https://orcid.org/0000-0001-9137-1218
Figures
Abstract
The commercial trade in wild meat is booming in Central Africa. Addressing this issue is a global priority because the trade poses a major threat to biodiversity and human health. We investigated the impact of socioeconomic factors, public health emergencies, and conservation efforts on the wild meat trade using daily surveys of wild meat markets on Bioko Island, Equatorial Guinea (EG), from 1997 through 2021. Bioko is an ideal location for examining how external factors impact the wild meat market trade. Although small, the island has large areas of intact forest that host populations of commercially valuable wildlife; low-cost protein substitutes are available; and Malabo, the island’s only large metropolitan area and wild meat trading hub, hosts a wealthy class of urbanites. We found significant associations between global market trends and the wild meat trade, especially China’s foreign investment and oil production in the US and EG. Economic crises like EG’s 2009 economic downturn that followed a global crash in oil prices and reduced production, redirected demand towards cheaper mainland wildlife carcasses amid reduced consumer demand. Public health emergencies had the most comprehensive impact on the wild meat trade. The 2014 Ebola outbreak and the COVID-19 pandemic both induced shifts in market demand, and the COVID-19 pandemic disrupted trade routes, affecting both urban and rural markets. Internally, we observed market decentralization over the last decade and changes in wildlife supply chains during public health emergencies. Conservation policies, including anti-poaching measures and educational outreach, temporarily influenced wildlife market trends, sometimes leading to trading surges in endangered primate carcasses. Our study highlights the importance of monitoring global market trends, public health campaigns, and adapting conservation strategies to disrupt wildlife supply chains and curb consumer demand for wild meat.
Author summary
This study presents a detailed analysis of a 23-year survey of wild meat markets on Bioko Island, Equatorial Guinea. It focused on assessing the impacts of various socioeconomic changes across the study period. These included the rise and fall of the national oil industry, the expansion of infrastructure, the repercussions of two public health emergencies, and the implementation of conservation measures aimed at curtailing the supply of wild meat and reducing its demand among urban consumers. The results offer valuable insights into how these diverse factors collectively shape the dynamics of the wild meat trade in an urban context and pathways to discourage luxury wild meat consumption.
Citation: Mitchell DV, Woloszynek S, Mitchell MW, Cronin DT, Zhao Z, Rosen GR, et al. (2024) Growth and globalization of the Central African wildlife economy: Insights from a 23-year study of wild meat markets on Bioko Island, Equatorial Guinea. PLOS Sustain Transform 3(11): e0000139. https://doi.org/10.1371/journal.pstr.0000139
Editor: Adeyemi Oladapo Aremu, North-West University, SOUTH AFRICA
Received: February 15, 2024; Accepted: October 3, 2024; Published: November 20, 2024
Copyright: © 2024 Mitchell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All wild market data and metadata are available from the Open Science Framework: https://osf.io/9a6py/?view_only=906b64b999a243c284c5b31e4b118669.
Funding: This research was supported by grants to MKG from the US Fish and Wildlife Service (F13AP00506, F17AP00425, and F19AP00513), the ExxonMobil Foundation, Conservation International, the Los Angeles Zoo, Margot Marsh Biodiversity Foundation, the National Geographic Conservation Trust, the Phoebe Haas Charitable Trust B, and the Sally Hunter Challenge Fund. The sponsors had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Central Africa, encompassing the Gulf of Guinea and the Congo Basin rainforests, is a region of paramount global biodiversity importance. These areas represent some of the largest remaining tracts of rainforest on Earth, housing a substantial portion of the world’s plant and animal species, including a rich array of endemic flora and fauna [1,2]. Despite its ecological significance, the region faces intense anthropogenic pressures driven by weak and unenforced laws, a rapidly growing human population, and rural-to-urban migration. The expansion of trade networks and foreign investment in local resource extraction, including timber, minerals, and hydrocarbons, further exacerbate these pressures [3,4]. This escalating extraction surpasses resource availability due to global demands, leading to habitat loss, deforestation, resource over-exploitation, pollution, and various human-wildlife conflicts [5].
The wildlife trade in Central Africa is widely recognized as a major threat to biodiversity [6–9]. Meat from terrestrial animals, or “wild meat,” is a large part of the wildlife trade. It is an important part of the diets, culture, and livelihoods of millions of people across the region and includes a range of invertebrates, amphibians, reptiles, birds, and mammals [3]. Driven by a blend of factors ranging from status to cultural affinity for wild meat, demand for wild meat is widespread in urban areas where alternative forms of protein are available [4] turning traditional subsistence hunting into a profit-driven, commercialized industry [7]. The urban appetite for wild meat, coupled with easier access to remote areas [10] and trade networks that connect remote areas with urban areas, has led to the establishment of large wildlife trade hubs that support soaring rates of wild meat consumption in cities [11]. Wild meat trading at these urban markets often involves species protected by national and international laws limiting or banning hunting [3,12]. Weak regulatory frameworks, infrastructure development, and market connectivity further facilitate this trade boom. Confounding factors like rural poverty, inadequate wildlife protection, insufficient resources for protected areas management, and chronic political instability all contribute to accelerating the urban wildlife trade [3,4,13].
Beyond posing an existential threat to the world’s biodiversity, the consumption and commercial trade of wild meat is linked with public health emergencies [14]. Zoonotic diseases, which can be transmitted directly to humans from wild or domestic animals, are a significant concern in this context [15,16]. Central Africa, with its frequent human-wildlife interactions, is a focal point for the emergence and spread of such diseases, including HIV-AIDS [17], Ebola [18,19], Lassa fever [20], tuberculosis [21], malaria [22], COVID-19 [23], and a variety of other infections [16]. The region’s widespread wildlife hunting trade elevates the risk of zoonotic transmission [24]. Markets dealing in wild meat, lacking in sanitation and protective measures, pose another serious risk since these markets can potentially become hotspots for zoonotic pathogen transmission and disease spread [25]. The global COVID-19 pandemic, caused by the zoonotic SARS-CoV-2 virus [23], highlights the urgent need to address the risks that wildlife markets pose to global human health and economic prosperity.
In light of these challenges, the wild meat trade hinders the global effort to achieve the United Nations Sustainable Development Goals (SDGs) [26], particularly SDG 15 (Life on Land) and SDG 3 (Good Health and Well-being). SDG 15 focuses on halting biodiversity loss and promoting the sustainable use of terrestrial ecosystems, while SDG 3 emphasizes the importance of combating global health threats, including those posed by zoonotic diseases. Examining the factors that impact the trade is important because it addresses a critical regional issue and underscores the interconnectedness between successful biodiversity conservation with economics and public health.
The wildlife economy on Bioko Island, Equatorial Guinea (EG), is a natural laboratory to improve understanding of regional wild meat trade dynamics and how the trade impacts achieving SDGs in Central Africa. Bioko, a volcanic island in the Gulf of Guinea, is notable for its high density of primates [27,28], endangered sea turtles [29], and other endemic species [1,2], particularly within the Gran Caldera Scientific Reserve (Fig 1) [30,31]. The island’s wildlife economy, driven by urban demand and weak enforcement of wildlife laws, threatens its biodiversity [12]. Like in many Central African urban centers, the trade in wild meat is a luxury market, with alternative protein sources available in rural and urban areas at lower prices [32,33]. Malabo, Bioko’s capital, epitomizes this issue with its thriving wildlife market, where traditional and commercial hunting practices converge with one another. A previous study on the Malabo wildlife market encompassing the first decade of our dataset reported large numbers of Bioko’s primates available for sale and an unsustainable level of urban demand unrestricted by attempted governmental interventions such as a complete ban on primate hunting. It was predicted that in the absence of any significant conservation interventions at this point, the Semu market would continue to grow until Bioko’s primate populations were hunted to depletion [12]. Ten years after this prediction, the Malabo market is thriving with higher numbers of carcasses available for sale than ever before. Our study builds upon these previous findings in order to understand how Bioko’s wildlife trade is sustained.
This map shows surveyed wildlife market locations, protected areas, and biomonitoring stations mentioned in the text. Data sources: Left map was made with Natural Earth 4.0, center map created in ArcGIS, country boundaries are from Natural Earth 4.0, elevation and slope raster files from worldclim.org, and roads are from the Global Roads Open Access Data Set (gROADS) from NASA SEDAC. Protected area shape files were obtained from https://zenodo.org/record/4781344#.YKlLtWZKiX0, which are available to anybody. The shape file for the Grand Caldera Scientific Reserve was modified to include Ureca Village. The adjacent marine protected area was drawn by hand to indicate its approximate location.
In this study, we explored the factors driving the unexpected growth in Bioko Island’s wildlife trade by building on previous findings using 23 years of data from the island’s primary and secondary wild meat markets, enriched with environmental and socioeconomic metadata. Using these data, we examined: (1) the impact of hypothesized market inflection points regarding economic disruptions, public health emergencies, and conservation measures on wildlife commerce; (2) how the magnitude and composition of supply changed over time; and (3) the relationship of market trends to prevailing economic, environmental, and annual trends.
