Orsolya Vincze
email: vincze.orsolya@ecolres.hu

Fernando Colchero

Jean-François Lemaître

Dalia Lemaître

Samuel Conde

Margaux Pavard

Araxi Bieuville

Beata Urrutia

Amy Ujvari

Carlo Boddy

Maley

Cancer risk across mammals

Cancer risk across mammals

Orsolya Vincze 1,2,3,4 ✉ , Fernando Colchero 5,6,7 , Jean-Francois Lemaître 8 , Dalia A. Conde 6,7,9 , Samuel Pavard 10 , Margaux Bieuville 10 , Araxi O. Urrutia [START_REF] Nunney | Size matters: height, cell number and a person's risk of cancer[END_REF][START_REF] Caulin | s paradox: evolution's prescription for cancer prevention[END_REF] , Beata Ujvari [START_REF] Nunney | s paradox and the promise of comparative oncology[END_REF] , Amy M. Boddy [START_REF] Peto | Epidemiology, multistage models, and short-term mutagenicity tests[END_REF] , Carlo C. Maley [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF] , Frédéric Thomas 1 & Mathieu Giraudeau [START_REF] Leroi | Cancer selection[END_REF][START_REF] Armitage | The age distribution of cancer and a multi-stage theory of carcinogenesis[END_REF] Cancer is a ubiquitous disease of metazoans, predicted to disproportionately affect larger, long-lived organisms owing to their greater number of cell divisions, and thus increased probability of somatic mutations [START_REF] Leroi | Cancer selection[END_REF][START_REF] Armitage | The age distribution of cancer and a multi-stage theory of carcinogenesis[END_REF] . While elevated cancer risk with larger body size and/or longevity has been documented within species 3-5 , Peto's paradox indicates the apparent lack of such an association among taxa [START_REF] Peto | Origins of Human Cancer[END_REF] . Yet, unequivocal empirical evidence for Peto's paradox is lacking, stemming from the difficulty of estimating cancer risk in non-model species. Here we build and analyse a database on cancer-related mortality using data on adult zoo mammals (110,148 individuals, 191 species) and map age-controlled cancer mortality to the mammalian tree of life. We demonstrate the universality and high frequency of oncogenic phenomena in mammals and reveal substantial differences in cancer mortality across major mammalian orders. We show that the phylogenetic distribution of cancer mortality is associated with diet, with carnivorous mammals (especially mammal-consuming ones) facing the highest cancer-related mortality. Moreover, we provide unequivocal evidence for the body size and longevity components of Peto's paradox by showing that cancer mortality risk is largely independent of both body mass and adult life expectancy across species. These results highlight the key role of life-history evolution in shaping cancer resistance and provide major advancements in the quest for natural anticancer defences.

Complex multicellular organisms are built of millions to quadrillions of cells, ultimately all being derived from a single cell, the zygote. During the course of the organisms' lifetime and owing to various mutational processes, cell lineages tend to accumulate mutations [START_REF] Couzin-Frankel | The bad luck of cancer[END_REF][START_REF] Chatterjee | Mechanisms of DNA damage, repair, and mutagenesis[END_REF] . While the majority of mutations are harmless, some enable cells to escape cell cycle control, to grow and proliferate uncontrollably, resulting in cancer [START_REF] Nunney | The real war on cancer: the evolutionary dynamics of cancer suppression[END_REF][START_REF] Ujvari | Ecology and Evolution of Cancer[END_REF] . Cancer is a multistage process, where a set of mutations is required for both initiation and malignant progression. Given that every cell division carries a risk of generating mutations, organisms with large bodies (composed of more cells) and extended longevities (with a longer time to accumulate mutations) should be more likely to develop cancer [START_REF] Leroi | Cancer selection[END_REF][START_REF] Peto | Origins of Human Cancer[END_REF][START_REF] Nunney | Size matters: height, cell number and a person's risk of cancer[END_REF][START_REF] Caulin | s paradox: evolution's prescription for cancer prevention[END_REF] . Indeed, within humans [START_REF] Wirén | Pooled cohort study on height and risk of cancer and cancer death[END_REF][START_REF] Nunney | Size matters: height, cell number and a person's risk of cancer[END_REF] and dogs [START_REF] Fleming | Mortality in North American dogs from 1984 to 2004: an investigation into age-, size-, and breed-related causes of death[END_REF] , larger individuals are more likely to develop cancer than smaller ones. Similarly, increasing age is one of the most potent carcinogenic factors in species in which cancer aetiology is well studied. Yet, while current evidence suggests that large body size and extended longevity result in increased cancer risk within species, this relationship may not hold across taxa [START_REF] Nunney | s paradox and the promise of comparative oncology[END_REF] .

Limited data available so far indicate that vertebrates do not face clear size-dependent cancer risks despite their size and longevity varying by orders of magnitude. This poses a logical challenge, first formulated by Sir Richard Peto [START_REF] Peto | Origins of Human Cancer[END_REF][START_REF] Peto | Epidemiology, multistage models, and short-term mutagenicity tests[END_REF] . He noted that although mice have approximately 1,000 times fewer cells and >30 times shorter lifespans than humans, their risk of carcinogenesis is not markedly different (coined as Peto's paradox) [START_REF] Nunney | Lineage selection and the evolution of multistage carcinogenesis[END_REF] . Peto's paradox is an evolutionary conundrum that has puzzled the scientific community and has led to lively debate regarding the evolution of anticancer mechanisms. It is often postulated that natural selection on large size or extended longevity is inherently inseparable from the evolution of anticancer defences. Knowledge gained from investigating Peto's paradox might thus largely contribute to our knowledge on natural anticancer mechanisms that could potentially be harnessed for medical use. Further, understanding cross-species variation of cancer vulnerability is an important next step in animal health and welfare. While a few studies aimed to establish cross-species variation in cancer risk [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF][START_REF] Møller | Life history, immunity, Peto's paradox and tumours in birds[END_REF] , most estimates and analyses have considerable limitations. These include small cross-or within-species sample sizes [START_REF] Leroi | Cancer selection[END_REF][START_REF] Peto | Origins of Human Cancer[END_REF][START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] , lacking information on the age distribution of cancer [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF][START_REF] Møller | Life history, immunity, Peto's paradox and tumours in birds[END_REF][START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] , data heterogeneity (for example, biases due to domestication [START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] or combining data from multiple taxa [START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF][START_REF] Tollis | Elephant genomes reveal accelerated evolution in mechanisms underlying disease defenses[END_REF] ) or lack of control for phylogenetic relatedness among species [START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] . Moreover, the effect of longevity was generally tested using the much-debated metric of maximum reported lifespan [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF][START_REF] Møller | Life history, immunity, Peto's paradox and tumours in birds[END_REF][START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] . Nonetheless, cancer prevalence (the parameter exclusively considered by earlier studies [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF][START_REF] Møller | Life history, immunity, Peto's paradox and tumours in birds[END_REF][START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] ) is expected to correlate with life expectancy (the average time lived by individuals in the population of interest), not maximum lifespan potential (that very few individuals achieve), making these analyses inherently flawed.

To characterize cancer incidence in a homogeneous sample and across a wide taxonomic range, here we used the Zoological Information Management System (ZIMS), managed by Species360 (a non-profit organization custodian of zoo and aquarium data) [START_REF] Conde | Data gaps and opportunities for comparative and conservation biology[END_REF] . We assembled information on 110,148 adult non-domesticated mammals distributed over 191 species, including data on their age, sex, dead/alive status and postmortem pathological records for 11,840 individuals. Cancer is registered in this database only for deceased animals and only if the inspecting veterinary pathologist considered it to be a factor that contributed to the individual's death. First, to characterize species longevities, we used survival modelling (n = 110,148) and calculated species-specific adult life expectancies, representing average adult longevity in our sample [START_REF] Ronget | Assessing ageing patterns for comparative analyses of mortality curves: going beyond the use of maximum longevity[END_REF][START_REF] Tidière | Comparative analyses of longevity and senescence reveal variable survival benefits of living in zoos across mammals[END_REF] . Second, to estimate cancer mortality risk, we used two metrics, both estimating the proportion of individuals dying of cancer. We first calculated a simple measure of cancer mortality risk (hereafter CMR; that is, the ratio between the number of cancer-related deaths and the total number of individuals whose postmortem pathological records were entered in the database, n = 11,840), a measure adopted by earlier comparative studies [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF][START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] . This measure relies solely on dead individuals, ignoring the incomplete records of live animals, potentially introducing bias in cancer mortality estimates. Therefore, we also calculated the cumulative incidence of cancer mortality (hereafter ICM), a metric of cancer mortality risk eliminating potential biases due to disregarding left-truncation (that is, cancer before individuals enter the study) and right-censoring (individuals alive, thus with unknown fate at data extraction). Using these two metrics, we explored the phylogenetic distribution of cancer-related mortality across mammals. We then investigated Peto's paradox and tested whether cancer mortality risk is associated with body size or the mean number of years lived by adults in the explored populations (that is, adult life expectancy).

