority of species, thus conservation prioritization is hampered. To address this data limitation, we developed a novel approach to spatial prioritization based on a model of potential connectivity derived from empirical data on species abundance and distance traveled between sites during migration. We applied the approach to migratory shorebirds of the East Asian-Australasian Flyway. Conservation strategies that prioritized sites based on connectivity and abundance metrics together maintained larger populations of birds than strategies that prioritized sites based only on abundance metrics. The conservation value of a site therefore depended on both its capacity to support migratory animals and its position within the migratory pathway; the loss of crucial sites led to partial or total population collapse. We suggest that conservation approaches that prioritize sites supporting large populations of migrants should, where possible, also include data on the spatial arrangement of sites.