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\begin{document}

\title{Long-term effect of the COVID-19 lockdown on the dynamics of the
bushmeat trade in West Africa (C\selectlanguage{ngerman}ôte d'Ivoire)\selectlanguage{english}}



\author[1]{Jules Koffi Gossé}%
\author[1]{Sean Paul Heighton}%
\author[1]{Philippe Gaubert}%
\author[1]{Sery Gonedelé Bi}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\textbf{Introduction}

The wildlife trade is a major societal issue challenging both
biodiversity conservation and global health (Bezerra-Santos et al.,
2021; Hughes, 2021). At an estimated annual value of several US\$
trillions, it also constitutes a crucial --often parallel-- economy for
countries, in which actors of the supply-chain source their revenues
(Andersson et al., 2021). It is also an important food system for a
number of rural households across the tropics (Fa et al., 2016).

The advent of the COVID-19 pandemic has generated a vivid debate on
whether or not the wildlife trade should be banned. Contrasting calls
from and within the scientific community, the political spheres and the
public have been put forward (Fang et al., 2021), from an outright
permanent ban (Sills et al., 2020) to maintenance of the wildlife trade
for the sake of social development (Roe et al., 2020).

West and central African rainforests are a particular hotspot for the
wildlife trade, where \emph{bushmeat} --the terrestrial vertebrates
hunted for food-- has traditionally been a vital source of protein and
income for rural communities (Ingram et al., 2021). With the
globalization of the trade, bushmeat offtakes have become likely
unsustainable for many species (reaching c. 5 million tons each year;
Nasi et al., 2011), an extinction trend coined under the term ``bushmeat
crisis'' (Bennett et al., 2002).

With the COVID-19 pandemic, the African continent was struck by national
trade bans of various length and effectiveness (Harvey-Carroll et al.,
2022; Meseko et al., 2020). However, because the bushmeat trade is a
poorly regulated, parallel economy across most of Africa, the genuine
economic stakes behind the trade are poorly known (van Vliet et al.,
2017). Moreover, the impact of bans on trade activities has rarely been
quantified (but see Funk et al., 2022), notably in terms of post-ban
recovery.

We conducted long-term monitoring of bushmeat sites in C\selectlanguage{ngerman}ôte d'Ivoire
(West Africa) that encompassed pre-, during, and post-COVID-19 lockdown.
In Côte d'Ivoire, hunting has been illegal since 1974 (law n°94--442),
but the bushmeat trade is openly tolerated, notably in major urban zones
(Gossé et al., 2022). Quantitative data on the bushmeat trade are scarce
and mostly outdated, but large levels of supply from protected areas has
been observed recently (Bi Gonedelé et al., 2017; Bi Gonedelé et al.,
2022). The trade seems non-selective in terms of species and strictly
motivated by financial income (Gossé et al., 2022), highlighting both
the likely unsustainability and important economic role of the national
bushmeat trade network.

Controls of the bushmeat trade by Ivorian state agencies have been
regularly implemented as conservation-oriented measures or public health
emergencies after the earlier epidemic of Ebola virus disease in 1994
(Dindé et al., 2017). However, there is to date no information available
on how such control measures have affected the market. Our main
objective was to assess the dynamics of the bushmeat trade in Côte
d'Ivoire as a response to the COVID-19 pandemic and explore
post-lockdown recovery trends depending on market type. We posit that
(i) bushmeat trade dynamics was negatively impacted by governmental
measures, with trends being similar between market types (as a measure
of the effectiveness of government interventions), and that (ii)
bushmeat trade activities quickly recovered after the lockdown --and the
bushmeat ban-- were lifted (as a measure of market network resilience).

\textbf{Material and Methods}

Data collection

The study covered five of the most active bushmeat sites in Côte
d'Ivoire. It includes (i) four bushmeat markets in the district of
Abidjan, namely Yopougon Siporex (5°21'N, -4°04'W), Abobo Grand Marché
(5°26'N, -4°01'W), Abobo Mairie (5°25' N, -4°01'W) and Adjamé (5°21'N,
-4°01'W), and (ii) the restaurants of Toumodi (6°33'N, -5°01'W) as a
renowned hub for bushmeat trafficking in central Côte d'Ivoire (Dindé et
al., 2017).

