Global hotspots of bushmeat hunting risk for mammals

Global hotspots of bushmeat hunting risk for mammals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global hotspots of bushmeat hunting risk for mammals Michela Pacifici, Andrea Cristiano, Giordano Mancini, Dario Nania, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6278255/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bushmeat hunting is a significant threat to mammal species worldwide 1 , 2 , yet global assessments of its impact are extremely scarce. This study provides the first comprehensive evaluation of the biological, environmental, and socioeconomic factors driving bushmeat hunting risks for terrestrial mammals, both currently and in the future. We identify key drivers such as low GDP, high population density, and extreme climate events. Our findings reveal that regions with lower economic development and higher human population density face the greatest hunting pressures. Additionally, we project future hotspots where socio-economic changes, including population growth, infrastructure expansion, and climate shifts, will intensify hunting threats, particularly for species already vulnerable to other environmental pressures. We also highlight specific groups, including primates, sloths, and fruit bats, which are most at risk due to their biological characteristics and increasing human encroachment. These species, often with slow reproductive rates or restricted distribution, are projected to face growing threats from bushmeat hunting in the coming decades. By identifying regions and species at risk, this study provides actionable insights for guiding future conservation priorities and mitigating the impacts of bushmeat hunting on biodiversity. Biological sciences/Ecology/Conservation biology Earth and environmental sciences/Environmental sciences/Environmental impact Figures Figure 1 Figure 2 Figure 3 Introduction Bushmeat hunting consists in the practice of hunting wild animals for food. It has historically been key to the subsistence of several indigenous communities 3 , especially in regions of the world characterized by lower socioeconomic conditions where bushmeat remains among the primary sources of protein and an important economic commodity 4 . While often being a necessity for subsistence, harvesting species for bushmeat consumption can become a long-term unsustainable practice with profound and devastating ecological consequences 5 . In fact, in recent decades, bushmeat hunting rose to prominence as a major threat for the conservation of biodiversity, especially in the case of large-bodied mammals which are often the primary target of hunting because of their high yield 1 , 2 . According to the International Union for Conservation of Nature (IUCN), approximately 23% of mammals are threatened with extinction 6 . Many of these species often play important roles in regulating ecosystems functioning 7 , for instance exerting bottom-up regulation of primary consumers (most herbivores), providing seed dispersal (rodents, primates, bats), stimulating vegetation regeneration and biogeochemical fluxes (ungulates and primates), and actively modifying habitats heterogeneity (ungulates 8 , 9 ). The decline of keystone species may trigger self-reinforcing negative feedback loops, further exacerbating disruption of ecosystem functioning in areas already under high anthropogenic pressure 10 . The risk faced by mammal species to increased levels of bushmeat hunting can be attributed to several intrinsic and extrinsic factors. Intrinsic drivers that can make a species susceptible to hunting include slow reproductive rates, which means hunting pressures can significantly reduce their populations and hamper their ability to rebound, and behavioural aspects that can make a species more or less dangerous or challenging to hunt 2 . Extrinsic drivers of bushmeat hunting can be associated with global socioeconomic and environmental changes of the last decades, such as the rapid growth of human population in tropical areas, increased rates of habitat destruction and fragmentation, and facilitated access to previously remote areas due to infrastructure development, which can facilitate wild meat trading 11 , 12 , 13 , 14 , 15 . Additionally, deforestation to boost agricultural expansion further facilitates hunters’ and poachers’ access to wildlife in previously undisturbed areas, increasing hunting pressure on mammal species 12 , 16 . Weak enforcement of wildlife protection, together with economic hardship and the lack of alternative livelihoods, further perpetuate the prevalence of harvest and bushmeat hunting in socioeconomic contexts where it represents a major source of income and livelihood 14 , 17 , 18 . Climate change may also play a role in determining species’ exposure to bushmeat hunting, as distributional shifts or species ability to adapt locally drive hunting patterns 19 . Despite its relevance for conservation assessments of threatened species, no comprehensive global analysis has yet evaluated the vulnerability of species to bushmeat hunting and their future risks on a spatially-explicit basis. Global analyses on the threat that bushmeat hunting poses to mammal species is limited by data availability, variability of regional patterns, and the complexity of socio-environmental interactions. Reliable, high-resolution data on hunting pressure are scarce, as bushmeat hunting is often unregulated, occurs in remote areas, and is driven by informal markets, making it difficult to quantify its true impact. Additionally, the factors influencing hunting risk — such as human population density, economic conditions, habitat accessibility, and cultural preferences — vary significantly across regions, complicating the development of globally applicable models 20 . Differences in data availability and quality between well-studied and data-deficient areas further introduce biases, potentially underestimating threats in regions with limited monitoring 2 . Moreover, environmental and socioeconomic drivers of bushmeat hunting are interconnected, requiring complex modeling approaches to disentangle their effects 21 . To assess the vulnerability of mammal species to bushmeat hunting, we analyzed key biological, environmental, and socioeconomic factors that may influence their susceptibility to this threat. We first identified terrestrial mammal species currently classified as threatened by hunting for human consumption according to the IUCN Red List. We then selected a set of species traits — such as body mass, reproductive parameters, trophic level, and foraging stratum — that could be linked to hunting risk. Using a binomial random forest model, we assessed how these traits correlate with the likelihood of being hunted. To further explore future risks, we incorporated environmental and socioeconomic variables projected for 2050 under scenarios that depict a highly unequal world, where wealth and technological advancement are concentrated in a small elite, including human population density, gross domestic product (GDP), tree cover, climatic extremes, and accessibility to urban centers. These factors were integrated using high-resolution spatial datasets and analyzed through machine learning models to predict which species may become threatened in the future. Finally, we mapped current and emerging hotspots of bushmeat hunting pressure, providing insights into regions and species most at risk. Results and discussion Large-bodied and long-lived mammals face the highest bushmeat hunting pressure Our analysis reveals that intrinsic traits are fundamental in determining the probability of a species being hunted for bushmeat (OOB estimate of error rate 8.01%; mtry selected = 4; mean accuracy of cross validation = 0.92; accuracy train/test validation = 0.91). We observed a strong positive relationship between body mass and the probability of a mammal species being hunted for bushmeat, with the probability increasing as body mass increases and reaching an asymptote at a logarithmic value of approximately 6 (around 1000 kg; Fig. 1 ). This result suggests that species with low to intermediate body mass in the dataset have a lower probability of being hunted for bushmeat compared to larger species. However, beyond the asymptote (which is approximately the weight of rhinoceroses), the probability of being hunted does not significantly increase, even for species with extremely high body mass values. This pattern highlights that while body mass is a strong predictor of hunting pressure, other ecological and practical constraints may limit the selection of the largest species. Several factors likely contribute to the asymptote in body mass and hunting risk relationship. First, large-bodied species often pose logistical challenges for hunters due to the difficulty in capturing, killing, and transporting such heavy carcasses over long distances, particularly in forested or remote environments. Additionally, larger species frequently have lower population densities and slower reproductive rates 22 , making them less abundant and more challenging to encounter in the wild 2 . From a behavioral perspective, many of the largest terrestrial mammals exhibit heightened wariness and adaptive anti-predator behaviors, including increased vigilance, cryptic movement, or occupation of less accessible habitats, which may further reduce their exposure and vulnerability to hunting 23 . Moreover, the hunting of very large species may be restricted by cultural or legal constraints, as some of these taxa are often protected under national and international conservation frameworks 24 , 25 . Interestingly, our results align with previous studies indicating that medium-to-large-sized mammals are the primary targets for subsistence and commercial bushmeat hunting 25 . This selection is likely driven by an optimal balance between the effort required to hunt and process an animal and the nutritional or economic return obtained. The asymptotic trend observed in our study may reflect the diminishing cost-benefit ratio of hunting species beyond a certain body size threshold, where the challenges outweigh the advantages. We also found a non-linear relationship between species’ maximum longevity and their probability of being hunted for bushmeat. Hunting probability increases up to a maximum longevity value of approximately 1000 days, then stabilizes. The relationship between the probability of bushmeat hunting and maximum longevity of a species likely reflects an ecological balance between the vulnerability and adaptability of species to human hunting pressures. Species with longer lifespans often have slower reproductive rates, which can make them more vulnerable to overexploitation, especially in environments where hunting pressures are high 2 . However, species with extremely long lifespans may have evolved adaptations that allow them to better evade hunting, such as larger home ranges or more complex social structures. Species with low reproductive rates experience high hunting pressure, but as reproductive rates increase, the probability of being hunted declines, reaching a minimum at approximately 3 litters per year (Fig. 1 ). Beyond this point, hunting probability increases again but stabilizes at high fecundity values. This pattern suggests that low-fecundity species are the most heavily targeted for bushmeat, whereas species with moderate reproductive rates experience the least pressure, and those with high reproductive rates are hunted at a consistent but relatively lower level. The high exploitation of species with low reproductive rates is likely due to links with other traits, such as larger body size, slower life history, and greater meat yield, making them prime targets for both subsistence and commercial bushmeat hunting 2 . Many large mammals, such as ungulates, primates, and carnivores, have long gestation periods, few offspring per year, and slow population recovery, increasing their vulnerability to overexploitation 20 . Despite their low reproductive output, their high market value and nutritional benefits make them preferred targets, often leading to population declines and local extinctions where hunting is intense. Conversely, species with moderate reproductive rates are the least targeted for bushmeat. This may be because they lack the high meat yield of large, slow-reproducing species, making them less attractive for hunters. Additionally, their faster life histories and more stable populations may allow them to persist under moderate hunting pressure, reducing their risk of overexploitation 26 . At higher