The data in our study represent the longest running market survey globally and, therefore, provides unique insights into market behavior. Most market studies are restricted to short periods of time and lack the continuity needed to examine the long-term impact of large-scale socioeconomic changes [34–39]. While many studies have investigated consumption patterns qualitatively through hunter and household questionnaires [40–46], our dataset provides a unique opportunity to use concrete daily data over a long period of time in order to quantitatively examine the drivers of the wildlife trade. Having an evidence-driven model is important for crafting conservation interventions necessary for reducing the demand for and supply of wild meat harvested from Africa’s last remaining intact ecosystems. Results from our study have allowed us to identify and quantify the most impactful changes on the wildlife trade and have therefore allowed us to make data-driven refinements to conservation strategies designed to disrupt carcass supply and reduce consumer demand for wild meat. Our study can act as a model system to examine how the region’s supply of and demand for wild meat has been impacted by socioeconomic factors in recent years and how shifting regional demographics will alter the supply of and demand for wildlife.
Materials and methods
We collected and analyzed 23 years of data detailing wildlife commercial activities on Bioko Island, including the main municipal wild meat market supplemented by new data from three secondary wild meat markets, environmental metadata [47,48], and socioeconomic metadata [47,49–57] (Table 1).
Data collection
From October 1997 to March 2021, we gathered daily data of carcass availability at Semu Market, Bioko Island. Operated six days a week, this market occasionally opens on Sundays and closes for cleaning on Tuesdays. Local researchers, trained by the National University of Equatorial Guinea, collected carcass data, noting age, condition, capture method, sex, origin, and price in Central African Francs. Carcasses were identified by scientific and common Spanish names and their origin (island or mainland). The regularity of data collection, vendor familiarity, and preference for fresh carcasses ensured unique data.
Secondary wildlife markets appeared in the late 2000s as EG’s road network improved and suburbs developed to house EG’s growing middle-class population. We monitored three secondary markets daily from January 2018 through March 2021: Timbabe, Fishtown, and Boloco (Table 1). The Timbabe and Fishtown secondary markets are located on the outskirts of Malabo, west and east of the city’s center, respectively. The Boloco secondary market is in the more rural Bioko Sur region near the city of Luba (Fig 1). Secondary markets operate on a variable and opportunistic schedule, with closures occurring approximately 1–3 times weekly.
We developed profiles for each market by classifying carcasses into several categories: Bioko’s birds, reptiles, primates, rodents, duikers, other mammals, and mainland carcasses. These categories were consistent with those used in the previous publication by Cronin et al. [12] of Semu market’s earlier decade (1997–2010). Subsequent analyses focused on major market groups, including Bioko’s primates, rodents, duikers, and mainland carcasses. We calculated the carcass rates for these groups in each market by dividing each month’s total carcasses by the number of survey days. We excluded species that were infrequently observed with arbitrary thresholds at Semu Market (fewer than 40 occurrences) and in secondary markets (less than 15 occurrences across all three markets). Those that were unidentifiable were also omitted to minimize misidentification errors. To account for data records lost due to a 2001 fire, we used the average carcass rates from January and March 2001.
Ethics statement
This study was approved by Drexel University’s Institutional Review Board (IRB) under IRB ID number 1308002258A003. Verbal and written consent was obtained when required. Data on carcass counts were completed based on visible products available for sale in public markets. Socioeconomic data were obtained from publicly available online sources.
Metadata collection
We aimed to determine how factors presumed and/or hypothesized to broadly influence market trade similarly impacted trade at Bioko’s wildlife markets. We assembled several datasets, including international environmental conditions, wild meat product substitutes, local environmental indicators, and international environmental indicators. Information regarding these datasets and their sources can be found in Table 1. Daily data were converted to monthly data through averaging since not all datasets were sampled at equivalent resolution. If data did not extend through the end of the study period, we forecasted the dataset to March 2021 using the auto.arima function in the R package Forecast [58,59].
Identifying interventions and external variables that impact market behavior by predictive modeling analysis
We hypothesized intervention events on wildlife commerce, including economic disruption, two public health emergencies, and various conservation measures summarized in Table 2. The legal commercial harvesting of wildlife is a multi-trillion-dollar industry [60]. In contrast, the scale of the illegal wildlife trade is unclear [4], but it is estimated to generate billions in revenue [61]. Both the legal and illegal wildlife trades play an important role in supporting the livelihoods of millions of people across Central Africa [3,41]. Given the importance of the wildlife trade and wild meat consumption to the economy, we examined the impact of a national economic crisis in 2009 [62]. EG’s economy heavily depends on its petroleum industry, making it vulnerable to oil price fluctuations. EG initially maintained economic stability during the 2008 global financial crisis, aided by high oil prices. The situation changed when global oil prices plummeted, reaching a low point in December 2008. This decline in oil prices led to a regional and national economic downturn, which became evident a year after the global fall in oil prices [63].
We also investigated how two international public health emergencies impacted Bioko’s wild meat markets since both affected the commercial wild meat trade in other Central African nations: the 2014–2016 Ebola outbreak [64,65] and the COVID-19 pandemic [66–68]. In EG, COVID-19 mitigation measures (S1 Table) significantly disrupted trade and employment, resulting in job losses for half of the surveyed workers [69]. To offset the pandemic’s negative impact on non-oil tax revenues, which dropped by 25.9% from 2019, EG’s government increased oil production by 8.3% and continued a liquified natural gas project. Collectively, these actions contributed to a GDP contraction of -0.9%, better than the projected -4.9% [69,70].
There have been calls to reduce the supply of and demand for wild meat for decades, growing with urgency over time as the effects on biodiversity became increasingly evident [3,71]. These became acute following the COVID-19 pandemic [68,72,73]. Yet data are sparse about the effectiveness of such measures. We assessed the impact of conservation policies and direct conservation action on wild meat supply and demand on Bioko. The conservation policies included a 2002 national biodiversity roundtable that addressed EG’s environmental issues [12] and a 2007 ban on primate hunting, sales, and consumption [74]. Previous research showed the impacts of the 2002 roundtable and 2007 ban on primate sales were significant with the primate ban resulting in an unintended rise in the Semu market’s primate supply [12]. Re-testing the impact of these events with an updated methodology and a larger dataset allowed us to assess the long-term impacts of conservation measures on Bioko.
Finally, we hypothesized that conservation actions in the Gran Caldera Scientific Reserve impacted wild meat supply. These interventions included regular biomonitoring patrols that began in October 2009 and the establishment of two biomonitoring stations on Bioko’s southern beaches. The Moaba station (circa 2008) is located along Bioko’s southeastern shore. The Moraka station (circa 2011) is located along Bioko’s southwestern shore and is the gateway to the Gran Caldera, which has a high density of primates (Fig 1). Both stations provide a passive conservation presence serving as a base station for wildlife patrols, research teams, and ecotourists.
To assess the impact of hypothesized market inflection points, we created a predictive Bayesian time series model using the full Semu market dataset from November 1997 to March 2021 with the CausalImpact package in the R framework [75,76]. Using these forecasted models, we examined the effects of each intervention on market behavior to understand the differences between actual observed carcass rates and our model’s predicted rate, allowing us to quantify the effect of intervention events.
For sequential events A and B, we fit a model to all data leading up to event A and then forecasted monthly carcass rates from event A to event B. This was repeated for all sequential hypothesized events. We created the model using prior distributions from a composite dataset of the monthly external metadata (e.g., temperature) previously described (termed a synthetic control) as well as pre-event carcass rates. The posterior estimate of the per month difference between the predicted and actual carcass rates, as well as the cumulative difference between the predicted and actual carcass rates across the entire forecasted period were quantified to determine the significance and impact of each event [75]. In total, we created 27 predictive models: five models of the hypothesized intervention events and three conservation-related models (8 total) in each of four market groups (e.g., primates) with the exception of five instances in mainland carcasses where models could not be created due to lack of data.
Quantifying differences between market periods and market groups
Principal components analysis.
To understand how the magnitude and composition of market supply changed over the study period, we performed Principal Components Analysis (PCA) [76]. PCA allows for the partitioning of the major axes of variation in the composition of carcass supply in the Semu market and can, therefore, provide insights into underlying variation in the data. This technique also allows us to potentially identify specific taxa contributing to changing market dynamics. We chose PCA as the ordination technique for this data to preserve underlying differentiation, include compositional differences that may be influenced by rare species, and allow for the inclusion of zero data, which in this study, represents the absence of market supply. We structured the data for the PCA by calculating the total number of carcasses per species for each monitored market day. Data were centered and scaled prior to analysis. We then categorized the results of the Semu market PCA into six periods for visualization, each separated by one of the hypothesized intervention events. We used the model’s loadings to assess variation in the abundance of specific species between the time periods. PCA was also used to better understand the spatial distribution of Bioko’s wildlife supply by determining differences in the supply of carcasses available within and between the four studied markets. In this case, the PCA used the total number of carcasses per species for each market day.
Analysis of Variance (ANOVA).
After identifying major variation in carcass supply across the four markets, we quantified differences in composition within each of the four market groups using an Analysis of Variance (ANOVA) in the R framework [76]. Since we could not assume normality, we used one-way Kruskal-Wallis Rank Sum Tests to identify significant differences between markets, market periods, and market groups. For mainland carcasses, we analyzed differences between the four markets, with the market as the fixed factor and the daily proportion of mainland carcasses as the response variable. We then used a Tukey pairwise comparison to determine pairwise differences of markets. For primates, duikers, and rodents, we performed a Kruskal-Wallis Rank sum test. For each calculation, we used the market as the fixed factor and the proportion of carcasses in one of the three market groups as the response variable. Non-parametric analysis was necessary for these market groups since the proportion of carcasses in each of the three market groups still violated the normality assumption even after data transformation. If significant differences were identified between the markets, we used a Dunn’s Test of Multiple Comparisons Using Rank Sums with a Bonferroni adjustment to identify which specific markets were significantly different from one another (significance score of greater than 95%). All analyses were performed in the R statistical framework [76].
