Introduction
Movement capacity and foraging performance are key traits in ecology
affecting survival and dispersal and have been explored in a large range
of animals, like marine predators (Humphries et al., 2010), mammals
(Clarin et al., 2013; Sinclair, 1992), birds (Naef Daenzer & Keller,
1999), and insects (Capaldi et al., 2000; Holway & Case, 1999; Sumpter
& Pratt, 2003). The miniaturisation of technology has allowed the
development of automated tracking devices (e.g. GPS, Argos; Kissling et
al., 2014) surpassing the performance of traditional Movement Capacity
Record (MCR) methods. However, automated tracking devices are often
restrictive in terms of sample size (1 to 20 individuals in general) and
size of animals targeted (related to the weight of the tracking device).
Different tracking technologies can be selected depending on the
locomotory mode of the targeted animal (e.g. flight, walk, swim), their
environment (e.g. water, air, ground surface), the weight of the
tracking device and its optimal attachment to the animal. But such
tracking tools require individual manipulation and frequently the
addition of extra weight can impact individual behavioural performance
(Batsleer et al., 2020).
Image-based tracking is a good alternative that is increasing in use in
animal ecology (Dell et al., 2014). These devices are not invasive as
they do not rely on catching the individual nor the attachment of e.g. a
microchip/GPS tag, and allow the tracking of several targets
simultaneously, enabling the observation of complex behaviours and
interactions between multiple individuals (Bozek et al., 2021; Gernat et
al., 2018). Two dimensional (2D) image-based tracking to study foraging
behaviour, learning, and/or vigilance of animals is quite common (Noldus
et al., 2002; Peters et al., 2016; Wajnberg & Colazza, 1998). However,
most animals move in three dimensions (3D). Birds, bats, and flying
insects move in 3D in the air, as do fish and sea mammals in the water,
which limits the accuracy and precision of 2D data recording. Given the
ability to assess the distance from the object in all three dimensions,
3D-image based tracking can describe adjusted flight/swim behaviours
(e.g. speed, curvature, orientation), even if individuals are close to
one another (Campbell et al., 2008; Chiron et al., 2015). An additional
advantage of 3D-imaging devices such as stereovision cameras is their
ability to recover target positions directly in metric coordinates, as
these systems are pre-calibrated in advance for a specific need (e.g.
close focal length, wide field of view). In comparison, traditional
2D-image based tracking devices (e.g. common cameras) would need
the use of a test chart to convert those 2D pixel expressed coordinates
to metric coordinates, and would be less accurate with a varying
3rd dimension. 3D-image based tracking devices have
been used to study Malaria mosquito flight for example (Spitzen et al.,
2013), and bat flight patterns in different landscapes (Falk et al.,
2014). Stereovision cameras are a tool often used in medicine (Skvara et
al., 2013) and engineering (Gao et al., 2011; Huynh et
al., 2016), but less in ecology (but see Theriault et al. (2010)
and Matzner et al. (2020) for bats and birds, and Rachinas-Lopes et al.
(2019) for water mammals, Chiron (2014) for insects, and more generally
Straw et al. (2011)) although benefits are numerous when studying the
behaviour of 3D-moving animals.
Here we applied 3D-image based tracking to a multi predator-prey
relationship, focussing on two model species: the invasive Asian hornetVespa velutina nigrithorax (also known as the
Yellow-legged Asian hornet; called “Hornet” in this study) and its
prey, the Western honey bee Apis mellifera (called “Honey bee”
in this study). We analysed time series in flight speed, flight
curvature, and hovering of this hornet and honey bees in front of one
beehive looking for potential effects of biotic and/or abiotic factors.
We also question whether predation success could be linked with a
mis-match in flight performances of both prey and predators.
Vespa velutina nigrithorax is an invasive alien hornet in Europe
that now needs to be considered amongst the multiple stressors affecting
honey bee survival (Monceau et al., 2014; Requier et al., 2019).
On top of being a generalist predator of insects, it is capable of
predating honey bees in high numbers in front of their hives,
demonstrating a specific predation behaviour described as “hawking”,
when the hornet hovers in front of the hive waiting for its prey
(Monceau et al., 2014; Tan et al., 2007). This increasing predation
pressure through summer and autumn is leading to heavy honey bee colony
losses (between 5-80% of colony losses in France, 30% of colony loss
in average; Kennedy et al., 2018; Requier et al., 2019), via two
phenomena. The first one is the direct impact of predation which
decreases the number of available foragers in the hive (Tan et al.,
2007). The second is “foraging paralysis”, when the honey bee colony
stops sending foragers out and there is a consequent decline in incoming
food resources (Requier et al., 2020).
Using a stereovision camera, we carried out automated processing of
3D-image based tracks in the field to record 3D-adjusted behavioural
parameters and to focus on specific inter-individual interactions. This
study aims at understanding the effects of predator density (the Asian
hornet) on both the flight behaviour of predators and prey (honey bees),
in a biogeographical context (western Europe) where the prey-predator
interaction did not evolve or co-adapt. First, we developed an automated
process to select “scenes of interest”, when both prey and predator
are present on the screen at the same time. Such an automated process
helps ecologists in video analyses and reduces potential observation
bias. Second, we explored flight performances in terms of speed,
curvature, and hovering by honey bees and hornets looking for potential
drivers of predation success. We assumed that the flight performances of
hornets and honey bees differ due to the morphological differences
between these species. Moreover, we hypothesized that the predation
success could be influenced by predator density due to a disturbance in
flight performance of both predator and prey.
Material and
methods