Palaeoclimatic and ecological determinants of mammalian host–pathogen exposure in Neanderthal-associated sites across Eurasia

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Trájer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8718874/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Understanding Neanderthal interactions with Late Pleistocene ecosystems requires examining the dynamic interplay between climate, faunal communities, and potential zoonotic exposures. This study analyses 42 Neanderthal-bearing fossil assemblages across Eurasia, focusing on stratigraphic units directly associated with skeletal remains and considering only extant mammal taxa or their close relatives to infer plausible pathogen risks. By integrating palaeoclimate reconstructions with contemporary host–pathogen data, we reconstruct site-specific environmental conditions, distributions of invertebrate vectors, and the presence of mammalian reservoir hosts. Rodents, ungulates, wild suids, and carnivores frequently emerge as potential pathogen carriers, while vector-borne transmission via ticks and sand flies appears climate-dependent. Substantial variation in species richness and inferred pathogen diversity reflects local ecological context and host composition. These results provide an ecologically grounded framework for understanding how environmental and climatic factors shaped Neanderthal exposures to zoonotic agents may have, highlighting the broader role of ecological interactions in hominin health and habitat use, and underscoring the need for further paleopathological evidence to validate predicted patterns. Neanderthals palaeoclimate mammal assemblages zoonotic pathogens vectors Late Pleistocene ecological networks Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Humans are active ecological agents, whose biology, culture, and behaviour are intertwined with their environments (Milner-Gulland, 2012 ). Patterns of subsistence, habitat use, social organization, and health both reflect ecological conditions and contribute to transforming them, with infectious disease emerging from the dynamic interactions among pathogens, hosts, and human behaviour (Alexander and McNutt, 2010 ). Within this framework, Neanderthals were exposed to complex disease environments structured by climate, animal communities, and arthropod vectors. These long-term exposures left immune-related genetic signatures that were later passed on to modern humans across Eurasia (Houldcroft and Underdown, 2016 ). This Neanderthal genomic legacy remains detectable in present-day non-African populations, where approximately 1–4% of the genome in many individuals derives from these archaic hominins (Prüfer et al., 2014 ). Genetic admixture took place during the Late Pleistocene, roughly overlapping with the transition from the late Middle to the early Upper Palaeolithic, with introgression events dated to around 65–45 ka (Prüfer et al., 2014 ; Sankararaman et al., 2014 ). Importantly, Neanderthal-derived DNA is especially common in genes involved in innate and antiviral immune responses, suggesting that these inherited variants helped early modern humans cope with the unfamiliar disease environments they encountered as they expanded beyond Africa (e.g., Dannemann et al., 2016 ; Enard and Petrov, 2018 ). Notable examples of immune-related introgression include genomic regions involved in innate and antiviral defence. One of the best-characterized cases is the TLR6–TLR1–TLR10 gene cluster, which contains haplotypes of Neanderthal origin that influence gene expression, improve microbial recognition, and modulate inflammatory responses (Dannemann et al., 2016 ). Similarly, the OAS gene cluster (OAS1–OAS3) harbours a Neanderthal-derived haplotype associated with changes in OAS1 expression and splicing, including the restoration of an ancestral, high-activity isoform. This variant has been linked to a reduced risk of severe viral infections in present-day populations (Huffman et al., 2022 ; Yousfi et al., 2024). Additional introgressed immune-related loci—such as GMEB2, GBP4/7, CCR9/CXCR6, and PNMA1/MIDEAS—further highlight the lasting influence of Neanderthal introgression on the structure and function of the modern human immune system (Reilly et al., 2022 ). Despite these genomic signals, direct evidence for infectious disease in Neanderthals remains limited. Recent advances in molecular palaeopathology and biomolecular archaeology, however, have begun to illuminate aspects of Neanderthal health and disease ecology (e.g., Pääbo et al., 2014). Metagenomic analyses of calcified dental plaque (calculus) from El Sidrón recovered typical oral commensals and pathogens, reconstructed a draft genome of Methanobrevibacter oralis dated to approximately 48,000 years ago, and detected the gastrointestinal microsporidian Enterocytozoon bieneusi in an individual with a dental abscess (Weyrich et al., 2017). These findings provide rare molecular insights into Neanderthal-associated microbial communities and systemic infections. Skeletal and biomolecular evidence further suggests exposure to zoonotic pathogens. A reassessment of the La Chapelle-aux-Saints 1 skeleton identified vertebral lesions consistent with brucellosis, supporting the possibility of infection acquired through contact with animal reservoirs (Rothschild and Haeusler, 2021 ). Independent biochemical and molecular investigations of Neanderthal fossils from Subalyuk Cave detected lipid biomarkers characteristic of the Mycobacterium tuberculosis complex (MTBC), including mycocerosates, alongside IS6110 PCR and spoligotyping signals and compatible pathological changes (Lee et al., 2023; Pálfi et al., 2023). While the Pleistocene ecology of MTBC and the relative roles of human- versus animal-adapted lineages remain debated, these results support the inference that Neanderthals experienced tuberculosis-like disease, plausibly acquired through zoonotic exposure during hunting, carcass processing, or competition with carnivores. Calculus metagenomics also indicates that Neanderthal disease ecology was embedded within complex multi-host food webs. The identification of Enterocytozoon bieneusi at El Sidrón, alongside plant secondary metabolites interpreted as possible evidence for self-medication, highlights how dietary practices and environmental exposure may have influenced morbidity patterns (Weyrich et al., 2017; Klaisnerová, 2022 ). While some parasite identifications from deep time rely on indirect evidence or derive from non-Neanderthal coprolites, the growing synthesis of skeletal pathology, lipid biomarkers (Lee et al., 2023), pathogen-specific DNA (Weyrich et al., 2017), and oral metagenomic data (Sankaranarayanan and Kodiveri Muthukaliannan, 2024 ) supports a view of Neanderthal health that included zoonotic spillovers—such as infections with Brucella spp., MTBC members, and microsporidia—superimposed upon persistent non-zoonotic burdens, including respiratory and oral disease. This integrated disease landscape was shaped by subsistence strategies, cave-based habitation, environmental exposure, and social care within Neanderthal groups (Rothschild and Haeusler, 2021 ). Assessing the plausibility of such pathogen exposures requires consideration of the evolutionary histories of candidate infectious agents. Several viral groups associated with arthropod vectors exhibit deep evolutionary roots compatible with Pleistocene circulation. Bunyaviruses—including Ťahyňa-, Bhanja-, and Akabane-like viruses—display extensive serogroup diversification and utilize mosquitoes, ticks, and biting midges as vectors (Elliott, 2014 ). Tick-borne flaviviruses diversified mainly during the Holocene, yet basal lineages may date back millions of years (Grard et al., 2007 ). Nairoviruses, including ancestors of Crimean–Congo haemorrhagic fever virus, likely originated in Africa, with more recent diversification events but an overall older lineage (Anagnostou and Papa, 2009 ). Phleboviruses such as Rift Valley fever virus diversified recently, yet the genus itself has deeper evolutionary roots (Bird et al., 2007 ). Lyssaviruses diversified mainly during the Holocene, but their bat reservoirs suggest older, possibly Pleistocene origins (Badrane and Tordo, 2001 ). Bacterial zoonoses with deep evolutionary histories suggest plausible Neanderthal exposure. Anaplasma phagocytophilum likely emerged in Eurasia with its tick vectors (Foley et al., 2008 ). Babesia species diverged by the Miocene and