To investigate differences in supply composition before and after the COVID-19 pandemic, we calculated the proportion of carcasses in market groups. We then identified differences in composition only between a one-year pre-COVID period (March 2019 through March 2020) and a one-year after the official start of the COVID-19 pandemic and the onset of the post-COVID lockdown (April 2020 through March 2021). In the Semu market, we used each market group as a fixed factor, the proportion of carcasses in that group as the response variable, and the period as the random effect. Non-parametric analysis was necessary since the proportion of carcasses in each of the four market groups could not be normalized through data transformation.
Multiple regression models.
To elucidate the relationship between observed market trends and prevailing economic and environmental trends, we applied backward stepwise multiple regression in R [76]. This methodology allowed us to analyze the impact of external factors on the Semu market trade. To select independent variables to include in the multiple regression models, we explored the results of our previously described Bayesian predictive model. During the creation of the predictive models for the five main hypothesized intervention events (e.g., the 2014 Ebola outbreak), inclusion probability values were assigned to each of the external variables in the synthetic control based on their inclusion as regression coefficients in the model. We extracted the inclusion probabilities from the CausalImpact model results for each of these models and chose the 8 variables with the highest inclusion probability for each as independent variables for the regression models. We also created a one-year lag for each variable by shifting variable values forward in time by 12 months. This led to a total of 16 variables. Variables were scaled and adjusted for seasonal trends using dummy variables for months. In Period 3, due to limited data points, we excluded lagged variables. Model quality was gauged using the Akaike Information Criterion (AIC) and adjusted R-squared. When models violated normality or heteroskedasticity assumptions, we applied log transformations to the dependent variables and coefficients. If these corrections did not resolve the criteria, we removed outliers based on visual inspection. This method allowed for a comprehensive evaluation of the variables’ influence on carcass rates.
Seasonal decomposition analysis.
Annual trends, such as weather changes or cultural events, may also impact market behavior. To investigate these relationships between annual trends and carcass rates, we implemented a seasonal decomposition analysis in the Statsmodels python package [77]. For each market group, we smoothed and de-trended the time series data by removing the moving average. We then used the remaining monthly signals across the 23-year period to compute the average seasonal signal for each month. We calculated residuals as the difference between the seasonal trend and the actual monthly seasonal signal within each year. We then used the residuals to quantify the variation or consistency of the seasonal signal throughout the study period.
Pearson’s correlation coefficient.
Trends within the market may also impact other facets of supply. We determined the correlation between market groups using a pairwise Pearson correlation coefficient analysis performed in R [76] to better understand these relationships. The correlation coefficient was calculated between the following groups within the Semu market: primates, duikers, Bioko’s rodents, all rodents, and all mainland carcasses. The market group of all rodents was added during this analysis due to their large proportion of carcasses in the Semu market and the taxa’s potential usage as a substituted protein source.
Results
Market profiles and trends
We categorized carcasses into market groups, including rodents, duikers, primates, birds, reptiles, and other mammals (including carnivores, hyrax, and pangolins) from Bioko. We separately enumerated carcasses from the mainland, which predominantly consisted of mammals and reptiles. Over 23 years of monitoring Semu, Bioko’s main municipal market, we recorded 659,460 carcasses from 33 species across 6,659 market days (Fig 2). Rodents, particularly Emin’s pouched rat (Cricetomys emini, 27.4%), were most prevalent, followed by Blue duiker (Philantomba monticola, 17.8%). Specific species most frequently imported from the mainland to Bioko were the Tree pangolin (Manis tricuspis), the Brush-tailed porcupine (Atherurus africanus), and the African palm civet (Nandinia binotata). Mainland primates were notably rare at the Semu market.
Secondary wildlife markets appeared in the late 2000s as EG’s road network improved and suburbs developed to house the growing urban population. Daily monitoring began in January 2018 and continued through March 2021. We recorded a total of 38,845 carcasses across three secondary markets: Boloco (13,659), Fishtown (16,589), and Timbabe (8,597), involving 28 species (Fig 2). The most common species were Blue duiker, Emin’s pouched rat, and Brush-tailed porcupine. Carcasses from the mainland were rare in secondary markets, especially in Boloco where none were found throughout the study period.
To examine changes in market volume and composition during the study period, we completed a trend analysis of carcass rates in four market groups: rodents, duikers, primates, and mainland carcasses. Fig 3 shows the trend analysis of carcass rates in the Semu market from November 1997 through March 2021. On-island carcasses constituted the bulk of the market (74.3%). However, mainland carcasses became a regular feature in September 2003, with a steady increase until 2014, when they began to rise dramatically, occasionally surpassing on-island carcasses between 2014 and early 2020. The start of the COVID-19 pandemic drastically reduced carcass rates, dipping to about 50 carcasses per market day in June 2020, largely due to fewer mainland carcasses and a decline in primate carcasses—marking the lowest rates since late 2013. Nonetheless, rodents and duikers persisted as primary contributors to the market for the full study period.
Carcass rates for the four studied market groups are shown from October 1997 through March 2021: Bioko’s primates (purple), mainland carcasses imported to Bioko Island (blue), Bioko’s rodents (orange), and duikers (green). Total carcass rates are shown in black. For convenience, the time series is divided into five hypothesized intervention events indicated above the graph and six time periods identified during downstream analysis.
Fig 4 shows time series plots for all monitored markets from January 2018 –March 2021. Although Boloco, Fishtown, and Timbabe did not surpass Semu in overall wildlife supply, they represented 20–25% of the island-wide carcasses available for sale monthly between 2018–2021. Monthly carcass rates averaged 15–20 individuals daily in the secondary markets (Fig 4A). Following March 2020, carcass rates in all secondary markets declined but not as dramatically as at Semu. Fig 4B shows carcass rates for the secondary markets only. In the year before the onset of the COVID-19 pandemic (March 2019—March 2020), we counted 22,156 carcasses across the markets, compared to 12,733 in the period from April 2020—March 2021, hereafter referred to as “post-COVID”. Despite the decline, the distribution of species remained similar. Mainland carcasses continued to be rare in the secondary markets in the year following the onset of the pandemic, with none in Boloco. Timbabe experienced a larger increase in carcass rates after the pandemic onset than the other markets.
(A) Island-wide carcass rates in four monitored markets from January 2018 –March 2021. Rates for all carcasses in each of the three secondary markets: Boloco (blue), Fishtown (red), and Timbabe (green). Total carcass rates are shown in black. Carcass rates sums for all monitored secondary markets are shown in the yellow dotted line. Semu market rates for all carcasses for sale are represented by the solid black line with island-wide carcass rate sums shown by the black dotted line. (B) Secondary market carcass rates in three monitored markets from January 2018 –March 2021. Rates for all carcasses in each of the three secondary markets: Boloco (blue), Fishtown (red), and Timbabe (green). Carcass rate sums for all three monitored secondary markets are indicated by the yellow dotted line.
Identifying external variables and interventions that impact market behavior
General trends.
Following the approach outlined in Broderson et al. (2015) [75], we modeled the Semu market dataset from 1997 to 2021 to evaluate the impact of various interventions on the wildlife economy. This analysis involved the creation of over 27 distinct models, each developed to forecast carcass rates between consecutive market and conservation events, using a combination of synthetic controls and historical carcass rates. Each model spanned the period from one event to the next, with projections extending to the subsequent event. This methodology was followed for each of the major species’ groups commonly available in wild meat markets, including primates, mainland carcasses, rodents, and duikers. This approach allowed for a detailed examination of the significance and impact of each event on market behavior.
Bayesian predictive model results for hypothesized events are shown in Table 3. Model histograms for the full data set are shown in S1 and S2 Figs. The predicted and actual impacts varied among different market groups depending on the type of intervention and major market groups, including primates (S1A, S2A and S3 Figs), mainland carcasses (S1B, S2A and S4 Figs), rodents (S1C, S2C and S5 Figs), and (S1D, S2D and S6 Figs). Our analysis revealed that each hypothesized intervention event had a significant but distinct impact on the market. The Ebola outbreak and the COVID-19 pandemic had the most pronounced overall effects on wild meat availability at the Semu market (S1 and S2 Figs). Fig 5 shows the impact of these zoonotic events on primate availability as an example, although the 2007 primate ban had the greatest overall impact on primates.
(A) Ebola Outbreak (February 2014) and (B) COVID-19 pandemic (March 2020). Both actual carcass rates (solid black line) and predicted carcass rates (blue dashed line) are shown. Confidence intervals for the predicted model are shown by blue shading. Green shading contains pre-event carcass rates used to train the model, and orange shading contains the period over which the model was projected.
We next calculated the probabilities that each of the 18 external independent variables (Table 1) was included in the Bayesian predictive models to identify independent variables with large influences on Semu market behavior. External variables that had the highest inclusion probabilities, excluding the COVID-19 pandemic period, were China Foreign Investments, Dow Jones Industrial Average (DJIA), EG Crude Oil Production, Food Price Index, Temperature (in EG), US Crude Oil Production, US Gas Prices, and US Exports to EG (S2 Table). Our closer examination of public health emergencies, including (1) before and after the Ebola outbreak and (2) in the period leading to the COVID-19 pandemic, included Food Price Index, US Crude Oil Production, and Brent Spot Oil Price (S3 Table).
Market group trends
Primates.