reproductive rates, hunting probability increases slightly but remains stable, suggesting that high-fecundity species are not primary targets for bushmeat hunting. Many small-bodied, fast-reproducing species — such as rodents and small carnivores — may still be hunted, but likely as secondary or fallback resources, particularly when larger prey becomes scarce 27 . In addition, their rapid population turnover and high reproductive potential make them more resilient to hunting pressures, preventing drastic population declines even under sustained exploitation 28 . Economic and urban development shape spatial patterns of bushmeat hunting The probability of a species being hunted for bushmeat is highest at lower gross domestic product (GDP) values (Fig. 1 ). This suggests that regions with lower economic development face the greatest hunting pressures, while regions with higher GDP exhibit much lower probabilities of bushmeat exploitation. Several socio-economic mechanisms may explain this pattern. In low-income regions, bushmeat hunting is often driven by food insecurity and a lack of access to alternative protein sources 29 . In these areas, despite economic hardship, the demand for bushmeat remains high due to its critical role in subsistence. As GDP increases, there is likely a reduction in hunting pressures, potentially due to improved access to alternative food sources, greater economic diversification, and better enforcement of wildlife protection laws 30 . At higher levels of GDP, hunting probability continues to stabilize at low values. This decline is likely driven by factors such as increased availability of domesticated meat, the availability of more sustainable livelihoods, and changing dietary preferences associated with urbanization and rising living standards. Additionally, stronger wildlife conservation policies, increased enforcement, and the development of ecotourism and alternative income sources may further reduce hunting pressures 25 , 31 . However, after reaching a GDP log value of approximately 17 (corresponding to 1.23 Purchasing Power Parity, Million USD in 2005 years rate), the probability of hunting for bushmeat increases until it reaches a peak at a log value of around 20 (1.3). This could be due to economic disparities within regions where, despite overall wealth, inequality exacerbates resource pressures. Our analysis shows that hunting probability increases with human population density (Fig. 1 ), highlighting the role of demographic pressure in driving bushmeat exploitation. As population density rises, demand for wild meat grows due to both subsistence needs and market-driven trade 32 , 33 . In densely populated areas, the accessibility of wildlife is a key factor: proximity to human settlements facilitates hunting, while infrastructure development, such as roads and market networks, further accelerates extraction rates 11 , 26 . However, the interaction with distance from major urban centers adds complexity to this pattern. The probability of hunting is lower near cities (Fig. 1 ) probably because of the reduced availability of wildlife populations 20 . Hunters target more remote areas where wildlife remains abundant. This trend bends at very large distances, where hunting may eventually be constrained by logistical challenges, law enforcement, and the shift to subsistence rather than commercial exploitation 33 . These findings suggest a dual effect of urbanization on hunting patterns: while high human density amplifies hunting pressure, the spatial dynamics of exploitation are shaped by both market demand and accessibility. Near large cities, bushmeat hunting is often commercialized, supplying urban consumers with wildlife products 27 . As hunters move deeper into remote areas, extraction rates increase, reflecting the ongoing expansion of hunting frontiers into less disturbed ecosystems 20 , 26 . This highlights the cumulative effect of different anthropogenic threats, such as deforestation or habitat fragmentation, with overexploitation pressure on mammals. Extreme climate favours overexploitation There is a complex relationship between the number of days with extreme events and the likelihood of a species being hunted for bushmeat. As the number of dry days increases, there is a point at which wildlife populations begin to suffer from food and water scarcity, and hunting efficiency starts to decline. With animals becoming more scattered in their search for resources, they become harder to locate and hunt. Consequently, hunting pressure decreases as logistical challenges and resource depletion take their toll, making it more difficult to maintain high levels of hunting success. On the other hand, with prolonged dry periods, when water sources become scarce, animals tend to congregate around the few remaining water sources, significantly increasing their vulnerability to hunters. In these conditions, hunting pressure is high, as animals are more easily located and hunted by humans. While dry spells reduce the availability of food and other resources, leading to more concentrated wildlife populations, hunters are more likely to exploit this predictability for easier access to prey. Moreover, in regions with limited access to alternative sources of protein, bushmeat hunting becomes a crucial survival strategy, and the reliance on hunting intensifies during these periods 11 . The relationship between the number of very heavy precipitation days and hunting pressure follows a similar pattern. When the number of very rainy days is limited, hunting pressures tend to decrease as humans can better survive off agricultural products and other protein sources 33 . However, as the number of very heavy precipitation days increases from approximately 12 to 45, the probability of a species being hunted for bushmeat rises. This could reflect an increased vulnerability of animals, which may be forced to congregate in more accessible areas or become more easily trapped due to limited movement or altered behaviors during extreme weather events. After 45 days of heavy precipitation, the probability stabilizes, suggesting that extended periods of such extreme weather may discourage hunting due to impassable conditions or a decrease in animal availability 26 . Tropical hotspots of emerging risk At present, the majority of species targeted for bushmeat hunting are found in tropical areas, with the highest hunting pressures observed in Africa and southeast Asia (Fig. 2 ). These regions are home to a high diversity of wildlife, much of which is currently threatened 34 . Species such as primates, ungulates, and bats are increasingly being hunted due to their accessibility and high demand for meat. As human populations continue to grow and expand into previously undisturbed regions, the risk of bushmeat hunting pressures on mammal wildlife is expected to intensify. In fact, we found 85 species that are not currently considered threatened by bushmeat hunting, but could become so by 2050 (Table S1 ). Of the 85 species identified as at potential future risk, 59 are already classified as threatened on the IUCN Red List, including 18 as Critically Endangered. Additionally, one is Near Threatened and 5 are Data Deficient 6 (Table S1 ), meaning their vulnerability may be underestimated. If bushmeat hunting becomes more widespread due to shifting cultural practices, increased demand for wild meat, or greater accessibility to previously unexploited populations, these species could experience rapid population declines. For Critically Endangered species, even low levels of hunting could push them toward extinction, while Data Deficient species may already be at high risk without being recognized as such. The species most at risk of becoming threatened from bushmeat hunting in the future are concentrated mainly in the Primates (44 species), Cetardtiodactyla (13 species), Chiroptera orders (13 species; Table S1 ). Though many of these species may not yet be heavily exploited, ecological and socio-economic trends indicate that they could become more vulnerable to hunting as human activity increases in their regions 35 . Primates are especially sensitive to hunting pressures due to the combination of slow reproductive rates, low population densities, and relatively restricted geographic ranges 36 due to high dependency to forests. As human settlements grow, these primate species are increasingly likely to be targeted for bushmeat, especially those living near urban areas or agricultural expansions. Hunting of primates has been linked to both subsistence needs and market demand, further exacerbating their vulnerability 37 . Cultural taboos play a crucial role in mitigating primate hunting pressure in various regions. In several African and Southeast Asian communities, traditional beliefs and spiritual customs discourage the consumption or killing of certain primates, effectively serving as an informal conservation mechanism 38 . For example, among the Batek people of Malaysia, primates such as gibbons and macaques are considered sacred and are rarely hunted. Similarly, in parts of Nigeria and Ghana, some ethnic groups avoid consuming primates like colobus monkeys due to beliefs that they are ancestral spirits or bring misfortune if killed. Studies have shown that adherence to these taboos can significantly reduce primate hunting, in some cases by up to 95% 39 . However, these cultural safeguards are eroding due to urbanization, globalization, and changing socio-economic conditions. Younger generations often prioritize economic survival over traditional customs, leading to increased participation in bushmeat hunting. Moreover, commercial hunting for bushmeat markets frequently overrides local taboos, as hunters may kill primates to sell rather than consume them personally 40 . In these areas, reinforcing traditional beliefs through cultural heritage initiatives may help sustain primate populations. The situation is different in the Amazon, including countries such as Peru, Brazil, and Colombia, where many indigenous communities rely on bushmeat as a primary protein source, and primates are frequently hunted 41 . Unlike in parts of Africa, where strong spiritual taboos can restrict primate hunting, South America lacks widespread, consistent prohibitions against consuming primate meat. While some Indigenous groups, such as the Matsés in Peru, associate certain primates with spiritual beliefs, this does not always translate into a strict hunting ban. Instead, hunting is often dictated by ecological factors (e.g., availability) rather than spiritual prohibitions 42 . The absence of strong cultural taboos suggests that conservation efforts in the Amazon should focus on alternative protein sources, hunting regulations, and community-based conservation programs rather than relying on cultural deterrents. Species in the Pilosa order, especially sloths, are vulnerable to exploitation, though for different reasons. Those animals have slow movements and low reproductive rates, making them easy targets for hunters 43 . These species, primarily arboreal and often hidden in the high canopy, are less visible to hunters, but as deforestation and forest fragmentation continue, their habitats are shrinking, and they may find themselves pushed into more accessible areas. As human populations grow and demand for land increases, human settlements will likely expand into sloth habitats, leading to more frequent encounters between humans and wildlife. Although these species are not typically the first choice for bushmeat hunters, the pressures of subsistence hunting and the growing need for protein in rural areas could make sloths a more attractive target. By 2050, species in the genus Pteropus , commonly known as flying foxes, may as well face increasing threats from bushmeat hunting due to a combination of changing human populations, expanding access to previously remote roosting areas, and the growing demand for bushmeat. While there is currently no strong evidence of Pteropus species being commonly hunted for food, such as in the case of Pteropus livingstonii , which roosts in inaccessible areas far from towns 44 , this may change as human activity and infrastructure continue to expand. In some regions, cultural taboos prevent the consumption of fruit bats, but these prohibitions could weaken over time, especially if economic or food security pressures increase 45 . Additionally, the increasing human encroachment into Pteropus habitats, facilitated by improved roads and access to previously remote areas, may expose these species to hunting pressures. For example, while