Cancer across the mammalian phylogeny

CMR was highly variable among species, ranging from 0% (in 47 species out of 191) to 57.14% in the kowari (Dasyuroides byrnei). CMR exceeded 10% in 41 species (21.5% of all species inspected), indicating that the oncogenic process is a prevailing source of mortality of many mammalian species distributed along the phylogeny, at least in managed populations (Fig. 1). ICM showed strong consistency with CMR (Pearson correlation test, r = 0.89, t = 25.14, df = 170, P < 0.0001). Nonetheless, all models were performed using both metrics to test for consistency in the results.

Cross-species variation in cancer risk showed strong phylogenetic signal [START_REF] Revell | phytools: an R package for phylogenetic comparative biology (and other things)[END_REF] (CMR: n = 191, λ = 0.87, P < 0.0001; ICM: n = 172, λ = 0.69, P < 0.0001). To explore this, we compared cancer risk among mammalian orders represented by at least two species using linear regressions. Results indicated that the phylogenetic signal was mostly driven by cancer mortality risk in Carnivora, which was significantly higher than in Primates or in Artiodactyla (Extended Data Table 1, Fig. 1 and Extended Data Fig. 1). Both cancer mortality risk metrics indicated that Artiodactyla is the least cancer-prone mammalian order, despite the frequency of large-bodied species in this group (Extended Data Table 1).

High cancer risk in managed populations of Carnivora has previously been reported [START_REF] Moresco | Taxonomic distribution of neoplasia among non-domestic felid species under managed care[END_REF][START_REF] Moresco | The Pro-carcinogenic Effects of Progestogens on Carnivore Target Tissues[END_REF] . Possible explanations include the use of hormonal contraception (for example, progestins) and pregnancy postponement in zoo carnivores, both being significant risk factors for certain cancers in humans as well as non-domestic felids [START_REF] Moresco | The Pro-carcinogenic Effects of Progestogens on Carnivore Target Tissues[END_REF][START_REF] Harrenstien | Mammary cancer in captive wild felids and risk factors for its development: a retrospective study of the clinical behavior of 31 cases[END_REF][START_REF] Munson | Comparative pathology of mammary gland cancers in domestic and wild animals[END_REF] . Nonetheless, if contraception was the key factor driving elevated cancer risk in Carnivora, a significant sex bias in cancer risk would be expected in this group, because hormonal contraception is usually administered to females. To test for sex bias in cancer risk across Carnivora, we estimated sex-specific CMR and ICM (only species with a minimum of ten males and ten females with available postmortem pathological records: n = 36 and n = 30 species, respectively). Pairwise comparison between sexes revealed no sex bias in either measure of cancer mortality risk (phylogenetic paired t-tests, CMR: t = 0.52, df = 33, P = 0.6061; ICM: t = -0.6815, df = 27, P = 0.5014) (ref. [START_REF] Moresco | Taxonomic distribution of neoplasia among non-domestic felid species under managed care[END_REF] ). Therefore, the generally high cancer risk in Carnivora is unlikely to be driven solely by the carcinogenic effects of reproductive management in zoo populations.

A high-fat, low-fibre diet, a known risk factor for carcinogenesis, has also been suggested to explain the elevated cancer risk in Carnivora [START_REF] Munson | Comparative pathology of mammary gland cancers in domestic and wild animals[END_REF][START_REF] Chao | Meat consumption and risk of colorectal cancer[END_REF] . Moreover, carnivores are on the top of the food chain, exposing them to bio-magnified effects of carcinogenic compounds [START_REF] Kelly | Food web-specific biomagnification of persistent organic pollutants[END_REF] , such as pollutants [START_REF] Munson | Comparative pathology of mammary gland cancers in domestic and wild animals[END_REF] . Importantly, the consumption of raw meat can also expose carnivores to pathogens that can drive oncogenic transformation [START_REF] Pesavento | Cancer in wildlife: patterns of emergence[END_REF] . For instance, in humans it was estimated that 10-20% of all cancers are of viral origin [START_REF] Bogolyubova | Human oncogenic viruses: old facts and new hypotheses[END_REF] . While this figure is unknown in any other animal species [START_REF] Pesavento | Cancer in wildlife: patterns of emergence[END_REF] , it is arguable that raw meat consumption might exacerbate the spread of carcinogenic pathogens [START_REF] Khatami | Bovine leukemia virus (BLV) and risk of breast cancer: a systematic review and meta-analysis of case-control studies[END_REF] . Exploring the association between diet and cancer risk could help to disentangle the influences of these risk factors.

To explore the link between carnivorous diet and cancer risk, we collected data on the species' natural diet (that is, consumption of animals, including invertebrates or vertebrates, and specifically of fish, reptiles, birds and mammals) from the literature [START_REF] Kissling | Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide[END_REF] . Phylogenetic generalized least-squares (PGLS) regressions controlling for differences in longevity and body mass run separately for each diet item (Fig. 2, Extended Data Figs. 2 and3 and Extended Data Table 2) indicated that species with animal-based diets have comparable cancer mortality risks (both CMR and ICM) to species that rarely or never consume animals. Nonetheless, consumption of vertebrate but not invertebrate prey was associated with increased cancer mortality risk. Specifically, mammals frequently consuming mammalian prey had significantly higher cancer mortality risk compared to mammals that rarely or never consume other mammals. Similar differences could not be detected in the case of fish, reptile or bird prey frequencies. These results indicate that a carnivorous diet has significant costs in terms of heightened oncogenic predisposition across mammals, particularly for diets high in mammalian prey. The nonsignificant association between cancer mortality risk and diet content of invertebrate, fish, reptile or bird indicates bio-magnification as a less likely source of elevated cancer risk among Carnivora. Nonetheless, the limited number of species primarily consuming these preys in our sample does not allow for definitive conclusions regarding this hypothesis. By contrast, the result that mammals consuming other mammals appear to have the highest cancer risk of all diet categories is consistent with a pathogenic origin of elevated cancer mortality risk among Carnivora. Host jumping of pathogens is most likely to occur in the case of phylogenetic proximity between the reservoir prey and the predator species [START_REF] Olival | Host and viral traits predict zoonotic spillover from mammals[END_REF] , making a mammal-to-mammal transmission the most likely host jump scenario. These results suggest that pathogen-driven oncogenesis might have a considerable role in shaping cancer mortality risk in mammals and urges the search for pathogens in various cancer types, while considering the notorious difficulty of proving oncogenic properties of pathogens [START_REF] Bogolyubova | Human oncogenic viruses: old facts and new hypotheses[END_REF] . Alternatively, high cancer risk in carnivorous animals might be related to their low microbiome diversity [START_REF] Ley | Evolution of mammals and their gut microbes[END_REF] , limited physical exercise under human care or other aspects of their physiology. Nonetheless, a lack of bias in these results, caused by potential alterations in the diet of housed carnivores, should be confirmed by studying natural populations. Importantly, these results probably reflect a complex, maybe indirect evolutionary link between diet and cancer vulnerability; therefore, the effect of meat consumption on cancer risk should be interpreted with caution.

Test of Peto's paradox

Owing to the large number of zero cancer mortality risk estimates and thus non-Gaussian distributions, cancer risks were analysed using zero-inflated phylogenetic models (Methods), as a function of sample size, body mass and life expectancy. The probability of detecting at least one individual with cancer in a species increased steeply with increasing number of individuals with available postmortem pathological records (Extended Data Table 3). In fact, cancer was detected in at least one individual in almost all species with more than 82 individual pathological records available (Extended Data Figs. 4 and5). Exceptions were the blackbuck (Antilope cervicapra) and the Patagonian mara (Dolichotis patagonum), where no cancer was detected despite postmortem pathological records being available for 196 and 213 individuals, respectively. 2 that also control for body mass, life expectancy and phylogeny.