Bushmeat sites were surveyed from December 2019 to January 2021,
including the period during which governmental measures were taken in
response to the COVID-19 pandemic (Milleliri et al., 2021: see Fig. 1).
We chose to measure the weekly effect of governmental measures from the
first week of April 2020, i.e. the week following that of when the
declaration of state of health emergency and of the closure of chop chop
bars was announced. The end of governmental measures was fixed to the
second week of October 2020, as there was no specific date associated to
the final release of constraints.

Sites were visited by JKG, SGB and a trained field assistant twice per
week between 08.00 am and 2.00 pm, every three days. An additional
control survey was conducted in September 2021 (week 92 after survey
start) in order to compare the observed number of sellers with that from
predictive growth curve modelling (see below).

We counted the number of sellers on each site as a proxy of the bushmeat
activity. We systematically verified that each seller had his/her own
stall(s), and was not a collaborator of another seller. We posit that
the number of sellers --as one of the supply determinants of a market
(Key et al., 2000)-- is expected to positively correlate with the
quantity of bushmeat supply. Weekly estimates per site were derived from
the mean counts of sellers per week per bushmeat site (Appendix Table
1). Sellers were informed of the objectives of the study and
participated to the survey on a voluntary basis.

Statistical analyses

All statistical analyses were conducted in the Rstudio interface of R
v4.2.1 (Appendix 1).

A changepoint analysis was implemented with the
package\emph{changepoint} (Killick and Eckley, 2014) to identify the
weeks at which the statistical properties of a sequence of observations
for each trend changed in its mean seller counts. We used the PELT
(pruned exact linear time) method with AIC values to identify multiple
changepoints (Killick et al., 2012), and the AMOC (at most one change)
method with the non-parametric CUSUM test statistic (penalty = 0.8) to
identify the largest changepoint in each trend (Csörg\selectlanguage{polish}ő \selectlanguage{english}and Horv\selectlanguage{ngerman}áth,
1997). We used\emph{imputeTS} (Moritz and Bartz-Beielstein, 2017) to
undertake a time series imputation of missing data for Toumodi (January
2021) and Adjamé (April 2020).

We used a non-parametric two-sample Kolmogorov-Smirnov test (Marsaglia
et al., 2003) to identify approximate significant differences between
pairs of curves, both by the distance between curves and curve shape.
These pairs included the comparison between the restaurants (Toumodi)
and all markets through averaging the number of sellers across all four
markets. To look more deeply into differences between pairs of curves,
we calculated Fréchet distances using \emph{kmlShape} (Genolini et al.,
2016), which too account for the location and ordering of the points in
each curve. We used a small `timeScale' of 0.1 in order to measure
differences of general curve shape across time (Dynamic Time Warping)
and reduce the influence of time on the curve distance (Euclidean
distance) (Genolini et al., 2016). Both the Kolmogorov-Smirnov and
Fréchet distance tests were measured for the entirety of the study
period and only during the period of governmental measures against
COVID-19.

A growth curve analysis using \emph{growthcurver} was conducted for each
curve from the time governmental measures were enforced in order to
determine relative rate of growth for the markets and restaurants under
a logistic regression model (Sprouffske and Wagner, 2016).

We used the Random Forest algorithm with the package
\emph{randomForest}(Breiman, 2001) to undertake a machine learning
approach in order to predict what would likely happen in terms of
bushmeat site growth after the main study period for each site (from
January to September 2021, time of the control survey). Training and
test datasets were divided into a ratio of around 80:20, while machine
learning robustness was validated using a Root Mean Square Error (RMSE;
better models have values closer to zero). We followed two scenarios for
the evolution of the sites from January 2021 until when we revisited the
sites in September 2021: (i) knowing the initial maximum number of
sellers prior to COVID-19 (constraint by maximum capacity for each
site), and (ii) not knowing any values post initial study period (no
constraint on the predictions).

