Background Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission. Methods We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus). Results Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. Conclusions Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

Aim We examined three potential enhancements of the stable isotope technique for elucidating migratory connectivity in birds inhabiting poorly studied areas, illustrated for Eurasian cranes (Grus grus) that overwinter in and migrate through Israel. First, we examined the use of oxygen stable isotopes (d 18 O), seldom applied for this purpose. Second, we examined the relationship between ambient water d 18 O and hydrogen stable isotope (d 2 H) values derived from various models, to determine the geographical origins of migrants. Third, we introduced the use of probabilistic distribution modelling to refine the assignment to origin of migrants lacking detailed distribution maps.Location Feather samples were collected in the Hula Valley (northern Israel) and across the species breeding range in north Eurasia.Methods We analysed d 18 O and d 2 H in primary and secondary flight feathers using standard mass spectrometry. The maximum entropy (MAXENT) model was used to map the probability surface of potential breeding areas, as a Bayesian prior for assigning Hula Valley cranes to potential breeding grounds.Results We found that d 18 O was suitable and informative. The soil water isoscape performed better for d 18 O while precipitation isoscape was preferable for d 2 H. The MAXENT-based probability surface largely refined assignments. Overall, most (>85%) cranes were assigned to the area west of the Ural Mountains, but for two individuals, most of the assigned area (>90%) was farther east, suggesting, for the first time, that Eurasian cranes may undertake the North Asia-Middle East (and perhaps Africa) migration flyway.Main conclusions Our results call for broader use of d 18 O in migratory connectivity studies and for application of probabilistic distribution modelling. We also encourage investigation of factors determining d 18 O and d 2 H integration into animal tissues. The proposed framework may help improve our understanding of migratory connectivity of species inhabiting previously unexplored areas and thus contribute to the development of efficient conservation plans.

Human activities shape resources available to wild animals, impacting diet and probably altering their microbiota and overall health. We examined drivers shaping microbiota profiles of common cranes (Grus grus) in agricultural habitats by comparing gut microbiota and crane movement patterns (GPS-tracking) over three periods of

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In ecological and conservation studies, responsible researchers strive to obtain rich data while minimizing disturbance to wildlife and ecosystems. We assessed if samples collected noninvasively can be used for faecal microbiome research, comparing microbiota of noninvasively collected faecal samples to those collected from trapped common cranes at the same sites over the same periods. We found significant differences in faecal microbial composition (alpha and beta diversity), which likely did not result from noninvasive sample exposure to soil contaminants, as assessed by comparing bacterial oxygen use profiles. Differences might result from trapped birds' exposure to sedatives or stress. We conclude that if all samples are collected in the same manner, comparative analyses are valid, and noninvasive sampling may better represent host faecal microbiota because there are no trapping effects. Experiments with fresh and delayed sample collection can elucidate effects of environmental exposures on microbiota. Further, controlled tests of stressing or sedation may unravel how trapping affects wildlife microbiota.