This highlights the quasi-universality of oncogenic phenomena across mammals, illustrating that with adequate sampling, cancer is likely to be detected in all mammals. Our results further emphasize the fact that some members of the order Artiodactyla, besides rodents, are particularly cancer-resistant. Rodents have long been subject to scrutiny in the search for natural cancer resistance mechanisms [START_REF] Seluanov | Mechanisms of cancer resistance in long-lived mammals[END_REF] , owing to notoriously low cancer incidence in some species [START_REF] Herrera-Álvarez | How to make a rodent giant: genomic basis and tradeoffs of gigantism in the capybara, the world's largest rodent[END_REF] . Nonetheless, cancer mortality risk in our dataset was lowest among ruminants, complying with rare cancer case reporting in this taxonomic group [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF][START_REF] Albuquerque | From humans to hydra: patterns of cancer across the tree of life[END_REF] . This indicates that other mammalian groups, especially Artiodactyla, might serve as informative model organisms in cancer research.

The probability of detecting cancer (CMR: n = 188; ICM: n = 141) (Extended Data Table 3 and Extended Data Figs. 4 and5), as well as non-zero cancer mortality risk (CMR: n = 141; ICM: n = 128) (Extended Data Table 3 and Fig. 3), tended to decrease with larger body masses and to increase with longer life expectancies. These effects were not significant, and were consistent between the two cancer mortality metrics. These associations were also largely independent of each other, and were similar in single-predictor models (Supplementary Table 3). The effect of body mass on cancer risk was even slightly negative, but the models indicated only a 2.8-2.9% decrease in cancer risk for a doubling of the body weight (for example, CMR changes from 3.82% (1 kg) to 3.71% (2 kg), or from 2.86% (1,024 kg) to 2.78% (2,048 kg), respectively, predictions obtained from the model presented in Extended Data Table 3, with an arbitrary life expectancy of 27 years, Extended Data Fig. 6). Additionally, body mass accounted for only 0.78% of the cross-species variance in CMR (that is, partial coefficient of determination). Similarly, cancer mortality risk only minimally and nonsignificantly increased with higher life expectancy, indicating a 24.7-25.2% increase in cancer risk for a doubling of the adult life expectancy (for example, CMR changes from 0.89% (1 year) to 1.18% (2 years), or from 2.80% (16 years) to 3.72% (32 years), respectively, predictions obtained from the model presented in Extended Data Table 3 with an arbitrary body mass of 10 kg, Extended Data Fig. 6). Adult life expectancy accounted for only 2.94% of the variance observed in CMR (that is, partial coefficient of determination). Overall, these results provide the largest-scale and most robust support to the body size and life expectancy components of Peto's paradox in mammals. They suggest that lifespan extension and larger body size jointly evolved with better anticancer mechanisms across mammals.

Since the first indication of species differences in cancer predisposition, an intense search has been conducted to identify mechanisms explaining cancer resistance in certain species, mostly rodents [START_REF] Seluanov | Mechanisms of cancer resistance in long-lived mammals[END_REF] , and very large animals [START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF] . Although these studies demonstrated key species-specific anticancer mechanisms [START_REF] Abegglen | Potential mechanisms for cancer resistance in elephants and comparative cellular response to DNA damage in humans[END_REF][START_REF] Kelly | Food web-specific biomagnification of persistent organic pollutants[END_REF][START_REF] Seluanov | Mechanisms of cancer resistance in long-lived mammals[END_REF] , a considerable gap remains in our knowledge on the taxonomic and phylogenetic diversity and distribution of tumour-suppressing mechanisms. Our results provide a solid foundation for future studies scrutinizing these questions, by providing information on the generality and frequency of oncogenic phenomena across the mammalian phylogeny. We also highlight the exceptional resources provided by zoos for studies of cancer in wildlife [START_REF] Seluanov | Mechanisms of cancer resistance in long-lived mammals[END_REF][START_REF] Herrera-Álvarez | How to make a rodent giant: genomic basis and tradeoffs of gigantism in the capybara, the world's largest rodent[END_REF] .

Our study indicates that death due to oncogenic phenomena is frequent and taxonomically widespread in mammals. In some species more than 20-40% of the managed adult population die of cancer-related pathologies. This estimate is staggering, especially knowing that cancer incidences estimated here are conservative (Methods). This observation urges the extensive exploration of cancer in wildlife, especially in the context of recent environmental perturbations [START_REF] Giraudeau | Human activities might influence oncogenic processes in wild animal populations[END_REF] , as serious threats to animal welfare [START_REF] Pesavento | Cancer in wildlife: patterns of emergence[END_REF] .

Online content

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-021-04224-5.

Methods

Documenting cancer in wild animals is extremely challenging in most cases owing to the lack of information on the age of individuals, the difficulty retrieving the bodies for necropsy and the likelihood of cancer negatively influencing survival before cancer itself could be detected. Although data on cancer incidence from wild populations would be indispensable to describe natural incidences of malignancies, such data, especially with corresponding ages and demographic histories, are unfortunately still far from our reach. Therefore, to estimate cancer mortality risk, we used data provided by Species360 and the Zoological Information Management System (ZIMS, Data Use Approval Number 73836), an international non-profit organization that maintains a real-time and centralized database of animals under human care (regrouping information from over 1,200 zoos worldwide). Although we recognize that the interpretation of data gathered on zoo animals requires caution, owing to strong human control on the diet, health, mortality factors, environment or standard biological functions of the animals, zoos provide exceptionally high data resolution on the demography and cause of death for a wide range of species. Here we rely on the high probability of body retrieval of deceased zoo animals and the necropsy routinely performed on most of them (unless found in an advanced stage of decomposition), aiming to identify the most likely pathology causing the death of the animal. These examinations are likely to reveal most solid tumours, but (although possible) benign tumours, liquid tumours (for example, leukaemia) or early-stage cancers are unlikely to be recorded here, either owing to their diagnostic difficulty or their perceived limited role in contributing to the death of the animal. Specifically, here we use the husbandry module of ZIMS, providing information on birth, death, sex and pre-defined categories of pathological findings, including neoplasia (by definition tumours that contributed to the death, albeit with no option to specify the cancer type or other details). No statistical methods were used to predetermine sample size, but to minimize bias caused by potential temporal heterogeneity in data management practices and necropsy record-keeping [START_REF] Boddy | Lifetime cancer prevalence and life history traits in mammals[END_REF] , here we focused on individuals alive or born after 1 January 2010 (data extraction: 30 May 2020). This sample was then used to characterize species-specific life expectancies and cancer incidence, but only after the exclusion of data that did not fulfil a series of criteria, to ensure the highest and most homogeneous data quality possible. First, cancer is an age-related disease that rarely occurs in juveniles, and pediatric cancers are usually medically distinct from adults' cancers. As such, infant mortality differences observed across species can significantly confound cancer incidence estimates. Therefore, we gathered sex-specific or species-specific (wherever the former was not available) ages at sexual maturity and we considered individuals for analyses only if they reached maturity before or during our sampling period. For individuals of unknown sex (about 12% of all individuals in the raw data extract), the maximum of ages at sexual maturity of males and females was used as an inclusion age threshold. Sex-specific age at sexual maturity for each species was obtained from Conde et al. [START_REF] Conde | Data gaps and opportunities for comparative and conservation biology[END_REF] or from published literature resources (see data sources in https://github.com/OrsolyaVincze/ VinczeEtal2021Nature/blob/main/SupplementaryData.xls). Second, given that age is a key predictor of cancer emergence, we considered only individuals for which date of birth was recorded precisely or within a narrow (maximum 30 days) time interval. Third, we considered only species in which postmortem pathological records were available for at least 20 adult individuals, irrespective of the cause of death (for example, infection, accident, geriatric disease and so on). Nonetheless, models presented were performed with increased thresholds of 40, 60, 80 and 100 individuals to check for consistency in the results (Supplementary Table 2). Fourth, given that the process of domestication is widely regarded as a major contributing factor to inbreeding depression and higher incidence of cancer [START_REF] Thomas | Rare and unique adaptations to cancer in domesticated species: an untapped resource?[END_REF] , we excluded all species that were subject to domestication as well as their wild ancestors (taxa excluded owing to being subject to domestication are listed in Supplementary Table 1). Following these restrictions, data extraction on age and cause of death resulted in information for 110,148 (62,556 live and 47,592 dead) individuals (n = 191 species). For calculation of ICM, we included only species in which survival is correctly estimated until old ages (that is, data allowing the estimation of age-specific survival until the age at which only 10% of individuals are surviving, n = 172 species). While these restrictions removed multiple sources of bias in our cancer mortality risk estimates, we cannot exclude the possibility that some species (for example, more charismatic ones) are subject to more frequent or more detailed necropsies. Nonetheless, our statistical approach, especially the complete case analysis, is largely insensitive to such biases, as individuals not having available postmortem diagnostic records are considered censored (see below). Also, while the depth of necropsy might vary slightly among species, neoplasia that had a significant contribution to the death of the animals (the focus of our study) are generally detected even at gross necropsies. Additionally, large species are considered of key importance for zoos, also reflected by the fact that the proportion of dead individuals with postmortem pathological records is larger in larger species (Pearson's correlation: r = 0.24, t = 3.35, df = 189, P = 0.001). Accordingly, if charisma had a role in cancer detection, we would expect a larger cancer risk in large mammals, opposite to the (nonsignificant) negative body mass effect in our models. Consequently, we believe that charisma is unlikely to represent a major source of bias in our analysis.