\textbf{Results}

Our study represented 60 weeks of continuous survey of 132 bushmeat
sellers. The largest market was Yopougon Siporex (26 sellers before the
COVID lockdown), whereas the smallest market was Abobo (Grand Marché;
four sellers). From the two-sample Kolmogorov-Smirnov tests, all curves
differed significantly from each other for the entire study period (p
\textless{} 0.05). Fréchet distances also showed a certain level of
heterogeneity among bushmeat sites, with Yopougon Siporex and Abobo
Grand Marché curves having the greatest distance between each other
(distance = 625.85) across the whole study period (Fig. 2).

The number of sellers was stable before lockdown enforcement and started
decreasing more or less abruptly from the first week of April in all the
bushmeat sites, except in Toumodi restaurants. This was mirrored by the
changepoint analysis, as the biggest change in mean for all trends was
at the first week of April 2020, except the Toumodi restaurants that had
their most important change at the third week of March 2020, i.e. right
after the announcement of the ban on bushmeat consumption (Fig. 1,
Appendix Table 1). Bushmeat site re-opening was first visible in Abobo
Grand Marché and Toumodi (June 2020), before the end of state of health
emergency (30 June 2020). Adjamé started reopening in July 2020
(concomitantly with end of lockdown in Abidjan), whereas Abobo Mairie
(September 2020) and Yopougon Siporex (October 2020) reopened later,
once sanitary measures were lifted. Growth curve analysis suggest that
Toumodi restaurants saw the most rapid, but non-significant, growth rate
(r = 0.927 ± 0.67, p = 0.2), followed by Yopougon Siporex market (r =
0.508 ± 0.143, p = 0.001). Adjamé market had the slowest growth rate (r
= 0.105 ± 0.043, p = 0.02), followed by Abobo Grand Marché market (r =
0.128 ± 0.072, p = 0.08). Toumodi restaurants, Abobo Grand Marché and
Adjamé markets had relatively high levels of standard error in the
growth rate estimates.

During the period under governmental measures, two-sample
Kolmogorov-Smirnov tests indicated that Toumodi restaurants
significantly differed from the combined bushmeat markets (D =
0.57,\emph{p} -value \textless{} 0.001), with the markets being
completely closed following the first months of lockdown and the
restaurants only closing for a period c. two months and a half. This
period saw significant differences across most comparisons, except
between the Toumodi restaurants and Adjamé market (D= 0.2, \emph{p}
-value= 0.379), as well as Yopougon Siporex and Abobo Mairie markets (D=
0.14,\emph{p} -value= 0.524) trends. Fréchet distances suggested
Yopougon Siporex market and Toumodi restaurants were the most divergent
(distance = 130.88), while Yopougon Siporex and Abobo Mairie markets
were the most similar (distance = 30.42; Fig 2).

In January 2021 (end of the survey), all the bushmeat sites exhibited
lower numbers of sellers than before lockdown, whilst the control survey
in September 2021 yielded the same result with the exception of Abobo
(same as before lockdown) and Toumodi (higher than before lockdown). In
all cases and models (with or without constraint), forecasting
predictions after 76 weeks since the beginning of lockdown were inferior
to the initial numbers of sellers (Fig. 3), with unconstrained models
yielding lower estimations. Both models yielded predictions of number of
sellers inferior to observed values after 76 weeks for Abobo Mairie and
Grand Marché markets, and Toumodi restaurants. The number of sellers in
Adjamé market was over-predicted by both models, while for Yopougon
Siporex market the unconstrained model yielded inferior predictions and
the constrained model higher predictions relative to the observed
number. The two models yielded the most dissimilar predictions for
Adjamé market (unconstrained model yielded simulated value closer to
observed value) and Toumodi restaurants (constrained model yielded
simulated value closer to observed value). Toumodi predictive models had
the highest averaged RMSE values (0.47) and lowest proportion of
variance explained (50.47\%), while Yopougon Siporex had the lowest
averaged RMSE (0.16) with high levels of variance explained (92.51\%)
(Table 1).

\textbf{Discussion}

Our study encompassed a large timespan (60 weeks) and a variable
spectrum of bushmeat site categories including small to large urban
bushmeat markets and chop chop bars (\emph{maquis} ). Although more
accurate proxies of the trade dynamics were not investigated (e.g.,
trends in biomass and income), our unprecedented survey design allows,
for the first time, to assess African bushmeat trade dynamics during and
after a national ban.

