Prey preferences of modern human hunter-gatherers

Prey preferences of modern human hunter-gatherers Cassandra K. Bugir1, Carlos A. Peres2,3, Kevin White4, Robert A. Montgomery5, Andrea S. Griffin1,7, Paul Rippon6, John Clulow1, Matt W. Hayward1, 8 1 Conservation Science Research Group, School of Environmental and Life Sciences, University of Newcastle, Callaghan, NSW, Australia 2 Centre for Ecology, Evolution and Conservation, School of Environmental Sciences, University of East Anglia, Norwich, Norfolk, NR4 7TJ, UK 3 Departamento de Sistemática e Ecologia, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil. 4 Alaska Department of Fish and Game, Division of Wildlife Conservation, PO Box 110024, Juneau, AK 5 Research on the Ecology of Carnivores and their Prey Laboratory, Department of Fisheries and Wildlife, Michigan State University, 480 Wilson Road, 13 Natural Resources Building, East Lansing, MI 48824, USA 6 School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia 7 School of Psychology, University of Newcastle, Callaghan, NSW, Australia 8 Mammal Research Institute, University of Pretoria, Tshwane, South Africa X001 Email: cassandra.bugir@uon.edu.au ORCIDs Cassandra K. Bugir https://orcid.org/0000-0002-4861-7777 Carlos A. Peres https://orcid.org/0000-0002-1588-8765 Kevin White https://orcid.org/0000-0002-5231-6045 Robert A. Montgomery https://orcid.org/0000-0001-5894-0589 Andrea S. Griffin https://orcid.org/0000-0003-4624-9904 Paul Rippon https://orcid.org/0000-0002-4353-2627 John Clulow https://orcid.org/0000-0001-8991-1449 Matt W. Hayward https://orcid.org/0000-0002-5574-1653 Abstract Understanding traditional hunter-gatherer lifestyles in our modern world is fundamental to our understanding of their viability, as well as the role of humans as predators in structuring ecosystems. Here, we examine the factors that drive prey preferences of modern huntergatherer people by reviewing 85 published studies from 161 tropical, temperate and boreal sites across five continents. From these studies, we estimated Jacobs' selectivity index values (D) for 2243 species/spatiotemporal records representing 504 species from 42 vertebrate orders based on a sample size of 799,072 kill records (median = 259). Hunter-gatherers preferentially hunted 11 large-bodied, riskier species, and were capable of capturing species ranging from 0.6 to 535.3 kg, but avoided those smaller than 2.5 kg. Human prey preferences were driven by whether prey were arboreal or terrestrial, the threats the prey afforded hunters, and prey body mass. Variation in the size of prey species pursued by hunter-gatherers across each continent is a reflection of the local size spectrum of available prey, and historical or prehistorical prey depletion during the Holocene. The nature of human subsistence hunting reflects the ability to use a range of weapons and techniques to capture food, and the prey deficient wildlands where people living traditional lifestyles persist. 1 Keywords Prey preference, human subsistence, group hunters, foraging, hunter-gatherers, predator-prey interactions, hominid, human ecology, human evolution 1 Introduction Hunting and meat consumption of non-domesticated animals are integral components of traditional modern human hunter-gatherer lifestyles (Lee et al., 2020; Bennett and Robinson, 2000). Modern human hunter-gatherer groups tend to have a set of behaviors and motives that direct what or when to hunt, and how to hunt safely. These behaviors, which are passed from generation to generation, are often shaped by needs within each group and likely follow the tenets of the optimal foraging theory (Chacon, 2012; Chang & Drohan, 2018). Optimal foraging theory posit that hunting preferences are shaped by the cost:benefit ratio of searching, handling and ingesting specific prey items (Stephens & Krebs, 1986). Specifically, prey items are selected to minimize the energetic and injury-related costs of prey acquisition and handling, while maximizing energy ingested (Belovsky, 1988; Pyke, 1984). Energetic hunting costs may vary by habitat and/or season because of differences in prey communities and their accessibility; taking into consideration prey traits such as body mass, herd or group size, population density, and degree of arboreality in forest habitats. Large-bodied animals tend to pose a greater threat to hunters due to their size, unpredictable temperament as well as physical self-defense features, including teeth, tusks, antlers, horns, or powerful legs with sharp hooves (Crosmary et al., 2012), yet yield large energetic returns if safely captured (Broughton et al., 2011). However, other animals, like venomous snakes or small animals possessing weapons (Kerley, 2018), can also be dangerous even if they are relatively small. Modern human hunter-gatherers have developed a suite of technologies to reduce energetic costs, for example by using snares/traps to capture prey with minimal proximity, energy expenditure, projectile weaponry to bring down riskier prey from a distance, or dogs to detect and subdue prey (Koster, 2008). Thus, it is vital for hunter-gatherers to develop a formative understanding of prey behaviour, seasonal changes, and their distribution in the environment before deploying hunting strategies (Hawkes et al., 1982). Energy-maximizing prey preferences are, in a sense, a form of food security. Knowing where prey resources are, when and how to harvest them effectively, and achieving optimal nutritional value, all reduce the energetic costs associated with foraging (Webster & Webster, 1984). 2 Here, we aimed to determine whether modern human hunter-gatherers preferentially select specific prey to satisfy their dietary requirements (Speth, 2010), what those preferences are, and what factors drive such patterns. Based on studies of large carnivores, we predicted that modern human hunter-gatherers would prefer to kill large-bodied herbivores due to the high energetic yields afforded by these species (Hayward et al., 2012, Hayward & Kerley, 2005). We tested these hypotheses using a comprehensive review of the literature synthesizing prey density, biomass, hunting method and dietary data to describe hunting patterns of modern hunter-gatherer people that still practice an extractive lifestyle in different biomes across the world. Addressing these questions will advance our understanding of the roles of modern humans in structuring ecosystems, and the characteristics necessary to maintain traditional livelihoods in the face of global wildlife declines. 