Estimation of adult life expectancy

As we have no reason to believe that censored individuals would not have the same prospect of survival as those who continue to be followed, we estimate adult life expectancy from age-specific survival estimated using the Kaplan-Meier procedure (using the survfit function in the R package survival [START_REF] Therneau | Package 'survival[END_REF] ). Individuals older than their age at sexual maturity on 1 January 2010 were left-truncated at their age at this date; individuals reaching sexual maturity after this date were left-truncated at their age at sexual maturity. Individuals still alive at the time of data extraction were considered right-censored (samples per species varied from 42 to 5,816 individuals), while known fate individuals were assigned as dead (n = 47,592), irrespective of whether their cause of death was specified or not.

Estimation of ICM

ICM was calculated using a competing risk approach, based on the cumulative hazard of cancer-related deaths and survival probability of the species under human care. First, age-specific survival S x , at age x, was estimated from KM analysis as above. However, here we performed a complete-case analysis, using only 11,840 individuals for which the cause of death was specified together with right-censored survivors. Complete-case analysis assumes that missingness in the cause of failure is random, but we had no reason to believe that this was not the case in our dataset. Postmortem examinations are routinely carried out on most recovered bodies in zoos, and once examinations are performed the results are equally likely to be entered in the database irrespective of the pathologies identified. ICM estimates were thus based on n = 74,396 individuals, n = 179 species. Second, the cancer mortality hazard h c x was estimated using a KMx1 analysis where only deaths by cancer were incorporated as a death event. ICM is then such that

∑ S h ICM = x α x x = ∞ c
where α is the age at sexual maturity. The only difference with classic estimation is that we extracted h c x (and S x ) for each time unit with discrete jumps (and falls) at event times at age t and with h c t≤x<t+1 = 0 (S x constant) between these events; instead of estimating discrete

hazard h d n = / t t t c c
on these time intervals (where d c t is the number of deaths by cancer within the interval and n t is the number of survivors at the beginning of the interval). We chose this method to reflect true variation in the data for the interspecific comparison where species differ greatly in the number of events and time interval between these (sometimes a third of the organism's adult lifespan), a situation rarely met when comparing human groups.

Covariates and statistical analyses

For each species, we obtained sex-specific adult body mass data from Species360's ZIMS (see https://github.com/OrsolyaVincze/VinczeEtal 2021Nature/blob/main/SupplementaryData.xls). Species-specific body mass was calculated as the average of all body mass measurements recorded in the ZIMS database in adults, while species-specific values were obtained by averaging the body masses of males and females. These were calculated only in species for which there were at least 100 adult body mass records; otherwise, body mass was taken from the literature and database review by Conde et al. [START_REF] Conde | Data gaps and opportunities for comparative and conservation biology[END_REF] . We verified that there was a one-to-one correspondence in the body mass information for species with records in both datasets.

Diet information was obtained from a global diet dataset for terrestrial mammals [START_REF] Kissling | Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide[END_REF] , providing information on diet composition at four hierarchical levels of food items (never consumed, occasionally consumed, secondary food item, primary food item). We collected information on animal content in diet, as well as subcategories of this diet class, namely invertebrate or vertebrate consumption, as well as specifically fish, reptile, bird and mammal consumption. Given that most food items had few species in the intermediate levels (occasional consumption and secondary food item), we re-categorized the diet variables at two levels: never/rarely consumed or representing the primary/secondary food item of the species. The effect of diet was tested in PGLS regressions using only species with non-zero cancer mortality risks. Models were run separately for each food item that were entered to a base model including body mass and adult life expectancy as covariates. Results are shown in Extended Data Table 2.

To account for statistical non-independence due to phylogenetic relationships, we obtained a sample of 1,000, equally plausible phylogenetic trees, from the posterior distribution published by Upham et al. [START_REF] Upham | Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation[END_REF] , covering 5,911 species. We then obtained a rooted consensus tree using the sumtrees Python library [START_REF] Sukumaran | DendroPy: a Python library for phylogenetic computing[END_REF] . Two species recently raised to species level were manually added to the tree as sister taxa of the species it was recently separated from (that is, Cervus canadensis to Cervus elaphus and Gazella marica to Gazella subgutturosa). Phylogenetic signal of cancer risk was assessed using the function phylosig from the R package phytools [START_REF] Revell | phytools: an R package for phylogenetic comparative biology (and other things)[END_REF] . Partial coefficients of determinations were calculated using the function R2.pred from the R package rr2 (ref. [START_REF] Ives | R 2 s for correlated data: phylogenetic models, LMMs, and GLMMs[END_REF] ), based on models presented in Extended Data Table 3.

Models of cancer risk testing Peto's paradox were performed using zero-inflated logistic models, which allow us to make inferences on the probability of detecting at least one cancer case in the species and, given that cancer was detected, inferences on the CMR or ICM. Therefore, the first part of this consisted of a phylogenetic binomial regression (using the function binaryPGLMM, in the R package ape [START_REF] Paradis | Analyses of Phylogenetics and Evolution in R language[END_REF] ), where the dependent variable explained the presence of zeros and non-zeros in CMR or ICM. This model contained the log number of deceased individuals with available postmortem pathological records as an explanatory variable, due to the higher probability of detecting cancer with a higher number of dead individuals inspected. Additionally, the model contained body mass and adult life expectancy as covariates. The second part of the model consisted of a PGLS regression that investigated variance only in non-zero cancer risks. ICM and CMR were logit-transformed in all PGLS models as recommended when analysing proportions [START_REF] Warton | The arcsine is asinine: the analysis of proportions in ecology[END_REF] . These models were weighted by log number of deceased individuals with available postmortem pathological records, as the precision of cancer mortality risk estimates is expected to increase with the number of dead individuals subject to postmortem examination, but it is not expected to explain bias in the estimation of the dependent variable in any particular direction (as in the case of the binomial models). These models also contained body mass and adult life expectancy as explanatory variables. Given the expected additive effect of body mass and longevity, the interaction between body mass and longevity metrics was also tested in all four models (binomial and logistic regressions for CMR and ICM), but these interactions did not increase model fit in any case and are therefore not presented. Both models were controlled for phylogenetic relatedness among species, where the scaling parameter of phylogenetic dependence (that is, s2/Pagel's λ in PGLMMs and PGLSs respectively) was set to the most appropriate values assessed by likelihood ratio statics in each model separately. PGLS models in which Pagel's λ converged to negative values were refitted with Pagel's λ fixed at 0. Three species (Lagurus lagurus, Cricetus cricetus and Dasyuroides byrnei) had been removed from the latter models, due to their high leverage caused by their very low adult life expectancy compared to the rest of the species and therefore concerns of strong influence of these points over model fit. Nonetheless, all models were performed using the entire dataset, and the results were highly consistent with and without the exclusions (Supplementary Table 4 and Extended Data Fig. 7).

Order differences in cancer incidence were tested using standard linear regressions, built using only taxonomic orders in which at least two species had their cancer incidence estimated. The model contained CMR or ICM (non-transformed) as dependent variables and order as the sole explanatory factor. Pairwise order differences were assessed using the R package emmeans [START_REF] Lenth | emmeans: estimated marginal means, aka least-squares means[END_REF] . All analysis were performed in the R Statistical and Programming Environment, version 4.0.4 (ref. [START_REF] Core | R: A Language and Environment for Statistical Computing[END_REF] ). Cancer mortality risks were transformed to percentages in figures and in the analysis performed on order differences (Extended Data Table 1), for easier interpretation. Models presented in Extended Data Tables 2 and3 and Supplementary Tables 2-4 are based on probabilities. 2. Diet is characterized by variables on three taxonomic levels: I. animal content (including any vertebrate or invertebrate prey); II. invertebrate or vertebrate prey; and III. within vertebrates fish, reptile, bird and mammal preys. Each variable is coded as a two-level factor: rarely/never occurring in diet and representing the primary/secondary food item of the species. P-values shown were obtained from models presented in Extended Data Table 2.