Primate carcass rates at the Semu market were significantly influenced by each intervention event (Fig 5, S1A, S2A and S3 Figs), leading to varying impacts (Table 3). During the November 2009 Economic Crisis, contrary to the predictions of an increase, there was a notable decline in primate carcass rates, with the largest absolute change being a decrease of 33 carcasses per month. This decreasing trend against the forecasted rise was also observed during public health emergencies (Fig 5 and S2 Fig). During the Ebola outbreak, primate carcass rates dropped, in contrast to the expected rise. The post-COVID period revealed a similar trend, with actual carcass rates falling significantly below the predicted rates, resulting in a relative decrease of 37% (S1 Fig and Table 3).
Conservation interventions yielded mixed results for primates (Table 3). The March 2002 Biodiversity Roundtable led to the most significant relative change among the interventions, causing a 275% increase in primate carcasses (Table 3). This contrasted with the predictive models that forecasted decreased carcass rates without such interventions. Despite the November 2007 primate hunting ban, the rapid growth in the market supply of primates during the late 2000s suggests a lack of enforcement and possible consumer anticipations of future enforcement of the ban. This pattern was noted in previous reports of primates in the Semu market by Cronin et al. (2015) [12], as well as in other species with increasingly vulnerable statuses by CITES [78]. The establishment of biomonitoring stations also had notable effects (Table 3). The initiation of regular biomonitoring patrols and the establishment of the Moraka biomonitoring station in 2011 by the Bioko Biodiversity Protection Program resulted in decreases in primate carcass rates, contrary to the predicted increases. In particular, the Moraka station led to an estimated monthly decrease of 24 carcasses. However, establishing the Moaba biomonitoring station in 2008 did not significantly impact the occurrence of primates at the Semu market.
Rodents.
The November 2007 Primate Ban, the November 2009 Economic Crisis, the February 2014 Ebola Outbreak, and the COVID-19 pandemic each played a role in altering rodent carcass rates (Table 3, S1C, S2C and S5 Figs). During the November 2009 Economic Crisis, an unexpected increase in rodent carcasses was observed, contradicting the predictions of a decline. The crisis disrupted the usual market trends, stabilizing carcass rates at higher-than-average levels.
This upward trend was especially noticeable during documented endemics and pandemics. The Ebola outbreak resulted in the largest absolute and relative change in rodent carcass rates. The outbreak caused an estimated increase of 50 carcasses per month, translating to a dramatic 547% relative change. Following the Ebola outbreak, a predicted decline in rodent carcass rates did not materialize; instead, actual rates remained significantly higher. The COVID-19 pandemic further amplified the discrepancy between predicted and actual rodent carcass rates. During the post-COVID period, rodent carcasses saw the largest absolute and relative change, with an increase of approximately 62 carcasses per market day, marking an 81% rise compared to the expected rates (S1 Fig).
Conservation interventions had varied impacts on rodent carcass rates (Table 3). The March 2002 Biodiversity Roundtable did not significantly influence rodent populations. However, the November 2007 Primate Ban had a complex effect, leading to fluctuations in carcass rates and a low overall impact. Establishing biomonitoring stations, particularly the Moraka biomonitoring station in 2011 on Bioko, significantly altered rodent carcass rates. This intervention led to a 595% relative change in carcass rates, highlighting the substantial impact of targeted conservation efforts on rodent populations.
Duikers.
Duiker carcass rates at the Semu market were significantly influenced by four interventions: the November 2007 Primate Ban, the November 2009 Economic Crisis, the February 2014 Ebola Outbreak, and the COVID-19 Pandemic (Table 3, S1D, S2D and S6 Figs). During the November 2009 Economic Crisis, there was a significant deviation from the predicted trends for duikers. Contrary to the predicted rapid increase in the absence of the crisis, actual carcass rates were substantially lower. This suggests that the economic crisis played a critical role in altering the market dynamics for duikers, leading to a decrease in carcass rates contrary to the predictions.
The Ebola outbreak caused the largest absolute and relative change, with an estimated 307% increase in carcass rates, equating to about 16 additional carcasses per month. This surge significantly differed from the predicted decrease post-event, indicating that the Ebola outbreak drastically altered duiker population dynamics. Similarly, the COVID-19 pandemic led to an unexpected increase in duiker carcasses, with a 60% rise per market day compared to the predicted rates (S1 Fig). This was part of a broader trend observed during the pandemic, where both duikers and rodents experienced significant increases in carcass rates, highlighting the substantial differential impact of public health crises on wildlife populations.
Conservation interventions also played a role, although their impacts were varied (Table 3). The March 2002 Biodiversity Roundtable did not significantly affect duiker carcass rates. However, the November 2007 Primate Ban had a complex influence, leading to a lower-than-predicted decline in duiker carcasses. Establishing both the Moraka and Moaba biomonitoring stations significantly impacted duiker carcass rates, mirroring the effects observed in primate populations. These interventions indicate a notable sensitivity of duiker populations to conservation efforts, reflecting the importance of research and wildlife protection personnel in discouraging illegal hunting activities.
Mainland carcasses.
Between March 2002 and November 2007, despite the presence of mainland carcasses recorded at the Biodiversity Roundtable and the subsequent Primate Ban, the data showed low, fluctuating rates with numerous zero values (S1B, S2A and S4 Figs). This erratic pattern rendered the predictive model unreliable, leading us to exclude mainland carcasses from major national and international intervention events prior to 2007 (Table 3). EG’s 2009 economic crisis significantly influenced mainland carcass rates. Predictive models indicated that, had the crisis not occurred, mainland carcass rates would have stabilized. However, actual carcass rates surged due to the economic turmoil, resulting in notable positive relative and absolute changes, detailed in Table 3.
The Ebola outbreak dramatically affected mainland carcass rates more than any other event, predicting a monthly increase of 76 carcasses, equivalent to a 2011% relative change (Table 3, S1 Fig), surpassing even the effects of the COVID-19 pandemic. Without the Ebola outbreak, carcass rates would have likely stabilized, as shown in (S1 Fig). The actual rates, however, escalated significantly due to the pandemic. Post-COVID predictions also indicated a continued decrease in carcass rates, estimated at about 19 per market day, further demonstrating the varied impacts of public health crises on carcass rates.
Quantifying specific differences between market periods and market groups
Principal Components Analysis (PCA).
PCA using the Semu market data allowed us to confirm distinct differences in market supply between each of the market periods punctuated by the hypothesized intervention events confirmed previously. Fig 6A and 6B show the PCA for the Semu market divided into each hypothesized market period. Fig 6A revealed that market days from period 5 were distinctly separated from periods 1–4. Period 5 corresponds to March 2014 to October 2019 and marks the period when mainland carcass availability began to serge at Semu (Fig 3). The influx of mainland carcasses is clearly a defining characteristic of the Semu over the last decade. Despite there being very little change in the distribution of market days between Period 5 and Period 6, the PCA in Fig 6B shows a large amount of differentiation between market days in Period 6. This suggests that major changes after the onset of the COVID-19 pandemic are not due to changes in the availability of specific species, but instead due to quantity differences alone. We next used PCA to look specifically for differences in the composition of supply available between the 12 months before and after the start of the COVID-19 pandemic (defined as the pre- and post-COVID periods) in the Semu market and the secondary markets to consider the impact of the COVID-19 pandemic more systematically on carcasses supply. Market days in this Semu PCA remained dispersed, and therefore, show no specific differences in the supply of species driving market shifts after the COVID-19 pandemic (S7A Fig). The same was true for the pre- and post-COVID periods in all secondary markets (S7B Fig).
Points represent a single market day during the time periods created by hypothesized intervention events. Colors represent each of these periods. Time intervals include (A) Period 1 –October 1997 to March 2002; Period 2 –April 2002 to November 2007; Period 3 –December 2007 to November 2009; Period 4 –December 2009 to February 2014; and Period 5 –March 2014 to October 2019. (B) The sixth period from November 2019—March 2021, which coincides with year 1 of the COVID-19 pandemic, was added to the PCA to show the effect of the pandemic on the overall composition of the market supply.
One-way Analysis of Variance (ANOVA) and Kruskal-Wallis Rank Sum Tests.
Since we could not assume the market data were normally distributed, we used both ANOVA and Kruskal-Wallis Rank Sum Tests to identify significant differences in supply between markets, market periods, and market groups. Results for each market group for periods 1 to 5 are shown in S8, S9, S10 and S11 Figs. Our analysis indicated significant variation in primate carcasses across all periods (Kruskal-Wallis test: Chi-square = 1123.8, p < 0.001), with notable differences between most periods, except Period 2 and Period 4. Similarly, mainland carcasses differed significantly over the periods, with a continuous proportion increase between periods 1 to 5 (Kruskal-Wallis test: Chi-square = 1793.8, p < 0.001). Despite their seemingly consistent abundance, rodent carcasses exhibited significant variation across the study period (Kruskal-Wallis test: Chi-square = 140.14, p < 0.001). Significant differences were found between several periods, notably Period 1 compared with Periods 3, 4, and 5, and Period 2 compared with Period 5 (Dunn-test with Bonferroni adjustment: p < 0.01 for all). However, no significant differences were observed between Periods 1 and 2, Periods 2 and 3, Periods 2 and 4, and Periods 3 and 4 (Dunn-test with Bonferroni adjustment: p > 0.05 for all). In contrast, duiker carcasses decreased consistently across the periods, with significant variation in their availability (Kruskal-Wallis test: Chi-square = 1955.2, p < 0.001). The final period had the lowest average proportion of duiker carcasses.