co-evolved with large herbivores, while Ixodes and Rhipicephalus ticks harbour ancient Rickettsia and Borrelia lineages (Lack et al., 2012 ). Evidence for Bacillus anthracis in Pleistocene fauna or hominins is lacking (Rigou et al., 2022 ). Fungal pathogens also indicate ancient exposure. Histoplasma capsulatum lineages diverged hundreds of thousands of years ago (Kasuga et al., 2003 ), whereas dermatophytes evolved more recently and were likely less widespread among Neanderthals. Protozoan and helminth parasites show long-standing host associations. Leishmania species and their sandfly vectors plausibly circulated across Pleistocene Eurasia (Schönian et al., 2018). Broad lineages of Leptospira , Cryptosporidium , and Giardia predate the Holocene (Giraud-Gatineau et al., 2024 ). Helminths such as Echinococcus diverged prior to domestication, and Fasciola species split during the Miocene–Pliocene (Knapp et al., 2011 ; Mas-Coma et al., 2005). Ancient coccidians ( Sarcocystis ) and nematodes ( Gongylonema ) further indicate deep evolutionary host–pathogen systems (Doležel et al., 1999 ; Chabaud and Bain, 1994 ). Together, genomic evidence of adaptive introgression, emerging palaeopathological data, and the deep evolutionary histories of candidate pathogens support the view that Neanderthals inhabited complex disease landscapes shaped by climate, ecology, and subsistence practices. Reconstructing these palaeopathogenic environments provides a framework for understanding not only Neanderthal health but also the selective pressures that shaped immune variation inherited by modern human populations. 2. Materials and methods 2.1. Aim and analytical workflow This study reconstructs palaeopathogenic exposure landscapes for Neanderthals by integrating fossil mammal assemblages, palaeoclimatic reconstructions, and modern host–pathogen–vector associations. By combining palaeoecological data with machine learning–based climatic and ecological projections, it assesses how environmental conditions shaped potential zoonotic risks in Neanderthal habitats. The analytical workflow comprised five main steps: Compilation of fossil mammal assemblages : Mammalian faunas from 42 stratigraphic units securely associated with Neanderthal remains were compiled to ensure reliable human–fauna co-occurrence. Not all Mousterian sites can be attributed to Neanderthals, as similar lithic assemblages were produced by early modern humans, particularly in the Levant (Shea & Bar-Yosef, 2005). Restricting the dataset to Neanderthal-bearing units avoids conflation of tool culture with species identity. Palaeoclimate classification : Modern Köppen–Geiger climate types were embedded in a two-dimensional climatic space using PCA of bioclimatic variables (Köppen, 1936 ; Peel, 2007). A K-nearest neighbour (KNN) classifier applied to palaeoclimate reconstructions assigned climate types to Neanderthal site-age pairs. Some palaeoclimatic conditions may lack direct modern analogues (Hill, 2015 ), so these assignments represent approximate analogies. Prediction of vector suitability : The PCA–KNN framework was used to project palaeoclimates onto modern distributions of invertebrate vectors linked to zoonotic pathogens, estimating potential vector presence. Mapping potential mammalian hosts : Fossil assemblages were cross-referenced with contemporary host–pathogen databases to identify taxa capable of carrying bacterial, viral, and protozoan agents (e.g., Borrelia burgdorferi s.l., Coxiella burnetii , TBEV). Only extant taxa or close relatives were included, as the zoonotic potential of extinct species lacking modern analogues cannot be reliably evaluated. Host richness and relative frequency were quantified at each site. Diversity metrics : Shannon and Simpson indices were used to evaluate diversity patterns in mammalian hosts and associated pathogens across the 42 sites. 2.2. Neanderthal remains-related fossil mammal assemblages A total of 42 Middle Paleolithic sites (Fig. 1 ) were analysed in Eurasia, where Neanderthal remains and fossil mammal assemblages were found in the same strata. Most of the sites are concentrated in Europe, but Middle Eastern and Central Asian sites also can be found among them. Data was obtained from the OCEEH Out of Africa Database (ROAD) (Kandel et al., 2023 ). All sites and layers contain both human remains and mammal assemblages. Supplementary Table S1 shows the main data of studied Neanderthal sites. 2.3. Selection of pathogens Direct evidence for infectious diseases in Middle Palaeolithic populations is scarce; therefore, pathogen selection was based on ecological plausibility rather than direct detection. To identify pathogens that could realistically have affected Neanderthal populations, three criteria were applied: Evolutionary antiquity : the pathogen or its close relatives must predate or overlap with the Pleistocene. Zoonotic ecology : the pathogen must circulate in wild animal reservoirs or environmental niches, without reliance on agriculture, domestication, or dense human populations. Transmission mode : vector-borne, environmentally transmitted, or wildlife-associated pathogens were considered more plausible than those requiring sustained human-to-human transmission. 2.4. Machine learning-based paleoclimate and host–vector predictions For the classification of palaeoclimatic reconstructions, as well as the prediction of likely hosts, pathogens, and vectors associated with Neanderthal and faunal assemblages, a k-nearest neighbor algorithm (k-NN), a non-parametric supervised learning method, was applied (Halder et al., 2024 ). 2.4.1. Paleoclimate reconstruction Training data for palaeoclimate classification were derived from the georeferenced global Köppen dataset (Beck et al., 2018 ), with climatic data for 1970–2000 obtained from WorldClim v2.1 (Fick et al., 2017). Test data consisted of fossil Neanderthal and mammalian occurrences, including site-specific ages and geographic coordinates, while palaeoclimatic reconstructions were based on the PALEO-PGEM-Series, spanning the last 5 million years (Barreto et al., 2023 ). Supplementary Table S2 shows the Köppen-like climatic categories used. Training and test datasets were structured as bioclimatic tables. The training dataset comprises extant species with known distributions and corresponding climate classifications ( Supplementary Table S3 ), while the test dataset contains palaeoclimate reconstructions for Neanderthal-associated sites. Missing or infinite values were replaced with column-wise means, and predictor variables were standardized. Bioclimatic features were selected to capture key environmental dimensions for species distributions, with the species label as the response variable for k-NN classification. Classification was implemented using scikit-learn’s KNeighborsClassifier (Mahesh & Amanullah, 2025 ), with 20 neighbors, Manhattan distance (p = 1), and distance-based weighting. k-NN is widely used in ecological classification for its interpretability and ability to handle nonlinear boundaries (Altman, 1992 ). The trained model predicted palaeoclimate classes for each fossil site, and accuracy was evaluated internally using scikit-learn metrics (Pedregosa et al., 2011 ). Predicted sites were visualized in two-dimensional feature space with matplotlib (Hunter, 2007 ), using a predefined RGB palette for Köppen codes and highlighting test sites with enlarged red markers. This allowed direct comparison between training climate classes and predicted palaeoclimates within ecological and archaeological contexts. 2.4.2. Selection of fossil mammals for analysis In this study, fossil mammal taxa were limited to species that are extant or have closely related modern counterparts (e.g., Bison priscus → Bison bonasus ; Bos primigenius → domestic cattle) to allow meaningful inferences about their potential as zoonotic reservoirs. Extinct taxa lacking close modern analogues, particularly large-bodied megafauna, were excluded. This approach focuses on plausible reservoirs, especially small- to medium-sized mammals such as rodents, lagomorphs, and carnivores, while potentially underrepresenting pathogen diversity linked to extinct hosts. Supplementary Data S1.xlsx provides the zoonotic potential of mammals and the fossil assemblages for Neanderthal site–stratum pairs, including only taxa of zoonotic relevance in a binary presence–absence format. 