2 Materials and Methods To assess preferential prey selection by modern human hunter-gatherer groups, we used methods established for large carnivores from Hayward and colleagues (2005, 2012, 2017). We conducted a review using JSTOR, Web of Science, and Google Scholar for the following keywords – “human” AND “prey preference” OR “hunt*” OR “diet” OR “subsistence” OR “harvesting” OR “hunting strategies”. These returned both peer-reviewed journal articles and grey literature. In our secondary search, we reviewed the reference lists of each of these papers to attain any additional studies not captured in the primary search. Studies were excluded from consideration when they included insufficient data, or involved nonsubsistence motivation for prey acquisition such as trophy hunting. Insufficient data were classified as cumulative abundance and kill numbers less than 20, with only 1 or 2 species reported as killed at a particular site, or a sample size <3 for particular species collected. Where only kill or abundance data was provided, we contacted authors to solicit supplementary information or referred to other researchers who worked at the same site, around the same time ± 1 year, to obtain the missing information. If an author did not respond, we searched for missing information from the same study area around the same year using Google Scholar and https://journalmap.org (Table 1). From each paper, we recorded site information (site coordinates, site name, and country), biome, and continent. We extracted variables, from these papers, including the prey species killed (scientific names included and referred to in Table 2), hunting strategy (e.g. firearms, gun-traps, snares, bow-and-arrow, regardless of hunting legalities), degree of prey threat to 3 hunter-gatherers based on morphological defense traits or large body size, prey population abundance or density (actual or relative) of those species, reported prey numbers killed, and prey body mass (kg). In cases where body mass was not reported, we used the lower end of values presented in Wilson & Mittermeier (2009), and multiplied mean adult prey body mass by ¾ to account for young, juvenile, sub-adult, and sexually dimorphic prey consumed (Jooste et al., 2013). Prey threat was assigned to a scale of 0-2 with small or slow moving prey scored as 0; mid-sized species armed with some defense trait such as horns/antlers/tusks as moderate threat as 1; and megaherbivores, venomous reptiles, or large carnivores as 2 (Table 2) based on Hayward (2006) using Estes (1991). Using the variables prey population abundance and prey species killed, we calculated the proportional abundance (p) and kills (r) for each species within the prey community at each site and then determined the Jacobs’ selectivity index value for each species at each site. The Jacobs’ index equation is D = (r p)/(r + p 2rp) and results in a score ranging from 1 (total avoidance) to +1 (maximum preference). Jacobs’ index diminishes the bias of rarer species by actively accounting for species rarity in relation to the total prey population at a given site and considering the heterogeneity of the confidence intervals (Jacobs, 1974). This metric also takes into consideration some of the other techniques, such as the forage ratio and Ivlev’s electivity index (Ivlev, 1961), addressing the overstated accuracies in results presented, and is preferred in determining the prey preferences of large carnivores (Hayward et al., 2017). We quantified whether each prey species was significantly preferred or avoided with t-tests of the Jacobs’ index values against zero (no preference or avoidance) where data were normally distributed, or a binomial (sign) test where they were not normally distributed. We also tested for preferred and accessible prey body mass (kg) ranges using breakpoints in segmented models in the segmented package of R (Muggeo, 2015) and evaluated preferences between continents using t-tests of the Jacobs’ index values (D) on either side of the breakpoints (Clements et al., 2014). The line between breakpoints indicated the relationship of body mass (kg) influencing preference, with the steepest line showing the preferred range of prey body mass (Clements et al., 2014). We subsequently tested the degree of preference (D) of species either side of each breakpoint with a t-test. We also excluded the outlying largest megaherbivores from the dataset to test whether modern human hunter-gatherers exhibit linear increases in preference with increasing prey body mass, as exhibited by other apex carnivores (Hayward & Kerley, 2005). To determine the ideal prey body mass, we calculated the ratio of the body mass of humans (46.5 kg = 0.75 × 62 kg for adult women; 4 Wadpole et al., 2012) to the body mass of their significantly preferred prey species (Hayward et al., 2012). To determine the factors that affected modern hunter prey preferences, we used a linear model based on the global equation: Jacobs’ Index preference value (D) ~ Body mass (kg) + Biome + Kill method + Continent + Threat + Prey arboreality [terrestrial (T) or arboreal (A)]. These were variables, extracted from the literature, determined by the selection process under optimal foraging theory: prey density, prey location within the environment, the type of biome prey were found, prey body mass, and tools used to hunt prey. We used the mean Jacobs’ index value of species recorded from 3 or more sites in these models, and hence do not believe there are pseudoreplication issues with these data. We ran similar models (linear and segmented) using broader taxonomic groupings — both family and order — as the dependent variable, to gain a broader picture of the taxa targeted and their influence on preferences. We used maximum likelihood methods to select the most supported models using Akaike’s Information Criterion (Burnham and Anderson, 1998) and considered those with a AIC value < 2 to be strongly supported (Akaike, 1974). We examined the most-supported models for uninformative parameters (Leroux, 2019). The sum of the AIC weights (Table 3) determined the importance of each variable and the relationship between the main factors and hunter-gatherer prey preferences. We performed all analyses in R statistical software 1.42.1 (Development Team, 2013) using the MuMIn (Barton, 2018) and tidyverse packages (Wickham, 2017). 