\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[T2A]{fontenc}
\usepackage[ngerman,polish,english]{babel}



\usepackage{float}








\begin{document}

\title{Density of predating Asian hornets at hives disturbs the 3D flight
performance of honey bees and decreases predation success}



\author[1]{Juliette Poidatz}%
\author[2]{Guillaume Chiron}%
\author[1]{Peter Kennedy}%
\author[1]{Juliet  Osborne }%
\author[3]{Fabrice Requier}%
\affil[1]{University of Exeter}%
\affil[2]{Université de La Rochelle}%
\affil[3]{IRD}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Automated 3D-image based tracking systems are new and promising devices
to investigate the foraging behaviour of flying animals with great
accuracy and precision. 3D analyses can provide accurate assessments of
flight performance in regard to speed, curvature, and hovering. However,
there have been few applications of this technology in ecology,
particularly for insects. We used this technology to analyse the
behavioural interactions between the Western honey bee Apis melifera and
its invasive predator the Asian hornet, Vespa velutina nigrithorax. We
investigated whether predation success could be affected by flight
speed, flight curvature, and hovering of the Asian hornet and honey bees
in front of one beehive. We recorded a total of 603,259 flight
trajectories and 5,175 predator-prey flight interactions leading to 126
successful predation events, representing 2.4\% predation success.
Flight speeds of hornets in front of hive entrances were much lower than
that of their bee prey; in contrast to hovering capacity, while
curvature range overlapped between the two species. There were large
differences in speed, curvature and hovering between the exit and
entrance flights of honey bees. Interestingly, we found hornet density
affected flight performance of both honey bees and hornets. Higher
hornet density led to a decrease in the speed of honey bees leaving the
hive, and an increase in the speed of honey bees entering the hive,
together with more curved flight trajectories. These effects suggest
some predator avoidance behaviour by the bees. Higher honey bee flight
curvature resulted in lower hornet predation success. Results showed an
increase of predation success when hornet number increased up to 8
individuals, above which predation success decreased, likely due to
competition among predators. Although based on a single colony, this
study reveals interesting outcomes derived from the use of automated 3D
tracking to derive accurate measures of individual behaviour and
behavioural interactions among flying species.%
\end{abstract}%



\sloppy


\textbf{Density of predating Asian hornets at hives disturbs the 3D
flight performance of honey bees and decreases predation success}

Juliette Poidatz\textsuperscript{1,2,++}, Guillaume
Chiron\textsuperscript{3}, Peter Kennedy\textsuperscript{1}, Juliet
Osborne\textsuperscript{1}, Fabrice Requier\textsuperscript{4,*,++}

\textsuperscript{1} Environment and Sustainability Institute, University
of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE, UK.

\textsuperscript{2} CIRAD UMR PVBMT, 97410, Saint-Pierre, La R\selectlanguage{ngerman}éunion,
France

\textsuperscript{3} Laboratoire L3i, Université de La Rochelle, 17000 La
Rochelle, France.

\textsuperscript{4} Université Paris-Saclay, CNRS, IRD, UMR Évolution,
Génomes, Comportement et Écologie, 91198, Gif-sur-Yvette, France.

* Corresponding author: Fabrice Requier,\emph{fabrice.requier@ird.fr}

\selectlanguage{english}\textsuperscript{++} Equal contribution

\textbf{ORCID}

\emph{Juliette Poidatz https://orcid.org/0000-0003-2942-2541}

\emph{Fabrice Requierhttp://orcid.org/0000-0003-1638-3141}

\emph{Peter Kennedy https://orcid.org/0000-0002-2999-7823}

\emph{Juliet Osborne https://orcid.org/0000-0002-9937-172X}

\textbf{Type of article:} Research Article

\textbf{Word count}

Running title: 44 characters

Abstract: 305 words

Keywords: 6 words

Main text: 4,938 words

References: 47 references

Tables and figure legends: 617 words

Artwork: 2 tables and 6 figures

\textbf{Running title:} Predator-prey flight performances in insects

\textbf{Abstract}

Automated 3D-image based tracking systems are new and promising devices
to investigate the foraging behaviour of flying animals with great
accuracy and precision. 3D analyses can provide accurate assessments of
flight performance in regard to speed, curvature, and hovering. However,
there have been few applications of this technology in ecology,
particularly for insects. We used this technology to analyse the
behavioural interactions between the Western honey bee \emph{Apis
melifera} and its invasive predator the Asian hornet, \emph{Vespa
velutina nigrithorax} . We investigated whether predation success could
be affected by flight speed, flight curvature, and hovering of the Asian
hornet and honey bees in front of one beehive. We recorded a total of
603,259 flight trajectories and 5,175 predator-prey flight interactions
leading to 126 successful predation events, representing 2.4\% predation
success. Flight speeds of hornets in front of hive entrances were much
lower than that of their bee prey; in contrast to hovering capacity,
while curvature range overlapped between the two species. There were
large differences in speed, curvature and hovering between the exit and
entrance flights of honey bees. Interestingly, we found hornet density
affected flight performance of both honey bees and hornets. Higher
hornet density led to a decrease in the speed of honey bees leaving the
hive, and an increase in the speed of honey bees entering the hive,
together with more curved flight trajectories. These effects suggest
some predator avoidance behaviour by the bees. Higher honey bee flight
curvature resulted in lower hornet predation success. Results showed an
increase of predation success when hornet number increased up to 8
individuals, above which predation success decreased, likely due to
competition among predators. Although based on a single colony, this
study reveals interesting outcomes derived from the use of automated 3D
tracking to derive accurate measures of individual behaviour and
behavioural interactions among flying species.