P. livingstonii has so far been relatively safe due to its roosting habits, its close relative, P. seychellensis comorensis , which roosts in more visible, accessible locations, has already been targeted for hunting in some areas 44 . If roosts of other Pteropus species become more easily accessible, particularly in regions where hunting is already occurring, Pteropus populations could face significant risks. In particular, the expansion of human settlements and urban sprawl into these regions could lead to a dramatic increase in bushmeat hunting, posing a serious threat to the survival of these bat species by 2050. The expansion of bushmeat hunting into new regions and taxa could lead to irreversible biodiversity loss, underscoring the urgent need for monitoring and early intervention. In 2050, both southeast Asian islands and tropical Africa are projected to become critical hotspots for mammal species newly targeted for bushmeat hunting (Fig. 3 ) due to the synergistic impacts of habitat loss, increased human population pressure, and climate change. These areas, characterized by vast tropical rainforests, already face significant deforestation driven by agricultural expansion, illegal logging, and infrastructure development 46 . Continued deforestation and forest fragmentation, exacerbated by a projected increase in human population and demand for land, will likely push both humans and wildlife into increasingly constrained areas, promoting hunting of forest-dwelling mammals, which could exacerbate local extinction risks 47 . These pressures, compounded by the anticipated rise in global temperatures, will lead to the shrinkage of species' distributions to narrower suitable habitats, potentially hindering species' movement and making them more vulnerable to hunters. In addition to that, the limited mobility of island species could further exacerbate their vulnerability, as they may not be able to escape hunting pressures or find new, safe habitats. The hotspots of emerging risk we identified, which contain high unique biodiversity, are often simultaneously some of the poorest areas in the world, where people rely on wildlife as a key resource for sustenance. This dependence on bushmeat, combined with rising population pressures and limited alternatives, could drive increased hunting of species that were not previously targeted. Conservation efforts, including stricter wildlife protection laws, community engagement, and sustainable alternatives to bushmeat consumption, will be essential to mitigate this emerging threat. Methods Selection of species’ traits We first selected all terrestrial species of mammals classified as threatened by “Hunting and trapping terrestrial animals” in the IUCN Red List, and filtered those used for “Food - human” in the Use and Trade classification scheme (N = 928). This allowed us to identify species currently threatened by bushmeat hunting. Then, we selected traits that could potentially be correlated with a higher risk of being hunted for food. We used the traits in the COMBINE database 48 and retained only those with data coming from direct observations available for > 35% of the species in the database. The traits we tested in our models were activity cycle (time of day when the species is most active), adult mass (body mass of an adult individual), maximum longevity (maximum reported age at death for the species), litter size (number of offspring born per litter per female), litters per year (number of litters per female per year), trophic level (divided into herbivore, omnivore and carnivore), and foraging stratum (divided into marine, ground level, scansorial, arboreal and aerial). See Table 1 for hypotheses associated with variable selection. Table 1 Hypotheses for variable selection. Rationale for the choice of variables included in the random forest models, along with acronyms and hypotheses. NAME HYPOTHESES Activity cycle Diurnal species are more easily detected and hunted by humans, whereas nocturnal species might be less exposed Body mass Larger-bodied mammals tend to provide more meat per individual; larger mammals can also be more dangerous to hunt Maximum longevity Long-lived species often have slower life histories, making them more vulnerable to overexploitation Litter size Species with lower reproductive output are at higher risk of population declines Litters per year Species that reproduce slowly may take more time to recover from overexploitation Trophic level Herbivores and omnivores are often more abundant and easier to hunt than carnivores; herbivores are often preferred for their meat Foraging stratum Ground-dwelling species are generally easier to capture than arboreal or aerial species Habitat breadth Species with broader habitat breadth are more likely to be hunted for bushmeat due to their wider distribution and higher encounter rates with hunters across diverse environments Consecutive dry days Water availability may influence crop yields and agricultural productivity, creating a direct incentive for increased hunting as a coping strategy in drier periods Minimum value of daily maximum temperature Lower minimum daily maximum temperatures may reduce the likelihood of mammal species being hunted for bushmeat by influencing their distribution, behavior, and accessibility to hunters Very heavy precipitation days Dual effect: while making certain areas impassable, they may also influence wildlife migration and increase hunting opportunities for certain species Tree cover percentage Forests provide more wildlife species to hunt, yet increased deforestation and landscape fragmentation could create gaps or corridors that may make hunting more efficient and accessible to humans Travel time to major cities Improved infrastructure may bring urban demand for bushmeat closer to rural areas Human population density Denser populations typically drive higher consumption of bushmeat Gross domestic product Poorer regions with fewer alternatives may turn to wildlife as a food source Environmental and socioeconomic variables In order to understand the role of current and potential future environmental and socioeconomic changes in determining species’ exposure to bushmeat hunting, we selected a set of variables and projected them into the future for 2050. Since the aim here is to quantify the role of environmental and socioeconomic variables (provided as raster maps) within the geographic range of species (polygon maps), we used the native resolution of the raster maps to avoid reducing accuracy through spatial resampling. All raster values of the variables described below were calculated as the median within the species' current range, as defined by the IUCN Red List 6 , using the time periods available for the present (centered on 2020) and the future (centered on 2050). All spatial analyses have been done in R version 4.4.2 or GRASS GIS 7. The Shared Socioeconomic Pathways 4 (SSP4) is a socioeconomic scenario characterized by high inequality within and between countries 49 . In this pathway, global development is uneven, with wealth and technological advancements concentrated in a few regions, while others face limited access to resources and slower economic growth. This scenario is the most suitable for studying bushmeat hunting because it reflects regions where inequality leads to greater pressures on natural resources, particularly in poorer areas with limited access to alternatives. For human population density, we used the data for 2020 and 2050 from Wang et al. 50 , covering 248 countries or areas with 5-year intervals. The authors combined national censuses and official population estimates, socioeconomic scenarios, environmental projections, urbanization and migration models to derive a dataset of global human population up to 2100. For 2020, the population density data were based on observed population distributions from recent census data. The data were mapped onto a 1 km grid to reflect the actual distribution of the human population at that time. For 2050, the population density projections were derived using the SSP4 scenario. We used Gross Domestic Product (GDP) as a proxy for economic development. We used data from Murakami et al. 50 to derive GDP estimates at a finer resolution than the country level. In this work the authors estimated GDPs for the period between 1850 and 2100 in 1/12 grids at 10-year intervals. This was achieved by downscaling actual GDPs from 1850 to 2010 and projecting GDPs under SSPs 1–5 from 2020 to 2100. We utilized data for 2020 and 2050 based on the SSP4 scenario. Tree cover is another important factor affecting species’ presence and hunters’ accessibility to natural resources. We used the data from 51 , who utilized the Global Change Assessment Model and a spatial disaggregation model (Demeter) to generate land use projections at a high resolution (0.05°). The resulting dataset provides gridded land use projections from 2015 to 2100 under 15 SSP-RCP scenarios, representing plausible future socio-economic and climate conditions. The projections are disaggregated into 32 land cover types, more consistent with the land cover classifications used in Earth System Models. The files contain the percentage of a cell covered by each land cover type. To obtain the percentage of tree cover in each cell, we summed the percentages of Plant Functional Type classes 1 to 12, which correspond to tree functional types, using the SSP4 scenario. As extreme climate can deeply alter human and animal behaviour, we utilized climate data from the Copernicus Climate Data Store 52 , specifically the climate extreme indices and heat stress indicators derived from the Coupled Model Intercomparison Project Phase 6 global climate projections. The indices are provided for historical and future climate projections. Since SSP4 is not available in CMIP6, we opted for the more similar scenario, that is SSP3-7.0 and the MIROC6 model, characterized by low economic development, limited adaptation and mitigation policies, and strong social inequalities. The dataset provides yearly climate indices at a spatial resolution of 0.5°x0.5°. We focused on three key climatic variables: number of consecutive dry days (cdd), minimum value of daily maximum temperature (txn), and number of very heavy precipitation days (r20mm). The cdd index is calculated by identifying the longest consecutive period of days with daily precipitation below 1 mm. The txn index represents the minimum value of daily maximum temperature recorded in each year, and the R20mm index counts the number of days with daily precipitation exceeding 20 mm. For each variable, we computed the annual values for both the present (2015–2024) and future (2045–2054) scenarios, and then averaged them over the respective 10-year periods to obtain the decadal averages. To account for accessibility, we used the high-resolution global map of travel time to the nearest major city (with at least 50,000 inhabitants) for the year 2015, produced by Weiss et al. 54 . This map has a 1×1 km resolution and integrates global-scale datasets that capture factors influencing human movement rates. Since the travel time to cities map was only available for the year 2015 (used here as the ‘present’ reference), we developed a scenario for 2050 based on recent urban expansion rates. Blei et al. 55 found that during the period 2000–2014, the median urban extent growth rate was 5.7% per year for cities in less developed countries, compared to 1.1% per year for cities in more developed countries. We used the World Bank's annual country classification by income for all countries with a population over 30,000 56 . Then, we applied a yearly 1.1% reduction in travel time to major cities — measured by Weiss et al. 54 in 2015 — to raster cells in countries classified as ‘Upper-middle-income’ and ‘High-income.’ For countries classified as ‘Low-income’ and ‘Lower-middle-income,’ we applied a yearly 5.7% reduction, extending to 2050. Statistical models We first examined the distribution of the variables to identify those that exhibited skewness or non-linear relationships, which would benefit from a log transformation. This step ensured that all variables included in the model were appropriately scaled. Then, we applied a Random Forest classification model to predict the likelihood of species being hunted for bushmeat based on a set of intrinsic traits, socioeconomic factors and environmental predictors. The analysis was conducted using the ‘randomForest’ and ‘caret’ packages. We used two datasets: one representing present-day conditions, with all variables based on present data, and another for future projections using 2050 data. To validate the model, we applied both 5-fold cross-validation and a 70/30 train-test split. Cross-validation was performed using the trainControl function, with a hyperparameter search for mtry (1–16) to optimize performance. Additionally, class imbalance was addressed by applying inverse frequency-based weights to the minority class (bushmeat = "Y"). The final model’s performance was evaluated using a confusion matrix and accuracy calculation. To determine the optimal probability threshold for future risk classification, we tested thresholds ranging from 0.1 to 1 in increments of 0.01, calculating the F1-score at each step. The F1-score is the harmonic mean of precision and recall, providing a balanced measure of a model's performance, especially in imbalanced classification problems. The final threshold was selected based on the highest F1-score, balancing precision and recall. Finally, we used the current range maps of species 6 to create richness maps for mammals that are currently classified as threatened by bushmeat hunting in the IUCN Red List 6 , as well as for those predicted to become threatened by 2050, at a resolution of approximately 10x10 km. This allowed us to identify both current hotspots of bushmeat hunting pressure and emerging risk hotspots. Declarations Data availability The range and threat data that support the findings of this study are available upon request on the IUCN Red List website ( https://www.iucnredlist.org/ ). References Rija, A. A., Critchlow, R., Thomas, C. D., & Beale, C. M. (2020). Global extent and drivers of mammal population declines in protected areas under illegal hunting pressure. PLoS One, 15(8), e0227163. Ripple, W. J., Abernethy, K., Betts, M. G., Chapron, G., Dirzo, R., Galetti, M., ... & Young, H. (2016). Bushmeat hunting and extinction risk to the world's mammals. Royal Society Open Science, 3(10), 160498. https://doi.org/10.1098/rsos.160498. Luz, A. C., Paneque-Gálvez, J., Guèze, M., Pino, J., Macía, M. J., Orta-Martínez, M., & Reyes-García, V. (2017). Continuity and change in hunting behaviour among contemporary indigenous peoples. Biological Conservation, 209, 17-26. Brashares, J. S., Arcese, P., Sam, M. K., Coppolillo, P. B., Sinclair, A. R., & Balmford, A. (2004). Bushmeat hunting, wildlife declines, and fish supply in West Africa. Science , 306 (5699), 1180-1183. Damania, R., Milner-Gulland, E. J., & Crookes, D. J. (2005). A bioeconomic analysis of bushmeat hunting. Proceedings of the Royal Society B: Biological Sciences, 272(1560), 259-266. IUCN (2024). The IUCN Red List of Threatened Species. Version 2024-2. https://www.iucnredlist.org. Accessed on 22-08-2024. Effiom, E. O., Nuñez-Iturri, G., Smith, H. G., Ottosson, U., & Olsson, O. (2013). Bushmeat hunting changes regeneration of African rainforests. Proceedings of the Royal Society B: Biological Sciences, 280(1759), 20130246. Enquist, B. J., Abraham, A. J., Harfoot, M. B., Malhi, Y., & Doughty, C. E. (2020). The megabiota are disproportionately important for biosphere functioning. Nature communications, 11(1), 699. Nunez-Iturri, G., Olsson, O., & Howe, H. F. (2008). Hunting reduces recruitment of primate-dispersed trees in Amazonian Peru. Biological Conservation, 141(6), 1536-1546. Cowlishaw, G. U. Y., Mendelson, S., & Rowcliffe, J. M. (2005). Evidence for post‐depletion sustainability in a mature bushmeat market. Journal of applied ecology, 42(3), 460-468. Benitez-Lopez, A., Alkemade, R., Schipper, A. M., Ingram, D. J., Verweij, P. A., Eikelboom, J. A. J., & Huijbregts, M. A. J. (2017). The impact of hunting on tropical mammal and bird populations. Science, 356(6334), 180-183. Deith, M. C., & Brodie, J. F. (2020). 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Assessing Africa‐wide pangolin exploitation by scaling local data. Conservation Letters, 11(2), e12389. Bielby, J., Mace, G. M., Bininda-Emonds, O. R., Cardillo, M., Gittleman, J. L., Jones, K. E., ... & Purvis, A. (2007). The fast-slow continuum in mammalian life history: an empirical reevaluation. The American Naturalist, 169(6), 748-757. Cromsigt, J. P. G. M., Prins, H. H. T., & Olff, H. (2013). Habitat heterogeneity as a driver of ungulate diversity and distribution patterns: interaction of body mass and digestive strategy. Diversity and Distributions, 15(3), 513-522. Cawthorn, D.-M., & Hoffman, L. C. (2015). The bushmeat and food security nexus: A global account of the contributions, conundrums, and ethical collisions. Food Research International, 76, 906-925. Lindsey, P. A., Balme, G., Becker, M., Begg, C., Bento, C., Bocchino, C., ... & Zisadza-Gandiwa, P. (2013). The bushmeat trade in African savannas: Impacts, drivers, and possible solutions. 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Economic and biological conditions influence the sustainability of harvest of wild animals and plants in developing countries. Ecological Economics, 140, 14-21. Duffy, R., St John, F. A., Büscher, B., & Brockington, D. (2016). Toward a new understanding of the links between poverty and illegal wildlife hunting. Conservation Biology, 30(1), 14-22. Peres, C. A. (2013). Biodiversity Conservation Performance of Sustainable‐Use Tropical Forest Reserves. Conservation Biology: Voices from the Tropics, 245-253. Lindsey, P. A., Nyirenda, V. R., Barnes, J. I., Becker, M. S., McRobb, R., Tambling, C. J., ... & t’Sas-Rolfes, M. (2017). Underperformance of African protected areas and the case for new conservation models: Insights from Zambia. PLoS ONE, 9(5), e94109. https://doi.org/10.1371/journal.pone.0094109. Ceballos, G., & Ehrlich, P. R. (2006). Global mammal distributions, biodiversity hotspots, and conservation. Proceedings of the National Academy of Sciences, 103(51), 19374-19379. Torres, P. C., Morsello, C., Parry, L., Barlow, J., Ferreira, J., Gardner, T., & Pardini, R. (2018). Landscape correlates of bushmeat consumption and hunting in a post-frontier Amazonian region. Environmental Conservation, 45(4), 315-323. Creighton, M. J., & Nunn, C. L. (2023). Explaining the primate extinction crisis: predictors of extinction risk and active threats. Proceedings of the Royal Society B, 290(2006), 20231441. Koné, I., Refisch, J., Jost Robinson, C. A., & Ayoola, A. O. (2023). Hunting of Primates in the Tropics: Drivers, Unsustainability, and Ecological and Socio-Economic Consequences. In Primates in Anthropogenic Landscapes: Exploring Primate Behavioural Flexibility Across Human Contexts (pp. 45-59). Cham: Springer International Publishing. Baker, L. R., Tanimola, A. A., & Olubode, O. S. (2018). Complexities of local cultural protection in conservation: the case of an Endangered African primate and forest groves protected by social taboos. Oryx, 52(2), 262-270. Colding, J., & Folke, C. (2001). Social taboos:“invisible” systems of local resource management and biological conservation. Ecological applications, 11(2), 584-600. Bachmann, M. E., Nielsen, M. R., Cohen, H., Haase, D., Kouassi, J. A., Mundry, R., & Kuehl, H. S. (2020). Saving rodents, losing primates—Why we need tailored bushmeat management strategies. People and Nature, 2(4), 889-902. Francesconi, W., Bax, V., Blundo-Canto, G., Willcock, S., Cuadros, S., Vanegas, M., ... & Torres-Vitolas, C. A. (2018). Hunters and hunting across indigenous and colonist communities at the forest-agriculture interface: an ethnozoological study from the Peruvian Amazon. Journal of ethnobiology and ethnomedicine, 14, 1-11. Thoisy, B. D., Richard-Hansen, C., & Peres, C. A. (2009). Impacts of subsistence game hunting on Amazonian primates. In South American primates: Comparative perspectives in the study of behavior, ecology, and conservation (pp. 389-412). New York, NY: Springer New York. Lopes, G. S., Cassano, C. R., Mureb, L. S., Miranda, F. R., Cruz‐Neto, A. P., & Giné, G. A. F. (2023). Combined effect of ambient temperature and solar radiation on maned sloths' behaviour and detectability. Austral Ecology, 48(7), 1344-1360. Sewall, B.J., Young, R., Trewhella, W.J., Rodríguez-Clark, K.M. & Granek, E.F. 2016. Pteropus livingstonii. The IUCN Red List of Threatened Species 2016: e.T18732A22081502. https://dx.doi.org/10.2305/IUCN.UK.2016-2.RLTS.T18732A22081502.en. Accessed on 11 February 2025. Mickleburgh, S.P., Hutson, A.M. and Racey, P.A. 1992. Old World Fruit-Bats - An Action Plan for their Conservation. IUCN, Gland, Switzerland. Caballero, C. B., Biggs, T. W., Vergopolan, N., West, T. A., & Ruhoff, A. (2023). Transformation of Brazil's biomes: The dynamics and fate of agriculture and pasture expansion into native vegetation. Science of the Total Environment, 896, 166323. Symes, W. S., Edwards, D. P., Miettinen, J., Rheindt, F. E., & Carrasco, L. R. (2018). Combined impacts of deforestation and wildlife trade on tropical biodiversity are severely underestimated. Nature communications, 9(1), 4052. Soria, C. D., Pacifici, M., Di Marco, M., Stephen, S. M., & Rondinini, C. (2021). COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology, 102, e03344 Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., ... & Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change, 42, 153-168. Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563. Murakami, D., Yoshida, T., & Yamagata, Y. (2021). Gridded GDP projections compatible with the five SSPs (shared socioeconomic pathways). Frontiers in Built Environment, 7, 760306. Chen, M., Vernon, C. R., Graham, N. T., Hejazi, M., Huang, M., Cheng, Y., & Calvin, K. (2020). Global land use for 2015–2100 at 0.05 resolution under diverse socioeconomic and climate scenarios. Scientific Data, 7(1), 320. Copernicus Climate Change Service (2022): Climate extreme indices and heat stress indicators derived from CMIP6 global climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.776e08bd (Accessed on 12-12-2024) Weiss, D. J., Nelson, A., Gibson, H. S., Temperley, W., Peedell, S., Lieber, A., ... & Gething, P. W. (2018). A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature, 553(7688), 333-336. Blei, A. M., Angel, S., Civco, D. L., Galarza, N., Kallergis, A., Lamson-Hall, P., Liu, Y., & Parent, J. (2018). Introduction. In Urban Expansion in a Global Sample of Cities, 1990 – 2014 (pp. 1–3). Lincoln Institute of Land Policy. http://www.jstor.org/stable/resrep22037.3 World Bank (2024) – with major processing by Our World in Data. “World Bank's income classification” [dataset]. World Bank, “Income Classifications” [original data]. Source: World Bank (2024) – with major processing by Our World In Data Additional Declarations There is NO Competing Interest. Supplementary Files NCOMMS2521842TableS1.docx Table S1 Pacificietal.SI.docx Table S2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6278255","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444891176,"identity":"546d861a-97d2-444d-99d5-3962cc27802c","order_by":0,"name":"Michela Pacifici","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie3PsWqEMBjA8U8C6RLq6tFyfYLCdwjKDfVepUXoZMuNQofTRZd7ALe+RedIhi6BWwUXS8HpBrt1uKFKLBQanW/IHwJJyI8kACbTOUaAAmzVnMcIzB4mw4qClUwTHInsySIZySVYqd78ISD7gXxcLEF/zW1O2qbDAPwL8cH5Nrh2q6dSxPHbDQWSNxriCeqvCgxhvX9EzjFkXvV8X0pZr7KJh3mC0SuGpH9PBKJD3pMIyzSrrVlywh3g4Qj9LZy5hSKbWQIoAKtIEXQUeZgm1Fvs8Z2ti1b9xZFHLBNZhxmx0kJHDqJ1vuOXpW+Hnx0/BRs7j9yvJK7vXvO86TTkN4b/98jM+SENMZlMJpPqB/sXaIvbPZV5AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-4468-4710","institution":"Department of Biology and Biotechnology, Sapienza University of Rome","correspondingAuthor":true,"prefix":"","firstName":"Michela","middleName":"","lastName":"Pacifici","suffix":""},{"id":444891177,"identity":"abfc86e1-1a96-4f34-8a7f-6f7035c10c79","order_by":1,"name":"Andrea Cristiano","email":"","orcid":"https://orcid.org/0000-0002-8197-9518","institution":"Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Cristiano","suffix":""},{"id":444891178,"identity":"fd028ee4-0d22-4d50-b17b-cfe755d221cf","order_by":2,"name":"Giordano Mancini","email":"","orcid":"","institution":"Department of Biology and Biotechnology, Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Giordano","middleName":"","lastName":"Mancini","suffix":""},{"id":444891179,"identity":"b528be79-6984-4eaf-a105-536a8ea46956","order_by":3,"name":"Dario Nania","email":"","orcid":"","institution":"Department of Biology and Biotechnology, Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Dario","middleName":"","lastName":"Nania","suffix":""},{"id":444891180,"identity":"f126f813-fe72-4415-a5fd-5ed975bf2b6e","order_by":4,"name":"Marco Davoli","email":"","orcid":"","institution":"Department of Biology and Biotechnology, Sapienza University of Rome","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Davoli","suffix":""}],"badges":[],"createdAt":"2025-03-21 14:00:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6278255/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6278255/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81019997,"identity":"5d9384f8-7141-42f4-b96c-295ed3e3e972","added_by":"auto","created_at":"2025-04-21 09:33:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePartial dependence plots and variable importance. \u003c/strong\u003eThe partial dependence plots on the left show the relationship between the top 10 most important variables in the random forest model and the probability of being a species hunted for bushmeat. The barplot on the right represents decreasing variable importance according to the Mean Decrease Accuracy metric. Green represents species traits, blue socio-economic variables and fuchsia indicates environmental variables.