Results
Model development with data from
Switzerland
We trained our models with Red Kite tracking data from Switzerland that
encompassed 697 individual seasons from a total of 258 tracked
individuals, many of which were tracked over several years (range 1 -
7). The presence (n = 360) or absence (n = 337) of a home range and a
nesting attempt (yes = 296, no = 401) were known for all individual
seasons, and the outcome of a nesting attempt was known for 257
individual seasons, of which 177 successfully raised at least one
fledgling, while 80 nesting attempts failed.
Our model to predict the presence of a home range classified 97.6% of
individual seasons correctly in the cross-validated test data (Table 1).
The most influential variables in predicting the existence of a home
range were the total amount of daytime spent within a potential nest
radius (residence_time_day), the median distance between daytime and
the most frequently used nighttime locations
(median_day_dist_to_max_night), and the number of revisits to a
potential nest site during the day (revisits_day; Fig. S2).
Our model predicting the presence of a nesting attempt classified 96.7%
of individual seasons correctly in the cross-validated test data (Table
1), while 9.3% of individual seasons could not be classified with
certainty (Fig. 1). The most influential variables in predicting a
nesting attempt were again the total amount of daytime and nighttime
spent within a potential nest radius (residence_time_day,
residence_time_night), and the median distance between daytime and the
most frequently used nighttime locations
(median_day_dist_to_max_night; Fig. S3). The process to prepare the
data, train the model, and predict the existence of nesting attempts for
697 individual seasons took 17 minutes.
Our model to predict the outcome of a nesting attempt classified 83.1%
of individual seasons correctly in the cross-validated test data (Table
1). If only those seasons with high (> 75%) probability of
assignment were used, the accuracy increased to 97.9% of correctly
classified outcomes, while 26.5% of all seasons could not be classified
with 75% certainty and were therefore flagged up for manual annotation
(Fig. 2). The most influential variables in predicting breeding success
were the number of revisits to the nest site during the second chick
phase (revisitsChick2), and the amount of time spent within the nest
radius during the second chick phase (timeChick2; Fig. S4). The process
to train the model, and predict the outcome of nesting attempts for 257
individual seasons took 2 minutes.
Table 1. Proportion of correctly predicted reproductive parameters from
GPS tracking data of Red Kites in Germany and Switzerland using the
newly developed NestTool. Data subset indicates whether the data were
used for model fitting (‘training’) or not (‘validation’). nindicates the number of individual breeding seasons that were
classified, but note that the number for breeding success was lower
because not all individuals initiated a nesting attempt.