\textbf{Key-words:} \emph{Apis mellifera} ; Flight performance;
Image-based tracking; Predator-prey interaction; Stereovision;

\emph{Vespa velutina}

\textbf{Graphical abstract}

\textbf{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 (\emph{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
3\textsuperscript{rd} 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\emph{.,} 2011; Huynh et
al\emph{.,} 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
hornet\emph{Vespa velutina} \emph{nigrithorax} (also known as the
Yellow-legged Asian hornet; called ``Hornet'' in this study) and its
prey, the Western honey bee \emph{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.

\emph{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\emph{.,} 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.

\subsection*{Material and methods}

{\label{material-and-methods}}

Automated 3D-image based tracking system

A high-speed stereovision camera (G3 Evo 3, TYZX(r)) was fixed on the top
of a beehive (10-frame Dadant type) to track the flights of bees and
hornets at the beehive entrance (\textbf{Figure 1a} ). A controller
laptop was used to schedule the recordings and to encode the raw RGB-D
videos before being dumped on a NAS (Network Attached Storage). Video
surveillance was deemed as a robust and effective method to measure the
exact number of honey bees at the entrance of the colony. The
experimental beehive was located nearby the town of La Rochelle (France)
(46deg8'N, 1deg8'W), 200 km from where the Asian hornet was first spotted in
2004 (Haxaire et al., 2006; Villemant et al., 2011). The video
surveillance was carried out from October 16\textsuperscript{th} to
October 25\textsuperscript{th} 2015. In this oceanic climate, the
ambient temperature ranged between 6degC and 17degC, wind speed ranged
between 11km.h\textsuperscript{-1} and 39km.h\textsuperscript{-1}, and
the relative humidity ranged between 65 and 82\%\textbf{.} Video
tracking was performed from 9am to 6pm over 10 consecutive days,
providing a total of 90 hours of recorded activity, corresponding to
more than a terabyte of compressed data (i.e. RGB-D videos above 50
fps). The monitored beehive was isolated in the apiary (i.e. no other
beehives in a 300m radius) in order to ensure that all observed
trajectories belonged to the target hive. The stereovision camera was
placed 50 cm above the flight board of the beehive to ensure the
non-trivial trade-off between the device intrusiveness (no nearby source
of disturbance), the image definition (at least 8 pixels per bee on the
flight board) and the observed volume (that must include the 50 cm wide
flight board). The Western honey bees (\emph{Apis mellifera} ) were not
bred from any particular genetic strain, while the native populations
are Apis mellifera melliefra in the region (Requier et al., 2019b) with
common hybridization towards \emph{A. mellifera mellifera}
x\emph{caucasica} (Requier et al., 2017). The Asian hornet is present in
high abundance in the region since 15 years (Rome and Villemant, 2022).

Flight trajectories of the Asian hornet and honey bees

The RGB-D videos collected by the stereo-cameras were processed using
the method presented in Chiron et al. (2013) resulting in a dataset made
of the trajectories of every insect flying in front of the beehive. The
subject of each trajectory (either Asian hornet or honey bee) was
determined using both the flight dynamics such as max speed (i.e. using
a clustering approach inspired by Chiron et al. 2014) as well as
appearance features such as the body size. More details on size/depth
ratio of targets as observed on RGB-D images are available in Requier et
al. (2016). Finally, for each trajectory, we extracted the period when
it occurred (date, start time), the flight speed
(m.s\textsuperscript{-1}), the orientation (3D vector), the curvature
(m), and the proportion of time spent in static flight (hovering). The
Asian hornet count for each measurement was made by counting them in the
five seconds before and after the measurement point. We differentiated
hovering behaviour into two categories: those where the individual
remained very steady (threshold 1 = less than 2 mm of drifting between 2
images), and those where the individuals drifted slightly from their
initial position (threshold 2 = less than 10mm of drifting between 2
images).

Automated selection of interest scenes

Each video segment, for which both hornet(s) and honey bee(s) were
detected, using the process just described, was automatically extracted
using specifically developed software. This software included a
step-by-step procedure composed of the following processes: (\emph{i} )
stereovision acquisition, (\emph{ii} ) target detection, in each image
independently, on RGB-D, (\emph{iii} ) temporal aggregation for
multi-target tracking in 3D (Chiron et al., 2013), (\emph{iv} )
signature extraction from the individual trajectories, (\emph{v} )
hierarchical segmentation of the trajectory data into temporal entity,
and (\emph{vi} ) behavioural modelling by multi-level clustering (Chiron
et al., 2014). The video segments were then visually reviewed by an
observer in order to detect potential successful predation of a honey
bee by a hornet (\textbf{Supporting Information, Figure S1} ). We
considered a predation to be successful when a hornet caught a honey bee
and flew out of video view with its caught prey, taking into account the
limited field of view (about 1.5m\textsuperscript{2} around the beehive
entrance). Each video was reviewed twice by the observer to confirm the
successful predation events. A predation was considered as a failure
when observing both hornet(s) and honey bee(s) in the same scene but
with no predation success (e.g. no catch).

Data analysis

All the analyses were conducted with R, version R 4.0.2 (R Core Team,
2020).

\emph{Daily temporal patterns of flight activities} - To understand how
flight performance of both honey bee and hornet vary over the time, we
used Generalized Additive Models (GAMs, using the\emph{mgcv} R-package)
of the following flight parameters: the number of trajectories per unit
of time, the maximum flight speed, the flight trajectory curvature and
the percentage of time spent hovering for each flight trajectory of bees
and hornets.

\emph{Flight performance --} To compare flight performances between
honey bees entering or leaving and hornets, we analysed their
distribution of maximum speed, curvature and hovering percentage. We
checked for normality using an Anderson-Darling normality test (adapted
for large datasets \textgreater{}5000pts, using the
\emph{nortest}R-Package) and for variance homogeneity with a Levene test
(using the\emph{car} R-Package). We then used Kruskal-Wallis rank sum
tests to assess differences in flight parameters between the three types
of flight (hornets, honey bees entering, honey bees leaving the hive,
followed by a pairwise Wilcoxon test as a Post-Hoc test with a
Bonferroni P value adjustment method (using the \emph{stats} R-Package).