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278255/v1/17dde6bb9fd967dc94dc9e58.jpg"},{"id":81019999,"identity":"ee8f2659-878b-492a-83a7-0d8ba2e1dc4e","added_by":"auto","created_at":"2025-04-21 09:33:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRichness of species currently threatened by bushmeat hunting. \u003c/strong\u003eNumber of species that, according to the IUCN Red List of Threatened Species, are currently threatened by “Hunting and trapping terrestrial animals” and used for “Food - human”. Dark blue areas represent sites rich in species threatened by bushmeat hunting today.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278255/v1/fa9fc83d78efc891e9d74044.jpg"},{"id":81020000,"identity":"92a1003e-6e86-4f47-95f4-d01152ed6b81","added_by":"auto","created_at":"2025-04-21 09:33:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEmerging risk of bushmeat hunting. \u003c/strong\u003eNumber of new species that could become hunted for bushmeat in 2050 if socio-economic conditions change. Areas in yellow are hotspots of emerging risk.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6278255/v1/dd33e900cc52ec60aed24d4f.jpg"},{"id":84422020,"identity":"88e3cd55-0217-4de6-80b5-717a8ef47590","added_by":"auto","created_at":"2025-06-11 18:23:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":891726,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6278255/v1/9f850122-6751-4e68-937b-1a8a99266702.pdf"},{"id":81021019,"identity":"63247a5d-2e43-4857-a249-b4639f4693a0","added_by":"auto","created_at":"2025-04-21 09:41:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21991,"visible":true,"origin":"","legend":"Table S1","description":"","filename":"NCOMMS2521842TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6278255/v1/56e102d2821888cc94793d0a.docx"},{"id":81020002,"identity":"202bcc9f-5535-44e9-886e-7a345971fba9","added_by":"auto","created_at":"2025-04-21 09:33:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11708,"visible":true,"origin":"","legend":"Table S2","description":"","filename":"Pacificietal.SI.docx","url":"https://assets-eu.researchsquare.com/files/rs-6278255/v1/b17c4d486bd6a4ef17603a36.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global hotspots of bushmeat hunting risk for mammals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBushmeat hunting consists in the practice of hunting wild animals for food. It has historically been key to the subsistence of several indigenous communities\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, especially in regions of the world characterized by lower socioeconomic conditions where bushmeat remains among the primary sources of protein and an important economic commodity\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While often being a necessity for subsistence, harvesting species for bushmeat consumption can become a long-term unsustainable practice with profound and devastating ecological consequences\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In fact, in recent decades, bushmeat hunting rose to prominence as a major threat for the conservation of biodiversity, especially in the case of large-bodied mammals which are often the primary target of hunting because of their high yield\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. According to the International Union for Conservation of Nature (IUCN), approximately 23% of mammals are threatened with extinction\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Many of these species often play important roles in regulating ecosystems functioning\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, for instance exerting bottom-up regulation of primary consumers (most herbivores), providing seed dispersal (rodents, primates, bats), stimulating vegetation regeneration and biogeochemical fluxes (ungulates and primates), and actively modifying habitats heterogeneity (ungulates\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e). The decline of keystone species may trigger self-reinforcing negative feedback loops, further exacerbating disruption of ecosystem functioning in areas already under high anthropogenic pressure\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe risk faced by mammal species to increased levels of bushmeat hunting can be attributed to several intrinsic and extrinsic factors. Intrinsic drivers that can make a species susceptible to hunting include slow reproductive rates, which means hunting pressures can significantly reduce their populations and hamper their ability to rebound, and behavioural aspects that can make a species more or less dangerous or challenging to hunt\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Extrinsic drivers of bushmeat hunting can be associated with global socioeconomic and environmental changes of the last decades, such as the rapid growth of human population in tropical areas, increased rates of habitat destruction and fragmentation, and facilitated access to previously remote areas due to infrastructure development, which can facilitate wild meat trading\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Additionally, deforestation to boost agricultural expansion further facilitates hunters\u0026rsquo; and poachers\u0026rsquo; access to wildlife in previously undisturbed areas, increasing hunting pressure on mammal species\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Weak enforcement of wildlife protection, together with economic hardship and the lack of alternative livelihoods, further perpetuate the prevalence of harvest and bushmeat hunting in socioeconomic contexts where it represents a major source of income and livelihood\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Climate change may also play a role in determining species\u0026rsquo; exposure to bushmeat hunting, as distributional shifts or species ability to adapt locally drive hunting patterns\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite its relevance for conservation assessments of threatened species, no comprehensive global analysis has yet evaluated the vulnerability of species to bushmeat hunting and their future risks on a spatially-explicit basis. Global analyses on the threat that bushmeat hunting poses to mammal species is limited by data availability, variability of regional patterns, and the complexity of socio-environmental interactions. Reliable, high-resolution data on hunting pressure are scarce, as bushmeat hunting is often unregulated, occurs in remote areas, and is driven by informal markets, making it difficult to quantify its true impact. Additionally, the factors influencing hunting risk \u0026mdash; such as human population density, economic conditions, habitat accessibility, and cultural preferences \u0026mdash; vary significantly across regions, complicating the development of globally applicable models\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Differences in data availability and quality between well-studied and data-deficient areas further introduce biases, potentially underestimating threats in regions with limited monitoring\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Moreover, environmental and socioeconomic drivers of bushmeat hunting are interconnected, requiring complex modeling approaches to disentangle their effects\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo assess the vulnerability of mammal species to bushmeat hunting, we analyzed key biological, environmental, and socioeconomic factors that may influence their susceptibility to this threat. We first identified terrestrial mammal species currently classified as threatened by hunting for human consumption according to the IUCN Red List. We then selected a set of species traits \u0026mdash; such as body mass, reproductive parameters, trophic level, and foraging stratum \u0026mdash; that could be linked to hunting risk. Using a binomial random forest model, we assessed how these traits correlate with the likelihood of being hunted. To further explore future risks, we incorporated environmental and socioeconomic variables projected for 2050 under scenarios that depict a highly unequal world, where wealth and technological advancement are concentrated in a small elite, including human population density, gross domestic product (GDP), tree cover, climatic extremes, and accessibility to urban centers. These factors were integrated using high-resolution spatial datasets and analyzed through machine learning models to predict which species may become threatened in the future. Finally, we mapped current and emerging hotspots of bushmeat hunting pressure, providing insights into regions and species most at risk.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eLarge-bodied and long-lived mammals face the highest bushmeat hunting pressure\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eOur analysis reveals that intrinsic traits are fundamental in determining the probability of a species being hunted for bushmeat (OOB estimate of error rate 8.01%; mtry selected\u0026thinsp;=\u0026thinsp;4; mean accuracy of cross validation\u0026thinsp;=\u0026thinsp;0.92; accuracy train/test validation\u0026thinsp;=\u0026thinsp;0.91). We observed a strong positive relationship between body mass and the probability of a mammal species being hunted for bushmeat, with the probability increasing as body mass increases and reaching an asymptote at a logarithmic value of approximately 6 (around 1000 kg; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This result suggests that species with low to intermediate body mass in the dataset have a lower probability of being hunted for bushmeat compared to larger species. However, beyond the asymptote (which is approximately the weight of rhinoceroses), the probability of being hunted does not significantly increase, even for species with extremely high body mass values. This pattern highlights that while body mass is a strong predictor of hunting pressure, other ecological and practical constraints may limit the selection of the largest species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeveral factors likely contribute to the asymptote in body mass and hunting risk relationship. First, large-bodied species often pose logistical challenges for hunters due to the difficulty in capturing, killing, and transporting such heavy carcasses over long distances, particularly in forested or remote environments. Additionally, larger species frequently have lower population densities and slower reproductive rates\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, making them less abundant and more challenging to encounter in the wild\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. From a behavioral perspective, many of the largest terrestrial mammals exhibit heightened wariness and adaptive anti-predator behaviors, including increased vigilance, cryptic movement, or occupation of less accessible habitats, which may further reduce their exposure and vulnerability to hunting\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Moreover, the hunting of very large species may be restricted by cultural or legal constraints, as some of these taxa are often protected under national and international conservation frameworks\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Interestingly, our results align with previous studies indicating that medium-to-large-sized mammals are the primary targets for subsistence and commercial bushmeat hunting\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This selection is likely driven by an optimal balance between the effort required to hunt and process an animal and the nutritional or economic return obtained. The asymptotic trend observed in our study may reflect the diminishing cost-benefit ratio of hunting species beyond a certain body size threshold, where the challenges outweigh the advantages.