Comparing the Semu market with secondary markets, we found significant differences in the daily proportions of primate, mainland, rodent, and duiker carcasses (Kruskal-Wallis test: Chi-square values ranging from 194.13 to 435.45, all p < 0.001). Notably, the Boloco market exhibited the highest proportions of primate and duiker carcasses, while the Semu market had the highest proportion of mainland carcasses. Rodent carcasses varied considerably, with Timbabe and Fishtown having the largest supplies.
To assess the impact of the COVID-19 pandemic on market supply, we also conducted Wilcoxon Rank Sum Tests for the pre- and post-COVID periods, shown in Fig 7A and 7B, respectively. S12, S13, S14 and S15 Figs show Wilcoxon Rank Sum Tests for primates, mainland carcasses, rodents, and duikers, respectively. At the Semu market, post-COVID trends showed a significant decrease in mainland carcasses (W = 39,666, p < 0.0001) and increases in both rodent (W = 16,124, p < 0.0001) and duiker carcasses (W = 21,480, p < 0.0001), with no change in primate carcasses (W = 30,178, p = 0.2445). In Bioko’s secondary markets, notable changes were observed primarily in the Boloco market, with an overall increase in carcass rates post-COVID (W = 25, p = 0.005934). The Fishtown and Timbabe markets showed no significant changes. The total carcass rates in the Semu market remained stable (W = 75, p = 0.8874). However, the proportion of primate carcasses in Boloco decreased significantly post-COVID (W = 7280, p = 0.006), while mainland carcasses decreased in the Semu market (W = 9,427.5, p < 0.0001). Fishtown was the only market to show a significant post-COVID decrease in mainland carcasses. Duiker carcasses experienced a decrease in both Boloco and Timbabe (W = 56,664, p < 0.0001 in Boloco; W = 11,269, p = 0.0115 in Timbabe), but increased in Semu (W = 18394, p = 0.0003), with Fishtown remaining unchanged. The Semu market experienced a significant increase in rodent carcasses post-COVID (W = 13,396, p < 0.0001). These findings highlight the varied impacts of the pandemic on different market groups and locations.
(A) Proportion of total Semu market carcasses in each of the four market groups prior to and after the COVID-19 outbreak. Data in the pre-COVID period extends from March 2019 through March 2020 and in the post-COVID period from April 2020 through April 2021. Significant differences between the pre-COVID and post-COVID period are denoted by asterisks (*p < 0.0001). (B) Total carcass rates in each of the four market groups prior to and after the COVID-19 outbreak. Data in the pre-COVID period extends from March 2019 through March 2020 and in the post-COVID period from April 2020 through April 2021. Significant differences between the pre-COVID and post-COVID period are denoted by asterisks (*p < 0.01). Additional tests are shown in S8–S16 Figs.
Examining factors that impact market supply and consumer demand
External economic and environmental variables.
To further assess the impact of external market influencers on trade at the Semu market, we fit backward stepwise multiple regression models to carcass rates in each of the Semu market groups for each market period. Independent variables with high inclusion probabilities in each of the predictive models created as well as a 12-month lagged variable of each were included in the regression model: China Foreign Investments, DJIA, EG Crude Oil Production, Food Price Index, Temperature in EG, US Crude Oil Production, US Gas Prices, and US Exports to EG (S2 and S3 Tables). Table 4 summarizes the findings of multiple regression models in various market groups over different study periods, highlighting the most impactful independent variables and key observations for each group. Detailed results of the backward stepwise multiple regression models and coefficient estimates are given for primates in S4 Table and S16 Fig, rodents in S5 Table and S16 Fig, duikers S6 Table and S17 Fig, and mainland carcasses in S7 Table and S17 Fig.
Primate carcass rates in periods 1 to 5 were significantly associated with the external independent variables, especially in periods 3 and 4, in which our model had the best fit (Table 4, S4 Table, and S16 Fig). Key variables for these periods included EG Crude Oil Production (p < 0.01 in Periods 3 and 4), Temperature (p < 0.01 in Period 3 and p = 0.02 in Period 4), and US Crude Oil Production (p < 0.01 in Period 4), with the highest coefficients in period 4. China Foreign Investment, US Gas Prices, and temperature were frequently significant across most periods, while periods 1 and 5 showed poorer model fit with fewer contributing coefficients. Rodent carcass rate models were most accurate in periods 2 and 5, with period 3 unable to predict rates (S5 Table and S16 Fig). Period 5’s models had large significant coefficients, including lagged China Foreign Investment (p < 0.01), EG Crude Oil Production (p < 0.01), and Food Price Index (p < 0.01). Lagged China Foreign Investment was a common predictor in four periods. US Crude Oil Production was also significant in at least three periods. Duiker carcass rate models showed high explanatory power, particularly in period 4 (S6 Table and S17 Fig). China Foreign Investment, EG Crude Oil Production, and US Gas Prices were common significant predictors. Temperature, despite its large standard error, was a frequent but less reliable predictor. Over time, more US-related variables became significant in these models. AIC values for mainland carcass rate models were lower than primate models, with period 2 providing the best explanatory power despite limited significant coefficients (S7 Table and S17 Fig). Period 3’s model, unique in having no significant coefficients, indicated weaker explanatory capacity. Food Price Index was a common variable but not always a significant predictor, while lagged China Foreign Investment, US Crude Oil Production, and Temperature showed high coefficients across all periods (S6 Fig). All months in period 5 were significant (S5 Table).
Seasonal trends.
Climatic variables, national or religious holidays, and local economies are often cyclic and could, therefore, cause underlying seasonal patterns or changes in the Semu market. The effect of these cycles on market carcass rates is important to identify and allocate resources for wildlife protection during the most vulnerable times of the year. Seasonal decomposition analysis showed that in addition to external variables, seasonal trends influenced Semu market dynamics across years and were distinct for different market groups (Fig 8). Primate carcass supply was greater in December than the rest of the study period (p = 0.018). Rodent and duiker carcasses were generally lower in the first half of the year and higher in the latter half. Specifically, rodent carcasses decreased in February (p = 0.01) and April (p = 0.002) and increased in August (p = 0.002) and September (p < 0.001). Similarly, duiker carcasses were lower in February (p = 0.005) and April (p = 0.003), and higher in August (p = 0.015) and September (p < 0.001). Mainland carcass rates showed no significant seasonal patterns.
(A) Primate carcass rates were significantly greater in December than the rest of the study period (p = 0.018). (B & C) Rodent and Duiker carcass rates in August (p = 0.002 and p = 0.015, respectively) and September (p = 0.0003 and p = 0.003, respectively) were significantly greater than all other months in the study period. Rodent and Duiker carcass rates in February (p = 0.01 and p = 0.005, respectively) and April (p = 0.002 and p = 0.003, respectively) were significantly lower than the rest of the year. (D) Mainland carcasses did not have any significant seasonality.
Market substitutions.
Changing market dynamics in the Semu market might result from substitutions within market groups, where demand shifts from one type of wild meat to another. To investigate if supply variations among market groups were related to one another, we conducted pairwise Pearson correlation analyses across different market groups. These groups included primates, duikers, Bioko’s rodents, all rodents, and all mainland carcasses. Strong positive correlations would indicate similar demand patterns, while negative correlations could point to substitutions driven by varying supply or demand shifts. We included an “all rodents” group due to their significant presence and potential as an alternative protein source in the market. Results of the Pearson correlation analysis for the Semu market are shown in S8 Table and Table 5 after de-trending the data by removing a 12-month moving average. The Semu market had strong and significant relationships between many of the market groups. The highest Pearson correlation coefficients were found between Bioko’s rodents, all rodents, and all mainland carcasses, suggesting similar market dynamics for these groups throughout the study period. The only negative correlation (p = 0.028) occurred between primates and mainland carcasses after September 2003 when wild meat from the mainland began to arrive at Semu. Two comparisons lacked significant correlations: (1) rodents in the fifth market period with primates and (2) mainland carcasses with primates.
Discussion
The wildlife economy on Bioko Island presented a unique lens to study wild meat supply and demand in response to international, national, and local trends. We demonstrated that Bioko’s wildlife economy closely mirrors the region’s progression and that external forces have affected market dynamics and the sale of wildlife on Bioko. Our study, spanning over two decades, documented how socioeconomic change, economic disruptions, public health emergencies, conservation policy, and direct conservation actions in protected areas impact wildlife trading trends. The outcomes of these analyses have important lessons that may assist in designing future conservation strategies to disrupt the supply of wild meat, especially in luxury urban markets.
Wild meat market trends from 1998–2021
Over the 23-year study period, we documented 659,460 carcasses from 33 species in Bioko’s main market, Semu, and 38,845 carcasses in Bioko’s secondary markets. Rodents and duikers were the most common carcasses available in all markets and accounted for 74.3% of the volume at Semu and 64.48% of the combined carcasses across all markets (Fig 2), reflecting consistent consumer demand for these species. The prevalence of rodents and duikers at Semu is likely driven by their ecological abundance, cultural significance, and economic incentives [4]. Rodents, such as cane rats, thrive in agricultural areas, making them easily accessible. Their meat is often considered a delicacy, ensuring steady demand. Duikers are valued for their taste and are commonly hunted due to their abundance in forested areas. Both species reproduce rapidly and are adaptable, making them resilient to hunting pressure. Duikers’ elusiveness and the use of snares, a low-cost hunting method, further contribute to their market prevalence [79,80].
