References
Andersson AA, Tilley HB, Lau W et al (2021) CITES and beyond:
illuminating 20 years of global, legal wildlife trade. Global Ecology
and Conservation 26: e01455.
Bennett E, Eves H, Robinson J, Wilkie D (2002) Why is eating bushmeat a
biodiversity crisis. Conservation Biology in Practice 3: 28-29.
Bezerra-Santos MA, Mendoza-Roldan JA, Thompson RCA et al (2021) Illegal
wildlife trade: A gateway to zoonotic infectious diseases. Trends in
Parasitology 37: 181-184.https://doi.org/10.1016/j.pt.2020.12.005
Bi Gonedelé S, Koné I, Béné J et al (2017) Bushmeat hunting around a
remnant coastal rainforest in Côte d’Ivoire. Oryx 51: 418-427.
Bi Gonedelé S, Kramoko B, Bené J et al (2022) Year-round longitudinal
monitoring of a bushmeat market in central-western Côte d’Ivoire:
implication for wildlife conservation. Journal for Nature Conservation.
Breiman L (2001) Random Forests. Machine Learning 45: 5-32.
10.1023/A:1010933404324
Csörgő M, Horváth L (1997) Limit theorems in change-point
analysis. Hoboken, NJ, John Wiley and Sons
D’Cruze N, Green J, Elwin A, Schmidt-Burbach J (2020) Trading tactics:
time to rethink the global trade in wildlife. Animals 10: 2456.
Dindé AO, Mobio AJ, Konan AG et al (2017) Response to the Ebola-related
bushmeat consumption ban in rural Côte d’Ivoire. Agriculture & Food
Security 6: 28. 10.1186/s40066-017-0105-9
Fa JE, van Vliet N, Nasi R (2016) Bushmeat, food security, and
conservation in African rainforests. In: Aguirre AAS, Raman (ed)
Tropical conservation: perspectives on local and global priorities.
Oxford, UK: Oxford University Press, pp 331-344
Falola A, Ajewole OO, Ajibade TB et al (2015) Assessment of welfare
status of bushmeat traders in the post-Ebola era in Kwara State,
Nigeria. Journal of Multidisciplinary Studies 4: 1-26.
Fang G, Liu H, Wang Q (2021) Wildlife conservation legislation at a fork
in the road. BioScience 72: 223-225. 10.1093/biosci/biab122
Funk SM, Fa JE, Ajong SN et al (2021) Impact of COVID-19 on wild meat
trade in Nigerian markets. Conservation Science and Practice 4.
Funk SM, Fa JE, Ajong SN et al (2022) Impact of COVID-19 on wild meat
trade in Nigerian markets. Conservation Science and Practice 4: e599.https://doi.org/10.1111/csp2.599
Genolini C, Ecochard R, Benghezal M et al (2016) kmlShape: an efficient
method to cluster longitudinal data (time-series) according to their
shapes. Plos One 11: e0150738.
Ghosh P, Neufeld A, Sahoo JK (2022) Forecasting directional movements of
stock prices for intraday trading using LSTM and random forests. Finance
Research Letters 46: 102280.
Gossé KJ, Gonedelé-Bi S, Justy F et al (2022) DNA-typing surveillance of
the bushmeat in Côte d’Ivoire: a multi-faceted tool for wildlife trade
management in West Africa. Conservation Genetics.
Harvey-Carroll J, Simo FT, Sonn-Juul T et al (2022) Continued
availability and sale of pangolins in a major urban bushmeat market in
Cameroon despite national bans and the COVID-19 outbreak. African
Journal of Ecology 60: 146-152.https://doi.org/10.1111/aje.12969
Hughes AC (2021) Wildlife trade. Current Biology 31: R1218-R1224.https://doi.org/10.1016/j.cub.2021.08.056
Ingram DJ, Coad L, Milner-Gulland EJ et al (2021) Wild meat Is still on
the menu: progress in wild meat research, policy, and practice from 2002
to 2020. Annual Review of Environment and Resources 46: 221-254.
10.1146/annurev-environ-041020-063132
Key N, Sadoulet E, De Janvry A (2000) Transactions costs and
agricultural household supply response. American Journal of Agricultural
Economics 82: 245-259.
Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of
changepoints with a linear computational cost. Journal of the American
Statistical Association 107: 1590-1598.
Killick R, Eckley IA (2014) changepoint: an R package for changepoint
analysis. Journal of Statistical Software 58: 1-19.
Marsaglia G, Tsang WW, Wang J (2003) Evaluating Kolmogorov’s
distribution. Journal of Statistical Software 8: 1-4.
10.18637/jss.v008.i18
McNamara J, Robinson EJZ, Abernethy K et al (2020) COVID-19, systemic
crisis, and possible implications for the wild meat trade in sub-Saharan
Africa. Environmental and Resource Economics 76: 1045-1066.
10.1007/s10640-020-00474-5
Meseko C, Shittu I, Adedeji A (2020) The bush meat trade thrives in
Nigeria despite anxiety over coronavirus. Transactions of the Royal
Society of Tropical Medicine and Hygiene 114: 639-641.
10.1093/trstmh/traa060
Milleliri J-M, Coulibaly D, Lamontagne F (2021) La Covid-19 en Côte
d’Ivoire (mars 2020-avril 2021) une année sous le sceau du coronavirus.
Médecine Tropicale et Santé Internationale 102: 1-8.
Moritz S, Bartz-Beielstein T (2017) imputeTS: time series missing value
imputation in R. The R Journal 9: 207.
Nasi R, Taber A, Van Vliet N (2011) Empty forests, empty stomachs?
Bushmeat and livelihoods in the Congo and Amazon Basins. International
Forestry Review 13: 355-368.
Reynolds MG, Doty JB, McCollum AM et al (2019) Monkeypox re-emergence in
Africa: a call to expand the concept and practice of One Health. Expert
Review of Anti-infective Therapy 17: 129-139.
10.1080/14787210.2019.1567330
Roe D, Dickman A, Kock R et al (2020) Beyond banning wildlife trade:
COVID-19, conservation and development. World Development 136: 105121.https://doi.org/10.1016/j.worlddev.2020.105121
Sills J, Yang N, Liu P et al (2020) Permanently ban wildlife
consumption. Science 367: 1434-1434. doi:10.1126/science.abb1938
Sprouffske K, Wagner A (2016) Growthcurver: an R package for obtaining
interpretable metrics from microbial growth curves. BMC bioinformatics
17: 1-4.
van Vliet N, Schulte-Herbrüggen B, Muhindo J et al (2017) Trends in
bushmeat trade in a postconflict forest town: implications for food
security. Ecology and Society 22.
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).