Extended

SFig. 1 |

 1 Fig. 1 | Distribution of cancer mortality risk across the mammalian phylogeny. a, CMR in various mammals (scale to bar plots is provided on the left of the graph). b, Violin plots indicating order differences in CMR in orders

Fig. 2 |

 2 Fig. 2 | Cancer mortality risk in mammals as a function of animal content in diet. Violin plots show CMR as a function of seven diet items, each coded as rarely/never occurring in the diet or representing the primary/secondary food item of the species. Medians are marked with solid black lines. P values indicate pairwise differences as indicated by models presented in Extended Data Table2that also control for body mass, life expectancy and phylogeny.

Fig. 3 |

 3 Fig. 3 | Association between cancer mortality risk and body mass or adult life expectancy across mammals. a, b, Non-zero CMR plotted against body mass (a) or adult life expectancies (b). Slopes were obtained from the PGLS model presented in Extended Data Table 3a. Points are proportional to the log number of individuals with available postmortem pathological records.

Extended Data Fig. 1 |

 1 Phylogenetic distribution and order differences of ICM. a, Phylogenetic distribution of ICM (%). b, Violin plots indicating order differences in ICM (%) in orders with minimum two species assessed. Solid black lines indicate order medians. Animal silhouettes used to visually represent mammalian orders were downloaded from PhyloPic (http://www. phylopic.org). Extended Data Fig. 2 | ICM in function of diet animal content. Violin plots indicate ICM in function of diet animal content. Diet is characterized by variables on three taxonomic levels: I. animal content (including any vertebrate or invertebrate prey); II. invertebrate or vertebrate prey; and III. within vertebrates fish, reptile, bird and mammal preys. Each variable is coded as a two-level factor: rarely/never occurring in diet and representing the primary/ secondary food item of the species. Plots indicate the range and distribution of cancer risks in each category, where the width of each curve corresponds with the approximate frequency of data points in each region. Medians are marked with solid black lines. P values indicate pairwise differences as indicated by models presented in Extended Data Table 2 that also control for body mass, life expectancy and phylogeny. Extended Data Fig. 3 | Predicted cancer mortality risk in function of animal content in diet. Values represent estimated marginal means of a, CMR or b, ICM in function of diet animal content, based on models presented in Extended Data Table

  Data Fig. 4 | Association between occurrence of non-zero CMR and a, body mass or b, adult life expectancy across species. Occurrence of cancer in each species is plotted in function of the number of deceased individuals for which cause of death was known. Predictions were obtained for two scenarios, one for low and one for high a, body masses and b, adult life expectancies respectively. Random noise was added to cancer occurrence to facilitate the visualization of overlapping points. Predictions and associated 95% confidence intervals were obtained from a binomial GLM without phylogenetic control. Extended Data Fig. 5 | Association between ICM and a,c, body mass or b,d, adult life expectancy. Occurrence of cancer is plotted in function of the number of deceased individuals for which cause of death was known and predictions were obtained for two scenarios, one for low and high a, body masses and b, adult life expectancies respectively. Random noise was added to cancer occurrence to facilitate the visualization of overlapping points. Predictions and associated 95% confidence intervals were obtained from a binomial GLM without phylogenetic control. Non-zero cancer mortality risks were plotted against c, body mass or d, adult life expectancies. Slopes were obtained from the PGLS model presented in Extended Data Table 3b. Points are proportional to the log number of individuals with known cause of death. Extended Data Fig. 6 | Predicted CMR at various hypothetical life expectancies for small, medium, and large-bodies species. CMR was predicted based on the logistic model presented in Extended Data Table3afor a series of hypothetical adult life expectancies, ranging from one to 70 years (x-axis). Predictions were obtained for three body masses, corresponding to a small, medium and a large bodied mammal in our dataset. True life expectancies of the three species are marked with red stars. Distribution of life expectancies across the species set of the model is shown by a histogram. Vertical dashed line marks maximum life-expectancy in our dataset. Jaculus jaculus silhouette by Maija Karala. The other animal silhouettes used to visually represent mammalian orders were downloaded from PhyloPic (http://www. phylopic.org).Extended Data Fig.7| Association between cancer mortality risk and body mass as well as adult life expectancy across 191 mammal species. These analysis include the three species excluded in some models due to their high leverages. Plots show a-d, CMR or e-h, ICM. Occurrence of cancer in each species is plotted in function of the number of individuals with post-mortem pathological records. Predictions were obtained for two scenarios: for small or large a,e, body masses and low or high b,f, adult life expectancies respectively.Random noise was added to cancer occurrence to facilitate the visualization of overlapping points. Predictions and associated 95% confidence intervals were obtained from a binomial GLM without phylogenetic control. Non-zero cancer mortality risks were plotted against c,g, body mass or d,h, adult life expectancies. Slopes were obtained from the PGLS model presented in Supplementary Table

4

 4

  . Points are proportional to the log number of individuals with known cause of death.

Acknowledgements

We are grateful to P. Bustamante and T. Székely for constructive criticism on an earlier version of the manuscript, and R. Thompson and A. Teare for invaluable explanations of ZIMS data. We are grateful to more than 1,200 zoo and aquarium members of Species360 that record data in ZIMS, making this study possible. O.V. was financed by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences and by the New National Excellence Programme of the Hungarian Ministry of Innovation and Technology. F.T. is supported by the MAVA Foundation, the ANR TRANSCAN (ANR-18-CE35-0009) and a CNRS International Associated Laboratory Grant. C.C.M. and A.M.B. were supported in part by NIH grant U54 CA217376. C.C.M. was also in part supported by NIH grant U2C CA233254, as well as CDMRP Breast Cancer Research Program Award BC132057 and the Arizona Biomedical Research Commission grant ADHS18-198847. D.A.C. was financed by the Species360 CSA sponsors: Copenhagen Zoo, Wildlife Reserves of Singapore and the World Association of Zoos and Aquariums. The findings, opinions and recommendations expressed here are those of the authors and not necessarily those of the universities where the research was performed or the United States National Institutes of Health.

Data availability

Data used for the analysis presented in the paper are available at https:// github.com/OrsolyaVincze/VinczeEtal2021Nature/blob/main/Supple mentaryData.xls. Raw data used to estimate cancer risk (Species360 Data Use Approval Number 73836) cannot be publicly shared, as Spe-cies360 is the custodian (not the owner) of their members' data. Raw data are accessible through Research Request applications (form available at https://docs.google.com/forms/d/1znoy62VEkDlhAp_0RfEvF7Zsx03g 4W5AlppJHqo3_WQ/viewform?edit_requested=true&pli=1). Research Requests are reviewed by both the Species360 Research Committee and their Board of Trustees every four months. The Board of Trustees makes the final decision on data sharing, based on recommendations by the Research Committee. Once Species360 grants access to data, they are intended only for and restricted to use in the project they were approved for and for a single publication. The researcher cannot use them for other projects, publications and/or purposes, nor can the researcher share the data with third parties. For any other inquires, all of the details for the submission of research requests to Species360 can be found at https://conservation.species360.org/wp-content/uploads/2020/08/ Species360-Sharing-Data-v3-3_komprimeret.pdf. Any email communications should be directed to support@species360.org.

Code availability

Data and R code needed to reproduce the analysis are publicly available at https://github.com/OrsolyaVincze/VinczeEtal2021Nature.

Extended Data Table 1 | Order differences in cancer mortality risk

measured as a, CMR or b, ICM. Only orders represented by a minimum of two species were included in these analyses. Models were constructed using R function "lm" using cancer mortality risks as a dependent variables and order as a sole predictor. Values presented are estimated marginal means and their associated 95% confidence intervals. The number of species for which data was available in each order is also shown. Post-hoc test of order differences in c, CMR or d, ICM across mammalian orders are shown.

Extended Data Table 2 | Association between cancer risk and diet animal content

Models explain variance in a, CMR or b, ICM in function of body mass, life expectancy and diet animal content. Diet animal content is characterized by variables on three taxonomic level: 1. animal content (including any vertebrate or invertebrate prey); 2. invertebrate or vertebrate prey; and 3. within vertebrates fish, reptiles, birds and mammals. Each diet item is coded as a two-level factor: rarely/never occurring in diet and representing the primary/secondary food item of the species. Each diet variable is added one by one to a base model containing the two significant predictors of cancer risk: body mass and life expectancy. Akaike's Information Criteria (AIC) are directly comparable among models with the same independent variable. All models are PGLS regressions.