2.4.3. Host and vector prediction A k-nearest neighbour (k-NN) approach was used to predict potential hosts, vectors, and pathogens associated with fossil Neanderthal and faunal assemblages. The training dataset comprised global occurrences of hosts and vectors linked to climate and environmental variables, while the test dataset included fossil sites with Neanderthal and mammalian specimens ( Supplementary Table S4 ). Both datasets were pre-processed by imputing missing or infinite values with column-wise means, extracting feature matrices, and standardizing variables to improve classification accuracy (Elmore & Richman, 2001 ). Occurrence records were sourced from GBIF (Luo et al., 2021 ). Due to the large number of known zoonotic pathogens, the analysis focused on representative taxa, including ticks, insects, freshwater snails and crustaceans, terrestrial vertebrates, unicellular eukaryotic pathogens, and viruses. The k-NN classifier used 20 neighbours with Minkowski distance (p = 1) and distance-based weighting. Predictions for each fossil site were based on the nearest neighbours, with the 10 closest neighbours also examined to identify alternative plausible taxa. Training and test data were visualized in two-dimensional feature space, with test sites highlighted and annotated by predicted taxa. This integration of palaeoclimate reconstruction with host–vector prediction provides a quantitative and interpretable framework for assessing the ecological context of Neanderthals and associated mammalian assemblages. Only taxa with established host–pathogen relationships or close phylogenetic analogues were retained ( Supplementary Table S5 ). All computations were conducted using standard Python scientific libraries (pandas, numpy, scikit-learn, matplotlib) to ensure reproducibility. 2.5. Diversity metrics and software To quantify patterns of ecological and epidemiological complexity across sites, Shannon and Simpson diversity indices were calculated for mammalian host assemblages and associated pathogens. These indices capture both richness and evenness and were used to compare site-level differences in inferred pathogen exposure and host diversity. All analyses were implemented in Python (Harris et al., 2020 ) and executed using Python 3.10 (64-bit). Spatial analysis and geoprocessing operations were performed in QGIS 3.31.11 using GRASS GIS 8.4.0. 3. Results 3.1. Environmental background 3.1.1. Predicted palaeoclimates KNN-based palaeoclimatic reconstruction shows that Neanderthal-bearing stratigraphic units consistently fall within a restricted set of Köppen–Geiger climate regimes. These are dominated by Mediterranean temperate (Csa), oceanic temperate (Cfb), and continental climates (Dfb–Dsb), while steppe and boreal conditions are comparatively rare (Fig. 2 ). Mediterranean hot-summer temperate environments (Köppen Csa) are primarily associated with Levantine and southeastern European sites, including Dederiyeh Cave (Layers 1 and 3), Ras el-Kelb (Tunnel Trench Unit K), Ksar Akil (Layer XXV), Amud Cave (Layers B1, 2, and 4), Tabun C, and Kalamakia (Unit IV). In contrast, many Western European assemblages are linked to warm, humid temperate oceanic climates (Köppen Cfb), notably at Saint-Césaire I, Le Moustier, La Ferrassie, Combe-Grenal, Hortus, Grotta Guattari, and El Sidrón. Continental climates without a pronounced dry season are widely represented across Central and Eastern Europe. Cold-winter, hot-summer continental conditions (Köppen Dfa) were inferred for Vindija Cave (Units G1 + 2 and G3), whereas cold-winter, warm-summer continental climates (Köppen Dfb) characterize a broad range of sites, including Subalyuk, Krapina, Bordu Mare Cave, Fumane, Riparo Tagliente, Ciota Ciara, Spy, Feldhofer Grotte, Kulna, Zaskalnaya VI, Sakajia Cave, Rozhok I, and Scladina. More arid continental settings with dry summers (Köppen Dsb) are reconstructed for Shanidar (Layer D) and Teshik-Tash (Occupation Layers I–V). Climatic extremes are less common but clearly defined. Cold, arid steppe conditions (Köppen BSk) are predicted for Cova Negra (Level 2–2B) and Cova del Gegant (Episode 3a), whereas boreal environments with cold winters and humid summers (Köppen Dfc) characterize the Neanderthal-bearing deposits of Chagyrskaya Cave (Stratum 6a and c/1) and Denisova Cave (East Chamber, Layer 12.3). Taken together, these results indicate that Neanderthal occupations spanned a wide yet non-random climatic range, with a strong emphasis on temperate and continental environments and only limited expansion into ecological extremes. 3.1.2. Predicted invertebrate host and vector environment Using K-nearest neighbour predictions, Neanderthal-associated sites and faunal assemblages were linked to likely vector taxa, revealing climate-dependent patterns across Eurasia. As it was already mentioned, these predictions are based on modern vector distributions and ecological associations; while they provide plausible reconstructions for the Pleistocene, some vector–pathogen combinations may have differed from the actual Neanderthal-era communities. In Mediterranean and Levantine contexts (Sites 3–8), sand flies ( Phlebotomus ), the established vector of human-pathogenic Leishmania , frequently rank as top-1 or top-2 predicted vectors, with Chrysomyia or Hyalomma often appearing in the complementary position. Cold, arid steppe sites (Sites 1–2: Cova Negra, Cova del Gegant) show Hyalomma dominating top-1 predictions, while Phlebotomus appears as a secondary candidate, indicating a shift in vector composition under steppe conditions. Western European temperate sites display mixed top-1 predictions: Phlebotomus is often present, but Dermacentor and Ixodes occasionally dominate, reflecting local variability. Continental and boreal sites (Dfb–Dsb), including Vindija, Subalyuk, Krapina, and Chagyrskaya, are frequently dominated by ixodid ticks ( Ixodes , Dermacentor ) in top-1 predictions, with Phlebotomus or Hyalomma appearing as secondary vectors. Overall, while Phlebotomus occurs widely across sites, vector rankings vary regionally and by climate type, with ticks prevailing in northern and continental zones and Hyalomma in steppe environments (Fig. 3 ). Complete top-1 and top-2 predictions for all 42 sites are provided in Supplementary Table S6 . 3.2. Potential host and reservoir competence of mammal assemblages Mammalian assemblages associated with Neanderthal sites exhibit pronounced variation in potential pathogen load, host susceptibility, and site-level diversity. In the following sections, we examine patterns of pathogen distribution among host taxa, site-level richness, and diversity indices, revealing how ecological context and host composition shaped the structuring of pathogen communities across the fossil record. All inferred host-pathogen associations are based on extant or closely related taxa. Pathogen exposure mediated by extinct species without living analogues, especially megafauna, is not captured and may have contributed additional epidemiological complexity. 3.2.1. Host mammal and potential pathogen diversities Following the selection criteria outlined in Methods 2.3, a subset of representative pathogens was retained for analysis, focusing on taxa with ecological plausibility and Pleistocene relevance. Within this retained subset, pathogen spectra across Neanderthal-associated sites are dominated by generalist zoonoses. Toxoplasma gondii emerges as the most frequent, associated with 45 host species, followed by Francisella tularensis (40), Listeria monocytogenes (32), and Yersinia pseudotuberculosis (30). Other recurrent pathogens include Leptospira grippotyphosa , Coxiella burnetii , tick-borne encephalitis virus (Flavivirus TBE), Erysipelothrix rhusiopathiae , Salmonella enterica , and Lyssavirus s.s. (rabies group). Overall, this retained dataset captures a broad diversity of vector-borne bacteria and viruses, as well as directly transmitted protozoan and fungal agents (Fig. 4 a). By limiting analysis to pathogens meeting the predefined criteria, these results provide a tractable and ecologically meaningful representation of potential zoonotic exposure in Neanderthal-associated mammalian assemblages. Pathogen burden is unevenly distributed among host taxa. Rodents, particularly the Apodemus flavicollis–A. sylvaticus complex (31 pathogens) and Microtus arvalis (27), carry the highest loads, followed by larger mammals such as Bos primigenius (25) and Lepus europaeus (23). Other heavily burdened hosts include wild suids ( Sus scrofa ), voles ( Clethrionomys glareolus ), and a range of medium-sized carnivores and small ungulates, such as Arvicola sapidus , Sorex minutus , Sorex araneus , and Arvicola amphibius (16–22 pathogens each). These patterns indicate that a combination of rodents, lagomorphs, suids, ungulates, and small carnivores functioned as key pathogen reservoirs in Neanderthal-associated ecosystems (Fig. 4 b). 