3 Results We compiled data from a total of 161 sites from 85 studies (Fig. 1; Table S1), describing a total of 504 terrestrial vertebrate prey species, including 372 mammals, 107 birds and 25 reptiles (ranging from 0.002 to 2495.3 kg) hunted by humans. We estimated Jacobs’ selectivity index values (D) for 2,243 species/spatiotemporal records representing 504 species from 42 vertebrate orders based on a cumulative number of 806,443 killed individuals (median kills per study = 296). Overall, 39% of our data came from Africa, 34% from South 5 America, 19% from Asia, 5% from North America, and 3% from Oceania. These data were collected from tropical (79%), temperate (19%), and boreal (2%) biomes. Human hunter-gatherers significantly preferred species ranging in body mass from 17.4 to 535.0 kg with a mean ± SE of 128.5 kg ± 29.0 kg (Fig. 2a) such as sable antelope, Cape bushbuck, waterbuck, giant anteater, lowland tapir, bohor reedbuck, Peter’s duiker, greater kudu, white-lipped peccary, collared peccary, and common eland (scientific names and full data in Table 2). The ratio of preferred prey to mean human body mass (46.5 kg) was 2.76:1. Conversely, significantly avoided species were those whose body mass ranged from 0.4 to 56.0 kg ( = 13.7 ± 2.4 kg; Table 2) including dogs, suni, Bornean orang-utan, goldenhanded tamarin, saddle-back tamarin, and spiny rat. The significantly preferred vertebrate families were Tayassuidae, Tapiridae, and Suidae. The significantly avoided families (from most to least avoided) were Odontophoridae, Megalonychidae, Psittacidae, Bucerotidae, Timaliidae, Elephantidae, Hominidae, Tinamidae, Psophiidae, Didelphidae, Pitheciidae, Sciuridae, Aotidae, Cebidae, Cracidae, Cercopithecidae, and Equidae (Table S2). The only taxonomic order that was significantly preferred was the Artiodactyla. Six avian orders were significantly avoided: Coraciiformes, Psittaciformes, Passeriformes, Tinamiformes, Gruiformes, and Galliformes. Five mammalian orders were also significantly avoided: Proboscidea, Marsupialia, Primates, Carnivora, and Rodentia (Table S3). Hunter-gatherer prey preferences increased linearly with prey body mass when megaherbivores — African elephant, hippopotamus, and giraffe — were excluded, although the predictive ability was low (r2 = 0.104, n = 168, p < 0.001; Fig. 2b). The global segmented model for all study sites revealed only one breakpoint at 2.5 kg, which corresponds to a threshold represented by kinkajou, an arboreal procyonid, or larger (Fig. 3a). The 52 prey species weighing less than 2.5 kg were significantly avoided (t = -9.187 d.f. = 51, p <0.001), whereas the 126 species larger than 2.5 kg were killed in accordance with their availability within prey communities (t = -1.318, d.f. = 125, p = 0.189). Segmented models for Asia and South America revealed that hunter-gatherers preferentially pursued prey smaller than African hunter-gatherers (Fig. 3). African hunter-gatherers pursued species larger than steenbok (11 kg) according to their availability, and avoided smaller species (t = 6 0.16, d.f. = 40, p = 0.87; Fig. 3b). Asian hunter-gatherers hunted species larger than a banded leaf monkey (6.1 kg) according to their availability (t = -1.92, d.f. = 12, p = 0.08), and significantly avoided smaller species (t = -2.49, d.f. = 16, p = 0.02; Fig. 3c). South American hunter-gatherers killed smaller-bodied species such as razor-billed curassow (2.9 kg) and larger in accordance with their availability (t = 0.72, d.f. = 30, p = 0.48), but significantly avoided species smaller than 2.9 kgs (t = -11.31, d.f. = 30, p < 0.001; Fig. 3d). Spearman's test revealed a strong positive correlation between prey body mass and threat variables ( = 0.760, d.f. = 846, p < 0.001), which would suggest that the larger the prey, the more damage inflicted on the predator. Since these two variables are correlated, we ran separate linear models that determining that threat (w = 0.98) was slightly more important than body mass (w = 0.78) in prey selection. Prey that posed a threat category of 1 and 2 were more preferred than low threat (category 0) prey, which were avoided (Fig. 4). The most important variable that drove prey preferences in hunter-gatherers was a prey species' degree of arboreality or terrestriality (sum of Akaike's weight w = 1.00). Hunter-gatherers were most likely to avoid arboreal prey (t = 6.63, d.f. = 55, p < 0.001). Kill method was found to be an uninformative variable within the linear model (Table 3). 4 Discussion Historically, human hunters are thought to have targeted larger herbivores, and this purported prey preference has been a prevalent concept associated with hominid evolution (Redford, 1992) and subsequent conquest of new land masses and impact on previously naïve faunas (Martin, 1984). Our results quantify this with >799,000 kill records in 85 studies, showing that subsistence hunters over the past 36 years definitively prefer larger, more threatening herbivores, largely within the order Artiodactyla. This observation is reinforced by the stark contrast between the most significantly preferred species, that have a mean body mass of 128 ± 29 kg (the ideal prey body mass of modern hunter-gatherers), and the six avoided species with a mean body mass of 13.7 ± 2.4 kg. When exceptionally large, extant African megaherbivores are excluded (Fig. 2b), the right-skewed distribution of human prey preferences against prey body mass reveals that humans are apex predators, such as lions (Panthera leo) and tigers (Panthera tigris), increasingly preferring larger prey (Hayward et al., 2012; Hayward and Kerley, 2005). The preference for artiodactyls reinforces the view that humans have become major competitors of large carnivores (Treves and Naughton- 7 Treves, 1999). Optimal foraging theory suggests that preference is based on the energetic cost and risk of prey acquisition against the benefit of prey consumption, which coincides with the preferred artiodactyls, such as peccaries and antelopes. Our taxonomic order and family groupings indicate a clear, positive preference for ungulates (artiodactyls and perissodactyls) above a minimum size threshold. Large herbivores have long been hypothesized as preferred target prey for modern human hunter-gatherers (Reyna-Hurtado & Tanner, 2007), and our global review quantifies this for individual species (sable antelope, Cape bushbuck, waterbuck, lowland tapir, bohor reedbuck, Peter’s duiker, greater kudu, and common eland), ranging in body mass from 17.4 kg to 535 kg. This result, surprisingly, reveals no clear, distinct body mass preference among modern human hunter-gatherers (Fig. 3) in contrast to other apex predators such as lions and tigers, which prefer prey 190-550 kg (Hayward & Kerley, 2005) and 60-250 kg (Hayward et al., 2012) respectively. This is likely because modern humans are adept at capturing all available prey (Fig. 3), distinguishing the risks between apex carnivores