\emph{Overlapping flight performances and predation success behaviour -}
To assess which of the global parameters best explained the hornet
predation success (response variable), we ran a binomial Generalized
Linear Mixed Model (GLMM, using the \emph{GLMM} R-Package) with fixed
parameters being the number of hornets present, the hour of the day, the
number of honey bees present, the interaction between all those
parameters, and the quadratic parameters of hornets and hour. To select
parameters of interest, we ran a multimodel inference procedure by AIC
comparison (using the \emph{MuMIn} R-Package). In order to extract
specific behavioural patterns linked with the hornet predation success,
we analysed the distribution of maximum flight speed, flight curvature
and hovering percentage for the prey (pooling honey bees entering and
leaving their hive) and predator, in case of predation success or
failure. To test for statistical differences between predation success
and failure, we checked for variance homogeneity as described above. We
then used a Kruskal-Wallis rank sum tests to assess differences in
flight parameters between them, followed by a pairwise Wilcoxon test as
a Post-Hoc test with a Bonferroni \emph{P} value adjustment method
(using the \emph{stats} R-Package).

\emph{Impact of hornet density on bees and hornets flight performance
and predation success} -- To assess the impact of hornet density
(Log\textsubscript{10} number of hornets) on bee and hornet flight
performance traits (i.e. flight speed, curvature and hovering), we used
Linear Models (LM, using the \emph{stats} R-Package) on hornets, honey
bees leaving the hive or honey bees entering the hive. Using the same
statistical technique, we analysed whether the density of hornets
impacted the coefficient of variation in these three flight performance
traits in hornets and in honey bees leaving the hive and entering the
hive.

\subsection*{Results}

{\label{results}}

Time series of flight activities

Overall, a total of 603,259 trajectories were extracted, which included
5,175 predator-prey flight interactions with a total of 126 successful
predation events representing 2.4\% of the recorded interactions. The
daily time series of flight activity in hornets and honey bees in front
of the beehive followed nonlinear, quadratic patterns that increased
until 1pm for hornets (GAM, F=22.9, P\textless{}0.001) and 3pm for honey
bees (GAM, F=25.95, P\textless{}0.001) and then decreased
(\textbf{Figure 1b} ). The daily dynamic of flight speed in hornets and
honey bees in front of the beehive followed nonlinear patterns with an
increase until 11am for hornets (GAM, F=7.297, P\textless{}0.001) and
until 2pm for honey bees entering the hive (GAM, F=2.327,
P\textless{}0.001) and leaving the hive (GAM, F=16.34,
P\textless{}0.001), and then decreased (\textbf{Figure 2a} ).

The daily time series of flight curvature in honey bees entering the
hive followed a similar nonlinear pattern to flight activity and flight
speed, with an increase until 2pm (GAM, F=12.29, P\textless{}0.001) and
then decreased (\textbf{Figure 2b} ). Curvatures of hornets and honey
bees leaving the hive followed a positive trend over the day (GAM,
F=9.263, P=0.003 and F=17.03, P\textless{}0.001 respectively). On the
other hand, the daily dynamics of the percentage of time spent hovering
by hornets and honey bees followed nonlinear patterns that decreased
until 2pm and then increased for hornets (GAM, F=6.19,
P\textless{}0.001), and was more variable for honey bees (GAM, F=13.56,
P\textless{}0.001, \textbf{Figure 2c} ). For honey bees leaving the
hive, the percentage of time spent hovering increased over the day (GAM,
F=58.01, P\textless{}0.001).

Flight performance

Hornets and honey bees had different flight performances in term of
flight speed, curvature and static flight (\textbf{Figure 3} ). Honey
bees leaving the hive were 1.9 times faster than honey bees entering the
hive, and honey bees entering the hive were 1.25 times faster than
hornets (Kruskal-Wallis chi-squared=78018, df=2, P\textless{}0.001;
Pairwise Wilcoxon test, P\textless{}0.001; \textbf{Figure 3a} ). With
respect to flight curvature, the flight trajectories of honey bees
leaving the hive were significantly. straighter than for honey bees
entering the hive, and for the latter the trajectories were
significantly straighter than for hornets (Kruskal-Wallis
chi-squared=41384, df=2, P\textless{} 0.001 ; Pairwise Wilcoxon test,
P\textless{}0.001). Moreover, the curvature was less variable in honey
bees leaving the hive compared with the two other categories
(\textbf{Figure 3b} ).

Hornets hovered significantly for more time than honey bees
(p\textless{}0.001 (Pairwise Wilcoxon test), hornets hovered 2.1 times
more than honey bees entering the hive). Honey bees leaving the hive
hovered significantly less than the honey bees entering the hive
(Kruskal-Wallis chi-squared=27949, df=2, P\textless{}0.001; Pairwise
Wilcoxon test, P\textless{}0.001) (\textbf{Figure 3c} ). In hovering
hornets, static flight as defined by ``threshold 1'' (no more than 2mm
displacement) represented a very small proportion of flight time, under
10\%, while if defined by ``threshold 2'', up to 10mm displacement,
hovering hornets can be divided into mainly two categories. A smaller
group of hornets, spending 10\% of recorded flight time hovering, and a
larger group of hornets spending around 90\% of recorded flight time
hovering, demonstrating that this kind of flight pattern is used by many
for long periods of time in front of the hives (\textbf{Figure S2} ).

