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Individual variation is increasingly recognized as a central component of ecological processes, but its role in structuring environmental niche associations remains largely unknown. Species’ responses to environmental conditions are ultimately determined by the niches of single individuals, yet environmental associations are typically captured only at the level of species. Here, we develop scenarios for how individual variation may combine to define the compound environmental niche of populations, use extensive movement data to document individual environmental niche variation, test associated hypotheses of niche configuration, and examine the consistency of individual niches over time. For 45 individual white storks ( Ciconia ciconia; 116 individual-year combinations), we uncover high variability in individual environmental associations, consistency of individual niches over time, and moderate to strong niche specialization. Within populations, environmental niches follow a nested pattern, with individuals arranged along a specialist-to-generalist gradient. These results reject common assumptions of individual niche equivalency among conspecifics, as well as the separation of individual niches into disparate parts of environmental space. These findings underscore the need for a more thorough consideration of individualistic environmental responses in global change research. Understanding how individual niches vary can inform ecology and conservation. A study of 45 GPS-tracked white storks across three breeding populations reveals that individual environmental niches are nested, arranged along a specialist-generalist gradient that is highly consistent over time.

1. Early arrival at breeding grounds is of prime importance for migrating birds as it is known to enhance breeding success. Adults, males and higher quality individuals typically arrive earlier, and across years, early arrival has been linked to warmer spring temperatures. However, the mechanisms and potential costs of early arrival are not well understood. 2. To deepen the understanding of arrival date differences between individuals and years, we studied them in light of the preceding spring migration behaviour and atmospheric conditions en route. 3. GPS and body acceleration (ACC) data were obtained for 35 adult white storks (Ciconia ciconia) over five years (2012-2016). ACC records were translated to energy expenditure estimates (overall dynamic body acceleration; ODBA) and to behavioural modes, and GPS fixes were coupled with environmental parameters. 4. At the interindividual level (within years), early arrival was attributed primarily to departing earlier for migration and from more northern wintering sites (closer to breeding grounds), rather than to migration speed. In fact, early-departing birds flew slower, experienced weaker thermal uplifts and expended more energy during flight, but still arrived earlier, emphasizing the cost and the significance of early departure. Individuals that wintered further south arrived later at the breeding grounds but did not produce fewer fledglings, presumably due to positive carryover effects of advantageous wintering conditions (increased precipitation, vegetation productivity and daylight time). Therefore, early arrival increased breeding success only after controlling for wintering latitude. Males arrived slightly ahead of females. Between years, late arrival was linked to colder temperatures en route through two different mechanisms: stronger headwinds causing slower migration and lower thermal uplifts resulting in longer stopovers. 5. This study showed that distinct migratory properties underlie arrival time variation within and between years. It highlighted (a) an overlooked cost of early arrival induced by unfavourable atmospheric conditions during migration, (b) an important fitness trade-off in storks between arrival date and wintering habitat quality and (c) mechanistic explanations for the negative temperature–arrival date correlation in soaring birds. Such understanding of arrival time can facilitate forecasting migrating species responses to climate changes.

Supervised learning of behavioural modes from body‐worn sensor data, especially accelerometers, has become a transformative research tool in behavioural ecology over the past years. Due to the popularity of the methodology and diverging needs of users, there are a number of software packages dedicated to it, ranging from web based graphical user interfaces to R software packages. In pyecoacc, we aim to augment the functionality of the existing software by integrating recent methodological findings and recommendations. pyecoacc is an open‐source Python package for supervised learning of behavioural modes from accelerometer data. It is designed to work with minimum configuration, while remaining flexible enough to accommodate customization, additions and extensions. The pyecoacc software package includes the common accelerometer feature computations that have become standard in the field, and pipelines for traditional as well as deep learning‐based models. Model selection is facilitated via simple comparison tables with the recommended metrics. The correct computation of behavioural time budgets with the confusion matrix correction is also supported. We demonstrate the software using a dataset of body acceleration of a rodent species (Damaraland mole‐rat, Fukomys damarensis).

Studying the causes and consequences of route selection in animal migration is important for understanding the evolution of migratory systems and how they may be affected by environmental factors at various spatial and temporal scales. One key decision during migration is whether to cross ‘high transport cost’ areas or to circumvent them. Soaring birds may face this choice when encountering waterbodies where convective updrafts are weak or scarce. Crossing these waterbodies requires flying using energetically costly flapping flight, while circumventing them over land permits energetically cheap soaring. We tested how several atmospheric factors (e.g. wind, thermal uplift) and geographic, seasonal and state‐related factors (sex and age) affected route selection in migrating white storks Ciconia ciconia. We used 196 GPS tracks of 70 individuals either crossing or circumventing the north‐easternmost section of the Mediterranean Sea, over Iskenderun Bay in southern Turkey. We found that westward and southward winds promoted a cross‐bay journey in spring and autumn, respectively, acting as tailwinds. Also, overall weaker winds promoted a sea crossing in spring. Sea crossing was associated with flapping flight and higher values of overall dynamic body acceleration and resulted in higher ground speed than travel over land. The combined environmental conditions and the effects of route selection on movement‐related energy costs and speed were likely responsible for an increase in the time spent flying and distance travelled of migrating storks that decided to cross the bay during spring. Notably, daily travel distances of spring migrants crossing the bay were 60 km longer than those of land‐detouring birds, allowing them to reach their destination faster but likely incurring a higher energetic flight cost. No such benefit was found during autumn. Our findings confirm that atmospheric conditions can strongly affect bird route selection. Consequently, migration timing, speed and movement‐related energy expenditure differed considerably between the two migratory seasons and the two route choices, highlighting a time‐energy trade‐off in the migration of white storks. A free plain language summary can be found within the Supporting Information of this article. A free plain language summary can be found within the Supporting Information of this article.