3.2.2. Site-level patterns of species richness and pathogen load Species richness and mean pathogen burden per host species varied substantially across Neanderthal-associated sites. The highest richness was observed at Chagyrskaya S6a (35 species), Ciota Ciara (31), and Scladina (27), while the most depauperate assemblages occurred at Feldhofer Grotte (2 species). Notably, high species richness did not always translate into elevated pathogen loads. For instance, Chagyrskaya S6a, despite its 35 host species, exhibited only a moderate mean pathogen burden (6.91), whereas Cova del Gegant, with just three host species, supported the highest mean burden (20.33). Sites of intermediate richness often displayed a combination of moderate diversity and pathogen load, such as Vindija G1 (14 species, 10.87 mean pathogens), Vindija G3 (17 species, 9.50), and Fumane UA3 (14 species, 11.50). Conversely, some assemblages showed very low pathogen values despite moderate richness, including Teshik Tash (9 species, 3.11). Together, these patterns indicate that pathogen diversity was shaped by host composition and ecological context, rather than species richness alone (Fig. 5 ). 3.2.3. Spatial and taxonomic variation in pathogen-host associations Analysis of mammalian host associations across pathogens revealed clear spatial and taxonomic structuring across Europe, the Near East, Central Asia, and Southern Siberia (Fig. 6 a–l). While some pathogens were widespread, others exhibited geographically restricted distributions, reflecting their ecological specificity and host requirements. For example, Borrelia burgdorferi s.l. and Coxiella burnetii showed broad geographic coverage, with moderate to high host richness across central and Eastern Europe and the Levant, consistent with their reliance on diverse rodent and ungulate reservoirs. In contrast, Erysipelothrix rhusiopathiae and Leishmania tropica were more spatially limited, occurring sporadically in Europe or largely confined to the Eastern Mediterranean, highlighting their dependency on particular rodent or canine hosts. Other pathogens displayed intermediate or variable distributions. Tick-borne encephalitis virus (Flavivirus TBE) and Francisella tularensis were concentrated in central and Eastern Europe, reflecting local rodent and insectivore communities, while Leptospira grippotyphosa and Listeria monocytogenes appeared widely across much of Europe but with lower to moderate host richness at individual sites, consistent with opportunistic colonization patterns. Some pathogens were consistently generalist, such as Salmonella enterica and Toxoplasma gondii , which exhibited high relative host richness across nearly all sampled regions, from Western Europe to Southern Siberia. By contrast, Yersinia pseudotuberculosis showed a narrower geographic and host range, with elevated associations in Eastern Europe and Western Asia. Collectively, these patterns indicate that pathogen distributions were shaped by both host availability and ecological constraints, producing clear regional and taxonomic variation in Neanderthal-associated ecosystems. 3.2.4. Diversity indices (Shannon and Simpson) Shannon and Simpson diversity indices further highlight variation in pathogen-host assemblages across sites. The highest diversity scores were observed at Kalamakia (Shannon 3.76), Scladina (3.75), Bordu Mare (3.75), and Ciota Ciara (3.70), reflecting combinations of moderate-to-high species richness with relatively balanced pathogen distributions. Cova del Gegant presents an interesting contrast: despite an extremely high mean pathogen load per species, it exhibits a high Shannon index (3.57) but a relatively low Simpson value (0.54). This reflects a combination of moderate-to-high richness (captured by Shannon) alongside strong dominance by a few highly prevalent pathogens (captured by Simpson), highlighting how the two indices emphasize different aspects of diversity. At the lower extreme, Shukbah LD (Shannon 0.69, Simpson 0.00) and Amud Cave LB4 (Shannon 1.39, Simpson 0.50) exhibited minimal diversity, consistent with their restricted faunal assemblages. El Sidrón represents an intermediate case of note, combining moderate richness (8 species) with a very high mean pathogen load (12.13) and correspondingly high diversity (Shannon 3.45, Simpson 0.81), suggesting unusually complex pathogen-host networks at this site (Fig. 7 a–b). Overall, these indices reveal that both species richness and evenness of pathogen distributions contribute to site-level diversity, and that high pathogen loads do not necessarily coincide with maximal diversity. 4. Discussion 4.1. Paleoclimatic reconstruction of Neanderthal sites Using the K-nearest neighbour (KNN) algorithm, Neanderthal-bearing deposits were assigned to Köppen climate types, largely supported by faunal evidence. Cold, arid steppe conditions (BSk) are reflected by large mammals such as Equus caballus and Capra pyrenaica , exemplified at Cova Negra and Cova del Gegant (Fosse et al., 2020 ). Warm, dry Mediterranean-type conditions (Csa) are indicated by taxa like Gazella gazella , Camelus sp., and cervids such as Cervus elaphus , seen at Dederiyeh Cave, Tabun Cave, and Ksar Akil (Bar-Yosef, 1998). Transitional continental climates (Dfa–Dfb) are supported by Bison priscus , Bos primigenius , and Capra ibex , reflecting open grasslands, parklands, and rugged terrain; dental microwear evidence confirms mixed foraging and habitat use (Hofman-Kamińska et al., 2024). Overall, KNN-based climate assignments align closely with faunal patterns, validating the paleoclimatic reconstruction. 4.2. Host–pathogen patterns in Neanderthal sites Several patterns emerge from the analysis of potential zoonotic reservoirs. A central finding is the apparent decoupling of mammalian host species richness from potential pathogen burden in Neanderthal remains-bearing strata. For example, Cova del Gegant exhibited extraordinarily high mean pathogen loads despite very low mammalian richness, whereas faunally rich sites such as Chagyrskaya S6a contained only moderate pathogen burdens. Site-level comparisons of host richness, mean pathogen burden, and diversity indices indicated that high host richness did not necessarily translate into high pathogen load. Instead, evenness and community composition, as captured by Shannon and Simpson indices, appeared to be more important determinants of pathogen structure. Similarity metrics revealed that a limited set of generalist pathogens persisted across most sites, producing recurrent host–pathogen associations dominated by geographically widespread taxa. This pattern aligns with theoretical and empirical work showing that host–pathogen systems are often structured around ecologically flexible pathogens capable of exploiting diverse hosts (Altizer et al., 2003; Dobson & Foufopoulos, 2001 ; Keesing et al., 2006 ). These findings indicate that zoonotic pressure in Neanderthal habitats was not governed solely by overall biodiversity, but also by the presence and relative abundance of specific reservoir hosts. This mechanism parallels dynamics documented in modern disease ecology, including the “dilution effect” and the role of “amplifier hosts” (Levi et al., 2016 ). In such systems, pathogen prevalence is shaped less by total host richness than by the dominance of highly competent reservoir species. Classic examples include Peromyscus leucopus amplifying Borrelia burgdorferi in low-diversity North American forests (Ostfeld & Keesing, 2000 ) and hantavirus prevalence depending on the density of particular rodent hosts (Jonsson et al., 2010 ). 