Overlapping flight performances and predation success behaviour

When looking at predation success, this was four times higher when
hornets attempted to predate honey bees entering the hive (69.46\%
success in predation events of honey bees entering the hive) compared
with honey bees leaving the hive (15.27\% success in predation events of
honey bees leaving the hive). The only global parameter that had a
significant impact on hornet predation success was the number of hornets
in front of the hive, when considered as a quadratic
parameter\textbf{(Table 1, Table S1)} . Predation success had a positive
quadratic relationship with the square number of hornet present
(\textbf{Table 1} ) and this peaked at approximately 8 hornets
(\textbf{Figure 4} ). The number of honey bee flight trajectories, the
time of day and the interactions between those parameters did not have
any significant impact on hornet predation success \textbf{(Table 1)} .

We found no significant difference in hornet flight speed associated
with hornet predation success or failure (Kruskal-Wallis
chi-squared=0.44, df=2, P=0.80). In other words, the flight speed of
hornet that then successfully caught a honeybee did not differ from the
flight speed of those hornets that were unsuccessful. Similarly, we
found no difference in percentage of time hornets spent hovering
(Kruskal-Wallis chi-squared=0.32, df=2, P=0.85) nor with hornet flight
curvature (Kruskal-Wallis chi-squared=1.71, df =2, P=0.43) associated
with predation success or failure. Moreover, there were no significant
difference in honeybee flight speed associated with hornet predation
success or failure (Kruskal-Wallis chi-squared=2.26, df=2, P=0.32), and
no significant difference in honey bee time spent hovering
(Kruskal-Wallis chi-squared=0.93, df=2, P=0.63). But honey bee flight
curvature was significantly lower in case of hornet predation success
compared to unsuccessful predation attempts (Kruskal-Wallis
chi-squared=11.10, df=2, P=0.005), meaning that the capacity of honey
bees to have more curved trajectories, or less straight flight paths,
allowed them to be more successful in avoiding hornet predation.

\textbf{Impact of hornet density on bees and hornets flight performance}

The speed of honey bees leaving the hive was negatively affected by the
number of hornets present in front of the hive (LM, F=4.617, p=0.032).
However, the speed (LM, F=19.36, p\textless{}0.001) and the curvature of
the trajectories of honey bees entering the hive were positively
affected by the number of Asian hornets in front of the hive (LM,
F=59.74, p\textless{}0.001) (\textbf{Figure 5} ). The number of hornets
in front of the hive strongly reduced the variance of all parameters
(speed, curvature and percentage of hovering) of bees entering, leaving
the hive, and hornets \textbf{(Figure 6),} but only tend to decrease (no
significant statistic) the variance of flight curvature of bees entering
the hives (\textbf{Table 2, Figure S3, Table S2} ).

\subsection*{Discussion}

{\label{discussion}}

The automatic selection of scenes of interest is a significant step
forward for behavioural ecology to reduce the manual labour involved. In
this study it reduced 90 observation hours into a few hours, and allowed
us to switch from 603,259 registered trajectories to 5,175 scenes of
interest (i.e. short video segments) where both prey and predator were
present. The field of view was appropriately selected, as it included
the honey bees' speed deceleration area as they enter their colony,
through its reduced entrance, that is strategically exploited by
predating hornets. Such data enabled the analysis of flight performance
characteristics, namely speed, curvature, and hovering (established via
3D accuracy), of honey bees and hornets, to better understand their
prey-predator interaction and look for potential drivers of predation
success.

The flight speed of honey bees arriving and entering the colony was half
as fast as the honey bees leaving the colony, probably because of the
need of honey bees to slow down in vicinity of the nest entrance to
correct their flight and enter the nest safely, i.e. ``parachute''
behaviour. To illustrate the importance of such parameter on the
potential predation capacities of predators on bees, we can cite the
very particular ``crashing behaviour'' of some \emph{Melipona} bee
species. In order to prevent predation in front of their nests by
spiders, while they attempt to enter their earth built nest, these bees
have adapted the shape of the nest entrance into a tube with a flat
platform orientated in a specific way to allow these bees to bounce on
it to enter the nest quickly, without having to decrease speed,
therefore being harder to catch by predators waiting at their nest
(Shackleton et al., 2019). Concerning flight curvature, honey bees
leaving the hive had very straight trajectories, while honey bees
entering their hive had more curved trajectories. The time spent
hovering by honey bees leaving the colony was very low. Hovering seems
to be a rare behaviour in honey bees, linked with specific situations
(e.g. traffic jams), while in hornets this is characteristic of their
predation flight in the hive vicinity or in other ``hunting sites'' e.g.
at other insect aggregations (bins, animal carcasses).

The flight speed of honey bees in front of the hive increased in the
morning, then for honey bees leaving their hive we observed a slowing
around noon, to gently decreasing speed through the afternoon. It could
be connected with a ``traffic jam'' phenomenon: when honey bee foragers
departing the hive reach a maximum at the same time when young honey
bees exhibit their first orientation flights outside the hive (Capaldi
\& Dyer, 1999; Capaldi et al., 2000). Hovering flights were mostly
observed with hornets and returning honey bees. Hornet hovering
decreased through the morning until early afternoon, and thereafter
increased. This might be related to the quantity of potential prey
available around the colony entrance: hornets may have to hover, to wait
for predation opportunities. For the honey bees entering the hive, we
observed a similar pattern, but with a slight increase in hovering in
the early afternoon, potentially linked with the same traffic jam
phenomenon described above. This hovering activity of bees in front of
the beehive could provide hornets with more opportunity to prey on them.

During the course of the day, the number of honey bees leaving their
hives and hornets in front of it at any one time follows a quadratic
pattern, meaning that it is enhanced through the morning up to the
middle of the day, to then slowly decrease at the end of the day. The
optimum period of activity for hornets was around 12pm, while for honey
bees peak activity around 2pm. This interesting offset could be linked
with the predation success of hornets that decreases when there are too
many hornets hovering in from of the hive. Arriving early at the hive
would, even before the prey are abundant would limit the number of
predating competitors. The presence and the temporal dynamic of honey
bees in front of hive was very similar to what observed in Struye et al.
(1994). The daily dynamic of the presence of hornets in front of the
hive also matches with former works studying the rate at which hornets
were leaving their nests over a day (Monceau et al\emph{.} 2017; Poidatz
et al\emph{.} 2018).