Trade dynamics in Côte d'Ivoire as measured per number of sellers was
strongly, negatively impacted by the COVID-19 lockdown and bushmeat ban,
as all bushmeat sites went down to a null number of sellers right before
or after the first governmental decisions were taken. This is in line
with our original expectation on the efficiency of governmental
measures. A significant reduction in carcass numbers was also observed
in Nigeria after lockdown (Funk et al., 2021). The COVID-19 pandemic has
shown that African states can be efficient in mitigating the bushmeat
trade when resources are duly mobilized, a notable point considering the
ongoing bushmeat crisis and the zoonotic outbreaks to come (D'Cruze et
al., 2020; Reynolds et al., 2019).

However, we observed significant differences in the dynamics of bushmeat
sites both during and after the strongest measures taken against the
COVID-19 pandemic, deviating from our posited expectation on trends
being similar between market types (as a measure of the effectiveness of
government interventions). We speculate that bushmeat trade dynamics are
affected by a combination of intrinsic market characteristics and
dissimilar levels of governmental interventions. For instance, Toumodi
restaurants were first to be fully closed (mid-March 2020, concomitantly
with the announcement on the bushmeat ban) and first to start
re-opening, at a time when all the bushmeat markets in Abidjan were
closing (early June 2020). This suggests that predicting bushmeat trade
dynamics under bans or restrictions is geographically and typologically
(i.e., type of market) dependent. State authorities may have been more
zealous in Toumodi by quickly applying the bushmeat ban before the
decision on closing \emph{maquis} one week later, but prompt to release
pressure on sellers from a zone not affected by the lockdown (which was
restricted to Abidjan).

The Ivorian state was permissive with the resumption of the bushmeat
trade, as we observed a progressive return of sellers on the bushmeat
sites before the sanitary measures were lifted. Although global
trajectories among bushmeat sites were similar throughout the survey
period, significant trend differences were observed during the
constrained period. This reinforces our view that the dynamics of the
bushmeat trade are site-specific and shaped by multiple factors. In our
case, small (Abobo Grand Marché) or poorly accessible (Adjamé) markets
could have been less controlled than the largest bushmeat market of
Abidjan, Yopougon Siporex. The latter is clearly identified as the main
hub of the bushmeat trade in Côte d'Ivoire (Gossé et al., 2022) and was
the last to show sign of resumption, after the end of the sanitary
measures against COVID-19 (October 2020).

The COVID-19 lockdown and bushmeat ban had a long-term impact on the
bushmeat trade dynamics, as three months after the end of governmental
measures all the bushmeat sites in Côte d'Ivoire exhibited lower numbers
of sellers than before (c. 63 to 91\% of the initial numbers). This
effect was still prevailing in three of the bushmeat markets from
Abidjan during our control survey eight months later (c. 56 to 91\% of
the initial numbers), despite stronger growth rate in large markets such
as Yopougon Siporex. This clearly violates our initial assumption of
market network resilience to the lockdown.

Forecasting predictions after 92 weeks since the start of sanitary
measures in Côte d'Ivoire showed contrasting outputs relative to
observed number of sellers. Most of the bushmeat sites were not
optimally modelled due to their heterogeneity in growth, especially
Toumodi restaurants. Random Forest algorithms performed better in the
case of Yopougon and Abobo Mairie markets, where predicted values from
both models converged closer to observed values. Convergence between
constrained and unconstrained models may serve as an additional
confidence estimate of model performance, coupled with the intrinsic
estimates already available (RMSE and variance). Further investigations
on Random Forest algorithms as applied to bushmeat trade dynamics over
longer periods of time will have to be undertaken before the benefits of
such predictive approach can be considered (e.g., as in finance
research; Ghosh et al., 2022).