4.3. Key mammalian contributors In the present study, rodents, ungulates, and small carnivores repeatedly emerged as key contributors to potential pathogen diversity and persistence across sites. Their disproportionate representation among inferred pathogen hosts suggests that stable amplifier species were embedded in Pleistocene ecosystems, comparable to modern rodents and ungulates functioning as amplifying or diluting hosts for zoonotic agents such as Borrelia burgdorferi s.l. (Krawczyk et al., 2022 ). Consistent with this, potential mammal hosts of Borrelia burgdorferi s.l. and Leptospira grippotyphosa occurred in 30 and 40 of the 42 studied Neanderthal-associated assemblages, respectively. It should be noted that while rodents, ungulates, and small carnivores dominate predicted pathogen networks, some large extinct taxa are absent from the analysis, which may slightly bias assessments of host contribution and pathogen richness. Zooarchaeological evidence provides a plausible behavioural context for exposure. Multiple Lower and Middle Palaeolithic Mediterranean sites document intensive exploitation of small mammals, particularly rabbits. Leporid assemblages from MIS 11–3 contexts in southern France show cut marks and burning consistent with butchery and cooking (Morin et al., 2019 ), while at Gabasa (Spain) rabbits dominate the faunal assemblage and exhibit anthropogenic modifications (Jaouen et al., 2022 ). Evidence from Abri du Maras further indicates Neanderthal exploitation of fast small game during MIS 4 (Hardy et al., 2013 ). During processing and consumption, Neanderthals would have come into direct contact with blood, urine, and other body fluids—recognized transmission routes for pathogens such as Leptospira spp. and Francisella tularensis (Lo et al., 2025 ). Large-bodied ungulates likely played important roles in pathogen transmission. Sites show tightly clustered associations between a few dominant herbivores—such as Bos primigenius , Cervus elaphus , and Microtus arvalis —and multiple inferred pathogens, suggesting transmission systems stabilized by a restricted group of core hosts (Cattadori et al., 2005 ). Palaeopathological evidence indicates Neanderthals were affected by brucellosis and mycobacterial infections, consistent with frequent Brucella and Mycobacterium host taxa (Rothschild & Haeusler, 2021 ). Potential hosts of Brucella abortus and Mycobacterium bovis were present in most of the analysed site-age units. Carnivores, including mustelids and felids, also represent relevant reservoirs. Zooarchaeological evidence indicates that Neanderthals processed and modified carnivore carcasses for pelts and other purposes (Russo et al., 2023 ; Abrams et al., 2025 ), entailing direct contact with tissues and secretions. In modern Eurasian ecosystems, mesocarnivores are established reservoirs for multiple zoonoses, including Echinococcus multilocularis , Leptospira spp., and rabies virus (Ionică et al., 2022 ; Schneider et al., 2023 ). Potential hosts of E. multilocularis were present in key sites including Denisova Cave, Chagyrskaya, Kůlna, Zaskalnaya VI, and Scladina. 4.4. Vector ecology and climatic structuring Vector predictions further highlight the climatic structuring of Neanderthal disease landscapes. Predicted vector assemblages rely on modern analogues and KNN-based climate matching, so these results represent likely scenarios rather than direct evidence. KNN models indicate that Phlebotomus sand flies frequently rank among the top predicted vectors in Mediterranean, Levantine, and steppe contexts, while ixodid ticks ( Ixodes , Dermacentor ) dominate continental and boreal environments. Potential mammalian hosts of Leishmania infantum were present in 25 Neanderthal-associated sites, supporting the plausibility of recurring leishmaniasis as a zoonotic risk (Tuon et al., 2008). In colder steppe and continental settings, vector composition included both sand flies and ticks, with Hyalomma prominent at Shanidar and Teshik-Tash, reflecting modern Central and Southwest Asian ecologies where these ticks transmit Crimean–Congo haemorrhagic fever virus and Rickettsia (Estrada-Peña et al., 2012 ). Western European oceanic sites showed mixed vector assemblages, while boreal sites were dominated by ixodid ticks. 4.5. Genetic and immunological implications These ecological reconstructions are particularly relevant considering genetic evidence for introgressed Neanderthal immune alleles in modern humans. The persistence of Neanderthal-derived TLR6–TLR1–TLR10 haplotypes, associated with microbial recognition including Borrelia antigens (Dannemann et al., 2016 ; Oosting et al., 2011 ; Dedkov et al., 2017 ), and OAS1–3 haplotypes influencing antiviral responses (Sams et al., 2016 ; Banday et al., 2022 ), aligns with long-term exposure to vector-borne bacteria and viruses. Associations between OAS polymorphisms and tick-borne encephalitis outcomes further support the adaptive relevance of these loci in Eurasian pathogen landscapes (Bogovic & Strle, 2015 ). 4.6. Limitations and scope of inferences The analyses of this study are inherently constrained by the reliance on extant mammal species or those with close modern analogues, which allows for plausible inferences of zoonotic potential. This approach may underestimate total pathogen diversity, as extinct taxa without living relatives—some of which could have served as key reservoirs—are not represented. Consequently, certain Pleistocene host–pathogen interactions might be overlooked, and reconstructed disease landscapes may emphasize generalist pathogens associated with surviving taxa. Nonetheless, most excluded extinct taxa are large-bodied megafauna (e.g., proboscideans, woolly rhinoceroses), typically representing only 1–3 species per site and a minor fraction of overall host diversity. Contemporary evidence further suggests that known zoonotic reservoirs are disproportionately concentrated among rodents, bats, and primates, whereas large-bodied taxa such as elephants or rhinoceroses contribute minimally to human zoonotic disease burdens (Han et al., 2016 ). 5. Conclusions Neanderthal populations inhabited diverse host–vector–pathogen environments shaped by paleoclimate. Phlebotomus sand flies dominated Mediterranean and temperate settings, whereas ixodid ticks prevailed in continental and boreal contexts. Generalist pathogens, including Toxoplasma gondii , Francisella tularensis , and Listeria monocytogenes , infected a wide range of mammals, while specialists were rare. Site-level analyses revealed variability in host–pathogen composition, with the presence and abundance of key hosts strongly influencing local zoonotic risk. Ancient viral genomes in Neanderthal remains provide direct evidence of exposure to vector-borne agents. Overall, these findings demonstrate that Neanderthals were embedded in pathogen landscapes structured by paleoclimate, reservoir identity, and host community composition, highlighting the role of infectious diseases in their ecology and adaptive pressures. Declarations Funding Funding number: bo/00896/24/5. Ethics approval Not applicable. Clinical trial number Not applicable. References Abrams, G., Auguste, P., Pirson, S., De Groote, I., Halbrucker, É., Di Modica, K., Pironneau, C., Dedrie, T., Meloro, C., Fischer, V., Bocherens, H., Vanbrabant, Y., & Bray, F. 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Parasites & Vectors , 16 (1), 425. https://doi.org/10.1186/s13071-023-06026-2 Additional Declarations No competing interests reported. Supplementary Files SupplementaryDataS1.xlsx SupplementaryTableS1.docx SupplementaryTableS2.docx SupplementaryTableS3.docx SupplementaryTableS4.docx SupplementaryTableS5.docx SupplementaryTableS6.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 20 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 30 Jan, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 28 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8718874","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583163019,"identity":"d98bc73f-8a9e-440a-bde6-b7105ab131ec","order_by":0,"name":"Attila J. Trájer","email":"data:image/png;base64,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","orcid":"","institution":"University of Pannonia","correspondingAuthor":true,"prefix":"","firstName":"Attila","middleName":"J.","lastName":"Trájer","suffix":""}],"badges":[],"createdAt":"2026-01-28 09:57:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8718874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8718874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101652293,"identity":"ee36b9ba-b4bd-4c15-b40c-2bf273ce19ec","added_by":"auto","created_at":"2026-02-02 09:29:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6466154,"visible":true,"origin":"","legend":"\u003cp\u003eThe 42 studied fossil mammal assemblages containing Neanderthal fossil sites in Eurasia with some examples of Neanderthal fossil specimens found in the studied sites and strata. The details of the sites are given in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure1sitemapcraniums.