\u003c/p\u003e \u003cp\u003eWe also found a non-linear relationship between species\u0026rsquo; maximum longevity and their probability of being hunted for bushmeat. Hunting probability increases up to a maximum longevity value of approximately 1000 days, then stabilizes. The relationship between the probability of bushmeat hunting and maximum longevity of a species likely reflects an ecological balance between the vulnerability and adaptability of species to human hunting pressures. Species with longer lifespans often have slower reproductive rates, which can make them more vulnerable to overexploitation, especially in environments where hunting pressures are high\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, species with extremely long lifespans may have evolved adaptations that allow them to better evade hunting, such as larger home ranges or more complex social structures.\u003c/p\u003e \u003cp\u003eSpecies with low reproductive rates experience high hunting pressure, but as reproductive rates increase, the probability of being hunted declines, reaching a minimum at approximately 3 litters per year (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Beyond this point, hunting probability increases again but stabilizes at high fecundity values. This pattern suggests that low-fecundity species are the most heavily targeted for bushmeat, whereas species with moderate reproductive rates experience the least pressure, and those with high reproductive rates are hunted at a consistent but relatively lower level. The high exploitation of species with low reproductive rates is likely due to links with other traits, such as larger body size, slower life history, and greater meat yield, making them prime targets for both subsistence and commercial bushmeat hunting\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Many large mammals, such as ungulates, primates, and carnivores, have long gestation periods, few offspring per year, and slow population recovery, increasing their vulnerability to overexploitation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Despite their low reproductive output, their high market value and nutritional benefits make them preferred targets, often leading to population declines and local extinctions where hunting is intense. Conversely, species with moderate reproductive rates are the least targeted for bushmeat. This may be because they lack the high meat yield of large, slow-reproducing species, making them less attractive for hunters. Additionally, their faster life histories and more stable populations may allow them to persist under moderate hunting pressure, reducing their risk of overexploitation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. At higher reproductive rates, hunting probability increases slightly but remains stable, suggesting that high-fecundity species are not primary targets for bushmeat hunting. Many small-bodied, fast-reproducing species \u0026mdash; such as rodents and small carnivores \u0026mdash; may still be hunted, but likely as secondary or fallback resources, particularly when larger prey becomes scarce\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition, their rapid population turnover and high reproductive potential make them more resilient to hunting pressures, preventing drastic population declines even under sustained exploitation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEconomic and urban development shape spatial patterns of bushmeat hunting\u003c/h3\u003e\n\u003cp\u003eThe probability of a species being hunted for bushmeat is highest at lower gross domestic product (GDP) values (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This suggests that regions with lower economic development face the greatest hunting pressures, while regions with higher GDP exhibit much lower probabilities of bushmeat exploitation. Several socio-economic mechanisms may explain this pattern. In low-income regions, bushmeat hunting is often driven by food insecurity and a lack of access to alternative protein sources\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In these areas, despite economic hardship, the demand for bushmeat remains high due to its critical role in subsistence. As GDP increases, there is likely a reduction in hunting pressures, potentially due to improved access to alternative food sources, greater economic diversification, and better enforcement of wildlife protection laws\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. At higher levels of GDP, hunting probability continues to stabilize at low values. This decline is likely driven by factors such as increased availability of domesticated meat, the availability of more sustainable livelihoods, and changing dietary preferences associated with urbanization and rising living standards. Additionally, stronger wildlife conservation policies, increased enforcement, and the development of ecotourism and alternative income sources may further reduce hunting pressures\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. However, after reaching a GDP log value of approximately 17 (corresponding to 1.23 Purchasing Power Parity, Million USD in 2005 years rate), the probability of hunting for bushmeat increases until it reaches a peak at a log value of around 20 (1.3). This could be due to economic disparities within regions where, despite overall wealth, inequality exacerbates resource pressures.\u003c/p\u003e \u003cp\u003eOur analysis shows that hunting probability increases with human population density (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), highlighting the role of demographic pressure in driving bushmeat exploitation. As population density rises, demand for wild meat grows due to both subsistence needs and market-driven trade\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In densely populated areas, the accessibility of wildlife is a key factor: proximity to human settlements facilitates hunting, while infrastructure development, such as roads and market networks, further accelerates extraction rates\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, the interaction with distance from major urban centers adds complexity to this pattern. The probability of hunting is lower near cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) probably because of the reduced availability of wildlife populations\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Hunters target more remote areas where wildlife remains abundant. This trend bends at very large distances, where hunting may eventually be constrained by logistical challenges, law enforcement, and the shift to subsistence rather than commercial exploitation\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These findings suggest a dual effect of urbanization on hunting patterns: while high human density amplifies hunting pressure, the spatial dynamics of exploitation are shaped by both market demand and accessibility. Near large cities, bushmeat hunting is often commercialized, supplying urban consumers with wildlife products\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. As hunters move deeper into remote areas, extraction rates increase, reflecting the ongoing expansion of hunting frontiers into less disturbed ecosystems\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This highlights the cumulative effect of different anthropogenic threats, such as deforestation or habitat fragmentation, with overexploitation pressure on mammals.\u003c/p\u003e \u003cp\u003eExtreme climate favours overexploitation\u003c/p\u003e \u003cp\u003eThere is a complex relationship between the number of days with extreme events and the likelihood of a species being hunted for bushmeat. As the number of dry days increases, there is a point at which wildlife populations begin to suffer from food and water scarcity, and hunting efficiency starts to decline. With animals becoming more scattered in their search for resources, they become harder to locate and hunt. Consequently, hunting pressure decreases as logistical challenges and resource depletion take their toll, making it more difficult to maintain high levels of hunting success. On the other hand, with prolonged dry periods, when water sources become scarce, animals tend to congregate around the few remaining water sources, significantly increasing their vulnerability to hunters. In these conditions, hunting pressure is high, as animals are more easily located and hunted by humans. While dry spells reduce the availability of food and other resources, leading to more concentrated wildlife populations, hunters are more likely to exploit this predictability for easier access to prey. Moreover, in regions with limited access to alternative sources of protein, bushmeat hunting becomes a crucial survival strategy, and the reliance on hunting intensifies during these periods\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe relationship between the number of very heavy precipitation days and hunting pressure follows a similar pattern. When the number of very rainy days is limited, hunting pressures tend to decrease as humans can better survive off agricultural products and other protein sources\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. However, as the number of very heavy precipitation days increases from approximately 12 to 45, the probability of a species being hunted for bushmeat rises. This could reflect an increased vulnerability of animals, which may be forced to congregate in more accessible areas or become more easily trapped due to limited movement or altered behaviors during extreme weather events. After 45 days of heavy precipitation, the probability stabilizes, suggesting that extended periods of such extreme weather may discourage hunting due to impassable conditions or a decrease in animal availability\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTropical hotspots of emerging risk\u003c/p\u003e \u003cp\u003eAt present, the majority of species targeted for bushmeat hunting are found in tropical areas, with the highest hunting pressures observed in Africa and southeast Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These regions are home to a high diversity of wildlife, much of which is currently threatened\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Species such as primates, ungulates, and bats are increasingly being hunted due to their accessibility and high demand for meat.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs human populations continue to grow and expand into previously undisturbed regions, the risk of bushmeat hunting pressures on mammal wildlife is expected to intensify. In fact, we found 85 species that are not currently considered threatened by bushmeat hunting, but could become so by 2050 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Of the 85 species identified as at potential future risk, 59 are already classified as threatened on the IUCN Red List, including 18 as Critically Endangered. Additionally, one is Near Threatened and 5 are Data Deficient\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), meaning their vulnerability may be underestimated. If bushmeat hunting becomes more widespread due to shifting cultural practices, increased demand for wild meat, or greater accessibility to previously unexploited populations, these species could experience rapid population declines. For Critically Endangered species, even low levels of hunting could push them toward extinction, while Data Deficient species may already be at high risk without being recognized as such.