Data Policy information about availability of data

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Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

Author contributions O.V. and M.G. contributed to the study conception; O.V., F.C., M.B., M.G., D.A.C., J.-F.L. and S.P. contributed to data collection and interpretation. O.V. performed statistical analysis with significant contribution from F.C. and S.P.; O.V. led the writing of the manuscript; all authors contributed to the interpretation of the results and writing of the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Article Extended Data Table 3 | Results of phylogenetic models exploring variation in of cancer mortality risk

Results are presented for both a, CMR (%) and b, ICM (%). Binomial phylogenetic GLMMs are presented first and phylogenetic GLSs exploring variation in non-zero cancer mortality risks are presented second. For each model sample size (n) and phylogenetic inertia (s2/λ) are presented at the bottom of results.

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Policy information about availability of computer code Data collection No software or code was used to collect the data. All raw data was obtained from Species360 (a non-profit organization centralizing zoo data), through a research request. Raw data can be obtained directly from the organization (see https://www.species360.org/serving-conservation/ research-data-request/)

Data analysis

All data analysis and graphical presentation was performed using R Statistical and Programming Environment, version 4.0.4 We estimated species-specific adult life expectancies from age-specific survival, estimated using a Kaplan-Meier procedure (using the "survfit" function in R package "survival" v. 3.2-11. Models of cancer risk were performed using zero-inflated logistic models. The first part of this model consisted of a phylogenetic binomial regression (using the function binaryPGLMM, in R package "ape" v. 5.5), the second part of the model consisted of a phylogenetic generalised least squares regression (PGLS, built using the function gls in R package "nlme" v. 3.1-153). Phylogenetic signal of cancer risk was assessed using the function 'phylosig' from R package 'phytools' v. 0.7-80. All packages and functions are described and fully referenced in the methods. Consensus phylogenetic tree was obtained using the 'sumtrees' library (v. 4.4.0) in python (v. 2.7.16).

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We worked with the widest database and largest sample size available at Species360. Species were considered for statistical analysis if minimum 20 individuals had postmortem pathological records. Models were however run by increasing this sample size to 40, 60, 80 and 100 to confirm the robustness of the results. Results of this sensitivity analysis is given in Supplementary Table 2.

Data exclusions

We excluded all species that were subject to domestication, because the process of domestication is widely regarded as a major contributing factor to inbreeding depression and higher incidence of cancer. We list in the species excluded due to this reason in Supplementary Table 1 and provide references in the manuscript summarizing the potential biases caused by domestication. Due to their high leverages (unusually low life expectancies) three species (Lagurus lagurus, Cricetus cricetus, and Dasyuroides byrnei) were eliminated from the main analysis presented in the manuscript. The thee species are listed in the Methods section, and the reason for their exclusion is explained. All analysis were run however also including these three species to ensure that the results are robust and not affected by the above exclusion. Results of the latter sensitivity analysis are presented in Supplementary Table 4 and Extended Data Figure 7.

Replication

To ensure the replicability of the results, we estimated two different metrics of cancer mortality risk, both estimating the proportion of individuals dying of cancer. All models are presented using both metrics as dependent variables. Moreover, to ensure the replicability of the results we performed sensitivity analysis using different minimum threshold of number of individuals with available postmortem pathological records. Results of these analyses are presented in Supplementary Table 2.

Where collinearity was plausible, models were re-run with single predictors to ensure that effects are consistent, as shown in Supplementary Table 3.

Randomization Initial species pool used here, represented the widest available husbandry database of Species360 at the time of data extraction. Exclusion included species subject to domestication (as shown in Supplementary Table 1) and three species whose leverage was concerning (all analysis were repeated including species, as presented in Supplementary Table 4). Diet categories were pre-defined by an independent published work (Kissling et al. 2014 Ecol.Evol.) and were adopted without modifications.

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Sophia Perdikaris

Sandrine Grouard

George Hambrecht

Megan Hicks

Anjana Mebane- Cruz

Reaksha Persaud

The Caves of Barbuda's Eastern Coast: Long term occupation, ethnohistory and ritual

Barbuda is the sister island to Antigua, located in the Lesser Antilles, West Indies. This island belongs to the Miocene arch of the Lesser Antilles, along with Grande Terre of Guadeloupe, Marie Galante, and Anguilla. Barbuda, notwithstanding its small size and low elevation, has an exceptionally rich past. Recent investigations by a Brooklyn College, City University of New York led team, has discovered evidence of human activity in and around these caves from the Archaic Period down to the present day. The range of activity at these caves begins with scatters of Archaic lithics, through artifacts and faunal material possibly produced by Obeah rituals to the contemporary celebrations and feasting activities that take place within and around these caves to this day. These contemporary cave based activities are central to the Barbudan people's relationship to their land and follow in the footsteps of the many waves of peoples that have called this island home for thousands of years. The idea of living from the land is celebrated many times a year through gatherings at the caves in which the food served has both African and Amerindian origins. Barbudans continue a long-term tradition of cave usage as shelter and feasting places where the food is cooked and shared and the only food consumed is what can be hunted and/or gathered from the wild. This tradition has been kept alive in the face of westernization and the threat of modernization. The caves of Barbuda offer a powerful example of changing human activities in one specific place through a truly longitudinal perspective.

Introduction

Investigations over the last five years by Brooklyn College (City University of New York) have been shedding light on the complex history of each culture that has inhabited Barbuda (Perdikaris 2010;Bain et al. 2010;[START_REF] Faucher | Archaeobotanical Studies on Barbuda, Preliminary Results from 2011[END_REF][START_REF] Hambrecht | Archaeological Investigations of Codrington Castle[END_REF][START_REF] Hambrecht | Preliminary Report of the January 2011 Excavations at Highland House[END_REF][START_REF] Hambrecht | Highland House Survey Report for the Barbuda Historical Ecology Project[END_REF]Pedikaris 2009;Perdikaris 2010;Perdikaris 2011). Of central concern is investigating the dynamics between culture and natural systems in this low-lying limestone island in the Leewards. During the 2010 field season, one of the objectives was to better understand the cave complexes that are found along the eastern/Atlantic side of Barbuda's coast and are remnants of the last interglacial shoreline. Caves have long served humans as areas of habitation, shelter and ritual. In areas with massive hurricanes, piracy, slavery and insecurity, these caves served as safe havens against hazards, both human and climatic. They hold a central place in local folklore and provide a powerful forum for heritage preservation by hosting rituals that connect to ancestral lifeways. Barbuda has a series of caves both inland and along the coast. The coastal caves that are central to the current discussion are all part of an escarpment that starts around the Castle Hill area (Figure 1. Map of Barbuda with location of the Caves and other sites) and then gradually rises up to about 100 feet above sea level.

Figure 1. Map of Barbuda Site Areas

The Castle Hill Bay area occupies a transition zone between the low lying south west of Barbuda and the Highland zone to the NW. It also provides a boundary between the Atlantic seacoast, with its extensive reefs, shifting sand beaches, mangrove bordered lagoons, backwater seeps and seasonal pockets of freshwater along with the Hog Hole fresh water spring and the dry forest of the interior of the island. The limestone caves and the plateau above also act to trap fresh water during the rainy season, which seeps through several of the cave ceilings to form small stalactites regularly visited by birds seeking moisture. The entire ridge stretching over 7 miles to the north of Castle Hill Bay is full of caves of various sizes of which 20 were surveyed during January 2010. Only one cave, Cave 2 from the Nicey Complex, was excavated that season. A test pit was excavated in the mouth of the cave. An interior niche was excavated and there was also a surface collection of artifacts and ecofacts from the entire cave floor. The Castle Hill Cave and the Indian Cave at Two Foot bay were mapped and photographed. All caves were cored and samples were collected for phosphate testing [START_REF] Mcgovern | Cave Survey Report, Barbuda Historical Ecology Project[END_REF].