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/b60840d577593abf1f9acfa6.png"},{"id":101652300,"identity":"c7e4d564-8ec1-4701-abf9-2f0e468e4215","added_by":"auto","created_at":"2026-02-02 09:29:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4595353,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of modern Köppen–Geiger climate classes into bioclimatic space (PCA), with palaeoclimatic positions of 42 Neanderthal site-age pairs classified using a K-nearest neighbours (KNN) model. Each labelled point represents a stratigraphic horizon containing Neanderthal remains, positioned according to its climatic similarity to modern environments. The full list of sites and corresponding Köppen climate classes is given in \u003cstrong\u003eSupplementary Tables S1\u003c/strong\u003e and \u003cstrong\u003eS2\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure2Knnresultpalaeoclimates02.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/f103a80239cf7d00fa1a0222.png"},{"id":101652303,"identity":"1cd94f7a-38b4-48e8-81dc-fe0976b3e4bf","added_by":"auto","created_at":"2026-02-02 09:29:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2757766,"visible":true,"origin":"","legend":"\u003cp\u003eModern bioclimatic data were used to train a K-nearest neighbours (KNN) classifier, which then predicted the most similar parasitic, host, and vector invertebrate taxa for each palaeoclimatic layer. The plot shows a two-dimensional representation (Feature 1 and Feature 2) derived from principal component analysis (PCA) of the original bioclimatic variables. Neanderthal-bearing stratigraphic horizons are plotted according to their climatic similarity to modern environments. The full list of site-age pairs and associated fossil mammal assemblages is provided in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure3vKnnresultinvertebrates02.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/5ebbf47c296f27c1d247f109.png"},{"id":101652302,"identity":"da4a24cd-5905-4ea3-9d68-20806448d5dd","added_by":"auto","created_at":"2026-02-02 09:29:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":457567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e: The most common potential pathogens; \u003cstrong\u003eb\u003c/strong\u003e: the most infected host species. Mammal abbreviations: ApSy+Fl: \u003cem\u003eApodemus sylvaticus\u003c/em\u003e-\u003cem\u003eA. flavicollis\u003c/em\u003ecomplex, MiAr: \u003cem\u003eMicrotus arvalis\u003c/em\u003e, BoPr: \u003cem\u003eBos primigenius\u003c/em\u003e, LeEu: \u003cem\u003eLepus europeus\u003c/em\u003e, SuSc: \u003cem\u003eSus scrofa\u003c/em\u003e, ClGl: \u003cem\u003eClethrionomys glareolus\u003c/em\u003e, ArSa: \u003cem\u003eArvicola sapidus\u003c/em\u003e, SoMi: \u003cem\u003eSorex minutus\u003c/em\u003e, SoAr: \u003cem\u003eSorex araneus\u003c/em\u003e, ArAm: \u003cem\u003eArvicola amphibius\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure4Top10pathogeninfectedhost.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/b803f977f99ad907d18e85f7.png"},{"id":101652298,"identity":"44a0ced9-46e5-434d-9d18-cba38be2aee2","added_by":"auto","created_at":"2026-02-02 09:29:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":439185,"visible":true,"origin":"","legend":"\u003cp\u003ePotential pathogen diversity of mammal assemblages related to Neanderthal sites.\u003c/p\u003e","description":"","filename":"Figure5Pathogendiversitypersite.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/7b3620034d27ada1fda1ad79.png"},{"id":101652266,"identity":"76fbe5d8-3891-452e-a960-ef8934ec5b7d","added_by":"auto","created_at":"2026-02-02 09:29:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48116474,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative number of fossil host mammals found in Southern Siberian, Central and Western Eurasian Neanderthal remains-bearing strata related to different pathogen taxa. \u003cstrong\u003ea\u003c/strong\u003e: \u003cem\u003eBorrelia burgdorferi\u003c/em\u003es. l., \u003cstrong\u003eb\u003c/strong\u003e: \u003cem\u003eCoxiella burnetii\u003c/em\u003e, \u003cstrong\u003ec\u003c/strong\u003e: \u003cem\u003eErysipelothrix rhusiopathiae\u003c/em\u003e, \u003cstrong\u003ed\u003c/strong\u003e: \u003cem\u003eFlavivirus TBE\u003c/em\u003e, \u003cstrong\u003ee\u003c/strong\u003e: \u003cem\u003eFrancisella tularensis\u003c/em\u003e, \u003cstrong\u003ef\u003c/strong\u003e: \u003cem\u003eLeishmania tropica\u003c/em\u003e, \u003cstrong\u003eg\u003c/strong\u003e: \u003cem\u003eLeptospira grippotyphosa\u003c/em\u003e, \u003cstrong\u003eh\u003c/strong\u003e: \u003cem\u003eListeria monocytogenes\u003c/em\u003e, \u003cstrong\u003ei\u003c/strong\u003e: \u003cem\u003eLyssavirus rabies\u003c/em\u003e, \u003cstrong\u003ej\u003c/strong\u003e: \u003cem\u003eSalmonella enterica\u003c/em\u003e, \u003cstrong\u003ek\u003c/strong\u003e: \u003cem\u003eToxoplasma gondii\u003c/em\u003e, \u003cstrong\u003el\u003c/strong\u003e: \u003cem\u003eYersinia pseudotuberculosis\u003c/em\u003e. The details of the sites are given in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure6Relativenumbersreservoirs02.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/32f47b16d273c2d1e39c267f.png"},{"id":101652274,"identity":"b6fdece9-2b90-4f70-a49e-41333f44ff9a","added_by":"auto","created_at":"2026-02-02 09:29:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":738670,"visible":true,"origin":"","legend":"\u003cp\u003eShannon (\u003cstrong\u003ea\u003c/strong\u003e) and Simpson (\u003cstrong\u003eb\u003c/strong\u003e) values of potential pathogens and mammal species in Neanderthal sites.\u003c/p\u003e","description":"","filename":"Figure7SimpsonShannon.png","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/1e4cb81733a82400a4fa9e03.png"},{"id":101942801,"identity":"71b44d21-2c43-49c6-93a0-8a8a1a74c0ba","added_by":"auto","created_at":"2026-02-05 09:38:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":58639655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/c30fbf5f-5b89-4c6b-8019-493f32a9246f.pdf"},{"id":101652278,"identity":"4403e09b-04b4-4a32-a9eb-80e3b527efb3","added_by":"auto","created_at":"2026-02-02 09:29:07","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46162,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/7aaa95d9615aa00d1e48080e.xlsx"},{"id":101652296,"identity":"3d6e7361-7825-4d92-818d-f48ef6046f73","added_by":"auto","created_at":"2026-02-02 09:29:09","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21283,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/1ec77be9f8b7b871c22e55ea.docx"},{"id":101652301,"identity":"d705e24e-4801-4dc6-9858-bc0841ad8dd1","added_by":"auto","created_at":"2026-02-02 09:29:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16453,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/e8655ea9219c0f0ca15dca1e.docx"},{"id":101652249,"identity":"28493ebb-dc07-414d-8e4c-bef8ec89882b","added_by":"auto","created_at":"2026-02-02 09:29:02","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15521,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/a1d13e922d40c90c67cd3cee.docx"},{"id":101652275,"identity":"ef35a53b-64c6-4d97-a79d-732ffe2561c0","added_by":"auto","created_at":"2026-02-02 09:29:06","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15603,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/f8d0bd34aec7272429089a5d.docx"},{"id":101752562,"identity":"c51f1291-e1d8-4458-a8f3-83b494a9cbcc","added_by":"auto","created_at":"2026-02-03 10:28:11","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":23488,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/38cac09027dae8dea29309df.docx"},{"id":101652273,"identity":"a4ff3a31-fd5e-4461-a263-0de1daa5014a","added_by":"auto","created_at":"2026-02-02 09:29:05","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":18695,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718874/v1/edbe76b1d6084abaf59f01f8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Palaeoclimatic and ecological determinants of mammalian host–pathogen exposure in Neanderthal-associated sites across Eurasia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHumans are active ecological agents, whose biology, culture, and behaviour are intertwined with their environments (Milner-Gulland, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Patterns of subsistence, habitat use, social organization, and health both reflect ecological conditions and contribute to transforming them, with infectious