Discussion
We present a novel tool that allows users to predict reproductive
behaviour from GPS tracking data of red kites, and thus extract
important reproductive parameters that can inform the population
dynamics of this species (Katzenberger et al. 2021, Sergio et al. 2021,
Pfeiffer and Schaub 2023). We developed these models with a large
tracking dataset in Switzerland, but show that they can be transferred
to different populations in Germany and successfully predict
reproductive behaviour. We encourage the use of NestTool in other
populations and species to estimate demographic parameters and assess
the broader transferability of this tool to other species and
populations.
Although the movement patterns of birds repeatedly visiting a nest site
to incubate eggs or feed chicks can be highly diagnostic and allow an
accurate identification of a bird’s breeding status (Picardi et al.
2020, Schreven et al. 2021, Bowgen et al. 2022, Overton et al. 2022,
Ozsanlav-Harris et al. 2022, Eisaguirre et al. 2023), there are
limitations to automatic identification (van der Wal et al. 2015). Red
kites, in particular, can have individually variable home range
behaviour, and also display profound differences in movements both
between males and females and in different landscapes (Aebischer and
Scherler 2021, García-Macía et al. 2022b, Spatz et al. 2022). Our tool
provides probabilistic predictions and facilitates easy manual
inspection of cases that fall below a user’s tolerance level of
certainty. This tool is therefore not designed to work as a ‘black box’,
but rather to assist researchers in the otherwise time-consuming manual
inspection of events during the breeding season of tracked animals. By
specifying the level of uncertainty that is tolerable, the user retains
full control over the trade-off between accepting automated predictions
and manually annotating the reproductive behaviour of many individuals.
NestTool provides accurate predictions for red kites using the movement
metrics that we chose, but we would welcome further development and
improvement for this tool to potentially become useful for other
species. We selected 42 variables (Table S1) that are primarily focused
on durations, distances, and home range areas. However, many alternative
formulations are conceivable, including for example environmental
features and historical context to put the movements of an individual
into perspective of previous movements (Williams et al. 2020, Overton et
al. 2022). The extraction of environmental features from remote-sensing
data may be highly beneficial for some species, but may increase the
computational cost of the tool, and make the transferability of models
to other populations more problematic (Hothorn et al. 2011, Yates et al.
2018).
Because we did not use environmental or landscape variables, the
variables that explained most of the variation between birds with and
without a home range mostly described the amount of time individuals
spent in a certain radius around the centre of the home range, either
during the day, at night, or in total (Fig. S2). Territorial birds are
expected to spend most of their time within their territory to be able
to defend the territory against intruders and raise their brood (Hinde
1956, Kaufmann 1983), and the importance of time as a suitable predictor
is therefore not surprising. Similarly, the prediction of nesting
behaviour relied primarily on the amount of time spent in the vicinity
of a potential nest site, and the median distance between diurnal
locations and where birds roosted at night (Fig. S3), as most nesting
birds generally sleep either on (females) or very close to (males) the
nest (Aebischer and Scherler 2021).
By contrast, the prediction of breeding success relied primarily on
movement metrics in the late chick stages (Fig. S4), including how often
individuals revisited the nest during that phase, how much time they
spent during the later chick stages, and when they last visited the nest
site. Successful breeding requires chicks to be fed for 46 days, thus
requiring multiple daily revisits to the nest for the entire duration of
the breeding season, and regular presence at the nest until the chicks
fledge (Aebischer and Scherler 2021, Scherler et al. 2023a). Although
even unsuccessful breeders frequently remain in their territory and at
their nest site, the number of revisits to the nest decreases when there
are no more chicks that need to be fed (Scherler et al. 2023a).
Moreover, in red kites brood loss primarily occurs during incubation
(Nägeli et al. 2022), which then leads to a distinctive relaxation of
the repetitive visits to the nest and an increase in the distance from
the nest already during incubation (Fig. 2). These revisitation patterns
are broadly similar to what has been used to predict breeding success in
other altricial raptor species (Picardi et al. 2020, Eisaguirre et al.
2023, Murgatroyd et al. 2023), and we are therefore optimistic that this
tool could be adapted to identify reproductive behaviour for other
species as well.
Using an automated tool to classify and predict the existence and
outcome of reproductive behaviour has clear benefits when comparing the
population dynamics across several populations of a species. One key
advantage of using tracking data at a resolution that is available
across a range of studies is the reduced influence of survey effort,
which can affect the assessment of reproductive success (Harris et al.
2020, Eisaguirre et al. 2023, Murgatroyd et al. 2023). By using a
consistent algorithm across many populations, effects of survey effort
or other regional adaptations can be overcome when understanding the
range-wide population dynamics of species (Mattsson et al. 2022). This
is particularly important for demographic parameters that are difficult
to estimate from field observations, such as whether reproduction occurs
or not. Especially in long-lived species, and populations approaching
the carrying capacity of the environment, an increasing proportion of
adult birds may defer breeding or fail at early stages of the nesting
cycle, which would be very difficult to detect using field surveys
(López‐Sepulcre and Kokko 2005, Catlin et al. 2019). Our tool offers a
consistent classification approach that is independent of field survey
effort and could therefore overcome some of the challenges in
quantifying the proportion of a population that is attempting
reproduction.
The age when the first reproduction attempt occurs is another important
driver in the population dynamics of long-lived species (Saether and
Bakke 2000, Sergio et al. 2021), and may change in increasing
populations that approach carrying capacity (Ferrer et al. 2004,
Margalida et al. 2020, Katzenberger et al. 2021). Because the
pre-reproductive years of long-lived animals can be difficult to observe
and can be affected by emigration, our tool offers a consistent approach
to quantify at what age young individuals recruit into a population and
attempt reproduction. Together with reproductive success, and the use of
telemetry data to estimate survival (Sergio et al. 2019, Swift et al.
2020, Buechley et al. 2021), most of the key demographic parameters that
are required for understanding population dynamics can therefore be
inferred from telemetry data. Our tool shows that demographic parameters
can be estimated consistently for different populations using the same
approach, which could facilitate opportunities to assess population
dynamics at larger spatial scales without the problem of inconsistencies
in estimates between local or regional studies (Mattsson et al. 2022).
In summary, NestTool has the potential to facilitate the extraction of
important demographic parameters from widely available tracking data for
red kites in Europe. The availability of demographic information about
when young birds recruit into the breeding population, and how
successful they breed is useful to understand the population dynamics of
the red kite, which may have changed in recent decades as populations
have recovered (Katzenberger et al. 2019, 2021, Pfeiffer and Schaub
2023). We encourage red kite researchers to use this tool to assess the
reproductive performance of their tracked individuals, and other
researchers to adopt this tool for different species, as has been
successfully done for species breeding in remote arctic areas
(Eisaguirre et al. 2023, Ozsanlav-Harris et al. 2023).