Density regulation influences population dynamics through its effects on demographic rates and consequently constitutes a key mechanism explaining the response of organisms to environmental changes. Yet, it is difficult to establish the exact form of density dependence from empirical data. Here, we developed an individual-based model to explore how resource limitation and behavioural processes determine the spatial structure of white stork Ciconia ciconia populations and regulate reproductive rates. We found that the form of density dependence differed considerably between landscapes with the same overall resource availability and between home range selection strategies, highlighting the importance of fine-scale resource distribution in interaction with behaviour. In accordance with theories of density dependence, breeding output generally decreased with density but this effect was highly variable and strongly affected by optimal foraging strategy, resource detection probability and colonial behaviour. Moreover, our results uncovered an overlooked consequence of density dependence by showing that high early nestling mortality in storks, assumed to be the outcome of harsh weather, may actually result from density dependent effects on food provision. Our findings emphasize that accounting for interactive effects of individual behaviour and local environmental factors is crucial for understanding density-dependent processes within spatially structured populations. Enhanced understanding of the ways animal populations are regulated in general, and how habitat conditions and behaviour may dictate spatial population structure and demographic rates is critically needed for predicting the dynamics of populations, communities and ecosystems under changing environmental conditions.

The application of supervised machine learning methods to identify behavioural modes from inertial measurements of bio-loggers has become a standard tool in behavioural ecology. Several design choices can affect the accuracy of identifying the behavioural modes. One such choice is the inclusion or exclusion of segments consisting of more than a single behaviour (mixed segments) in the machine learning model training data. Currently, the common practice is to ignore such segments during model training. In this paper we tested the hypothesis that including mixed segments in model training will improve accuracy, as the model would perform better in identifying them in the test data. We test this hypothesis using a series of data simulations on four datasets of accelerometer data coupled with behaviour observations, obtained from four study species (Damaraland mole-rats, meerkats, olive baboons, polar bears). Results show that when a substantial proportion of the test data are mixed behaviour segments (above ~ 10%), including mixed segments in machine learning model training improves the accuracy of classification. These results were consistent across the four study species, and robust to changes in segment length, sample size, and degree of mixture within the mixed segments. However, we also find that in some cases (particularly in baboons) models trained with mixed segments show reduced accuracy in classifying test data containing only single behaviour (pure) segments, compared to models trained without mixed segments. Based on these results, we recommend that when the classification model is expected to deal with a substantial proportion of mixed behaviour segments (> 10%), it is beneficial to include them in model training, otherwise, it is unnecessary but also not harmful. The exception is when there is a basis to assume that the training data contains a higher rate of mixed segments than the actual (unobserved) data to be classified—such a situation may occur particularly when training data are collected in captivity and used to classify data from the wild. In this case, excess inclusion of mixed segments in training data should probably be avoided.

In cooperatively breeding species where rearing effort is shared among multiple group members, increases in group size typically reduce average per capita contributions to offspring care by all group members (load-lightening) but it is not known how changes in group size affect the distribution of workload among group members. The socioeconomic collective action theory suggests that, in larger groups, the incentives for free riding are stronger, leading to greater inequalities in work division among group members. Here, we use the Gini index to measure inequality at the group level in the contributions of helpers to three different cooperative behaviours (babysitting, pup-provisioning and raised guarding) in groups of varying size in wild Kalahari meerkats ( Suricata suricatta ). In larger groups, inequality in helpers' contributions to cooperative activities and the frequency of free riding both increased. Elevated levels of inequality were generated partly as a result of increased differences in contributions to cooperative activities between helpers in different sex and age categories in larger groups. After controlling for the positive effect of group size on total provisioning, increasing levels of inequality in contributions were associated with reductions in total pup-provisioning conducted by the group. Reductions in total pup-provisioning were, in turn, associated with reductions in the growth and survival of pups (but pup growth and survival were not directly affected by inequality in provisioning). Our results support the prediction of collective action theory described above and show how the Gini index can be used to investigate the distribution of cooperative behaviour within the group.