and humans for prey species, where all but the smallest species yield energetic benefits to humans when successfully hunted with non-specific methods, such as snares and traps (Lupo et al.,2020; Broughton et al., 2011). Modern human hunter-gatherer prey preferences are impacted by the declines in the availability of desirable vertebrate prey populations worldwide (Díaz et al., 2019), such that they are now using technological advances in hunting methods to capture any available prey above a minimum selective threshold (2.5 kg globally; Fig. 3). Widespread depletion of large-bodied prey in Asia and South America is likely to drive the need to hunt any species that can be captured, irrespective of its optimality (Jerozolimski & Peres, 2003), whereas truly large-bodied prey species remain abundant only in parts of Africa and North America (Lindsey et al., 2017). Predator-prey arms races mean large herbivores have often been selected for increased body mass, weapons and/or tough skin (Hopcraft et al., 2012). We suggest that modern huntergatherer prey preferences are most likely driven by species that can satisfy optimal foraging theory requirements, implementing multiple technologies (notably unselective snares used in conjunction with other hunting methods) to kill and consume them, especially in persistently overhunted areas across continents and biomes (Milner-Gulland et al., 2003). This diversity of hunting methods to capture all available prey may mean that modern human hunters are no 8 longer constrained by morphology in what they can capture – instead utilizing technology to capture almost any species. A lack of desirable prey species available in hunting catchments may lead to greater amounts of energy expenditure associated with longer travel distances from households and camp sites (Wood and Gilby, 2019). Even after incurring energy expenditure from greater travel distances, central-place hunters may encounter prey with reduced body mass (Smith et al., 2018) and thereby reduced nutrition, as well as facing the overall loss of preferred game species (Maisels et al., 2001). Reducing the viability of modern hunter-gatherer livelihoods may lead to the erosion, and in some instances, extinction of ethno-cultural practices as these people are forced into other lifestyles. These alternative lifestyles often include integration into agricultural societies or urbanization. This, in turn, incentivizes land use change that ultimately depletes natural habitats and displaces prey populations, pushing them further away from their natural ranges or into fragmented habitats. Such scenarios may also invoke apparent competition dynamics that are deleterious to viability of prey species. That is, as hunter-gatherers are increasingly subsidized by domestic food resources, population densities may increase resulting in greater hunter pressure and depletion of natural prey species, even if per capita human consumption is lower. Indeed, recreational hunting can also take place as hunters move in from urban areas to undertake cultural hunting (Hayward, 2009). Although modern hunter-gatherers often prefer wild meat compared to domestic livestock (Bennett and Rao, 2002), the switch between the two may not be easy, despite being necessary for their survival when facing chronic wildlife declines. Our study illustrates the important ecological roles humans play in predator-prey dynamics as central-place foraging apex predators with the ability to optimally forage upon all prey larger than 2.5 kg. Using prey preference information will enable us to predict the functional roles of both modern and extinct hunter-gatherer societies within the ecosystems we inhabit. This analysis thus provides novel insights into how the management of available wildlife resources can benefit modern hunter-gatherer livelihoods by ensuring that preferred prey resources can persist in the environment. Promoting appropriate game management efforts to increase or maintain the availability of wild prey populations has the potential to ensure the continuity of traditional lifestyles. 9 Conflict of Interests To the best of our knowledge, there are no conflicting interests. Informed Consent This research did not have any active, live participants, animals or human, therefore no consent was required. Funding The University of Newcastle - Australia is recognized and appreciated for scholarship funding to CB. Acknowledgments Offering sincere appreciation to Dr. Hanlie Winterbach for aerial data in Botswana and Elsabe van der Westhuizen for survey data in Zimbabwe. Thank you to two anonymous reviewers whose valuable comments improved this manuscript. Also, to Stephen Bugir, Taras Bugir, Robert Scanlon, and Rose Upton for reviewing an earlier version of this manuscript. References Akaike, H. (1974). A new look at the statistical model identification. 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Traditional management of hunting in a Xavante community in central Brazil: the search for sustainability. In J. G. Robinson and E. L. Bennett (Eds.). Hunting for subsistence in tropical forests (pp. 375–394). Columbia University Press, New York. Leroux, S. J. (2019). On the prevalence of uninformative parameters in statistical models applying model selection in applied ecology. PloS one, 14(2), e0206711. Liebenberg, L. (2006). Persistence hunting by modern hunter-gatherers. Current Anthropology, 47(6), 1017-1026. Lindsey, P. A., Romanach, S., Matema, S., Matema, C., Mupamhadzi, I., & Muvengwi, J. (2011). Dynamics and underlying causes of illegal bushmeat trade in Zimbabwe. Oryx, 45(1), 84-95. Lindsey, P. A., Chapron, G., Petracca, L.S., Burnham, D., Hayward, M.W., Henschel, P., Hinks, A.E., Garnett, S.T., Macdonald, D.W., Macdonald, E.A. and Ripple, W.J. (2017). Relative efforts of countries to conserve world’s megafauna. Global Ecology and Conservation, 10, 243-252. Lowell, R. E. (2014). Unit 3 black bear management report. Chapter 6, pages 6-1 through 626. In P. Harper and L. A. McCarthy (Eds.). Black bear management report of survey 12 and inventory activities 1 July 2010–30 June 2013. Alaska Department of Fish and Game, Species Management Report ADF&G/DWC/SMR-2014-5, Juneau. Lupo, K. D., Schmitt, D. N., & Madsen, D. B. (2020). Size matters only sometimes: the energyrisk trade-offs of Holocene prey acquisition in the Bonneville basin, western USA. Archaeological and Anthropological Sciences, 12(8), 1-18. Maisels, F., Keming, E., Kemei, M., & Toh, C. (2001). The extirpation of large mammals and implications for montane forest conservation: the case of the Kilum-Ijim Forest, Northwest Province, Cameroon. Oryx, 35(4), 322-331. Milner-Gulland, E. J., & Bennett, E. L. (2003). Wild meat: the bigger picture. Trends in Ecology & Evolution, 18(7), 351-357. Muggeo, V. (2015). Regression models with breakpoints/changepoints estimation. 