Predator capture rates are expected to depend on encounter probability
with prey, prey escape capability, and on predator agility (Kruse et
al., 2008). We confirm that hornets succeed in preferentially catching
honey bees going back into their colony (Shah and Shah, 1991): we first
hypothesised that the main explaining parameter could be their speed,
which is lower than for bees leaving their colony, as they come back
more slowly due to their nectar-filled crops or pollen baskets, and need
to slow down to be able to access the small hive entrance. But our
analysis showed that, whilst returning bees were slower, the flight
speed did not play a significant role in the probability of success of
hornet predation. We also reveal in this study that the quadratic number
of Asian hornets in front of the honey bee hive influences their
predation success on honey bees, reaching a peak at 8 hornets, above
which threshold their predation success decreases. In the increasing
phase, there are enough active prey, and we can hypothesise that the
predators are distributed at the hive entrance in a way that allows them
to optimize space and occupy more and more potential honey bee paths;
but above 8 hornets, hornet predation success decreases with their
increasing number. It could be due to inter-specific competition
(Monceau et al., 2014), and maybe also be due to the foraging paralysis
of honey bees, that do not exit the colony at the same rate anymore.

This result is congruent with the results of the escape success in
terrestrial predator-prey interactions model developed by Wilson et al.
(2020), where those authors concluded that smaller prey with higher
agility would force larger predators to run along curved paths that do
not allow them to use their superior speeds, and therefore could be a
critical parameter for escaping predation. Moreover, in their study of
the goshawk's \emph{Accipiter gentilis} (Linnaeus 1758) predation, Kane
et al. (2015) showed a similar conclusion: the prey's sharp sideways
turns caused the goshawk to lose visual fixation on the prey and thus
decreased their predation success.

The increasing number of hornets present in front of the hive strongly
affected both hornet behaviour and honey bee behaviour on entering and
leaving their hive. First, an increased number of hornets reduced the
speed of bees leaving the hive, suggesting more hesitancy from bees
going out to forage. The number of hornets also enhanced the speed and
the trajectory curvature of honey bees entering the hive, so the bees
are `racing' into the hive, and choosing an unpredictable flight path --
both of which may reduce their chances of capture. Very interestingly,
this result fits with the adaptive behaviour of \emph{Apis
cerana}foragers to escape \emph{V. velutina} predation (Tan et al.,
2007).\emph{Apis cerana} are native honey bees from Asia an coexist
with\emph{V. velutina} in this region where both the Asian honey bee and
Asian hornet are native. The similar behaviour of bees from Europe (our
study) and Asia in the presence of an abundance of hornet predators
suggests that increasing flight speed of honey bees entering the hive,
i.e. the preferential prey of hornets in comparison with honey bees
leaving their hive, would improve bee survival and limit hornet
predation success. Second, increasing the number of hornets reduced the
variance in flight patterns for bees and hornets. This could be
advantageous to hornets at first as they have a lower range of bee
flight trajectories to tackle and anticipate for predation success.

Although based on one single beehive, this study illustrates the
interest of those observation and analysis methods, as they provided
unique and very useful data, that allowed the observer to accurately
witness complex phenomena congruent with the literature, and provided
interesting leads for further studies. The automatic processing method
providing ''Filtered video sequences of potential predation'' from
''RGB-D sequences'' represents a very useful tool for video-based data
collection in ecology. Some improvement points can be recommended for
future studies. External uncontrolled events (e.g. other flying insects
coming into the field of view of the video camera, extreme weather
episodes, overcrowded flight area) are likely to induce biases in the
final statistics for the following reasons: target miss detections,
failure during tracking, erroneous built-in depth estimation by the
stereo-camera. Therefore, relative analysis and examination of trends is
more conservative. For absolute figures, for example of ''hornet
predation success rate'', the data would benefit from confirmation with
complementary studies. This method could also allow to go deeper in the
description of such behaviours: for example, through marking hornets'
hunters of different ages (different hunting experience) and colonies,
it could be possible to describe individual learning, and to detect
behavioural colonial differences. The same way through marking escaped
bees, their learning capacity could be further evaluated.

In summary, 3D flight analysis of both predator and prey has
demonstrated the characteristics of the bees' flight that change (speed
and trajectory curvature) as predatory pressure at the hive increases,
suggesting avoidance behaviour. It has also shown that curvature of the
bees' flight has more effect than flight speed on hornet predatory
success. As honey bee colonies in Europe are now under considerable
pressure from this predator, it would be of great interest for future
work to focus on whether different bee colonies show different flight
behaviours making them more or less resilient to attack by this
voracious predator.

\textbf{Authors' contributions}

F.R. and G.C. conceived the ideas and designed methodology; G.C.
collected the videos and built-up the dataset of trajectories; J.P. and
F.R. analysed the data; J.P. and F.R. led the writing of the manuscript
and all authors contributed significantly to the drafts and gave their
final approval for publication.

\textbf{Acknowledgments}

This work has received funding from the Biotechnology and Biological
Sciences Research Council (BBSRC); grant number BB/S015523/1, from the
European Union's Horizon 2020 research and innovation program under
grand agreement no 862665 ICT-AGRI-FOOD; grant number ANR-21-ICAF-0001,
and grants from the European Community Program (797/2004) for French
beekeeping (RISQAPI project). Special thanks go to M. Menard for help on
the video analyses.

\textbf{Disclosure statement}

The authors reported no potential conflict of interest.

\textbf{Data accessibility}

Data on time series in speed, curvature, and hovering of flight
performance in Asian hornets and honey bees are publically available
through the figshare repository https://doi.org/xxxx/m9.figshare.xxxxx
(Poidatz \emph{et al} ., 2022).