Agricultural activities and vegetation growth cause rapid small‐scale vegetation changes which dynamically alter habitat suitability. Time series enable to track down such variations of vegetation structure and are promising to examine their impact on animals' life. Nevertheless, their potential to characterize vegetation dynamics in ways pertinent to animals' fine‐scale habitat use has not been adequately explored and ecologically meaningful proxies are lacking. To address this gap, we exemplary investigated foraging activities of breeding white storks in an agricultural landscape. Reflecting on the understanding that storks require short vegetation to access prey, we examined if good foraging conditions – early growth and post‐harvest/mowing periods – are detectable using the points between local minima/maxima in NDVI profiles (half‐maximum). We processed 1 year of Landsat imagery to identify half‐maximum periods (HM: good prey access) and non‐half‐maximum periods (non‐HM: poor prey access) on field‐scale in croplands and grasslands. Additionally, we mapped used/unused fields and retrieved foraging duration/daily visitation rates from GPS tracks of the storks. We then explored habitat use, compared habitat use with habitat availability and tested temporal predictors distinguishing between HM/non‐HM in habitat selection models. Examining habitat use, storks revisited croplands and grasslands significantly more often during HM than during non‐HM, while foraging duration was only prolonged in croplands during HM. However, comparing habitat use with habitat availability, we observed that storks used croplands and grasslands in significantly higher proportions during HM than during non‐HM. Additionally, we found that temporal information affected storks' habitat selection and improved model performance. Our findings emphasize that the half‐maximum proxy enables to coarsely distinguish temporal resource variations in storks' foraging habitats, highlighting the potential of time series for characterizing behaviorally‐relevant vegetation dynamics. Such information helps to create more species‐centered landscape scenarios in habitat models, allowing to unravel effects of small‐scale environmental changes on wildlife to ultimately guide conservation and management. Agricultural activities and vegetation phenology continuously alter habitat suitability. Time series are promising for characterizing such vegetation dynamics, but their potential to infer ecologically meaningful information remains to be tested. Knowing that white storks require short vegetation to forage, we explore if good foraging conditions ‐ early growths and post‐harvest/mowing periods ‐ can be identified by the points between local minima/maxima in NDVI profiles (half‐maximum). We process 1 year of Landsat data to distinguish good (HM) and poor (non‐HM) prey accessibility in storks' foraging habitats. Additionally, we retrieve foraging locations/duration from GPS‐tracks of breeding storks and investigate their foraging habitat use and selection. Although not all our results are significant, we find that storks favor foraging during HM over non‐HM. Hence, we propose that time series are indeed suitable for deriving ecologically relevant information on small‐scale vegetation dynamics. Such information can help creating more species‐centered landscape scenarios to ultimately guide conservation and management.

In cooperatively breeding species where rearing effort is shared among multiple group members, increases in group size typically reduce average per capita contributions to offspring care by all group members (load-lightening) but it is not known how changes in group size affect the distribution of workload among group members. The socioeconomic collective action theory suggests that, in larger groups, the incentives for free riding are stronger, leading to greater inequalities in work division among group members. Here, we use the Gini index to measure inequality at the group level in the contributions of helpers to three different cooperative behaviours (babysitting, pup-provisioning and raised guarding) in groups of varying size in wild Kalahari meerkats (Suricata suricatta). In larger groups, inequality in helpers’ contributions to cooperative activities and the frequency of free riding both increased. Elevated levels of inequality were generated partly as a result of increased differences in contributions to cooperative activities between helpers in different sex and age categories in larger groups. After controlling for the positive effect of group size on total provisioning, increasing levels of inequality in contributions were associated with reductions in total pup-provisioning conducted by the group. Reductions in total pup-provisioning were, in turn, associated with reductions in the growth and survival of pups (but pup growth and survival were not directly affected by inequality in provisioning). Our results support the prediction of collective action theory described above and show how the Gini index can be used to investigate the distribution of cooperative behaviour within the group.

Migration conveys an immense challenge, especially for juvenile birds coping with enduring and risky journeys shortly after fledging. Accordingly, juveniles exhibit considerably lower survival rates compared to adults, particularly during migration. Juvenile white storks (Ciconia ciconia), which are known to rely on adults during their first fall migration presumably for navigational purposes, also display much lower annual survival than adults. Using detailed GPS and body acceleration data, we examined the patterns and potential causes of age‐related differences in fall migration properties of white storks by comparing first‐year juveniles and adults. We compared juvenile and adult parameters of movement, behaviour and energy expenditure (estimated from overall dynamic body acceleration) and placed this in the context of the juveniles’ lower survival rate. Juveniles used flapping flight vs. soaring flight 23% more than adults and were estimated to expend 14% more energy during flight. Juveniles did not compensate for their higher flight costs by increased refuelling or resting during migration. When juveniles and adults migrated together in the same flock, the juvenile flew mostly behind the adult and was left behind when they separated. Juveniles showed greater improvement in flight efficiency throughout migration compared to adults which appears crucial because juveniles exhibiting higher flight costs suffered increased mortality. Our findings demonstrate the conflict between the juveniles’ inferior flight skills and their urge to keep up with mixed adult–juvenile flocks. We suggest that increased flight costs are an important proximate cause of juvenile mortality in white storks and likely in other soaring migrants and that natural selection is operating on juvenile variation in flight efficiency.