disease emerging from the dynamic interactions among pathogens, hosts, and human behaviour (Alexander and McNutt, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Within this framework, Neanderthals were exposed to complex disease environments structured by climate, animal communities, and arthropod vectors. These long-term exposures left immune-related genetic signatures that were later passed on to modern humans across Eurasia (Houldcroft and Underdown, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This Neanderthal genomic legacy remains detectable in present-day non-African populations, where approximately 1\u0026ndash;4% of the genome in many individuals derives from these archaic hominins (Pr\u0026uuml;fer et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic admixture took place during the Late Pleistocene, roughly overlapping with the transition from the late Middle to the early Upper Palaeolithic, with introgression events dated to around 65\u0026ndash;45 ka (Pr\u0026uuml;fer et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sankararaman et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Importantly, Neanderthal-derived DNA is especially common in genes involved in innate and antiviral immune responses, suggesting that these inherited variants helped early modern humans cope with the unfamiliar disease environments they encountered as they expanded beyond Africa (e.g., Dannemann et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Enard and Petrov, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotable examples of immune-related introgression include genomic regions involved in innate and antiviral defence. One of the best-characterized cases is the TLR6\u0026ndash;TLR1\u0026ndash;TLR10 gene cluster, which contains haplotypes of Neanderthal origin that influence gene expression, improve microbial recognition, and modulate inflammatory responses (Dannemann et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, the OAS gene cluster (OAS1\u0026ndash;OAS3) harbours a Neanderthal-derived haplotype associated with changes in OAS1 expression and splicing, including the restoration of an ancestral, high-activity isoform. This variant has been linked to a reduced risk of severe viral infections in present-day populations (Huffman et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yousfi et al., 2024). Additional introgressed immune-related loci\u0026mdash;such as GMEB2, GBP4/7, CCR9/CXCR6, and PNMA1/MIDEAS\u0026mdash;further highlight the lasting influence of Neanderthal introgression on the structure and function of the modern human immune system (Reilly et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these genomic signals, direct evidence for infectious disease in Neanderthals remains limited. Recent advances in molecular palaeopathology and biomolecular archaeology, however, have begun to illuminate aspects of Neanderthal health and disease ecology (e.g., P\u0026auml;\u0026auml;bo et al., 2014). Metagenomic analyses of calcified dental plaque (calculus) from El Sidr\u0026oacute;n recovered typical oral commensals and pathogens, reconstructed a draft genome of \u003cem\u003eMethanobrevibacter oralis\u003c/em\u003e dated to approximately 48,000 years ago, and detected the gastrointestinal microsporidian \u003cem\u003eEnterocytozoon bieneusi\u003c/em\u003e in an individual with a dental abscess (Weyrich et al., 2017). These findings provide rare molecular insights into Neanderthal-associated microbial communities and systemic infections.\u003c/p\u003e \u003cp\u003eSkeletal and biomolecular evidence further suggests exposure to zoonotic pathogens. A reassessment of the La Chapelle-aux-Saints 1 skeleton identified vertebral lesions consistent with brucellosis, supporting the possibility of infection acquired through contact with animal reservoirs (Rothschild and Haeusler, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Independent biochemical and molecular investigations of Neanderthal fossils from Subalyuk Cave detected lipid biomarkers characteristic of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTBC), including mycocerosates, alongside IS6110 PCR and spoligotyping signals and compatible pathological changes (Lee et al., 2023; P\u0026aacute;lfi et al., 2023). While the Pleistocene ecology of MTBC and the relative roles of human- versus animal-adapted lineages remain debated, these results support the inference that Neanderthals experienced tuberculosis-like disease, plausibly acquired through zoonotic exposure during hunting, carcass processing, or competition with carnivores.\u003c/p\u003e \u003cp\u003eCalculus metagenomics also indicates that Neanderthal disease ecology was embedded within complex multi-host food webs. The identification of \u003cem\u003eEnterocytozoon bieneusi\u003c/em\u003e at El Sidr\u0026oacute;n, alongside plant secondary metabolites interpreted as possible evidence for self-medication, highlights how dietary practices and environmental exposure may have influenced morbidity patterns (Weyrich et al., 2017; Klaisnerov\u0026aacute;, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While some parasite identifications from deep time rely on indirect evidence or derive from non-Neanderthal coprolites, the growing synthesis of skeletal pathology, lipid biomarkers (Lee et al., 2023), pathogen-specific DNA (Weyrich et al., 2017), and oral metagenomic data (Sankaranarayanan and Kodiveri Muthukaliannan, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) supports a view of Neanderthal health that included zoonotic spillovers\u0026mdash;such as infections with \u003cem\u003eBrucella\u003c/em\u003e spp., MTBC members, and microsporidia\u0026mdash;superimposed upon persistent non-zoonotic burdens, including respiratory and oral disease. This integrated disease landscape was shaped by subsistence strategies, cave-based habitation, environmental exposure, and social care within Neanderthal groups (Rothschild and Haeusler, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssessing the plausibility of such pathogen exposures requires consideration of the evolutionary histories of candidate infectious agents. Several viral groups associated with arthropod vectors exhibit deep evolutionary roots compatible with Pleistocene circulation. Bunyaviruses\u0026mdash;including Ťahyňa-, Bhanja-, and Akabane-like viruses\u0026mdash;display extensive serogroup diversification and utilize mosquitoes, ticks, and biting midges as vectors (Elliott, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Tick-borne flaviviruses diversified mainly during the Holocene, yet basal lineages may date back millions of years (Grard et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Nairoviruses, including ancestors of Crimean\u0026ndash;Congo haemorrhagic fever virus, likely originated in Africa, with more recent diversification events but an overall older lineage (Anagnostou and Papa, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Phleboviruses such as Rift Valley fever virus diversified recently, yet the genus itself has deeper evolutionary roots (Bird et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Lyssaviruses diversified mainly during the Holocene, but their bat reservoirs suggest older, possibly Pleistocene origins (Badrane and Tordo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBacterial zoonoses with deep evolutionary histories suggest plausible Neanderthal exposure. \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e likely emerged in Eurasia with its tick vectors (Foley et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). \u003cem\u003eBabesia\u003c/em\u003e species diverged by the Miocene and co-evolved with large herbivores, while \u003cem\u003eIxodes\u003c/em\u003e and \u003cem\u003eRhipicephalus\u003c/em\u003e ticks harbour ancient \u003cem\u003eRickettsia\u003c/em\u003e and \u003cem\u003eBorrelia\u003c/em\u003e lineages (Lack et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Evidence for \u003cem\u003eBacillus anthracis\u003c/em\u003e in Pleistocene fauna or hominins is lacking (Rigou et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Fungal pathogens also indicate ancient exposure. \u003cem\u003eHistoplasma capsulatum\u003c/em\u003e lineages diverged hundreds of thousands of years ago (Kasuga et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), whereas dermatophytes evolved more recently and were likely less widespread among Neanderthals. Protozoan and helminth parasites show