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J. 2018. Moose management report and plan, Game Management Unit 12: Report period 1 July 2010–30 June 2015, and plan period 1 July 2015–30 June 2020. Alaska Department of Fish and Game, Species Management Report and Plan ADF&G/DWC/SMR&P-2018-17, Juneau. White, K. S., Crupi, A., Scott, R. & Seppi, B. E. (2012). Mountain goat movement patterns and population monitoring in the Haines-Skagway area, Region 1. Alaska Department of Fish and Game, Division of Wildlife Conservation. White, K. S., Mooney, P. W. & Bovee, K. (2010). Mountain Goat Movement Patterns and Population Monitoring on Baranof Island. Alaska Department of Fish and Game, Division of Wildlife Conservation. 13 Wickham, H. (2017). tidyverse: Easily Install and Load the'Tidyverse'. R package version 1.2. 1. R Core Team: Vienna, Austria. Wilkie, D. S., Curran, B., Tshombe, R., & Morelli, G. A. (1998). Managing bushmeat hunting in Okapi wildlife reserve, Democratic Republic of Congo. ORYX, 32(2), 131-144. Williams-Guillen, K., Camilo, D. G. J. P.-G., & Bauman, K. (2006). Abundancia de animales cazados y características de cacería en el territorio de Kipla SaitTasbaika, reserva de biosfera Bosawás. Wani 46, 37-61. Wilson, D. E., & Mittermeier, R. A. (2009). Handbook of the Mammals of the World. Lynx Edicions, Barcelona. Wood, B., & Gilby, I. (2019). From Pan to man the hunter: hunting and meat sharing by chimpanzees, humans, and our common ancestor. In Chimpanzees and Human Evolution. UCLA. 14 Figures and Tables Fig. 1. Location of 161 sites for which data were available for analysis in this study. A majority of these sites occurred along the tropical forest biome (a sample size of 151 species). Savannah and boreal forest sites accounted for 36 and 4 species used in the analysis, respectively. Colours in the figure represent biome differences according to the WWF. 15 Figure 2. a) Scatterplot of Jacobs’ prey selectivity index against log10 prey body mass with Lowess smoothed curve. Prey body mass importance weight was 0.94 from the Akaike’s Informative Criterion. We derived 0.39 as the logarithmic mass value from the segmented model, whose breakpoint was 40.98. This value corresponds to a prey preference mass of 2.5 kg and larger. Any species lower than this threshold body mass are generally avoided. b) Prey preference relationship with prey body size, excluding the three largest terrestrial herbivores — giraffe, hippopotamus, and African elephant. The right skewed positioning of the line is comparable to large carnivores such as lions, indicating that human hunter-gatherers are apex predators. Linear regression equation and R2-value are shown in bold letters. 16 Figure 3. Segmented models exhibiting the species mass rank (lowest to highest weighed species hunted) against the cumulative Jacobs’ Index (D). Breakpoints are in each regression line to show where the preferred prey mass starts. a) The global preference line is at 2.5 kg or about the mass of a kinkajou. b) African preferred prey are species above 11 kg (steenbok). c) Asian preferred prey items are above 6.1 kg (Sunda pangolin). d) South American prey items above 2.9 kg were preferred (bearded saki monkey). 17 Figure 4. These graphs represent the most important variables against preference (D). a) Variance in preference of arboreal and terrestrial species. This variable (T.A) was weighted 1.00 important in decisionmaking for preferred prey. There are reasons such as larger prey size, hunter locomotor skills, and more visibility for terrestrial species to account for being the more preferred category. b) The species threat level to hunters (Threat) was weighted 0.98 importance factor for influencing Jacobs’ Index (D). 18 Table 1. Assessed criteria of study sites and made assumptions for missing variables such as prey abundance, mass data, hunting methods, or exclusion of species. Country Site(s) Assumption Botswana Okavango Delta Aerial census of Botswana- dry season 2012 prey Kalahari density of Struthio camelus and Hippopotamus amphibius. Canada Ontario Anser caerulescens abundance (Cooch et al., 1989). Democratic Ituri Forest Republic of Congo Madagascar Makira Forest Malaysia Mexico Maliau Basin Site B, D, E Campeche Quintana Roo X-Hazil Sur Nicaragua Arang Dak Suma Pipi Paraguay Common names based on IUCN Red List Data. Primates not included because netting was the hunting strategy and nets don't catch arboreal primates. (Redford & Robinson, 1991) Maximum Production Equation was used in Table 1 from which data were extrapolated. Abundance data for all species (Fitzmaurice, 2014 #559) Abundance data- Mazama spp., Tayassu spp., and Tapirus spp. (Reyna-Hurtado & Tanner, 2007) Abundance data for all species (Escamilla et al., 2000) Source (Liebenberg, 2006) (Prevett et al., 1983) (Hart & Hart, 1986) (Wilkie et al., 1998) (Golden, 2009) (Brodie et al., 2015) (Escamilla et al., 2000) (Jorgenson, 1998 #397) Abundance data -Myrmecophaga tridactyla, Dasypus (Koster, 2008) spp., Cebus spp., Nasua nasua, Panthera once, Ateles spp., Cuniculus spp., and Testudines (Williams-Guillen et al., 2006) Abundance data (Hill & Padwe, 2000). (Hill et al., 1997) Mbaracayu Reserve Peru Pacaya- Samiria Abundance & mass averaged for Cebus spp., Ateles (Begazo & Bodmer, spp., and Dasyprocta spp. (Robinson & Redford, 1986) 1998) National (Leeuwenberg & Reserve Robinson, 2000) (Redford & Robinson, 1987) Peru Yavari Miri Mass data (Robinson & Redford, 1986). Abundance (Bodmer et al., 1997) Tahuayo data (Leeuwenberg et al., 2000). Alaska: Abundance data- Anseriformes (Service, 2018 #1141) (White et al., 2010, United 2012) States of Yukon DrainageAlces alces (Wells, 2018), Falcipennis canadensis, America Haine Lagopus lagopus, and Lepus spp. (Carroll & Merizon, Baranof Island 2017), 2 bear species (Lowell, 2014 #1142) Dall sheep (Battle & Stantorf, 2018). Zimbabwe Save Valley Illegal hunting. Snares and dogs as a hunting method. (Lindsey et al., 2011) Conservancy Abundance data from (Dunham, 2016 #1124) for Gonarezhou (Gandiwa et al., 2013) National Park Sylvicapra grimmia, Hippopotamus amphibious, Phacochoerus aethiopicus, and Raphicerus campestris. 