Understanding population dynamics requires estimation of demographic parameters like mortality and productivity. Because obtaining the necessary data for such parameters can be labour-intensive in the field, alternative approaches that estimate demographic parameters from existing data can be useful. High-resolution biologging data are now frequently available for large-bodied bird species, and can be used to estimate survival and productivity. We build on existing approaches to develop a new tool (‘NestTool’) that uses GPS tracking data at hourly resolution to estimate important productivity parameters such as territory acquisition, breeding propensity and breeding success. We developed NestTool with data from 258 individual red kites (Milvus milvus) from Switzerland tracked for up to 7 years. NestTool first extracts 42 movement metrics such as time within a user-specified radius, number of revisits, home range size, and distances between most frequently used day and night locations from the raw tracking data for each individual breeding season. These variables are then used in three successive random forest models to predict whether individuals exhibited home range behaviour, initiated a nesting attempt, and successfully raised fledglings. The models achieved > 95% accurate classification of home range and nesting behaviour in cross-validation data, but slightly lower (> 80%) accuracy in classifying the outcome of nesting attempts, because some individuals frequently returned to nests despite having failed. NestTool provides a graphical user interface to manually annotate those individual seasons for which model predictions fall below a user-defined threshold of certainty. When applied to tracking data from different red kite populations in Germany, NestTool yielded accurate predictions with > 80% accuracy in all parameters. NestTool is available as R package at https://github.com/Vogelwarte/NestTool and we encourage ornithologists to adapt it for different populations and species. NestTool will facilitate the more widespread estimation of demographic parameters from tracking data to inform population assessments

Parental investment theory proposes two non-mutually exclusive hypotheses to explain variation in anti-predator behaviour in relation to the age of offspring: the “reproductive value of offspring” hypothesis and the “harm to offspring” hypothesis. The relative importance of the two factors underlying the hypotheses, reproductive value and harm, may change depending on environmental conditions such as food availability. To test the relative importance of the two hypotheses under different food conditions, we conducted a supplementary feeding experiment in red kite (Milvus milvus) breeding pairs and used a live eagle owl (Bubo bubo) as decoy nest predator to trigger anti-predator behaviour. We used time-to-capture in mist nets mounted next to the decoy predator as proxy for mobbing intensity. Under natural food conditions we found a nearly constant mobbing intensity throughout the entire nestling period. However, under food-enhanced conditions mobbing intensity was reduced in parents with young nestlings and increased in parents with old nestlings. These results suggest greater importance of the “reproductive value of offspring” hypothesis in situations of favourable food availability. Moreover, mobbing intensity depended on brood size and weather conditions. The results suggest that parental anti-predator investment increases with the reproductive value of the brood under favourable breeding conditions, but that this pattern is adjusted to the current context, including the vulnerability of the brood and environmental conditions.