We conclude that neglecting the socio-geographical specificities of the
different types of bushmeat sites could lead to erroneous projections of
bushmeat trade dynamics. Eleven months after the end of governmental
measures in Côte d'Ivoire, most of the bushmeat sites had not fully
recovered in terms of number of sellers. Whether regular controls have
slowed down the resumption of the trade requires further investigations.
The social and economic implications behind the lack of the full
recovery of certain markets at the time of our study, including that of
the largest bushmeat market of Côte d'Ivoire, is unknown. Given the
possible precariousness of certain bushmeat sellers (see Falola et al.,
2015), it is likely that some could not economically support the
consequences of a national ban for several months. However, with the
data at hand, it remains hazardous to discuss the deleterious economic
shockwave foreseen by some authors in relation to wildlife trade bans
(e.g., McNamara et al., 2020), especially since future disease outbreaks
and reduction in resources through over-harvesting will likely influence
market dynamics under the current tolerated market scenario.

Understanding bushmeat trade dynamics in the context of mitigation
measures will require a multi-dimensional approach where the
characteristics of the different markets (e.g., type of markets,
socio-demography of vendors, ban record, species hunted and places
sourced) are clearly identified. Our study showed that wildlife trade
bans can have a long-lasting impact on bushmeat trade dynamics, and that
state power when guided by a clearly defined objective can efficiently
setup trade mitigation, at least in the short-term. Actual mitigation of
the bushmeat trade, whether conservation- or health-driven, will depend
on a comprehensive understanding of its specific dynamics and the
economic reliance of involved actors. To reach this objective, less
talk, and more on-the-ground data with comprehensive modelling will be
required.

\textbf{Acknowledgments}

KJG and SGB were supported by the Ministère de l'Enseignement Supérieur
et de la Recherche Scientifique (MESRS) of Côte d'Ivoire, as part of the
Debt Reduction-Development Contracts (C2Ds) managed by the Institut de
Recherche pour le Développement (IRD) (project TRACE-BROUSSE). KJG
received a PhD scholarship from ''Appui à la Modernisation et à la
Réforme des Universités et Grandes Ecoles de Côte d'Ivoire''
(AMRUGE-CI\_N°2). SPH received funding from PANGOGO (ANR
ANR-17-CE02-0001). PG was supported by BUSHRISK (FCT IC\&DT
02/SAICT/2017---n° 032130). PG and SGB were also supported by AFRICoV
(ANRS COV6).

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\textbf{Table 1.} Input variables and model fit results for the
predictive models per bushmeat site.

The models were either constrained (grey row) by the highest number of
sellers prior to COVID-19 for each site, or not constrained (no color).
They were run under a machine learning approach using the Random Forest
method in which we provided a training and testing dataset (ratio of
around 80:20 of data, excluding missing data). Model fit was determined
by percentage of variance explained and the Root Mean Square Error
(RMSE), which was standardized by number of sellers per site (RMSE
closer to zero equals more confidence).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Market\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Ratio training:test datasets\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Number of sellers (September 2021)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\% variance explained\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\% variance explained (averaged per site)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
RMSE\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Standardized RMSE (averaged by n sellers)\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yopougon\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35/10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
92.43\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
92.51\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.63\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.09403\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yopougon\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35/9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
92.59\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.223881\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Abobo Mairie\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35/10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
96.93\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
96.28\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.42\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.367876\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Abobo Mairie\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35/9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
95.63\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.181347\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Abobo Grand March\selectlanguage{ngerman}é\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
35/10\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
71.21\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
68.975\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.56\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.254545\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Abobo Grand March\selectlanguage{ngerman}é\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
35/9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
66.74\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.41\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.186364\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adjam\selectlanguage{ngerman}é\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
32/8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
83.93\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
79.725\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.45\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.239669\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Adjam\selectlanguage{ngerman}é\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
32/7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
75.52\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.22\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.201653\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Toumodi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
33/\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
46.56\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
50.465\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.49\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.559551\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Toumodi\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
33/7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
54.37\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.73\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.388764\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legend}

\textbf{Figure 1.} Variation in the number of sellers at the five
bushmeat sites surveyed in C\selectlanguage{ngerman}ôte d'Ivoire before, during and after the
governmental measures against the COVID-19 pandemic. X-axis represents
60 weeks of survey.