19 Table 2. This table shows the data used for the study. Species (including scientific name) hunted, body mass, proportions of abundance and kills, continent, habitat, and threat posed to hunters were collected from 85 studies. p Threat Habitat Continent 0.07 0 -2.13 0.09 0 14 -0.02 0.08 0 11 2.19 0.18 0 0.18 0.13 0 5 0.06 0.002 0 3.7 ± 1.8 4 0.63 0.53 2 South America South America South America South America South America South America Asia 0 ± 0.4 2.3 ± 2.4 7 0.02 <0.001 1 0.03 ± 0.23 2.7 ± 1.4 2.6 7 1.00 0.89 0 195 0.38 ± 0.13 0 ± 0.8 2.3 ± 1.1 8 0.29 0.05 1 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah Africa Hippotragus niger 172.3 0.54 ± 0.1 0 ± 0.6 6.2 ± 0.7 13 <0.001 1 Savannah Africa Armadillo, Giant Priodontes maximus 36.7 0.49 ± 0.19 0 ± 0.05 0.7 ± 0.4 6 0.69 0.16 0 Armadillo, Greater long-nosed Armadillo, NineBanded Babbler, ShortTailed Baboon, Yellow Dasypus kappleri 3.5 0.65 ± 0.12 0 ± 0.6 5 ± 0.6 3 0.25 0.06 0 Dasypus novemcinctus Trichastoma malaccense Papio cynocephalus 2.9 -0.14 ± 0.13 0 ± 1.8 9.9 18 0.41 0 0.002 -0.62 ± 0.38 3.4 ± 2.2 10 3 1.00 0.24 0 South America South America South America Asia 17.5 0.3 ± 0.2 0 ± 2.2 2.9 ± 1.2 7 0.45 0.29 1 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah Species Scientific Name Body Mass (kg) Jacobs Index (D) Abundance (p) Kills (r) n Sign test Acouchi, Green Myoprocta pratti 1.6 -0.14 ± 0.04 14.3 ± 3.3 3 0.25 Acouchi, Red Myoprocta acouchy 1 -0.36 ± 0.19 0 ± 0.3 11.3 ± 2.3 3.2 ± 1 12 Agouti, Black Dasyprocta fuliginosa Dasyprocta punctata 4.6 -0.3 ± 0.16 6.6 ± 1.3 7.1 ± 1.6 4 0.2 ± 0.14 8.9 ± 1.9 Dasyprocta leporina 3.9 0.41 ± 0.23 0 ± 1.7 9 Amazona farinosa 0.7 -0.84 ± 0.12 8.3 ± 1.7 12.2 ± 4.6 20.3 ± 8.4 1 ± 0.4 232 0.29 ± 0.41 0.7 ± 0.3 27.4 0.74 ± 0.06 Antelope, Pygmy Bubalus depressicornis Myrmecophaga tridactyla Neotragus batsei 3.6 Antelope, Roan Hippotragus equinus Antelope, Sable Agouti, Central American Agouti, Redrumped Amazon, Southern Mealy Anoa Anteater, Giant 20 t-test 2.43 -0.85 South America Africa Africa Badger, Honey Mellivora capensis 9 -0.65 ± 0.25 0 ± 0.02 0.1 ± 0.7 3 1.00 0.21 1 Savannah Africa Barbet Capitonidae 0.1 -0.5 ± 0.5 4.8 ± 1.6 20 3 1.00 0.42 0 Asia Bat, Insular Fruit Pteropus tonganus 0.6 0.15 ± 0.35 14 ± 6.2 3 1.00 0.71 0 Bear, Malayan Sun Helarctos malayanus 53 -0.35 ± 0.25 4.7 ± 2.7 16.3 ± 8.1 4.7 ± 1.1 6 0.69 0.21 2 Binturong Arctictis binturong 20 0.26 ± 0.3 0 ± 0.6 3.9 ± 5.6 3 1.00 0.58 0 Buffalo, African Forest Buffalo, Cape Syncerus caffer nanus Syncerus caffer 237.5 -0.84 ± 0.16 1 ± 0.6 4 0.13 0.01 2 335.4 -0.29 ± 0.11 0 ± 4.5 0.03 2 Bulbul Pycnonotidae 0.04 -0.64 ± 0.23 9 ± 0.6 0.04 ± 1.8 10.5 ± 0.4 3.9 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah 3 0.11 0 14 -1.27 0.25 Bushbuck, Cape Tragelaphus scriptus 43.4 0.5 ± 0.16 0 ± 0.5 3.9 ± 0.6 13 2.86 0.01 2 Capuchin, Brown Cebus apella 3.2 -0.11 ± 0.13 16.3 ± 2.8 25 -0.88 0.39 0 Capuchin, Wedgecapped Capuchin, Whitefronted Capybara Cebus olivaceus 4.5 -0.15 ± 0.3 0 ± 3.3 14.3 ± 3.7 20 ± 0.9 5 0.75 0 Cebus albifrons 4.2 -0.17 ± 0.14 3.5 ± 0.4 2.8 ± 2.9 14 0.23 0 34.9 -0.07 ± 0.16 0 ± 7.6 1.7 ± 0.8 4 1.00 0.84 0 Caribou Hydrochaeris hydrochaeris Rangifer tarandus 150 -0.17 ± 0.59 39.6 ± 30.5 3 1.00 0.81 1 Cat, Leopard Felis bengalensis 4.7 0.1 ± 0.33 0 ± 0.4 21.5 ± 3.1 3.4 ± 0.1 4 1.00 0.79 1 Chachalaca, Little Ortalis motmot 0.5 -0.53 ± 0.35 0 ± 5.8 1.1 ± 0.8 4 0.63 0.34 0 Chachalaca, Plain Ortalis vetula 0.4 -0.3 ± 0.25 0 ± 8.1 3 1.00 0.46 0 Chevrotain, Lesser Malay Chevrotain, Water Tragulus kanchil 3.6 0.1 ± 0.21 0±1 11.9 ± 0.5 6.6 ± 8.8 8 1.00 0.69 0 Hyemoschus aquaticus 10.9 0.13 ± 0.24 0 ± 0.7 3.4 ± 8.5 6 1.00 0.65 0 21 1.00 0.32 Tropical Forest Savannah Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tundra Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Oceania Asia Asia Africa Africa Asia Africa South America South America South America South America North America Asia South America North America Asia Africa Chimpanzee Pan troglodytes 38.7 -0.57 ± 0.34 0.8 ± 0.3 0.5 ± 5.7 4 0.63 0.19 2 Civet, African Palm Nandinia binotata 2.8 0.38 ± 0.24 0 ± 0.16 1.7 5 1.00 0.22 0 Civet, Banded Palm Hemigalus derbyanus 1.9 -0.02 ± 0.35 4.3 ± 1.2 6.1 ± 2 4 1.00 0.95 0 Civet, Malay Viverra tangalunga 11 -0.31 ± 0.2 13.8 ± 3.8 6.9 ± 0.7 3 0.25 0.25 0 Civet, Masked Palm Paguma larvata 4.5 -0.13 ± 0.2 2.6 ± 0.4 7.5 ± 0.9 12 0.53 0 Civet, Small Indian Viverricula indica 3 0.94 ± 0.03 0±1 42 ± 0.2 3 0.001 0 Coati, South American Colobus, Black Nasua nasua 3.3 -0.26 ± 0.11 0 ± 1.5 6.2 ± 1.1 20 0.08 0 Colobus satanas 13 -0.11 ± 0.14 4.7 ± 1 4.6 ± 0.7 5 1.00 0.47 1 Colobus, Guereza Colobus guereza 16.5 -0.39± 0.53 0 ± 2.7 1.4 ± 1.9 3 1.00 0.59 1 Colobus, Pennant’s Procolabus pennantii 7.9 -0.37 ± 0.24 0 ± 4.9 6 0.69 0.21 1 Curassow, Black Crax alector 3.3 -0.14 ± 0.27 0±1 11.1 ± 2.9 7.2 ± 9.6 6 1.00 0.65 0 Curassow, Great Crax rubra 3.2 0.27 ± 0.28 0 ± 1.1 Curassow, Nocturnal Curassow, Razorbilled Cuscus, Bear Nothocrax urumutum 2.2 -0.63 ± 0.25 Mitu tuberosa 2.9 Ailurops ursinus Cuscus, North-east -0.65 0.25 -1.87 3 1.00 0.53 0 0 ± 0.21 5.9 ± 16.1 0.7 ± 5.8 5 0.22 0.08 0 0.28 ± 0.17 0 ± 1.4 5 ± 4.4 5 1.00 0.23 0 3.5 0.56 ± 0.25 6.1 ± 2.3 3 0.25 0.15 0 Phalanger gymnotis 3.1 -0.28 ± 0.51 10.7 ± 0.2 22.5 ± 1.7 15 ± 3.5 3 1.00 0.63 0 Deer, Barking Muntiacus muntjak 15.75 -0.36 ± 0.17 13.6 ± 4.2 12 -1.86 0.06 1 Deer, Grey Brocket Mazama gouazoubira 17.22 0.14 ± 0.13 0 ± 3.1 13.4 ± 1.7 4.6 23 0.96 0.35 1 Deer, Red Brocket Mazama americana 28.09 -0.03 ± 0.11 6.8 ± 1.5 9.5 ± 0.5 39 -0.24 0.81 1 22 Tropical Forest Savannah Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Africa Africa Asia Asia Asia Africa South America Africa Africa Africa South America North America South America South America Asia Oceania Asia South America South America Deer, Sambar Cervus unicolor 134 -0.18 ± 0.23 0 ± 2.1 6.1 ± 0.1 5 Deer, White-tailed 46.61 0.06 ± 0.28 0 ± 1.3 3.6 ± 4.4 6 Dik-dik, Kirk's Odocoileus virginianus Madoqua kirkii 5.6 -0.23 ± 0.24 0 ± 0.02 2 ± 0.6 Dog Canis familiaris 20 -1 ± 0 0 ± 1.5 Dove, Emerald Chalcophaps indica 0.125 -0.89 ± 0.11 Drill Mandrillus leucophaeus Cephalophus dorsalis 13.23 Cephalophus nigrifrons Cephalophus monticola Cephalophus ogilbyi Duiker, Bay Duiker, Blackfronted Duiker, Blue Duiker, Ogilby's Duiker, Peter's 0.51 1 1.00 0.84 1 3 1.00 0.62 0 0 ± 0.2 3 0.25 <0.001 1 1.9 ± 0.3 0.8 ± 0.3 4 0.13 0.004 0 0.23 ± 0.19 0 ± 0.3 3.7 ± 2 5 1.00 0.37 2 17.63 0.09 ± 0.13 4.4 ± 1.6 5.5 13 0.47 1 13.68 -0.45 ± 0.17 0 ± 0.3 0.8 ± 1.6 8 0.04 1 6.11 -0.01 ± 0.12 15.9 ± 3.4 22 0.88 1 18.5 0.44 ± 0.20 2.7 ± 0.8 17.36 0.39 ± 0.12 0 ± 3.1 12.39 0.07 ± 0.17 0±2 16.6 ± 1.5 13.8 ± 0.1 13.9 ± 0.5 9.7 ± 0.2 14 16.39 -0.39 ± 0.16 0 ± 0.5 1.8 ± 1.8 9 63.65 -0.20 ± 0.21 1.1 ± 0.4 0.8 ± 0.2 13 0.26 ± 0.1 0 ± 0.7 3.2 ± 2.5 -0.89 ± 0.03 0 ± 3.1 -0.55 ± 0.32 -3.77 0.29 -0.16 4 0.63 0.12 1 9 0.04 0.01 1 0.75 1 0.05 1 0.74 0.36 1 12 -5.93 0.04 2.1 ± 1 12 -0.21 0 ± 12.8 3.4 ± 2 3 Duiker, Whitebellied Duiker, Yellowbacked Eland, Common Cephalophus callipygus Cephalophus natalensis Cephalophus leucogaster Cephalophus sylvicultor Taurotragus oryx Elephant, African Loxodonta africana Fanaloka, Spotted Fossa fossana 535.2 6 2495. 