\textbf{References}

Batsleer, F., Bonte, D., Dekeukeleire, D., Goossens, S., Poelmans, W.,
Van der Cruyssen, E., Maes, D., \& Vandegehuchte, M.L. (2020). The
neglected impact of tracking devices on terrestrial
arthropods.\emph{Methods in Ecology and Evolution, 11} , 350-361.
https://doi.org/10.1111/2041-210X.13356

Bozek, K., Hebert, L., Portugal, Y., \& Stephens, G.J. (2021).
Markerless tracking of entire honey bee colony. \emph{Nature
communications} , \emph{12,} 1733.

Campbell J., Mummert, L., \& Sukthankar, R. (2008). Video monitoring of
honey bee colonies at the hive entrance. \emph{Proceedings of Workshop
on Visual observation and analysis of animal and insect behavior} (in
conjunction of ICPR).

Capaldi, E.A., \& Dyer, F.C. (1999). The role of orientation flights on
homing performance in honeybees. \emph{The Journal of experimental
biology, 202} , 1655-66.

Capaldi, E.A., Smith, A., Osborne, J., Farris, S.M., Reynolds, D.R.,
Edwards, A.S., Martin, A., Robinson, G.E., Poppy, G.M., \& Riley, J. R.
(2000). Ontogeny of orientation flight in the honeybee revealed by
harmonic radar. \emph{Nature} , \emph{403,} 537--540.

Clarin, T.M., Ruczy\selectlanguage{polish}ń\selectlanguage{english}ski, I., Page, R.A., \& Siemers, B. M. (2013).
Foraging ecology predicts learning performance in insectivorous
bats\emph{. PloS one, 8(6),} e64823.

Chiron, G., Gomez-Kr\selectlanguage{ngerman}ämer, P., \& Ménard, M. (2013). Detecting and
tracking honeybees in 3D at the beehive entrance using stereo
vision.\emph{EURASIP Journal on Image and Video Processing, 59} .

Chiron, G., Gomez-Krämer, P., \& Ménard, M. (2014). Discovering emergent
behaviors from tracks using Hierarchical Non-parametric Bayesian
Methods. \emph{Proc. of the 22nd International Conference on Pattern
Recognition,} 2185-2190.

Dell, A. I., Bender, J.A., Branson, K., Couzin, I.D., de Polavieja,
G.G., Noldus, L. P.J.J., Pérez-Escudero, A., Perona, P., Straw, A.D.,
Wikelski, M., \& Brose, U. (2014). Automated image-based tracking and
its application in ecology. \emph{Trends in Ecology \& Evolution, 29} ,
417-428, ISSN 0169-5347.

Falk, B., Jakobsen, L., Surlykke, A., \& Moss, C.F. (2014). Bats
coordinate sonar and flight behavior as they forage in open and
cluttered environments. \emph{The Journal of Experimental Biology}
,\emph{217,} 4356. https://doi.org/10.1242/jeb.114132

Gao, Y., Ai, X., Wang, Y., Rarity, J., \& Dahnoun, N. (2011).
U-V-Disparity based obstacle detection with 3D Camera and steerable
filter. \emph{2011 IEEE Intelligent Vehicles Symposium (IV)} ,
Baden-Baden, 957-962. https://doi.org/10.1109/IVS.2011.5940425

Gernat, T., Rao, V.D., Middendorf, M., Dankowicz, H., Goldenfeld, N., \&
Robinson, G.E. (2018). Bursty interaction patterns in bee social
networks. \emph{Proceedings of the National Academy of Sciences}
,\emph{115 (7),} 1433-1438. https://doi.org/10.1073/pnas.1713568115

Haxaire, J., Bouguet, J.P., \& Tamisier, J.P. (2006). \emph{Vespa
velutina} Lepeletier, 1836, une redoutable nouveauté pour la faune de
France (Hym., Vespidae). \emph{Bulletin de la Société entomologique de
France} , \emph{111} , 194. https://doi.org/10.3406/bsef.2006.16309

Holway, D.A., \& Case, T.J. (1999). Mechanisms of dispersed
central-place foraging in polydomous colonies of the Argentine
ant.\emph{Animal Behaviour} , \emph{59(2),} 433-441, ISSN 0003-3472.
https://doi.org/10.1006/anbe.1999.1329

Humphries, N., Queiroz, N., Dyer, J., Pade, N.G., Musyl, KM.K.,
Schaefer, K.M., Fuller, D.W., Brunnschweiler, J.M., Doyle, T.K.,
Houghton, J.D.R., Hays, G.C., Jones, C.S., Noble, L.R., Wearmouth, V.J.,
Southall, E.J., \& Sims, D.W. (2010). Environmental context explains
Lévy and Brownian movement patterns of marine predators.~\emph{Nature,}
~\emph{465} \textbf{,~} 1066--1069. https://doi.org/10.1038/nature09116

Huynh, P., Ross, R., Martchenko, A., \& Devlin, J. (2016). 3D anomaly
inspection system for sewer pipes using stereo vision and novel image
processing. IEEE 11th Conference on Industrial Electronics and
Applications (ICIEA), Hefei. 988-993.
https://doi.org/10.1109/ICIEA.2016.7603726

Kane, S. A., Fulton, A.H., \& Rosenthal, L.J. (2015). When hawks attack:
animal-borne video studies of goshawk pursuit and prey-evasion
strategies. \emph{Journal of Experimental Biology, 218(2),} 212-222.
https://doi.org/10.1242/jeb.108597

Kennedy, P.J., Ford, S.M., Poidatz, J., Thiéry, D., \& Osborne, J.L.
(2018). Searching for nests of the invasive Asian hornet (\emph{Vespa
velutina} ) using radio-telemetry. \emph{Communications Biology, 1,}
1\selectlanguage{english}-8. https://doi.org/10.1038/s42003-018-0092-9

