Introduction
Understanding the dynamics of animal populations requires data on key
demographic processes such as mortality, productivity, and dispersal.
The widespread adoption of miniaturized automated tracking devices has
facilitated the study of seasonal migration patterns of many animal
species in great detail (Kays et al. 2015, Panuccio et al. 2021). So
far, however, relatively few studies have used these large-scale GPS
tracking data to extract information on survival (Klaassen et al. 2014,
Sergio et al. 2019, Swift et al. 2020, Buechley et al. 2021) or
reproduction (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).
Reproductive performance in long-lived species generally improves with
age, and the timing and outcome of the first reproduction can have major
implications for population dynamics (Blas et al. 2009, Sergio et al.
2011, Weimerskirch 2018). As young birds recruit into the breeding
population, their first breeding attempts often fail at an early stage
and can therefore be difficult to detect through field observations
(Péron et al. 2014, Kidd et al. 2015). Because the collection of
reproductive information from individuals in the field can be very time-
and labour-intensive, using tracking data to determine when the first
reproduction occurs and how successful it is may therefore offer
additional detail to help understand the population dynamics of
long-lived species (Murgatroyd et al. 2023).
Information on reproduction can be derived from animal tracking data if
individuals follow repeatable patterns that can be reliably identified
and distinguished. For example, the nest as a central location will be
visited frequently during the breeding phase, resulting in typical
central-place foraging movement patterns that can be detected in
tracking data (van der Wal et al. 2015, Picardi et al. 2020, Bowgen et
al. 2022, Overton et al. 2022, Eisaguirre et al. 2023). The
identification of nesting behaviour can be further improved by using
both GPS and accelerometery data, as periods of incubation lead to
unique patterns with near-zero movement (Schreven et al. 2021,
Ozsanlav-Harris et al. 2022). However, accelerometery data are not
ubiquitously available, and it is so far not clear whether models can be
developed that reliably identify nesting and breeding success of
different populations of the same species based solely on location data
(Picardi et al. 2020, Eisaguirre et al. 2023).
The red kite (Milvus milvus ) is a widespread European raptor
species that has recently recovered from historic persecution in central
Europe (Mammen et al. 2017, Aebischer and Scherler 2021), but is still
vulnerable to anthropogenic causes of mortality in south-western Europe
(Mateo-Tomás et al. 2020, Sergio et al. 2021). Despite being a
long-lived species with delayed maturity, the temporal fluctuation of
productivity was the most important demographic driver of the trajectory
of red kite populations (Sergio et al. 2021, Pfeiffer and Schaub 2023).
A better understanding of the age at which young red kites recruit into
the breeding population and contribute to the reproductive output of a
population is therefore a key demographic parameter that is so far
poorly studied (Katzenberger et al. 2021, Literák et al. 2022, Pfeiffer
and Schaub 2023). Developing a tool that allows an objective assessment
of this recruitment process would therefore be a useful advance for
studying red kite population dynamics.
Besides population dynamics, the movements of red kites have attracted
much research interest and > 2000 individuals have been
equipped with tracking devices in Germany, Austria, Switzerland, Spain
and France (Aebischer and Scherler 2021, Mattsson et al. 2022, Pfeiffer
and Meyburg 2022). These tracking efforts have revealed information on
mortality and survival (Sergio et al. 2019, Katzenberger et al. 2021,
Mattsson et al. 2022), migration and dispersal (Maciorowski et al. 2019,
García-Macía et al. 2022a, Literák et al. 2022, Scherler et al. 2023b),
and revealed that successfully breeding red kites showed smaller home
ranges than unsuccessfully breeding birds (Spatz et al. 2022).
Therefore, telemetry data obtained from individuals during the breeding
season may be sufficient to extract information on breeding propensity
and success. However, the ranging behaviour of red kites varies markedly
between countries and years, with territory sizes dependent on landscape
composition, habitat structure, and food availability (Pfeiffer and
Meyburg 2015, Aebischer and Scherler 2021, Spatz et al. 2022). There is
an urgent need to test whether the predictions of certain demographic
parameters based on movement patterns are transferrable across
populations (Mattsson et al. 2022).
We built on existing approaches (Picardi et al. 2020, Schreven et al.
2021, Overton et al. 2022) to develop a transferable algorithm
(hereafter referred to as ‘NestTool’) predicting important demographic
parameters (territory acquisition, breeding propensity, and breeding
success) of red kites using only GPS tracking data. We trained and
tested this model using data from 697 known-outcome individual breeding
seasons in Switzerland, and validated the utility of the algorithm with
data from two separate populations in Germany. We provide guidance on
what movement patterns indicate successful breeding and provide software
to rapidly annotate existing tracking data with information about
breeding propensity and success. We further examine the conditions under
which an accurate prediction of nest outcome is not possible, and
caution researchers that typical tracking data will not be able to
classify all breeding episodes correctly. Our NestTool facilitates the
rapid estimation of breeding propensity and success, and will therefore
contribute to a better understanding of important demographic processes
that drive red kite population dynamics (Pfeiffer and Schaub 2023).