Although rodents and duikers were the main species available at all markets, primates were consistently present in all markets, contributing between 7.57% and 12.17% of the total carcass volume depending on the market. Primate meat is hunted for both its meat and its cultural significance in certain traditions [4]. Despite legal and ethical concerns, the high value of primate meat in urban markets incentivizes hunting [12]. Primate social structure makes them vulnerable targets, and the weak enforcement of wildlife laws, coupled with growing urban demand, exacerbates the trade. Hunting primates requires shotguns and a high level of skill [30], adding complexity to their conservation and sustainable management. Other mammals, birds, and reptiles made up the remaining carcass supply.
While rodents, duikers, and primates are nearly always available, the Semu market in Malabo has gone through multiple periods of dramatic change. The composition of species available for sale is drastically different now than in the late 90s (Fig 6), reflecting the change in demand of a developing consumer base. Additionally, the market trend analysis revealed that from 1997 to 2014, the Semu market (Fig 3) was predominantly supplied by on-island carcasses, making up 74.3% of the total. Mainland carcass contributions became more common starting in 2003, notably including species like the Tree pangolin and Brush-tailed porcupine. Between 2014 and early 2020 mainland carcass supply surged, occasionally exceeding on-island supply (S8 Fig), pointing to a growing, organized wildlife trade network that connects Semu market traders with suppliers in Rio Muni, the mainland part of EG, and probably with suppliers from neighboring countries.
As the Semu market shifted to accommodate the pressures of a changing economy, increased connectivity, and evolving consumer demand, the Bioko wildlife commodity chain adapted. By monitoring secondary markets, we were able to document a key response–the decentralization of Bioko’s commodity chain. Secondary markets on Bioko have become an important source of the wild meat supply. Fishtown, Timbabe, and Boloco, collectively represented 6.25% of Bioko’s wildlife carcass trade from 2018 to 2021 (Fig 4A). Before the COVID-19 pandemic, these markets maintained a steady rate of about 15–20 carcasses daily (Fig 4B). Analysis of market trends showed a notable island-wide shift. For instance, as historically abundant supplies of primate carcasses become less common at the Semu market (Fig 3 and S12 Fig), it would be logical to conclude that Bioko’s unique primate populations have largely been depleted. However, we showed that the supply of primates shifted from Semu to the secondary markets and, in particular, were significantly more abundant in the Boloco market (Fig 3, S8 and S12 Figs). The Boloco market is located in Luba (Fig 1) at the intersection of the Malabo–Luba road and the Luba–Ureca road. The Luba–Ureca road was completed in 2015 and extends through the entire length of the Gran Caldera Scientific Reserve [81], providing easy access to areas with high densities of primates [27,28,82]. Newly constructed roads and more advanced public transportation facilitate the wild meat trade throughout Central Africa [10,83], which is also the case on Bioko.
Overall, the trend analysis indicates a new wildlife market model has emerged on Bioko Island over the past decade. The traditional, centralized wild meat trade at Semu has now diversified, with the Semu market evolving into an international hub. This hub not only deals in wild meat sourced from Bioko but has also developed a niche in trading less expensive carcasses from the mainland. Concurrently, a distinct "forest-to-table" model has emerged in other parts of the island. This is evident from the rise of secondary markets, predominantly trading in wild meat obtained directly from Bioko’s forests. This emerging model fosters connectivity between hunters and consumers, marking a notable shift in the dynamics of wildlife trade on the island.
Changing market dynamics in response to socioeconomic shifts
We identified six main market periods at Semu (Fig 3 and S7 Fig), punctuated by five main intervention events (Table 2). Although the Ebola outbreak and the COVID-19 pandemic had the most pronounced overall effects on wild meat availability at the Semu market (Fig 5, S1 and S2 Figs), each intervention event had a unique and substantial effect on carcass volumes, with varying consequences for different wildlife groups (Table 3). The differential effects show that market dynamics are complex and influenced by external factors, which is evident in the distinct patterns observed within the main market groups during these events, including primates (S1A, S2A and S3 Figs), mainland carcasses (S1B, S2A and S4 Figs), rodents (S1C, S2C and S5 Figs), and duikers (S1D, S2D and S6 Figs).
Impact of economic disruptors and external market trends on wildlife trade.
The Semu market in Malabo revealed how wildlife trade is susceptible to broader socio-economic changes (Tables 3 and 4, S2 and S3 Tables). EG’s 2009 Economic Crisis, triggered by a worldwide decline in oil prices, had a significant impact on consumer behavior. As incomes fell and job security weakened, urban consumers in Bioko Island shifted their demand from the more expensive local wild meat, such as primates and duikers, to more affordable alternatives like rodents sourced from the mainland. This substitution pattern highlights a common response in the illegal wildlife trade when the supply of a highly demanded product becomes restricted or exhausted. Similar market adaptations have been observed throughout Central Africa, where economic pressures drive shifts in wildlife consumption [46,84].
More broadly, fluctuating wild meat availability at Semu can also be partially attributed to external economic forces, particularly the influence of economic superpowers like China and the US, exemplified notably by China’s foreign investment in EG primarily via infrastructure development. For instance, EG and China have strengthened their relationship over the past decade, becoming major trade partners, with China responsible for a significant portion of imported construction materials and labor, which are key components of EG’s pathway to globalization [85,86]. This investment has affected not only urbanization but also wildlife habitats through road construction [81], indirectly facilitating commercial hunting. This was especially true for endangered and vulnerable wildlife. The primate multiple regression models frequently showed a positive influence from China’s Foreign Investment, underscoring its impact on the wildlife trade. Concurrently, fluctuations in US gas prices, which disproportionately impact EG’s oil-dependent economy, correlated with wild meat availability. This market behavior underlines a key aspect of the wildlife trade: its sensitivity to consumer purchasing power and the broader global economic context, illustrating how external economic factors can drive changes in wildlife trade patterns on Bioko Island.
Impact of public health emergencies on wildlife trade.
The Ebola outbreak and COVID-19 pandemic significantly influenced wildlife trade patterns (Table 3, Figs 5 and 7), likely due to travel restrictions, trade disruptions, and perhaps, due to increased public awareness of zoonotic diseases. The impact of the COVID-19 pandemic extended beyond consumer choices, significantly disrupting traditional wildlife trade routes (S1 Table). These effects were more systemic than during the Ebola outbreak because it interrupted supply chains through international travel and trade restrictions. This disruption led to a decreased supply of wildlife from the mainland (Fig 7A), affecting both the central Semu market and two secondary markets (Fig 7B).
The impact of increased public awareness on the trade is less clear and differed between each crisis. Both emergencies led to a marked reduction in the demand for certain wildlife species, especially primates, which are perceived as high-risk for disease transmission. During the Ebola outbreak, consumers notably shifted their demand from primates to other wildlife species based on carcass availability at Semu (S8 Fig). A similar trend was observed during the COVID-19 pandemic, where there was a substantial decline in the sales of primate carcasses (S12 Fig), with a preference emerging for rodents and duikers instead (Fig 7A). This shift in urban consumer behavior during two independent public health emergencies demonstrates that public health concerns and messaging can help reduce urban consumer demand for wild meat.
In contrast, the secondary markets responded differently. Throughout the COVID-19 pandemic, primates were available at the more rural Boloco market (Fig 7B), suggesting that local hunters adapt to supply chain gaps caused by disruptions in international trade. While at the suburban Fishtown wild meat market, carcass rates peaked during the height of the global shutdown in 2020 (Fig 4B) and were significantly more abundant than before the pandemic (Fig 7B), perhaps to bring wild meat products closer to potential suburban consumers. While these observations emphasize the deep interconnection between global health and wildlife trade, illustrating how international health crises can impact local economies and ecosystems, the demand for wild meat persists and the smaller more nimble secondary readily adapt to changing circumstances.
Influence of conservation measures on wildlife trade.
Our findings also shed light on the impact of conservation measures (Fig 3 and Table 3). The impact of conservation policy interventions was variable and, in the case of primates, counterproductive, exemplified by the dramatic 275% increase in primate carcasses in the period following the 2002 National Biodiversity Roundtable. EG’s 2007 primate hunting ban initially led to a decline in primate carcass availability at Semu, but the rate quickly rebounded and surpassed carcass availability before the ban by 54% (Table 3). This surprising result, previously reported by Cronin et al. (2015) [12], suggests that policy interventions that are not enforced are likely to fail. Legislation like EG’s 2007 Primate Hunting Ban, which aimed to reduce wild meat trade to mitigate the public health risk of wild meat handling and consumption, proved ineffective and undermined public trust. A similar situation occurred in Ivory Coast during the Ebola outbreak. There, a ban on wild meat sales and consumption that was poorly enforced and communicated with the public pushed the trade further underground, impeding disease control and increasing public fear and distrust towards health officials [87]. This underscores that legislation intended to protect people and wildlife must be introduced and implemented in a manner that fosters public confidence and trust in those the regulations are designed to assist.
Conservation measures in areas where commercially valuable wildlife live show more promise for successfully disrupting carcass supply. The supply of primate carcasses was especially informative in this regard. We evaluated conservation activities led by the local conservation organization, the Bioko Biodiversity Protection Program, in partnership with the National Institute for Forestry Development and Protected Area Management (INDEFOR-AP) in the Gran Caldera Scientific Reserve. Although a supply of primate carcasses from the Reserve was discovered in local secondary markets, the total volume of Bioko’s primates being sold in wildlife markets has remained low since 2014 (Fig 3). Additional predictive modeling testing the impact of conservation events (Table 3) supports the conclusion that reduced primate supply is at least partially linked to increased protection measures in the Gran Caldera Scientific Reserve over the previous decade leading to the decline of primate and duiker availability at Semu (Table 3). Increased protection measures, such as regular wildlife patrols and biomonitoring stations, have contributed to a decrease in the availability of primate carcasses in major markets. This finding demonstrates that targeted conservation initiatives can effectively curb the wildlife trade, particularly in protected areas. It proves that even modest investments in protected areas management in EG can protect commercially valuable endangered wildlife [80].