Kissling, D., Pattemore, D.E., \& Hagen, M. (2014). Challenges and
prospects in the telemetry of insects. \emph{Biological Reviews,
89,}511-530. https://doi.org/10.1111/brv.12065

Matzner, S., Warfel, T., \& Hull, R. (2020). ThermalTracker-3D: A
thermal stereo vision system for quantifying bird and bat activity at
offshore wind energy sites. \emph{Ecological Informatics} , \emph{57} ,
101069. https://doi.org/10.1016/j.ecoinf.2020.101069

Monceau, K., Bonnard, O., \& Thiery, D. (2014). \emph{Vespa Velutina} :
A new invasive predator of honeybees in Europe. \emph{Journal of Pest
Science} , \emph{87} , 1--16. https://doi.org/10.1007/s10340-013-0537-3

Monceau, K., Maher, N., Bonnard, O., \& Thiery, D. (2017). Daily and
seasonal extranidal behaviour variations in the invasive yellow-legged
hornet, \emph{Vespa velutina} Lepeletier (Hymenoptera:
Vespidae).\emph{Journal of Insect Behavior, 30} , 220-230.
https://doi.org/10.1007/s10905-017-9607-3

Naef-Daenzer, B., \& Keller, L.F. (1999). The foraging performance of
Great and Blue tits (\emph{Parus major} and \emph{P. caeruleus} ) in
relation to caterpillar development, and its consequences for nestling
growth and fledging weight. \emph{Journal of animal Ecology, 68(4),}
708-718. https://doi.org/10.1046/j.1365-2656.1999.00318.x

Noldus, L.P.J.J, Spink, A.J., \& Tegelenbosch, R.A.J. (2002).
Computerised video tracking, movement analysis and behaviour recognition
in insects. \emph{Computers and Electronics in Agriculture}
,\emph{35(2--3),} 201-227, ISSN 0168-1699.
https://doi.org/10.1016/S0168-1699(02)00019-4

Peters, S.M., Pinter, I.J., Pothuizen, H.H.J., de Heer, R.C., van der
Harst, J.E., \& Spruijt, B.M. (2016). Novel approach to automatically
classify rat social behavior using a video tracking system.\emph{Journal
of Neuroscience Methods, 268,} 163-170, ISSN 0165-0270.
https://doi.org/10.1016/j.jneumeth.2016.02.020

Poidatz, J., Monceau, K., Bonnard, O., \& Thiery, D. (2018). Activity
rhythm and action range of workers of the invasive hornet predator of
honeybees \emph{Vespa velutina} , measured by radio frequency
identification tags. \emph{Ecology and Evolution, 8(15),} 7588-7598.
https://doi.org/10.1002/ece3.4182

Rachinas-Lopes, P., Ribeiro, R., dos Santos, M.E., \& Costa, R.M.
(2019). Correction: D-Track---A semi-automatic 3D video-tracking
technique to analyse movements and routines of aquatic animals with
application to captive dolphins. \emph{PLoS ONE. 14(1),} e0211383.
https://doi.org/10.1371/journal.pone.0201614

R Core Team (2020). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.

Requier, F., Chiron, G., \& Menard, M. (2016). Decrypter le vol des
abeilles en 3D. \emph{Biofutur. 380} , 48--53.

Requier, F., Odoux, J.F., Henry, M., Bretagnolle, V. (2017). The
carry-over effects of pollen shortage decrease the survival of honeybee
colonies in farmlands. \emph{Journal of Applied Ecology} 54(4),
1161-1170. \emph{https://doi.org/10.1111/1365-2664.12836}

Requier, F., Rome, Q., Chiron, G., Decante, D., Marion, S., Menard, M.,
Muller, F., Villemant, C., \& Henry, M. (2019). Predation of the
invasive Asian hornet affects foraging activity and survival probability
of honey bees in Western Europe. \emph{Journal of Pest Science}
,\emph{92} , 567-578. https://doi.org/10.1007/s10340-018-1063-0.

Requier, F., Garnery, L., Kohl, P.L., Njovu, H.K., Pirk, C.W.W., Crewe,
R.M., Steffan-Dewenter, I. (2019b). The conservation of native honey
bees is crucial. \emph{Trends in Ecology \& Evolution} 34(9), 789-798.

\emph{https://doi.org/10.1016/j.tree.2019.04.008}

Requier, F., Rome, Q., Villemant, C., \& Henry, M. (2020). A
biodiversity-friendly method to mitigate the invasive Asian hornet's
impact on European honey bees. \emph{Journal of Pest Science, 93} , 1-9.
https://doi.org/10.1007/s10340-019-01159-9

Rome Q, Villemant C (2022) Le Frelon asiatique \emph{Vespa velutina} .
In: INPN - MNHN. Data available
at\emph{http://frelonasiatique.mnhn.fr/home}. Last accessed on September
2022.

Sinclair, A.R.E. (1992). Do large mammals disperse like small mammals?
In: Stenseth N.C., Lidicker W.Z. (eds) \emph{Animal Dispersal} .
Springer, Dordrecht.