Main governmental measures and relaxes (following Milleliri et al.,
2021): A - 16 March 2020: suspension from entry into Ivory Coast of
foreigners, closure of teaching establishments, prohibition of people
gathering, suspension of cultural events, application of barrier
measures, strict ban on the consumption of bushmeat (\emph{Communiqué du
Conseil National de Sécurité du 16 mars 2020} ); B- 23 March 2020:
declaration of state of health emergency, establishment of a curfew,
lockdown of Abidjan, closure of \emph{maquis} (chop chop bars) and
restaurants (\selectlanguage{english}\emph{Décret n°2020-351 du 23 mars 2020} ); C- 9 April
2020: obligation to wear masks in Abidjan, mandatory confinement for
vulnerable people, implementation of telework, reduction of the number
of passengers in public transports; D- 15 May 2020: end of the curfew in
Abidjan; E- 25 May 2020: reopening of teaching establishments; F- 30
June 2020: end of state of health emergency.; G- 1 July 2020: end of
suspension from entry into Ivory Coast of foreigners; H- 15 July 2020:
end of lockdown of Abidjan; E- October 2020: end of sanitary measures.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.} Heatmap of Fr\selectlanguage{ngerman}échet distances between trend curves of
seller numbers for the five bushmeat sites surveyed in Côte d'Ivoire.

A: whole survey period. B: period of governmental measures only. Below
grey diagonal (red tones): sum of the distances. Above the grey diagonal
(blue tones): maximum distance found across all sites.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{A} & Toumodi & All Markets & Yopougon Siporex & Abobo Grand
March\selectlanguage{ngerman}é & Abobo Mairie & Adjam\selectlanguage{ngerman}é\tabularnewline
\midrule
\endhead
Toumodi & x & 7,33 & 13 & 17 & 10 & 5\tabularnewline
All Markets & 238,84 & x & & & &\tabularnewline
Yopougon Siporex & 327,78 & & x & 22 & 15 & 11\tabularnewline
Abobo Grand March\selectlanguage{ngerman}é & 357,18 & & 625,85 & x & 7 & 12\tabularnewline
Abobo Mairie & 261,96 & & 361,72 & 245,12 & x & 5,02\tabularnewline
Adjam\selectlanguage{ngerman}é & 166,72 & & 329,26 & 355,9 & 141,38 & x\tabularnewline
\textbf{B} & Toumodi & All Markets & Yopougon - Market & Abobo Grand
March\selectlanguage{ngerman}é & Abobo Mairie & Adjam\selectlanguage{ngerman}é\tabularnewline
Toumodi & x & 7 & 10 & 7 & 7 & 4\tabularnewline
All Markets & 104,36 & x & & & &\tabularnewline
Yopougon Siporex & 130,88 & & x & 9 & 7 & 7\tabularnewline
Abobo Grand March\selectlanguage{ngerman}é & 57,24 & & 71,86 & x & 3 & 5\tabularnewline
Abobo Mairie & 77,37 & & 30,42 & 46,36 & x & 5\tabularnewline
Adjam\selectlanguage{ngerman}é & 41,81 & & 82,96 & 53,22 & 41,39 & x\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 3.} Modelled trends in number of sellers at the five
bushmeat sites surveyed in C\selectlanguage{ngerman}ôte d'Ivoire after the end of the survey
period. Red line indicates the modelled growth curve during the survey
period and stops at week 60 (survey completion). Blue line indicates
post-survey predictions without model constraint, while green line
indicates predicted values with model constrained by maximum number of
sellers prior to COVID-19 (ultimate circle on the top right of each
graph). Point ``x'' refers to the numbers of sellers during the
September 2021 control survey.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/572507/articles/617316-long-term-effect-of-the-covid-19-lockdown-on-the-dynamics-of-the-bushmeat-trade-in-west-africa-c%C3%B4te-d-ivoire}

\textbf{Appendix Table 1.} Number of sellers observed at the five
bushmeat sites surveyed in Côte d'Ivoire before, during and after the
COVID-19 lockdown. {[}Excel table{]}

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/572507/articles/617316-long-term-effect-of-the-covid-19-lockdown-on-the-dynamics-of-the-bushmeat-trade-in-west-africa-c%C3%B4te-d-ivoire}

\textbf{Appendix 1.} R scripts related to the analyses conducted as part
of this study {[}R document{]}

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