\u003c/p\u003e \u003cp\u003eThe species most at risk of becoming threatened from bushmeat hunting in the future are concentrated mainly in the Primates (44 species), Cetardtiodactyla (13 species), Chiroptera orders (13 species; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Though many of these species may not yet be heavily exploited, ecological and socio-economic trends indicate that they could become more vulnerable to hunting as human activity increases in their regions\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Primates are especially sensitive to hunting pressures due to the combination of slow reproductive rates, low population densities, and relatively restricted geographic ranges\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e due to high dependency to forests. As human settlements grow, these primate species are increasingly likely to be targeted for bushmeat, especially those living near urban areas or agricultural expansions. Hunting of primates has been linked to both subsistence needs and market demand, further exacerbating their vulnerability\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Cultural taboos play a crucial role in mitigating primate hunting pressure in various regions. In several African and Southeast Asian communities, traditional beliefs and spiritual customs discourage the consumption or killing of certain primates, effectively serving as an informal conservation mechanism\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. For example, among the Batek people of Malaysia, primates such as gibbons and macaques are considered sacred and are rarely hunted. Similarly, in parts of Nigeria and Ghana, some ethnic groups avoid consuming primates like colobus monkeys due to beliefs that they are ancestral spirits or bring misfortune if killed. Studies have shown that adherence to these taboos can significantly reduce primate hunting, in some cases by up to 95%\u003csup\u003e39\u003c/sup\u003e. However, these cultural safeguards are eroding due to urbanization, globalization, and changing socio-economic conditions. Younger generations often prioritize economic survival over traditional customs, leading to increased participation in bushmeat hunting. Moreover, commercial hunting for bushmeat markets frequently overrides local taboos, as hunters may kill primates to sell rather than consume them personally\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In these areas, reinforcing traditional beliefs through cultural heritage initiatives may help sustain primate populations. The situation is different in the Amazon, including countries such as Peru, Brazil, and Colombia, where many indigenous communities rely on bushmeat as a primary protein source, and primates are frequently hunted\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Unlike in parts of Africa, where strong spiritual taboos can restrict primate hunting, South America lacks widespread, consistent prohibitions against consuming primate meat. While some Indigenous groups, such as the Mats\u0026eacute;s in Peru, associate certain primates with spiritual beliefs, this does not always translate into a strict hunting ban. Instead, hunting is often dictated by ecological factors (e.g., availability) rather than spiritual prohibitions\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The absence of strong cultural taboos suggests that conservation efforts in the Amazon should focus on alternative protein sources, hunting regulations, and community-based conservation programs rather than relying on cultural deterrents.\u003c/p\u003e \u003cp\u003eSpecies in the Pilosa order, especially sloths, are vulnerable to exploitation, though for different reasons. Those animals have slow movements and low reproductive rates, making them easy targets for hunters\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These species, primarily arboreal and often hidden in the high canopy, are less visible to hunters, but as deforestation and forest fragmentation continue, their habitats are shrinking, and they may find themselves pushed into more accessible areas. As human populations grow and demand for land increases, human settlements will likely expand into sloth habitats, leading to more frequent encounters between humans and wildlife. Although these species are not typically the first choice for bushmeat hunters, the pressures of subsistence hunting and the growing need for protein in rural areas could make sloths a more attractive target.\u003c/p\u003e \u003cp\u003eBy 2050, species in the genus \u003cem\u003ePteropus\u003c/em\u003e, commonly known as flying foxes, may as well face increasing threats from bushmeat hunting due to a combination of changing human populations, expanding access to previously remote roosting areas, and the growing demand for bushmeat. While there is currently no strong evidence of \u003cem\u003ePteropus\u003c/em\u003e species being commonly hunted for food, such as in the case of \u003cem\u003ePteropus livingstonii\u003c/em\u003e, which roosts in inaccessible areas far from towns\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, this may change as human activity and infrastructure continue to expand. In some regions, cultural taboos prevent the consumption of fruit bats, but these prohibitions could weaken over time, especially if economic or food security pressures increase\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Additionally, the increasing human encroachment into \u003cem\u003ePteropus\u003c/em\u003e habitats, facilitated by improved roads and access to previously remote areas, may expose these species to hunting pressures. For example, while \u003cem\u003eP. livingstonii\u003c/em\u003e has so far been relatively safe due to its roosting habits, its close relative, \u003cem\u003eP. seychellensis comorensis\u003c/em\u003e, which roosts in more visible, accessible locations, has already been targeted for hunting in some areas\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. If roosts of other \u003cem\u003ePteropus\u003c/em\u003e species become more easily accessible, particularly in regions where hunting is already occurring, \u003cem\u003ePteropus\u003c/em\u003e populations could face significant risks. In particular, the expansion of human settlements and urban sprawl into these regions could lead to a dramatic increase in bushmeat hunting, posing a serious threat to the survival of these bat species by 2050.\u003c/p\u003e \u003cp\u003eThe expansion of bushmeat hunting into new regions and taxa could lead to irreversible biodiversity loss, underscoring the urgent need for monitoring and early intervention. In 2050, both southeast Asian islands and tropical Africa are projected to become critical hotspots for mammal species newly targeted for bushmeat hunting (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) due to the synergistic impacts of habitat loss, increased human population pressure, and climate change. These areas, characterized by vast tropical rainforests, already face significant deforestation driven by agricultural expansion, illegal logging, and infrastructure development\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Continued deforestation and forest fragmentation, exacerbated by a projected increase in human population and demand for land, will likely push both humans and wildlife into increasingly constrained areas, promoting hunting of forest-dwelling mammals, which could exacerbate local extinction risks\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese pressures, compounded by the anticipated rise in global temperatures, will lead to the shrinkage of species' distributions to narrower suitable habitats, potentially hindering species' movement and making them more vulnerable to hunters. In addition to that, the limited mobility of island species could further exacerbate their vulnerability, as they may not be able to escape hunting pressures or find new, safe habitats. The hotspots of emerging risk we identified, which contain high unique biodiversity, are often simultaneously some of the poorest areas in the world, where people rely on wildlife as a key resource for sustenance. This dependence on bushmeat, combined with rising population pressures and limited alternatives, could drive increased hunting of species that were not previously targeted. Conservation efforts, including stricter wildlife protection laws, community engagement, and sustainable alternatives to bushmeat consumption, will be essential to mitigate this emerging threat.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSelection of species\u0026rsquo; traits\u003c/p\u003e \u003cp\u003eWe first selected all terrestrial species of mammals classified as threatened by \u0026ldquo;Hunting and trapping terrestrial animals\u0026rdquo; in the IUCN Red List, and filtered those used for \u0026ldquo;Food - human\u0026rdquo; in the Use and Trade classification scheme (N\u0026thinsp;=\u0026thinsp;928). This allowed us to identify species currently threatened by bushmeat hunting. Then, we selected traits that could potentially be correlated with a higher risk of being hunted for food. We used the traits in the COMBINE database\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e and retained only those with data coming from direct observations available for \u0026gt;\u0026thinsp;35% of the species in the database. The traits we tested in our models were activity cycle (time of day when the species is most active), adult mass (body mass of an adult individual), maximum longevity (maximum reported age at death for the species), litter size (number of offspring born per litter per female), litters per year (number of litters per female per year), trophic level (divided into herbivore, omnivore and carnivore), and foraging stratum (divided into marine, ground level, scansorial, arboreal and aerial). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for hypotheses associated with variable selection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eHypotheses for variable selection.\u003c/b\u003e Rationale for the choice of variables included in the random forest models, along with acronyms and hypotheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAME\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHYPOTHESES\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiurnal species are more easily detected and hunted by humans, whereas nocturnal species might be less exposed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarger-bodied mammals tend to provide more meat per individual; larger mammals can also be more dangerous to hunt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum longevity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong-lived species often have slower life histories, making them more vulnerable to overexploitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLitter size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies with lower reproductive output are at higher risk of population declines\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLitters per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies that reproduce slowly may take more time to recover from overexploitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrophic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHerbivores and omnivores are often more abundant and easier to hunt than carnivores; herbivores are often preferred for their meat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForaging stratum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGround-dwelling species are generally easier to capture than arboreal or aerial species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat breadth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies with broader habitat breadth are more likely to be hunted for bushmeat due to their wider distribution and higher encounter rates with hunters across diverse environments\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsecutive dry days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater availability may influence crop yields and agricultural productivity, creating a direct incentive for increased hunting as a coping strategy in drier periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum value of daily maximum temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower minimum daily maximum temperatures may reduce the likelihood of mammal species being hunted for bushmeat by influencing their distribution, behavior, and accessibility to hunters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery heavy precipitation days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDual effect: while making certain areas impassable, they may also influence wildlife migration and increase hunting opportunities for certain species\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTree cover percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForests provide more wildlife species to hunt, yet increased deforestation and landscape fragmentation could create gaps or corridors that may make hunting more efficient and accessible to humans\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time to major cities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved infrastructure may bring urban demand for bushmeat closer to rural areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDenser populations typically drive higher consumption of bushmeat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross domestic product\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoorer regions with fewer alternatives may turn to wildlife as a food source\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEnvironmental and socioeconomic variables\u003c/p\u003e \u003cp\u003eIn order to understand the role of current and potential future environmental and socioeconomic changes in determining species\u0026rsquo; exposure to bushmeat hunting, we selected a set of variables and projected them into the future for 2050. Since the aim here is to quantify the role of environmental and socioeconomic variables (provided as raster maps) within the geographic range of species (polygon maps), we used the native resolution of the raster maps to avoid reducing accuracy through spatial resampling. All raster values of the variables described below were calculated as the median within the species' current range, as defined by the IUCN Red List\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, using the time periods available for the present (centered on 2020) and the future (centered on 2050). All spatial analyses have been done in R version 4.4.2 or GRASS GIS 7.