Shackleton, K., Balfour, N.J., Toufailia, H.A. \emph{et al.} (2019).
Unique nest entrance structure of \emph{Partamona helleri} stingless
bees leads to remarkable `crash-landing' behaviour. \emph{Insectes
Sociaux, 66} , 471--477. https://doi.org/10.1007/s00040-019-00709-9

Shah, F.A., \& Shah, T.A. (1991) \emph{Vespa Velutina} , a serious pest
of honey bees in Kashmir. \emph{Bee World, 72(4),} 161-164.
https://doi.org/10.1080/0005772X.1991.11099099

Skvara, H., Burnett, P., Jones, J., Duschek, N., Plassmann, P., \&
Thirion, J\selectlanguage{english}-P. (2013) Quantification of skin lesions with a 3D
stereovision camera system: validation and clinical
applications.\emph{Skin Research and Technology, 19,} 182-190.
https://doi.org/10.1111/j.1600-0846.2012.00625.x

Spitzen, J., Spoor, C. W., Grieco, F., ter Braak, C., Beeuwkes, J., van
Brugge, S.P., \ldots{} \& Takken, W. (2013). A 3D analysis of flight
behavior of \emph{Anopheles gambiae} sensu stricto malaria mosquitoes in
response to human odor and heat. \emph{PloS one, 8(5),} e62995.
https://doi.org/10.1371/journal.pone.0062995

Straw, A.D., Branson, K., Neumann, T.R., \& Dickinson, M.H. (2011).
Multi-camera real-time three-dimensional tracking of multiple flying
animals. \emph{Journal of The Royal Society Interface, 8395} , 409.
https://doi.org/10.1098/rsif.2010.0230

Struye, M.H., Mortier, H.J., Arnold, G., Miniggio, C., \& Borneck, R.
(1994). Microprocessor-controlled monitoring of honeybee flight activity
at the hive entrance. \emph{Apidologie, 25} , 384-395.
https://doi.org/10.1051/apido:19940405

Sumpter, D., \& Pratt, S.A. (2003). A Modelling framework for
understanding social insect foraging. \emph{Behavioral Ecology and
Sociobiology, 53,} 131--144. https://doi.org/10.1007/s00265-002-0549-0

Tan, K., Radloff, S.E., Li, J.J., Hepburn, H. R., Yang, M.X., Zhang,
L.J., \& Neumann, P. (2007). Bee-hawking by the wasp, \emph{Vespa
velutina} , on the honeybees \emph{Apis cerana} and \emph{A. mellifera}
.\emph{Naturwissenschaften, 94,} 469--472.
https://doi.org/10.1007/s00114-006-0210-2

Theriault, D.H., Wu, Z., Hristov, N.I., Swartz, S.M.,Breuer, K.S., Kunz,
T.H., \& Betke, M. (2010). Reconstruction and analysis of 3D
Trajectories of Brazilian Free-tailed Bats in flight. Boston University
Computer Science Technical Report No. BUCS-TR-2010-027. {[}Available
from: http://hdl.handle.net/2144/3803{]}

Villemant, C., Barbet-Massin, M., Perrard, A., Muller, F., Gargominy,
O., Jiguet, F., \& Rome, Q. (2011). Predicting the invasion risk by the
alien bee-hawking yellow-legged hornet \emph{Vespa velutina
nigrithorax}across Europe and other continents with niche models.
\emph{Biological Conservation} ,\emph{144} , 2142--2150.
https://doi.org/10.1016/j.biocon.2011.04.009

Wajnberg, E., \& Colazza, S. (1998). Genetic variability in the area
searched by a parasitic wasp: analysis from automatic video tracking of
the walking path. \emph{Journal of Insect Physiology, 44(5--6),}437-444,
ISSN 0022-1910. https://doi.org/10.1016/S0022-1910(98)00032-8

Wilson, R.S., Pavlic, T.P., Wheatley, R., Niehaus, A.C., \& Levy, O.
(2020). Modeling escape success in terrestrial predator--prey
interactions. \emph{Integrative and Comparative Biology,
60(2),}497--508. https://doi.org/10.1093/icb/icaa070

\textbf{Figure captions}

Figure 1. Examining 3D flight performance in honey bees and Asian
hornets. (a) Conceptualization of the experimental setup with a
stereovision camera fixed over a hive flight board, registering all
activity in the volume of air in front of the hive on a computer, which
analyses the trajectories of Asian hornets (blue) and honey bees
(green). (b) Daily time series of the total number of honey bees (green)
and hornets (blue) (trajectories). Lines represent model predictions and
shaded areas show the 95\% confidence intervals.

\textbf{Figure 2.} Daily time series of (a) the flight speed, (b)
curvature and (c) time spent hovering by honey bees entering (in red) or
leaving (in grey) their hive and Asian hornets (in blue). Differences in
letters denote significant difference using Kruskall-wallis tests. Lines
represent model predictions and shaded areas show the 95\% confidence
intervals. Dot lines show non-significant trends.

\textbf{Figure 3.} Flight performance in (a) speed, (b) curvature and
(c) time spend hovering of the Asian Hornet in blue, honey bees entering
the hive in red or leaving it in grey. Time spent hovering is based on
the threshold 2 indicator (see methods).

\textbf{Figure 4.} Predation success of the Asian hornets modelled as a
quadratic function of hornet numbers. Line represents the model
prediction and the shaded area shows the 95\% confidence intervals.

\textbf{Figure 5.} Hornet density (Log\textsubscript{10} trajectories)
effects on (a) speed of bees entering the hive, (b) speed of bees
leaving the hive, (c) curvature of bees entering the hive trajectories.
Lines represent model predictions and shaded areas show the 95\%
confidence intervals.

\textbf{Figure 6.} Hornet density (Log\textsubscript{10} trajectories)
effects on the coefficient of variation (CV) of (a) speed, (b)
trajectory curvature and (c) hovering percentage in hornets. Lines
represent model predictions and shaded areas show the 95\% confidence
intervals.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6/Figure-6}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Table 1.docx`  available at \url{https://authorea.com/users/513578/articles/589715-density-of-predating-asian-hornets-at-hives-disturbs-the-3d-flight-performance-of-honey-bees-and-decreases-predation-success}

\textbf{Hosted file}

\verb`Table 2.docx`  available at \url{https://authorea.com/users/513578/articles/589715-density-of-predating-asian-hornets-at-hives-disturbs-the-3d-flight-performance-of-honey-bees-and-decreases-predation-success}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Graphical-abstract/Graphical-abstract}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

