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This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Wiley., 1. Supervised learning of behavioral modes from body-acceleration data has become a widely used research tool in Behavioral Ecology over the past decade. One of the primary usages of this tool is to estimate behavioral time budgets from the distribution of behaviors as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behavior. In this paper we show that the widespread computation of behavioral time budgets is biased, due to ignoring the classification model confusion probabilities. 2. Next, we introduce the confusion matrix correction for time budgets - a simple correction method for adjusting the computed time budgets based on the model's confusion matrix. 3. Finally, we show that the proposed correction is able to eliminate the bias, both theoretically and empirically in a series of data simulations on body acceleration data of a fossorial rodent species (Damaraland mole-rat, Fukomys damarensis). 4. Our paper provides a simple implementation of the confusion matrix correction for time budgets, and we encourage researchers to use it to improve accuracy of behavioral time budget calculations.

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. published

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.

Supplementary text, Tables S1-S13, Figures S1,S2

Summary statistics for the seasonal niche overlap values with all environmental variables.;Figure and summary statistics for the seasonal niche tracking values with all environmental variables.;Tabel describing the posterior distribution estimates (mean and 95% confidence intervals) for the variability estimation result from the mixed models.

Supplementary text, Tables S1-S7, Figure S1-S4

Biotelemetry is increasingly used to study animal movement at high spatial and temporal resolution and guide conservation and resource management. Yet, limited sample sizes and variation in space and habitat use across regions and life stages may compromise robustness of behavioral analyses and subsequent conservation plans. Here, we assessed variation in (i) home range sizes, (ii) home range selection, and (iii) fine-scale resource selection of white storks across breeding status and regions and test model transferability. Three study areas were chosen within the Central German breeding grounds ranging from agricultural to fluvial and marshland. We monitored GPS-locations of 62 adult white storks equipped with solar-charged GPS/3D-acceleration (ACC) transmitters in 2013–2014. Home range sizes were estimated using minimum convex polygons. Generalized linear mixed models were used to assess home range selection and fine-scale resource selection by relating the home ranges and foraging sites to Corine habitat variables and normalized difference vegetation index in a presence/pseudo-absence design. We found strong variation in home range sizes across breeding stages with significantly larger home ranges in non-breeding compared to breeding white storks, but no variation between regions. Home range selection models had high explanatory power and well predicted overall density of Central German white stork breeding pairs. Also, they showed good transferability across regions and breeding status although variable importance varied considerably. Fine-scale resource selection models showed low explanatory power. Resource preferences differed both across breeding status and across regions, and model transferability was poor. Our results indicate that habitat selection of wild animals may vary considerably within and between populations, and is highly scale dependent. Thereby, home range scale analyses show higher robustness whereas fine-scale resource selection is not easily predictable and not transferable across life stages and regions. Such variation may compromise management decisions when based on data of limited sample size or limited regional coverage. We thus recommend home range scale analyses and sampling designs that cover diverse regional landscapes and ensure robust estimates of habitat suitability to conserve wild animal populations.

Supplementary text, Tables S1-S13, Figures S1,S2