However, the success of direct conservation measures can lead to unintended consequences. While there has been a notable decrease in primate carcass trade in EG, other species at other locations have not seen the same level of protection. The impact of direct conservation measures on primate carcass availability could at least partially explain the observed rise in rodent and mainland carcass availability following the establishment of the Moraka Biomonitoring Station (Table 3), situated strategically at the entrance to the Gran Caldera and adjacent lowland regions that are key habitats for primates. This shift suggests a possible transition by consumers in the Semu market toward more affordable and plentiful rodent-based protein sources and carcasses brought in from the mainland (Table 5). This highlights the need for conservation communication strategies adaptable to changing market conditions and shifting consumer demand.
Conservation strategy recommendations
Our study offers a nuanced understanding of the intricate relationship between socio-economic developments, public health crises, and wildlife trade dynamics in Central Africa, specifically focusing on Bioko Island. The findings highlight the need for multi-faceted, region-specific conservation strategies considering the complex interplay of economic, social, and environmental factors. A more holistic approach to wildlife conservation is urgently needed [80]. There needs to be a dual focus, one emphasizing urban market disruptors and another emphasizing rural wildlife market disruptors. In urban areas, conservation efforts should emphasize public perception changes, particularly concerning health risks associated with wildlife consumption, and the monitoring of national and international trade routes. In rural areas, initiatives should focus on direct wildlife protection, better protected areas management, and support for local communities.
Disrupting wild meat supply in a decentralized wildlife economy.
Our study highlights two significant changes in Bioko Island’s wildlife economy. The first is a marked increase in wild meat imports from the mainland at Semu, the island’s main municipal market, starting in 2014. The second is the rise and expansion of secondary markets in the 2010s, some of which specialize in wild meat from Bioko’s larger vertebrates. This trend represents a major shift in Equatorial Guinea’s wild meat trade. Together, these findings imply that Bioko Island’s wild meat trade networks have become more resilient to national supply chain disruptions and are increasingly catering to specialized demand for local wild meat. These developments present new challenges for wildlife conservation. Disrupting the wild meat trade, therefore, requires highly adaptable conservation strategies that consider the evolving complex trade patterns. These measures should not only focus on limiting the supply of wildlife carcasses in these newly established trade centers but also on reducing demand among urban populations that typically do not depend on wild meat for their survival [80]. Additionally, there is an urgent need for improved monitoring and stricter enforcement of existing laws. This is crucial both to prevent the influx of carcasses from the mainland and to combat illegal hunting on Bioko Island, particularly hunting of primates which is prohibited by presidential decree [74].
Disrupting consumer demand for wildmeat with responsible messaging.
Disrupting consumer demand for wild meat requires responsible messaging about zoonotic risks. Our study indicates that public health interventions such as these, especially during emergencies, can temporarily reduce wildlife demand. Educational campaigns about zoonotic risks should be a key component of these interventions. Furthermore, as evidenced by the impact of the global shutdown during the COVID-19 pandemic, consumer demand can be disrupted by identifying, monitoring, and regulating trade routes and supply points to disrupt, reduce, and regulate the wildlife trade in a more sustainable manner. These actions will have a broad effect on the overall supply of wild meat availability. Regulating these routes would support disrupting the trade in products from endangered species.
Protecting wildlife and supporting people.
The effective protection of wildlife and the improvement of protected area management are essential components in reducing wild meat supply and consumer demand for it. On Bioko Island, very modest improvements to the management of protected areas can help. Notably, increased protection efforts in the Gran Caldera Scientific Reserve, including frequent wildlife patrols and the operation of biomonitoring stations, have led to a decrease in the overall availability of primate carcasses for sale. This outcome underscores the importance of intensifying conservation efforts to protect commercially valuable endangered wildlife, and that even small investments that are responsibly managed can have a positive effect on wildlife protection.
Protecting wildlife requires supporting communities’ livelihoods so that they are not only dependent on wildlife trade but are also actively reinforcing conservation efforts. Integrating alternative livelihood projects with conservation efforts offers a sustainable solution to decrease the economic dependency of people living adjacent to protected areas on the wildlife trade, an all-too-common occurrence on Bioko and across Central Africa. These projects, encompassing areas such as technical education, business management, and entrepreneurial initiatives, provide new economic avenues for communities traditionally reliant on hunting wildlife. This multifaceted strategy not only directly combats the wildlife trade but also fosters sustainable development within communities, thereby safeguarding the well-being of both wildlife and human populations.
The findings of this study emphasize the critical importance of aligning conservation efforts in Central Africa with the UN SDGs [26]. The observed impacts of economic crises, public health emergencies, and conservation policies on the wildlife trade directly related to SDG 15 (Life on Land), which seeks to halt biodiversity loss, particularly, Target 15.7, which calls for urgent action to end poaching, trafficking of protected species, and to address the demand and supply of illegal wildlife products. Our findings also provide information that can help achieve SDG 3 (Good Health and Well-Being), which focuses on combating zoonotic diseases. Moreover, the ongoing challenges in regulating and enforcing wildlife trade policies underscore the need for stronger international cooperation and capacity-building, as highlighted in SDG 17 (Partnerships for the Goals). By providing insights into the dynamics of the wildlife trade on Bioko Island, our study not only advances understanding of regional issues but also contributes evidence-driven recommendations to help achieve the broader objectives of the SDGs. Future conservation efforts must be strategically aligned with these global goals to ensure long-term sustainability and biodiversity protection in Central Africa.
Future research
Future research should delve deeper into the individual drivers of wildlife trade, exploring how local cultural practices, international policies, and global economic trends influence consumer behavior and trade networks. More detailed studies are also urgently needed to assess the effectiveness of different conservation strategies and to evaluate their impact on wildlife populations and local communities. Finally, our study underscores the importance of adopting informed, adaptive, and collaborative approaches to wildlife conservation. As Central Africa continues economic and social transformation, understanding and addressing the changing dynamics of wildlife trade will be crucial for preserving biodiversity and the region’s sustainable development.
Supporting information
S1 Fig. Bayesian model created using the entire study period to predict on (A) Primates, (B) Mainland Carcasses, (C) Rodents, and (D) Duikers.
Each figure shows actual values of average carcasses per month (black), forecasted carcass rates (blue dotted line), and forecasted confidence intervals (blue shading). The horizontal dashed line represents the COVID-19 intervention event in March 2020. The entire study period before March 2020 was used to create the forecasted model.
https://doi.org/10.1371/journal.pstr.0000139.s001
(PDF)
S2 Fig. Bayesian model predicting the impact of the COVID-19 pandemic on (A) Primates, (B) Mainland Carcasses, (C) Rodents, and (D) Duikers.
Each figure shows actual values of average carcasses per month (black), forecasted carcass rates (blue dotted line), and forecasted confidence intervals (blue shading). The first horizontal dashed line represents the start of the pre-Ebola period. The second dashed line represents the COVID-19 intervention event in March 2020. The pre-Ebola period was used to create the forecasted model.
https://doi.org/10.1371/journal.pstr.0000139.s002
(PDF)
S3 Fig. Bayesian predictive model for primate carcass rates at the four identified intervention points: (A) March 2002, (B) November 2007, (C) November 2009, and (D) February 2014.
Each figure shows actual values of average primate carcasses per month (black), forecasted carcass rates (blue dotted line), and forecasted confidence intervals (blue shading). The first and second horizontal dashed lines represent the pre-event period. The second and third horizontal dashed lines represent the post-event period, the time over which the model is projected and quantified.
https://doi.org/10.1371/journal.pstr.0000139.s003
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S4 Fig. Bayesian predictive model for mainland carcass rates at the four identified intervention points: (A) November 2009, and (B) February 2014.
Each figure shows actual values of average mainland carcasses per month (black), forecasted carcass rates (blue dotted line), and forecasted confidence intervals (blue shading). The first and second horizontal dashed lines represent the pre-event period. The second and third horizontal dashed lines represent the post-event period, the time over which the model is projected and quantified.
https://doi.org/10.1371/journal.pstr.0000139.s004
(PDF)
S5 Fig. Bayesian predictive model for rodent carcass rates at the four identified intervention points: (A) March 2002, (B) November 2007, (C) November 2009, and (D) February 2014.
Each figure shows actual values of average rodent carcasses per month (black), forecasted carcass rates (blue dotted line), and forecasted confidence intervals (blue shading). The first and second horizontal dashed lines represent the pre-event period. The second and third horizontal dashed lines represent the post-event period, the time over which the model is projected and quantified.
https://doi.org/10.1371/journal.pstr.0000139.s005
(PDF)
S6 Fig. Bayesian predictive model for duiker carcass rates at the four identified intervention points: (A) March 2002, (B) November 2007, (C) November 2009, and (D) February 2014.
Each figure shows actual values of average duiker carcasses per month (black), forecasted carcass rates (blue dotted line), and forecasted confidence intervals (blue shading). The first and second horizontal dashed lines represent the pre-event period. The second and third horizontal dashed lines represent the post-event period, the time over which the model is projected and quantified.
https://doi.org/10.1371/journal.pstr.0000139.s006
(PDF)
S7 Fig. PCAs of market supply before and after the start of the COVID-19 pandemic.