The Castle Hill Bay caves are near the ocean with the southern-most being the furthest from the water and the northern-most the closest. Throughout the escarpment, fresh water resources are plentiful and in close proximity. Most of the caves show usage at various time periods at the surface. Some of the caves have collapsed while others are used extensively by feral donkeys and sheep whose trampling makes some of the earlier occupation markers harder to identify and require more extensive remote sensing. In future seasons, we will continue investigating the caves and perform extensive coring and testing to better understand their human use through time. The focus for the January 2010 field season was the complex of caves and shelters around the Castle Hill, Pigeon Cliff, Hog Hole and Two Foot Bay areas. Systematic survey and collection of surface finds was conducted for the three caves that are discussed in this paper [START_REF] Mcgovern | Cave Survey Report, Barbuda Historical Ecology Project[END_REF]. All surface finds were located by GPS and extensive photographic documentation and mapping of the interior and exterior of the caves was carried out. This survey was followed by more intensive soil coring and systematic collection combined with the stratigraphic excavation with 100% 3 mm mesh sieving of a single 2X2 test pit outside of Cave 2. These investigations documented prehistoric, Archaic, Ceramic Age, Historic, and recent 20 th -21 st century occupation, including a small shell midden of apparent Saladoid date AD 400-600 [START_REF] Mcgovern | Cave Survey Report, Barbuda Historical Ecology Project[END_REF]. Only the historic and modern time period finds will be discussed in this paper.

Castle Hill Cave

The Castle Hill Cave is a focus of contemporary feasting, hunting and gathering. It is the largest of the caves that are being used for these purposes by modern Barbudans. It is only when this cave reaches full capacity that the rest of the caves further north on the escarpment are occupied. All cave dwelling is on a first come first served basis. The spatial organization of these caves is quite intriguing and they all seem to have separate areas to represent the functions taking place during the events that the locals call "living from the land". Living from the land can be simple hunting parties that use the caves for shelter, dressing and cooking game meat over the course of a weekend or single individuals enjoying peace and tranquility who want to take time away camping and eating things they can collect or hunt. It can also mean entire families using time in the caves to connect with each other and friends, having communal meals, playing dominos or cards and exchanging stories. During the living from the land outings, all the protein cooked is either hunted or collected. A popular time in the year for those camping activities is around Easter and during late May/ early June when the hermit crabs migrate to the water to lay their eggs. The hermit crab roe is used for making fungi, a local dish with African roots. Castle Hill is similar in layout to two other caves that are used for "living from the land" today. It contains a central dwelling area with sleeping platforms. Two tables are placed at the front of the sleeping platform. One is for the food service and underneath there is charcoal storage. A second table lower to the ground is used for the serving of alcoholic beverages. Hanging from the cave ceiling by the food table is a spice rack holding matches and food seasoning. The placement of the spice rack allows it to survive bad weather and it is always left stocked ready for the next gathering.

To the west of the main cave and adjacent to it, is the turtle collecting pen. Scattered in a cascade fashion outside this pen are numerous carapaces and bones from consumed land tortoises (Chelonoidis carbonaria, [START_REF] Breuil | Histoire naturelle des Amphibiens et Reptiles terrestres de l'archipel Guadeloupeen. Guadeloupe, Saint Martin, Saint-Barthelemy[END_REF]) referred to as "turtles" by the Barbudans). With an estimated MNI of 130 individuals, many more are expected to be found when we excavate areas that are now no longer easily visible. A butcher block is used for the processing of the land tortoise. After being removed from their shell the tortoises are placed in boiling water to remove the skin and then are butchered, bone in, and placed in the cooking pot.

Figure 2. Castle Hill Cave

All pork processing is kept away from any other animal processing since the different animals are never cooked together in the same pot. Separate butcher blocks and tables are used for processing each of the animal groups involved. The variety of animals that are consumed during feasting include boar (Sus scrofa) , deer (Dama dama), cattle (Bos Taurus), land tortoises, caprines, birds, doves and Guinea Hens, as well as many varieties of fish and hermit crabs (Coenobita clypeatus). The ungulates are processed in the deer area, the fish and shellfish did not have assigned areas for processing but it is quite likely they already come preprocessed to the site.

Next to the tortoise processing area are shallow caverns that are used for the safe keeping of the cooking pots and utensils in between visits. In front of the cooking pot storage area is a table for processing boar and a few feet away a table for processing deer, along with a clothing line for drying laundry. The boar is shaved, singed, and then butchered and placed in the cooking pot. The cooking pit area has a long grill and all pots are placed next to each other for cooking.

The deer is first taken to the deer processing area in the northeast part of the cave where the animal is hung by the calcaneus on a stick that hangs from a tree (in other caves it is hung in the same manner from the cave ceiling itself) and is then dressed. The skull and the lower limbs are removed and discarded. The skulls of stags are exhibited on the outer cave wall. After the initial butchery, the deer carcass is then brought over to the processing table for further butchery before being placed in the cooking pot. All animals are hacked, bone in. No deboning takes places prior to consumption resulting in extensive splinter debris in the midden as well as parts of the cave floor.

Figure 3. Work table outside of Castle Cave.

In front of the cave along the feeding table numerous bones from other hunting and gathering activities were recorded, primarily barracuda (Sphyraena barracuda), parrot fish (genus Sparisoma), empty shells from hermit crab (locals call them soldier crabs) and conch.

About 100 feet away from the sleeping/eating area toward the east, there is a recreation area that has chairs and hammocks. This is in close proximity to nickel tree bushes where the seed "nickel" is extracted and used in playing wari.

Playing cards and dominoes are favorite activities and the traces of them are evident throughout the recreation area floor. Byproducts of the feasting activities are left behind. Some of the debris, primarily non-biodegradeable items such as bottles and cans are occasionally removed. In the following field seasons, systematic collection of the bone material will be performed and metrics will be collected for the major species. The tortoises will be sexed and samples will be retained for DNA analysis.

Two Foot Bay: Indian Cave

This cave marks the northern end of the escarpment that contains the coastal caves and it is one of the higher elevation caves. The cave has multiple rooms and two entrances. The most used entrance is to the east. A very narrow passageway with a low ceiling covers a distance of approximately 15 meters before arriving at the main chamber. The main chamber has a sunken feature to the north. To the south there is another low ceiling passage that leads to a wall containing two petroglyphs. The petroglyphs appear to represent two faces, possibly one male and the other female. Barbudans believe that they are Amerindian in origin and were placed there to keep bad spirits away from the cave. Both petroglyphs have deteriorated over the last few years from wind and humidity as well by being touched by visitors. Further exploration of the cave leads to a sinkhole feature in the middle of the cave. This chamber leads to a rock face entrance to the last northern cave chamber. The rock face above the entrance of this chamber coupled with the entrance itself has the appearance of a skull. Barbudans today show this cave to tourists visiting their island but will never use this cave as a shelter or dwelling during their "living from the land" celebrations. This cave is on occasion used for meals but it is uniformly avoided for overnight stays.

Nicey Cave Complex: Cave 2

This cave is a complex of five chambers with a rear entrance/skylight formed by a section of collapsed roof. There is an outer chamber directly accessible from outside, and three inner chambers divided by low ceiling ridges. Four of the chambers are substantial in size and one, chamber 5, is a very small circular crevice adjacent to the larger chamber 4. The cave has extensive stalagmites and stalactites and the cave floor is primarily sand that is covered by broken stalactites. The cave also features a circular stone enclosure similar to tortoise pens seen on modern sites except that the walls of the enclosure are too low to hold tortoises and the enclosure itself is far removed from daylight. (tortoise pens require light for the well being of the animals prior to harvesting). The artifacts recovered, with the exception of chamber 1 which is the cave mouth and is open to the outside, showed a complete lack of modern debris. The outer chamber had 2 hunting hangers (wood sticks hanging with rope from the cave ceiling and used for dressing game) and the mummified carcass of a donkey. Extensive entomological evidence and bones showed that often animals do die in the shelter of the outer chamber of this cave. About 10 feet away from the mouth of the cave, there were accumulations of stone tools, possibly archaic and a test excavation at the mouth of the cave uncovered an intact early Saladoid midden, both the tools and bones from the midden are under analysis and the results will be presented in a separate paper. The artifacts recovered from inside the cave are a 19 th century drinking glass, one late 18 th century or 19 th century fragment of a black glass bottle base, a dark brown gun flint (English) and a musket ball. A number of white clay pipe stems were found, none with maker's marks, as well as one fragment of a bowl. The diameter of the stems suggests dates of manufacture within the late 17 th century to the middle of the 18 th century. The bowl has no maker's mark and much of it has been broken off, obscuring the overall shape of the bowl, yet what remains can be tentatively dated to the late 17 th century or the early 18 th century.