3 1.6 Flowerpecker, Scarlet-backed Fossa Dicaeum cruentatum 0.01 -0.18 ± 0.47 8.1 ± 4.6 9.5 Cryptoprocta ferox 7.7 0.13 ± 0.28 17.5 ± 8.9 13.1 ± 1.1 Duiker, Red -0.73 23 4.58 0.18 Tropical Forest Tropical Forest Savannah Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah Asia North America Africa Africa Asia Africa Africa Africa Africa Africa Africa Africa 2 Tropical Forest Tropical Forest Savannah Africa Africa <0.001 2 Savannah Africa 1.00 0.27 0 Africa 3 1.00 0.73 0 5 1.00 0.67 1 Tropical Forest Tropical Forest Tropical Forest Africa Asia Africa Gazelle, Grant's Gazella granti 40 0.17 ± 0.05 0±1 5 ± 1.2 4 0.63 0.13 1 Savannah Africa Gazelle, Thomson's Gazella thomsoni 15 -0.05 ± 0.22 0±2 4 0.63 0.89 1 Savannah Africa Genet Genetta servalina 1.65 0.22 ± 0.02 0 ± 0.08 18.5 ± 3.8 0.4 ± 1 3 0.25 0.03 1 Africa Giraffe 906.1 -0.12 ± 0.25 3.7 ± 1.7 7 1.00 0.63 2 Goose, Canada Giraffa camelopardalis Branta canadensis Tropical Forest Savannah 4.7 0.63 ± 0.16 14 ± 9 3 0.25 0.06 0 Tundra Gorilla, Western Gorilla gorilla 78.1 -0.72 ± 0.22 0.6 ± 0.4 Guan, Marail Penelope marail 1.7 -0.68 ± 0.1 Guan, Spix's Penelope jacquacu 0.8 Guenon, Crested Mona Guenon, Mona Cercopithecus pogonias Cercopithecus mona Guenon, Moustached Guenon, Preuss’ Cercopithecus cephus Cercopithecus preussi Cercopithecus erythrotis Cercopithecus nictitans Alcelaphus buselaphus Hippopotamus amphibius Potamochoerus porcus Aceros cassidix Africa Aepyceros melampus Guenon, Red-eared Guenon, Whitenosed Hartebeest, Red Hippopotamus Hog, Red River Hornbill, Redknobbed Impala 5 0.38 0.03 2 0 ± 1.9 3.8 ± 14.2 34.1 ± 0.3 0.04 ± 1.2 2.8 ± 1.8 7 0.13 0.002 0 -0.32 ± 0.23 8.7 ± 3.1 7.5 ± 1.6 8 0.73 0.21 0 2 -0.72 ±0.09 -0.08 ±0.02 8 0.01 <0.001 1 5.7 -0.08 ± 0.2 10.2 ± 4 6 0.69 0.73 1 5.1 -0.59 ± 0.19 14.9 ± 5 -0.02 ±0.01 13.3 ± 4.1 4.5 ± 2.7 5 0.06 0.03 1 8.6 -0.26 ± 0.11 1.5 ± 0.5 1.1 ± 1.8 4 0.11 1 3.8 -0.16 ± 0.14 13 ± 4.8 10 ± 2.3 7 0.29 1 7.8 -0.29 ± 0.16 20.8 ± 5.2 11 1.44 0.10 1 94.5 -0.03 ± 0.16 9.2 ± 4.5 14.5 ± 2.3 4.7 ± 0.5 12 2.30 0.84 1 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah 1050 -0.07± 0.12 0±1 2.3± 0.7 8 0.65 2 Savannah Africa 60.7 0.3 ± 0.12 0 ± 0.4 2.3 ± 1.9 14 0.09 1 Savannah Africa 3.1 -0.64 ± 0.36 15.9 ± 11.1 6.3 ± 0.9 3 0.21 0 Asia 39.7 -0.002 ± 0.12 0 ± 3.9 14.1 ± 5.5 14 0.99 1 Tropical Forest Savannah 24 -2.34 0.45 0.73 1.84 1.00 -1.90 Africa North America Africa South America South America Africa Africa Africa Africa Africa Africa Africa Jaguar Panthera onca 57.1 -0.02 ± 0.26 0±1 0.95 ± 1 8 Kinkajou Potos flavus 2.5 -0.46 ± 0.17 0 ± 0.7 1.3 ± 6.2 11 Klipspringer Oreotragus oreotragus Tragelaphus strepsiceros Tragelaphus imberbis Macaca nigra 12 0.43 ± 0.16 0 ± 0.01 1.3 ± 1.7 3 150.4 0.39 ± 0.15 0 ± 0.7 5.3 ± 0.8 13 70 -0.19 ± 0.33 0 ± 0.1 5 4.1 -0.24 ± 0.41 65.2 ± 10.9 2.15 ± 0.5 51 Macaca fascicularis 2 -0.59 ± 0.41 4.3 ± 1.2 Macaca nemestrina 13.6 -0.4 ± 0.2 Mangabey, GreyCheeked Mongoose, Longnosed Monkey, Banded Leaf Monkey, Black Spider Monkey, Common Woolly Monkey, Dusky Titi Cercopithecus albigena Herpestes naso 3.4 Monkey, Guyanan Red Howler Monkey, Red-faced Spider Monkey, Spix's Night Monkey, Squirrel Kudu, Greater Kudu, Lesser Macaque, Crested Black Macaque, Longtailed Macaque, Pig-tailed 1 Tropical Forest Tropical Forest Savannah South America South America Africa 0.03 2 Savannah Africa 1.00 0.67 1 Savannah Africa 3 1.00 0.62 1 Asia 10 ± 0.2 3 1.00 0.28 0 7.1 ± 2.2 6.2 ± 2.2 7 0.13 0.12 1 -0.43 ± 0.34 5.8 ± 2.6 2.3 ± 2.9 3 1.00 0.33 1 3.6 -0.26 ± 0.23 0 ± 0.3 2.1 ± 3.2 3 1.00 0.51 0 3 1.00 0.46 1 9 0.51 0.61 1 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Presbytis melalophos cruciger Ateles paniscus 6.4 0.13 ± 0.15 10.6 ± 4.6 8.2 0.17 ± 0.28 0 ± 1.25 12.5 ± 0.5 9.8 ± 0.5 Lagothrix lagotricha 9.3 0.1 ± 0.14 0 ± 2.2 9.3 ± 1.8 12 Callicebus moloch 1.1 -0.39 ± 0.2 3.7 ± 0.9 2.4 ± 0.8 Alouatta macconnelli 6.8 -0.01 ± 0.27 0 ± 1.3 Ateles paniscus 1 -0.08 ± 0.27 6.3 ± 1.7 8.7 ± 12.9 5.4 ± 1.9 Aotus vociferans 0.9 -0.5 ± 0.17 0 ± 1.4 2 ± 0.4 7 Saimiri sciureus 0.8 -0.55 ± 0.24 0 ± 3.9 1.9 ± 3.7 22 25 1.00 -1.83 0.25 5.23 0.95 2 0.04 0 0.26 0.05 1 6 24.97 -0.04 0.97 0 13 -0.95 0.97 1 10 -2.73 0.02 0 0.22 0 0.88 0 0.13 -0.16 Asia Asia Africa Africa Asia South America South America South America South America South America South America South America Monkey, Venezuelan Red Howler Monkey, Whitebellied spider Monkey, Whitefronted Leaf Ocelot Alouatta seniculus 6.5 -0.02 ± 0.12 6.6 ± 1.6 6.5 ± 0.8 4 0.63 0.54 1 Tropical Forest South America Ateles belzebuth 8.3 -0.28 ± 0.33 0±2 9.2 ± 1 5 0.38 0.15 1 Presbytis frontata 7.4 -0.51 ± 0.29 7.2 ± 2.1 5 1.00 0.28 1 South America Asia Felis pardalis 11.1 -0.41 ± 0.23 0 ± 0.3 10.7 ± 0.3 0.5 ± 0.5 6 0.03 <0.001 1 Opossum, Common 1.3 -0.91 ± 0.06 0 ± 0.8 0.4 ± 0.3 3 0.25 <0.001 0 Orangutan Didelphis marsupialis Pongo pygmaeus 56 -1 ± 0 0.8 ± 0.01 0 3 0.25 0.09 2 Oribi Ourebia ourebi 14 0.71 ± 0.14 0 ± 0.01 2.2 ± 1.2 3 1.00 0.99 1 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah Oxen, Musk Ovibos moschatus 295 0.01 ± 0.45 15.2± 14.6 10.1± 0.7 25 0.57 2 Tundra Paca, Lowland Cuniculus paca 7.1 0.07 ± 0.1 0 ± 2.2 4 0.63 0.33 0 Pangolin, African White-Bellied Pangolin, Sunda Manis tricuspis 1.8 -0.32 ± 0.23 0 ± 0.5 11.5 ± 0.9 1.6 ± 0.9 4 0.63 0.83 0 Manis javanica 6.2 0.09 ± 0.29 0 ± 0.13 3.9 ± 0.4 49 3.85 <0.001 0 Peccary, Collared Pecari tajacu 22.7 0.29 ± 0.07 0 ± 1.1 20 3.20 0.005 1 Peccary, Whitelipped Pig, Bearded Tayassu pecari 30.8 0.34 ± 0.1 0 ± 2.8 14.5 ± 0.6 7.6 ± 0.8 Sus barbatus 115.8 -0.06 ± 0.25 12.7 ± 3.3 Pig, Sulawesi Sus celebensis 54 0.4 ± 0.2 12.9 ± 1.9 Pig, Wild Sus scrofa 54.7 -0.07 ± 0.32 0 ± 1.4 Porcupine, African Brush-tailed Porcupine, Longtailed Atherurus africanus 7.9 0.21 ± 0.09 0 ± 2.4 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah Trichys fasciculata 2 0.03 ± 0.47 0 ± 10.7 26 16.5 ± 5.9 35.9 ± 0.7 22.1 ± 1 10.8 ± 0.1 6.2 ± 2 0.58 8 0.73 0.82 1 4 0.63 0.16 1 3 1.00 0.90 1 0.14 1 11 -5.05 3 1.00 0.96 0 5 1.00 0.86 0 Tropical Forest South America South America Asia Africa North America South America Africa Asia South America South America Asia Asia Asia Africa Asia Porcupine, Thickspined Puma Hystrix crassispinis 4 0.65 ± 0.05 0 ± 0.7 5.2 ± 6.8 3 0.25 0.01 0 Felis concolor 