(A) Semu market. (B) Secondary markets.
https://doi.org/10.1371/journal.pstr.0000139.s007
(PDF)
S8 Fig. Proportion of primate carcasses in the Semu market in the five identified compositional periods.
Proportion of carcasses is calculated as the number of primate carcasses divided by the total number of carcasses counted each day. Data is divided into the five compositionally variant periods identified during PCA. The five periods are as follows: October 1997 –March 2002, April 2002 –November 2007, December 2007 –November 2009, December 2009 –February 2014, and March 2014 –October 2019.
https://doi.org/10.1371/journal.pstr.0000139.s008
(PDF)
S9 Fig. Proportion of mainland carcasses to the Semu market in the five identified compositional periods.
Proportion of carcasses is calculated as the number of carcasses imported to Bioko from the mainland (Cameroon and Rio Muni) divided by the total number of carcasses counted each day. Data is divided into the five compositionally variant periods identified during PCA. The five periods are as follows: October 1997 –March 2002, April 2002 –November 2007, December 2007 –November 2009, December 2009 –February 2014, and March 2014 –October 2019. All periods are significantly different from one another (Dunn-test).
https://doi.org/10.1371/journal.pstr.0000139.s009
(PDF)
S10 Fig. Proportion of rodent carcasses in the Semu market in the five identified compositional periods.
Proportion of carcasses is calculated as the number of rodent carcasses divided by the total number of carcasses counted each day. Data is divided into the five compositionally variant periods identified during PCA. The five periods are as follows: October 1997 –March 2002, April 2002 –November 2007, December 2007 –November 2009, December 2009 –February 2014, and March 2014 –October 2019.
https://doi.org/10.1371/journal.pstr.0000139.s010
(PDF)
S11 Fig. Proportion of duiker carcasses in the Semu market in the five identified compositional periods.
Proportion of carcasses is calculated as the number of duiker carcasses divided by the total number of carcasses counted each day. Data is divided into the five compositionally variant periods identified during PCA. The five periods are as follows: October 1997 –March 2002, April 2002 –November 2007, December 2007 –November 2009, December 2009 –February 2014, and March 2014 –October 2019.
https://doi.org/10.1371/journal.pstr.0000139.s011
(PDF)
S12 Fig. Proportion of primate carcasses in all markets before and after the COVID-19 outbreak.
The proportion of primate carcasses is calculated as the number of primate carcasses from each market divided by the total number of carcasses counted each day. Data in the pre-COVID period extends from March 2019 through March 2020 and in the post-COVID period from April 2020 through April 2021. Significant differences between the pre-COVID and post-COVID period are denoted by an asterisk (*p < 0.01).
https://doi.org/10.1371/journal.pstr.0000139.s012
(PDF)
S13 Fig. Proportion of mainland carcasses in secondary markets before and after the COVID-19 outbreak.
The proportion of imported carcasses is calculated as the number of imported carcasses from each market divided by the total number of carcasses counted each day. Data in the pre-COVID period extends from March 2019 through March 2020 and in the post-COVID period from April 2020 through April 2021. Significant differences between the pre-COVID and post-COVID period are denoted by an asterisk (*p < 0.001). Boloco is not included in the analysis since there were no imported carcasses recorded.
https://doi.org/10.1371/journal.pstr.0000139.s013
(PDF)
S14 Fig. Proportion of duiker carcasses in secondary markets prior to and after the COVID-19 outbreak.
The proportion of duiker carcasses is calculated as the number of duiker carcasses from each market divided by the total number of carcasses counted each day. Data in the pre-COVID period extends from March 2019 through March 2020 and in the post-COVID period from April 2020 through April 2021. Significant differences between the pre-COVID and post-COVID period are denoted by an asterisk (*p < 0.05, **p < 0.001).
https://doi.org/10.1371/journal.pstr.0000139.s014
(PDF)
S15 Fig. Proportion of rodent carcasses in secondary markets prior to and after the COVID-19 outbreak.
The proportion of rodent carcasses is calculated as the number of rodent carcasses from each market divided by the total number of carcasses counted each day. Data in the pre-COVID period extends from March 2019 through March 2020 and in the post-COVID period from April 2020 through April 2021. Significant differences between the pre-COVID and post-COVID period are denoted by an asterisk (*p < 0.001).
https://doi.org/10.1371/journal.pstr.0000139.s015
(PDF)
S16 Fig. Multiple regression model coefficient estimates for primates (left) and Bioko’s Rodents (right).
Multiple regression models were created using a backwards stepwise approach with model of best fit evaluation based on AIC values. Independent predictor variables with the highest inclusion probability from each of the Bayesian predictive models (16 total–four interventions per market group) were included in the multiple regression analysis. Five models were created for each market group based on the five identified Semu market periods. Coefficient estimates for each model of best fit are displayed here.
https://doi.org/10.1371/journal.pstr.0000139.s016
(PDF)
S17 Fig. Multiple regression model coefficient estimates for mainland taxa (left) and duikers (right).
Multiple regression models were created using a backwards stepwise approach with model of best fit evaluation based on AIC values. Independent predictor variables with the highest inclusion probability from each of the Bayesian predictive models (16 total–four interventions per market group) were included in the multiple regression analysis. Coefficient estimates for each model of best fit are displayed here.
https://doi.org/10.1371/journal.pstr.0000139.s017
(PDF)
S1 Table. Detailed summary of COVID-19 in Equatorial Guinea December 2019–March 2021.
https://doi.org/10.1371/journal.pstr.0000139.s018
(PDF)
S2 Table. Inclusion probabilities of external variables in Bayesian predictive intervention model.
Inclusion probabilities represent the probability of each external variable being used as a predictor for the forecasted model. Inclusion probabilities are shown for each intervention tested (4 total impacts) for all four market groups (duikers, mainland carcasses, primates, and rodents).
https://doi.org/10.1371/journal.pstr.0000139.s019
(PDF)
S3 Table. Inclusion probabilities of external variables in Bayesian predictive intervention model.
Inclusion probabilities represent the probability of each external variable being used as a predictor for the forecasted model. Inclusion probabilities are shows for each modeled period for all four market groups (duikers, mainland carcasses, primates, and rodents). The external variable with the highest inclusion probability for each model is bolded and highlighted in grey.
https://doi.org/10.1371/journal.pstr.0000139.s020
(PDF)
S4 Table. Results of the primate backward stepwise multiple regression model.
For each model, independent covariates with the highest inclusion probability for each of the Bayesian predictive models (16 total–four interventions per market group) were included in the analysis. Dummy variables were created for each month to account for seasonal behaviors in response variables. Five separate multiple regression models were created for each of the previously determined five market periods. Independent covariates were removed stepwise using AIC values to determine the model of best fit. Coefficient estimates, standard errors, and p-values for models from each of the five periods are shown here.
https://doi.org/10.1371/journal.pstr.0000139.s021
(PDF)
S5 Table. Results of the rodent carcass backward stepwise multiple regression model.
For each model, independent covariates with the highest inclusion probability for each of the Bayesian predictive models (16 total–four interventions per market group) were included in the analysis. Dummy variables were created for each month to account for seasonal behaviors in response variables. Five separate multiple regression models were created for each of the previously determined five market periods. Independent covariates were removed stepwise using AIC values to determine the model of best fit. Coefficient estimates, standard errors, and p-values for models from each of the five periods are shown here.
https://doi.org/10.1371/journal.pstr.0000139.s022
(PDF)
S6 Table. Results of the duiker carcass backward stepwise multiple regression model.
For each model, independent covariates with the highest inclusion probability for each of the Bayesian predictive models (16 total–four interventions per market group) were included in the analysis. Dummy variables were created for each month to account for seasonal behaviors in response variables. Five separate multiple regression models were created for each of the previously determined five market periods. Independent covariates were removed stepwise using AIC values to determine the model of best fit. Coefficient estimates, standard errors, and p-values for models from each of the five periods are shown here.
https://doi.org/10.1371/journal.pstr.0000139.s023
(PDF)
S7 Table. Results of the mainland carcass backward stepwise multiple regression model.
For each model, independent covariates with the highest inclusion probability for each of the Bayesian predictive models (16 total–four interventions per market group) were included in the analysis. Dummy variables were created for each month to account for seasonal behaviors in response variables. Five separate multiple regression models were created for each of the previously determined five market periods. Independent covariates were removed stepwise using AIC values to determine the model of best fit. Coefficient estimates, standard errors, and p-values for models from each of the five periods are shown here.
https://doi.org/10.1371/journal.pstr.0000139.s024
(PDF)
S8 Table. Pearson correlation coefficients from pairwise correlation of Semu market groups.
https://doi.org/10.1371/journal.pstr.0000139.s025
(PDF)
Acknowledgments
We thank the National University of Equatorial Guinea, and especially Mr. José Manuel Esara Echube, for hosting the project and researchers. We thank the wildlife market monitors including Reginaldo Aguilar Biacho, Olga Avomo, Gustavo Motoho, and Zoilo Obiang Ndong for their steadfast commitment to collecting wild meat survey data from 1998 to 2021. We thank the staff of the Bioko Biodiversity Protection Program for curating the wildlife monitoring data. We thank the National Institute for Forest Development and Protected Areas (INDEFOR-AP) personnel for their support and collaboration. Finally, we thank the anonymous reviewers for their efforts and comments which improved the manuscript.
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