\u003c/p\u003e \u003cp\u003eThe Shared Socioeconomic Pathways 4 (SSP4) is a socioeconomic scenario characterized by high inequality within and between countries\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In this pathway, global development is uneven, with wealth and technological advancements concentrated in a few regions, while others face limited access to resources and slower economic growth. This scenario is the most suitable for studying bushmeat hunting because it reflects regions where inequality leads to greater pressures on natural resources, particularly in poorer areas with limited access to alternatives.\u003c/p\u003e \u003cp\u003eFor human population density, we used the data for 2020 and 2050 from Wang et al.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, covering 248 countries or areas with 5-year intervals. The authors combined national censuses and official population estimates, socioeconomic scenarios, environmental projections, urbanization and migration models to derive a dataset of global human population up to 2100. For 2020, the population density data were based on observed population distributions from recent census data. The data were mapped onto a 1 km grid to reflect the actual distribution of the human population at that time. For 2050, the population density projections were derived using the SSP4 scenario.\u003c/p\u003e \u003cp\u003eWe used Gross Domestic Product (GDP) as a proxy for economic development. We used data from Murakami et al.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e to derive GDP estimates at a finer resolution than the country level. In this work the authors estimated GDPs for the period between 1850 and 2100 in 1/12 grids at 10-year intervals. This was achieved by downscaling actual GDPs from 1850 to 2010 and projecting GDPs under SSPs 1\u0026ndash;5 from 2020 to 2100. We utilized data for 2020 and 2050 based on the SSP4 scenario.\u003c/p\u003e \u003cp\u003eTree cover is another important factor affecting species\u0026rsquo; presence and hunters\u0026rsquo; accessibility to natural resources. We used the data from\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, who utilized the Global Change Assessment Model and a spatial disaggregation model (Demeter) to generate land use projections at a high resolution (0.05\u0026deg;). The resulting dataset provides gridded land use projections from 2015 to 2100 under 15 SSP-RCP scenarios, representing plausible future socio-economic and climate conditions. The projections are disaggregated into 32 land cover types, more consistent with the land cover classifications used in Earth System Models. The files contain the percentage of a cell covered by each land cover type. To obtain the percentage of tree cover in each cell, we summed the percentages of Plant Functional Type classes 1 to 12, which correspond to tree functional types, using the SSP4 scenario.\u003c/p\u003e \u003cp\u003eAs extreme climate can deeply alter human and animal behaviour, we utilized climate data from the Copernicus Climate Data Store\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, specifically the climate extreme indices and heat stress indicators derived from the Coupled Model Intercomparison Project Phase 6 global climate projections. The indices are provided for historical and future climate projections. Since SSP4 is not available in CMIP6, we opted for the more similar scenario, that is SSP3-7.0 and the MIROC6 model, characterized by low economic development, limited adaptation and mitigation policies, and strong social inequalities. The dataset provides yearly climate indices at a spatial resolution of 0.5\u0026deg;x0.5\u0026deg;. We focused on three key climatic variables: number of consecutive dry days (cdd), minimum value of daily maximum temperature (txn), and number of very heavy precipitation days (r20mm). The cdd index is calculated by identifying the longest consecutive period of days with daily precipitation below 1 mm. The txn index represents the minimum value of daily maximum temperature recorded in each year, and the R20mm index counts the number of days with daily precipitation exceeding 20 mm. For each variable, we computed the annual values for both the present (2015\u0026ndash;2024) and future (2045\u0026ndash;2054) scenarios, and then averaged them over the respective 10-year periods to obtain the decadal averages.\u003c/p\u003e \u003cp\u003eTo account for accessibility, we used the high-resolution global map of travel time to the nearest major city (with at least 50,000 inhabitants) for the year 2015, produced by Weiss et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. This map has a 1\u0026times;1 km resolution and integrates global-scale datasets that capture factors influencing human movement rates. Since the travel time to cities map was only available for the year 2015 (used here as the \u0026lsquo;present\u0026rsquo; reference), we developed a scenario for 2050 based on recent urban expansion rates. Blei et al.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e found that during the period 2000\u0026ndash;2014, the median urban extent growth rate was 5.7% per year for cities in less developed countries, compared to 1.1% per year for cities in more developed countries. We used the World Bank's annual country classification by income for all countries with a population over 30,000\u003csup\u003e56\u003c/sup\u003e. Then, we applied a yearly 1.1% reduction in travel time to major cities \u0026mdash; measured by Weiss et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e in 2015 \u0026mdash; to raster cells in countries classified as \u0026lsquo;Upper-middle-income\u0026rsquo; and \u0026lsquo;High-income.\u0026rsquo; For countries classified as \u0026lsquo;Low-income\u0026rsquo; and \u0026lsquo;Lower-middle-income,\u0026rsquo; we applied a yearly 5.7% reduction, extending to 2050.\u003c/p\u003e \u003cp\u003eStatistical models\u003c/p\u003e \u003cp\u003eWe first examined the distribution of the variables to identify those that exhibited skewness or non-linear relationships, which would benefit from a log transformation. This step ensured that all variables included in the model were appropriately scaled. Then, we applied a Random Forest classification model to predict the likelihood of species being hunted for bushmeat based on a set of intrinsic traits, socioeconomic factors and environmental predictors. The analysis was conducted using the \u0026lsquo;randomForest\u0026rsquo; and \u0026lsquo;caret\u0026rsquo; packages. We used two datasets: one representing present-day conditions, with all variables based on present data, and another for future projections using 2050 data. To validate the model, we applied both 5-fold cross-validation and a 70/30 train-test split. Cross-validation was performed using the trainControl function, with a hyperparameter search for mtry (1\u0026ndash;16) to optimize performance. Additionally, class imbalance was addressed by applying inverse frequency-based weights to the minority class (bushmeat = \"Y\"). The final model\u0026rsquo;s performance was evaluated using a confusion matrix and accuracy calculation.\u003c/p\u003e \u003cp\u003eTo determine the optimal probability threshold for future risk classification, we tested thresholds ranging from 0.1 to 1 in increments of 0.01, calculating the F1-score at each step. The F1-score is the harmonic mean of precision and recall, providing a balanced measure of a model's performance, especially in imbalanced classification problems. The final threshold was selected based on the highest F1-score, balancing precision and recall. Finally, we used the current range maps of species\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e to create richness maps for mammals that are currently classified as threatened by bushmeat hunting in the IUCN Red List\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, as well as for those predicted to become threatened by 2050, at a resolution of approximately 10x10 km. This allowed us to identify both current hotspots of bushmeat hunting pressure and emerging risk hotspots.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe range and threat data that support the findings of this study are available upon request on the IUCN Red List website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iucnredlist.org/\u003c/span\u003e\u003cspan address=\"https://www.iucnredlist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRija, A. A., Critchlow, R., Thomas, C. D., \u0026amp; Beale, C. M. (2020). Global extent and drivers of mammal population declines in protected areas under illegal hunting pressure. 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Lincoln Institute of Land Policy. http://www.jstor.org/stable/resrep22037.3\u003c/li\u003e\n\u003cli\u003eWorld Bank (2024) \u0026ndash; with major processing by Our World in Data. \u0026ldquo;World Bank\u0026apos;s income classification\u0026rdquo; [dataset]. World Bank, \u0026ldquo;Income Classifications\u0026rdquo; [original data]. Source: World Bank (2024) \u0026ndash; with major processing by Our World In Data\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6278255/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6278255/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBushmeat hunting is a significant threat to mammal species worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, yet global assessments of its impact are extremely scarce. This study provides the first comprehensive evaluation of the biological, environmental, and socioeconomic factors driving bushmeat hunting risks for terrestrial mammals, both currently and in the future. We identify key drivers such as low GDP, high population density, and extreme climate events. Our findings reveal that regions with lower economic development and higher human population density face the greatest hunting pressures. Additionally, we project future hotspots where socio-economic changes, including population growth, infrastructure expansion, and climate shifts, will intensify hunting threats, particularly for species already vulnerable to other environmental pressures. We also highlight specific groups, including primates, sloths, and fruit bats, which are most at risk due to their biological characteristics and increasing human encroachment. These species, often with slow reproductive rates or restricted distribution, are projected to face growing threats from bushmeat hunting in the coming decades. By identifying regions and species at risk, this study provides actionable insights for guiding future conservation priorities and mitigating the impacts of bushmeat hunting on biodiversity.\u003c/p\u003e","manuscriptTitle":"Global hotspots of bushmeat hunting risk for mammals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 09:33:01","doi":"10.21203/rs.3.rs-6278255/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df124f2f-aba2-4747-87d6-d2512872a7d1","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47369390,"name":"Biological sciences/Ecology/Conservation biology"},{"id":47369391,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-06-11T18:15:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 09:33:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6278255","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6278255","identity":"rs-6278255","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}