The most distinctive characteristic of the faunal assemblage is the presence of a significant number of cat bone elements, Felus catus spp (see figure 3). The cat elements do not represent the whole of the cat skeleton. The cat bones showed a preponderance of ulna, femur and humerus elements. The top of a skull was also recovered along with a number of mandibles. Only one vertebra and a couple of ribs were recovered. Figure 3 represents the frequency of bones recovered with black color indicating the highest density and white the lack thereof. A number of the long bones were broken and exhibit spiral fractures. Spiral fractures are generally caused by twisting the bone from both ends, a process that is most likely caused by human intervention. The cat bones from this assemblage do not show any signs of chewing by predators or scavengers. Coupled with the spiral fractures this faunal assemblage would seem to be the product of human activity. The rest of the assemblage shows characteristics of a cave that saw intermittent but clear human activity. The typical prey animals are represented (see figure 8) as are scavengers such as rats and land crabs. Though the prey animals are represented it should be pointed out that the assemblage is quite atypical for a dwelling or feasting site such as those described earlier in this paper. The faunal material was primarily found in the darkest most inaccessible part of this cave. The majority of the artifacts also were found in this part of the cave, many of them in Chamber 5, the same place where the cat bones were found. The approximate time span in which these activities could have taken place based on artifact dating points to an early modern date from the late 17th century into the 19th century. Most of the artifacts were collected as a surface collection and cannot be used to date any particular event yet they do suggest that the cave might have been in use during the Historic Period from as early as the late 17 th century. Other archaeological research in the region has confirmed cat remains in Obeah contexts (see Haviser, J. 2010, 426-441, Obeah One tool that enslaved Africans utilized to negotiate and resist their bondage was ritual activity and religion. In the Anglophone islands of the Caribbean the creole ritual activity that developed is often termed Obeah (Olmos, Paravisini-Gebert 2011) Obeah is a difficult phenomenon to describe, in part due to its underground nature. is variously described as 'witchcraft', 'religion', and 'folk practice'. It has elements of medicinal use as well as poisoning and magic [START_REF] De Barros | Dispensers, Obeah and Quackery: Medical Rivalries in Post-Slavery British Guiana[END_REF][START_REF] Handler | Slave Medicine and Obeah in Barbados, Circa 1650 to 1834[END_REF][START_REF] Lazarus-Black | Legitimate Acts and Illegal Encounters: Law and Society in Antigua and Barbuda[END_REF][START_REF] Mantz | Enchanting Panics and Obeah Anxieties: Concealing and Disclosing Eastern Caribbean Witchcraft[END_REF][START_REF] Mcclure | Parallel Usage of Medicinal Plants by Africans and Their Caribbean Descendants[END_REF][START_REF] Paton | Obeah Acts: Producing and Policing the Boundaries of Religion in the Caribbean[END_REF]. All creolized, syncretic African-American ritual activity was practiced in a context in which secrecy was important because of the Euro-American bans on their practice. Obeah, perhaps more than other African-American rituals, was practiced in secret. It has been argued that the English colonial world was particularly unsuited to the creation of African-American hybrid activity (Elliott 2006;Canizares-Esguerra 2006). Obeah embodied everything that was anathema to the white Anglo-Protestants who associated it with pagan African religion, devil worship, and rebelliousness. Its leaders were thought to have a corrupting influence on other enslaved people and owners believed that the Africans used herbs for "witchcraft" that could destroy their crops and herds and poison the minds of the blacks and the bodies of white Christians. Obeah became a catch-all term for many misunderstood African practices and laws were passed throughout the North American colonies and the British Caribbean making it illegal to practice or participate in Obeah rituals [START_REF] Lazarus-Black | Legitimate Acts and Illegal Encounters: Law and Society in Antigua and Barbuda[END_REF]. Obeah had to stay deep underground (sometimes literally) in order to be practiced. The secrecy which preserved the practices in turn makes it very difficult to turn towards the sort of popular and scholarly material available for the description and study of african-creole ritual that developed in the French and Spanish Empires.

The archaeological evidence from Nicey Cave strongly suggests ritual practice. Artifacts that could be associated with Obeah, as well as their placement support this possibility. While the dating of this material is by no means precise we suggest that this activity might have been African based and might be something describable as Obeah. The timing is important here though. If this activity is indeed late 17th century or early 18th century then the population engaged in this might very well have been African born. There is even the very limited possibility that if it is late 17th century that the activity could have been in part European, possibly Irish, as well as African. There is a long and well documented history of European folk-magic activity centered on the abuse of cats (Darnton 1984). If on the other hand this is an 18th or even 19th century activity then defining it as Obeah might be more solid. Regardless it is highly suggestive evidence of ritual activity taking place in an out-of-the-way part of the island in the most inaccessible part of a dark and difficult to enter cave. In looking at what has been described of specific components that constitute obeah rituals, it is obvious that while there are some similarities in the basis of praxis, obeah is also adapted to local availability of resources. Some basic components in its ritual activity include cat bones, goblets and hollowed bones, colored glass bottles, gunpowder and rum, among others as well as extensive knowledge of herbs and medicinal plants [START_REF] Lazarus-Black | Legitimate Acts and Illegal Encounters: Law and Society in Antigua and Barbuda[END_REF][START_REF] Mantz | Enchanting Panics and Obeah Anxieties: Concealing and Disclosing Eastern Caribbean Witchcraft[END_REF][START_REF] Sturge | The West Indies in 1837: Being the Journal of a Visit to Antigua[END_REF]. The assemblage of cave 2 provides strong indications of a possible Obeah practice having taken place

Conclusions

The caves and rock shelters of Barbuda have been used by humans for thousands of years, and the 2010 cave survey team was able to document occupation beginning possibly as early as the Archaic , c.4500 years BCE, through the Saladoid (ca. 200BCE-1200CE) and Historic Periods (17 th -19 th century CE) down to contemporary use by 21 st century Barbudans. The caves as multifunctional spaces with differential use through time have been used for shelter, feasting and ritual activity.

The Barbuda Historical Ecology Project will investigate each of these different periods of use for the caves of the Atlantic coast, including the contemporary use of the caves (Cruz pers. Comm. 2011). Barbuda is one of the last remaining Caribbean islands that has escaped intensive development. Barbudans have been able to maintain a relationship to their environment that is unparalleled. The 'living off the land' activities as seen at the caves are an excellent example of this. Barbuda is not immune however to development and the island is changing rapidly. The need for economic growth has given rise to building activity that has destroyed archaeological sites and potential plans of further development will no doubt affect the Barbudan life style and identity. As the elders are passing so does their knowledge to this prized connection of "living from the land". To what degree the new generations of Barbudans will choose to maintain these life ways remains to be seen. As archaeologists and anthropologists we have been recording this historic transitional moment and have been collaborating with local authorities in involving the local high school youth in all the studies and excavations of the ethnographic cave use. Following in the footsteps of past Barbudans, the Barbudans of today maintain a relationship to their island in part through the use of the caves. The longitudinal study of the caves of Barbuda by the Barbuda Historical Ecology Project will continue to study the relationship of these caves to each population that has lived on the island of Barbuda, especially in reference to the their importance to contemporary Barbudans.

Figure 4 .

 4 Figure 4. Indian Cave. Note the skull-like impression of the cave. Jay Haviser and SIMARC students are standing in the mouth of the skull.

  Coring indicated 3 layers of sedimentation and 25-30cm of total depth. The entire cave floor was mapped cored and phosphate tested. Surface collection of all artifacts and ecofacts was performed. Chamber 5 was excavated in its entirety (McGovern et al 2010).

Figure 5 .

 5 Figure 5. Survey of Cave 2 with artifact distributions.

Figure 6 .

 6 Figure 6. Core from Inner Chamber 2.

Figure

  Figure 7. Frequency of elements present from Dodson and Wexier 1979 drawing by M. Coutureau.

Acknowledgments

The authors would like to thank the Barbudan Council for their support and encouragement. Dr. Reg Murphy, Mr. Calvin Gore and Mr. John Mussington for all their ongoing help and generous sharing of knowledge of Barbudan archaeology, history and ecology as well as all the students and colleagues that have been collaborating in the Barbuda Historical Ecology Project and Research Experience for Undergraduates NSF. This research has been supported by grant 0851727 REU "Islands of Change" to Dr. Sophia Perdikaris, grant 0732327 "NABO International Polar Year" to Dr. Thomas H McGovern from the US National Science Foundation Office of Polar Programs Arctic Social Sciences Program and PSC CUNY grants 2005-2008.

Author Information

Sophia Perdikaris Dept of Anthropology and Archaeology

Brooklyn College CUNY 2900 Bedford Avenue Brooklyn NY 11210 (sophiap@brooklyn.cuny.edu)