Global environmental changes are predicted to lead to warmer average temperatures and more extreme weather events thereby affecting wildlife population dynamics by altering demographic processes. Extreme weather events can reduce food resources and mortality, but the contribution of such events to demographic processes are poorly understood. Estimates of season-specific survival probabilities are crucial for understanding mechanisms underlying annual mortality. However, only few studies have investigated survival at sufficient temporal resolution to assess the contribution of extreme weather events. Here, we analysed biweekly survival probabilities of 307 radio-tracked juvenile little owls (Athene noctua) from fledging to their first breeding attempt in the following spring. Biweekly survival probabilities were lowest during the first weeks after fledging in summer and increased over autumn to winter. The duration of snow cover in winter had a strong negative effect on survival probability, while being well fed during the nestling stage increased survival during the first weeks after fledging and ultimately led to a larger proportion of birds surviving the first year. Overall annual survival probability over the first year varied by 34.3 % between 0.117 (95 % credible interval 0.052 – 0.223) and 0.178 (0.097 – 0.293) depending on the severity of the winter, and up to 0.233 (0.127 – 0.373) for well-fed fledglings. The season with the lowest survival was the post-fledging period (0.508; 0.428 – 0.594) in years with mild winters, and the winter in years with extensive snow cover (0.481; 0.337 – 0.626). We therefore show that extreme weather events reduced the proportion of first-year survivors. Increasingly warmer winters with less snow cover may therefore increase annual survival probability of juvenile little owls in central Europe, but environmental changes reducing food supply during the nestling period can have similarly large effects on annual juvenile survival and therefore the viability of populations.

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