61.6 -0.72 ± 0.19 0 ± 0.1 0.3 ± 0.8 3 0.25 0.13 2 Rabbit, Brazilian Forest Rat, Forest Giant Pouched Rat, Giant Pouched Sylvilagus brasiliensis Cricetomys emini 1 -0.07 ± 0.34 0 ± 0.3 0.5 ± 0.5 4 0.63 0.77 0 2 -0.19 ± 0.16 0 ± 4.1 7 1.00 0.45 0 1.2 0.43 ± 0.21 8.8 ± 5.1 16.3 ± 3.5 16.3 4 0.63 0.13 0 5.6 0.01 ± 0.36 2.6 ± 1.9 7.5 ± 0.5 6 1.00 0.99 0 0.13 <0.001 0 0.05 1 0.13 0 0.001 0 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah South America Africa Tropical Forest Tropical Forest Tropical Forest Savannah South America South America South America Africa South America South America Asia South America Africa Africa 0.5 -0.98 ± 0.02 10.9 ± 0.7 0.5 4 Reedbuck, Bohor Cricetomys gambianus Thryonomys swinderianus Proechimys semispinosus Redunca redunca 35.1 0.4 ± 0.2 0 ± 0.3 3.7 ± 0.6 13 Saki, Bearded Chiropotes sagulatus 2.9 -0.58 ± 0.2 0 ± 1.1 3.8 ± 0.1 3 Saki, Monk Pithecia monachus 2.3 -0.62 ± 0.12 5.6 ± 2 1.3 ± 1.3 11 Saki, White-faced Pithecia pithecia 1.9 -0.82 ± 0.13 0±1 2.5 ± 6.5 3 0.25 0.04 0 Sitatunga Tragelaphus spekei 71.5 -0.45 ± 0.21 0 ± 0.3 1.7 ± 0.8 7 0.45 0.14 1 Sloth, Hoffman's Two-toed Sloth, Pale-throated Choloepus hoffmanni 5.9 -0.85 ± 0.09 12.3 ± 3.5 1.2 ± 0.3 3 0.25 0.01 0 Bradypus tridactylus 4.2 -0.87 ± 0.08 0 ± 6.5 6 0.03 <0.001 0 Squirrel, Indian giant Squirrel, Redlegged Sun Squirrel, South American Red Steenbok Ratufa indica 2.4 -0.54 ± 0.29 11.6 ± 3.4 1.7 ± 12.5 13 ± 6.1 5 0.38 0.14 0 Heliosciurus rufobrachium Sciurus spadiceus 0.3 -0.83 ± 0.14 0 ± 0.7 1.9 ± 1.5 4 0.13 0.01 0 0.3 -0.67 ± 0.15 0 ± 0.1 6 0.22 0.02 0 11 -0.58 ± 0.28 0 ± 0.04 4.2 ± 13.5 1.5 ± 0.2 4 0.63 0.26 1 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah 5 -1 0 ± 0.02 0.5 ± 4.8 3 0.25 <0.001 1 Savannah Rat, Marsh cane Rat, Spiny Suni Raphicerus campestris Neotragus moschatus 27 2.18 0.25 1.58 Asia South America South America Africa Africa Africa Africa Tamandua, Southern Tamarin, GoldenHanded Tamarin, Saddleback Tapir, Baird's Tamandua tetradactyla Saguinus midas 4.7 -0.05 ± 0.22 0 ± 0.3 1.3 ± 9.2 5 1.00 0.91 1 0.5 -1 0 ± 3.3 0.3 ± 1.6 5 0.06 <0.001 0 Saguinus fuscicollis 0.4 -0.99 ± 0.01 14.8 ± 4.8 0.1 ± 0.3 4 0.13 <0.001 0 Tapirus bairdii 254 -0.32± 0.37 4.6 ± 1.7 2.8 ± 0.4 5 1.00 0.44 2 Tapir, Lowland Tapirus terrestris 153.4 0.43 ± 0.11 0 ± 0.7 5.6 ± 0.8 25 0.001 2 Tayra Eira barbara 4.8 -0.44 ± 0.21 0 ± 1.1 0.3 ± 0.8 4 1.00 0.23 0 Tinamou, Brazilian Crypturellus strigulosus Tinamus major 0.6 -0.86 ± 0.11 0 ± 0.8 7.8 ± 1.6 6 0.03 0.001 0 1 -0.63 ± 0.08 0 ± 0.86 2.7 12 <0.001 0 Tinamou, Whitethroated Topi Tinamus guttatus 0.6 -0.77 ± 0.14 2.8 ± 0.5 0.7 ± 0.9 5 0.06 0.01 0 Damaliscus korrigum 83.1 0.12 ± 0.12 0 ± 1.8 5.9 ± 0.3 9 1.00 0.40 1 Tortoise, Redfooted Toucan, Cuvier’s Chelonoidis carbonaria Ramphastos cuvieri 4.5 0.5 ± 0.17 0 ± 2.6 8.9 ± 0.3 4 0.63 0.15 0 0.8 -0.26 ± 0.31 10.5 ± 0.5 13.4 5 1.00 0.45 0 Tree-Kangaroo, Grizzled Trumpeter, Darkwinged Trumpeter, Greywinged Turkey, Ocellated Dendrolagus inustus 14.8 -0.22 ± 0.24 3.6 ± 1.9 1.8 ± 4.6 3 1.00 0.46 0 Psophia viridis 1.1 -0.75 ± 0.17 0 ± 2.9 1.3 ± 1.5 3 0.25 0.08 0 Psophia crepitans 10 -0.66 ± 0.08 0 ± 0.8 3.2 ± 1.9 9 0.00 <0.001 0 Agriocharis ocellata 3.4 0.09 ± 0.34 1.8 ± 1 2 ± 3.6 3 1.00 0.82 0 Vontsira, Broadstriped Vontsira, Ringtailed Wallaby, WhiteStriped Galidictis fasciata 0.6 -0.75 ± 0.17 11.1 ± 2.9 3.9 ± 0.1 4 0.13 0.02 0 Galidia elegans 0.8 0.66 ± 0.09 7.9 ± 2.6 4 0.13 0.004 0 Dorcopsis hageni 5.5 -0.41 ± 0.05 12.3 ± 1.7 29.3 ± 0.4 5.6 ± 1.3 3 0.25 0.01 0 Tinamou, Great 28 3.61 -2.16 Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Savannah South America South America South America South America South America South America South America South America South America Africa Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest Tropical Forest South America South America Oceania South America South America North America Africa Africa Oceania Warthog, Cape Warthog, Common Waterbuck Wildebeest, Blue Zebra, Plains Phacochoerus aethiopicus Phacochoerus africanus Kobus ellipsiprymnus 76.8 0.36 ± 0.16 0 ± 0.9 6 ± 1.7 11 61.3 0.52 ± 0.1 4 ± 1.2 5 227 0.44 ± 0.1 0 ± 0.4 11.1 ± 1.4 4 ± 1.7 14 Connachaetes taurinus Equus quagga 231.9 -0.34± 0.13 0±8 7.6 ± 1.1 7 264.2 -0.25 ± 0.06 0 ± 1.7 7.4± 0.9 13 29 -2.36 0.06 -2.50 0.45 -0.03 0.05 1 Savannah Africa 0.01 1 Savannah Africa 0.001 2 Savannah Africa 0.11 2 Savannah Africa 0.003 2 Savannah Africa Table 3. Model selection results of factors driving human prey preferences and variable importance (sum of the weights, w). AICc refers to Akaike’s Information Criterion corrected for small sample size, and Weight refers to the relative likelihood of the model. Intercept Terrestrial/ Arboreal Threat Continent Biome Kill Method d.f. logLik AICc Weight 25 27 -0.45624 -0.52332 + + + + NA + NA NA NA NA 5 9 -84.964 -80.745 180.291 180.608 0 0.317 0.383 0.327 26 28 9 10 31 11 -0.34927 -0.42611 -0.3549 -0.21318 -0.58577 -0.3182 + + + + + + + + NA NA + NA NA + NA NA + + + + NA + NA NA NA NA NA NA + NA 7 11 3 5 18 7 -83.762 -79.527 -90.825 -89.021 -74.412 -87.74 182.211 182.715 187.793 188.405 189.324 190.167 1.92 2.425 7.502 8.115 9.033 9.876 0.147 0.114 0.009 0.007 0.004 0.003 29 32 30 12 18 17 -0.52235 -0.51169 -0.40482 -0.22564 -0.15584 -0.32963 + + + + NA NA + + + NA + + NA + NA + NA NA NA + + + + NA + + + NA NA NA 14 20 16 9 6 4 -79.94 -72.709 -78.343 -86.68 -90.282 -93.409 190.573 191.018 192.218 192.477 193.076 195.059 10.282 10.728 11.927 12.186 12.786 14.768 0.002 0.002 9.84E-04 8.65E-04 6.41E-04 2.38E-04 20 19 24 22 23 2 -0.20199 -0.32933 -0.3693 -0.25927 -0.45374 0.040497 NA NA NA NA NA NA + + + + + NA + + + NA + NA + NA + + NA + NA NA + + + NA 10 8 19 15 17 4 -87.331 -89.612 -77.325 -82.678 -80.797 -95.872 196.037 196.112 197.683 198.452 199.594 199.984 15.746 15.821 17.393 18.162 19.303 19.694 1.46E-04 1.41E-04 6.40E-05 4.36E-05 2.46E-05 2.03E-05 14 13 21 15 -0.20282 -0.3571 -0.43136 -0.28059 + + NA + NA NA + NA NA NA NA + + NA NA NA + + + + 14 12 13 16 -85.001 -87.5 -86.587 -83.921 200.695 200.974 201.493 203.374 20.405 20.684 21.203 23.083 1.42E-05 1.24E-05 9.53E-06 3.72E-06 30 4 16 3 1 6 8 0.040497 -0.20809 -0.07832 -0.16126 -0.00683 -0.00268 NA + NA NA NA NA NA NA NA NA NA NA + + + NA NA + + + NA NA + + NA + NA NA + + 8 18 6 2 13 17 -94.112 -82.66 -96.687 -101.456 -89.938 -88.156 205.113 205.82 205.885 206.984 208.194 214.313 24.822 25.529 25.595 26.694 27.904 34.022 1.56E-06 1.10E-06 1.06E-06 6.12E-07 3.34E-07 1.57E-08 7 5 -0.08107 -0.20288 NA NA 1 16 NA NA 0.98 16 + NA 0.45 16 NA NA 0.27 16 + + 0.01 16 15 11 -91.636 -96.855 216.368 217.369 36.078 37.079 5.61E-09 3.40E-09 w: N containing model 31