Fig. 2: Example plots of the temporal sequence of three movement metrics (median daily time spent at the nest, median of the daily maximum distance from the nest, and maximum time away from nest) for three adult female red kite individuals that failed to raise fledglings (left), bred successfully (centre), and exhibited behaviour that resulted in uncertain classification (right).
The overall breeding propensity of the tracked red kite population in our study area in Switzerland was 82.2% when inferred from tracking data (compared to 82.2% observed), and overall breeding success inferred from the tracking data was 70.9% (compared to 68.9% observed).

Transferring the models to populations in Germany

We tested our models with red kite tracking data from Germany that encompassed 163 individual seasons from a total of 65 tracked individuals, some of which were tracked over several years (range 1 - 7). After down-sampling the data to 60-min intervals and filtering the data to the seasonal phenology in Germany, we were able to predict reproductive parameters for 99 individual seasons, which contained 98 seasons with home range, 89 nesting attempts, and 55 successful broods. The process to prepare the data, predict the three responses of home range, breeding propensity and breeding success, and summarise the data took 130 seconds.
Our home range model predicted that 95 seasons had home range behaviour - an accuracy of 94.9% (Table 1). The single season without a home range was not correctly predicted, and had a higher predicted probability than four other seasons with home range behaviour.
Compared to the 89 individual seasons that initiated a nesting attempt, our nesting model predicted 87 seasons to breed - an accuracy of 89.9% (Table 1). Out of the 10 individual seasons without a nesting attempt, 4 seasons were predicted to have a nesting probability of >0.5. Conversely, of the 89 actual nesting attempts 6 had a predicted nesting probability of <0.5, and were therefore missed. When we classified only those nests for which predictions were made with high certainty (>75%), the overall accuracy increased to 91.8%.
The prediction of breeding success for the data from Germany was less accurate: out of the 55 individual seasons that raised at least one fledgling, our breeding success model predicted 83.6% as successful, but erroneously predicted 18.6% of failed nests as successful, leading to an overall accuracy of 82.7% (Table 1). For 69% of nesting attempts the outcome could not be predicted with high certainty (<75% assignment probability) and would have required manual annotation; omitting these cases with low predictive certainty increased the accuracy to 92.3%. When the data from Germany were used to train nest success models, the predictive accuracy increased to 93.1% for all cases and to 97.1% for cases that were classified with >75% certainty (Fig. S5).