long-standing host associations. \u003cem\u003eLeishmania\u003c/em\u003e species and their sandfly vectors plausibly circulated across Pleistocene Eurasia (Sch\u0026ouml;nian et al., 2018). Broad lineages of \u003cem\u003eLeptospira\u003c/em\u003e, \u003cem\u003eCryptosporidium\u003c/em\u003e, and \u003cem\u003eGiardia\u003c/em\u003e predate the Holocene (Giraud-Gatineau et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Helminths such as \u003cem\u003eEchinococcus\u003c/em\u003e diverged prior to domestication, and \u003cem\u003eFasciola\u003c/em\u003e species split during the Miocene\u0026ndash;Pliocene (Knapp et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mas-Coma et al., 2005). Ancient coccidians (\u003cem\u003eSarcocystis\u003c/em\u003e) and nematodes (\u003cem\u003eGongylonema\u003c/em\u003e) further indicate deep evolutionary host\u0026ndash;pathogen systems (Doležel et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Chabaud and Bain, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTogether, genomic evidence of adaptive introgression, emerging palaeopathological data, and the deep evolutionary histories of candidate pathogens support the view that Neanderthals inhabited complex disease landscapes shaped by climate, ecology, and subsistence practices. Reconstructing these palaeopathogenic environments provides a framework for understanding not only Neanderthal health but also the selective pressures that shaped immune variation inherited by modern human populations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Aim and analytical workflow\u003c/h2\u003e \u003cp\u003eThis study reconstructs palaeopathogenic exposure landscapes for Neanderthals by integrating fossil mammal assemblages, palaeoclimatic reconstructions, and modern host\u0026ndash;pathogen\u0026ndash;vector associations. By combining palaeoecological data with machine learning\u0026ndash;based climatic and ecological projections, it assesses how environmental conditions shaped potential zoonotic risks in Neanderthal habitats.\u003c/p\u003e \u003cp\u003eThe analytical workflow comprised five main steps:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCompilation of fossil mammal assemblages\u003c/b\u003e: Mammalian faunas from 42 stratigraphic units securely associated with Neanderthal remains were compiled to ensure reliable human\u0026ndash;fauna co-occurrence. Not all Mousterian sites can be attributed to Neanderthals, as similar lithic assemblages were produced by early modern humans, particularly in the Levant (Shea \u0026amp; Bar-Yosef, 2005). Restricting the dataset to Neanderthal-bearing units avoids conflation of tool culture with species identity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePalaeoclimate classification\u003c/b\u003e: Modern K\u0026ouml;ppen\u0026ndash;Geiger climate types were embedded in a two-dimensional climatic space using PCA of bioclimatic variables (K\u0026ouml;ppen, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1936\u003c/span\u003e; Peel, 2007). A K-nearest neighbour (KNN) classifier applied to palaeoclimate reconstructions assigned climate types to Neanderthal site-age pairs. Some palaeoclimatic conditions may lack direct modern analogues (Hill, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), so these assignments represent approximate analogies.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePrediction of vector suitability\u003c/b\u003e: The PCA\u0026ndash;KNN framework was used to project palaeoclimates onto modern distributions of invertebrate vectors linked to zoonotic pathogens, estimating potential vector presence.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMapping potential mammalian hosts\u003c/b\u003e: Fossil assemblages were cross-referenced with contemporary host\u0026ndash;pathogen databases to identify taxa capable of carrying bacterial, viral, and protozoan agents (e.g., \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e s.l., \u003cem\u003eCoxiella burnetii\u003c/em\u003e, TBEV). Only extant taxa or close relatives were included, as the zoonotic potential of extinct species lacking modern analogues cannot be reliably evaluated. Host richness and relative frequency were quantified at each site.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiversity metrics\u003c/b\u003e: Shannon and Simpson indices were used to evaluate diversity patterns in mammalian hosts and associated pathogens across the 42 sites.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Neanderthal remains-related fossil mammal assemblages\u003c/h2\u003e \u003cp\u003eA total of 42 Middle Paleolithic sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were analysed in Eurasia, where Neanderthal remains and fossil mammal assemblages were found in the same strata. Most of the sites are concentrated in Europe, but Middle Eastern and Central Asian sites also can be found among them. Data was obtained from the OCEEH Out of Africa Database (ROAD) (Kandel et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). All sites and layers contain both human remains and mammal assemblages.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e shows the main data of studied Neanderthal sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Selection of pathogens\u003c/h2\u003e \u003cp\u003eDirect evidence for infectious diseases in Middle Palaeolithic populations is scarce; therefore, pathogen selection was based on ecological plausibility rather than direct detection. To identify pathogens that could realistically have affected Neanderthal populations, three criteria were applied:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEvolutionary antiquity\u003c/b\u003e: the pathogen or its close relatives must predate or overlap with the Pleistocene.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eZoonotic ecology\u003c/b\u003e: the pathogen must circulate in wild animal reservoirs or environmental niches, without reliance on agriculture, domestication, or dense human populations.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTransmission mode\u003c/b\u003e: vector-borne, environmentally transmitted, or wildlife-associated pathogens were considered more plausible than those requiring sustained human-to-human transmission.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Machine learning-based paleoclimate and host\u0026ndash;vector predictions\u003c/h2\u003e \u003cp\u003eFor the classification of palaeoclimatic reconstructions, as well as the prediction of likely hosts, pathogens, and vectors associated with Neanderthal and faunal assemblages, a k-nearest neighbor algorithm (k-NN), a non-parametric supervised learning method, was applied (Halder et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Paleoclimate reconstruction\u003c/h2\u003e \u003cp\u003eTraining data for palaeoclimate classification were derived from the georeferenced global K\u0026ouml;ppen dataset (Beck et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with climatic data for 1970\u0026ndash;2000 obtained from WorldClim v2.1 (Fick et al., 2017). Test data consisted of fossil Neanderthal and mammalian occurrences, including site-specific ages and geographic coordinates, while palaeoclimatic reconstructions were based on the PALEO-PGEM-Series, spanning the last 5\u0026nbsp;million years (Barreto et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e shows the K\u0026ouml;ppen-like climatic categories used.\u003c/p\u003e \u003cp\u003eTraining and test datasets were structured as bioclimatic tables. The training dataset comprises extant species with known distributions and corresponding climate classifications (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e), while the test dataset contains palaeoclimate reconstructions for Neanderthal-associated sites. Missing or infinite values were replaced with column-wise means, and predictor variables were standardized. Bioclimatic features were selected to capture key environmental dimensions for species distributions, with the species label as the response variable for k-NN classification.\u003c/p\u003e \u003cp\u003eClassification was implemented using scikit-learn\u0026rsquo;s KNeighborsClassifier (Mahesh \u0026amp; Amanullah, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), with 20 neighbors, Manhattan distance (p\u0026thinsp;=\u0026thinsp;1), and distance-based weighting. k-NN is widely used in ecological classification for its interpretability and ability to handle nonlinear boundaries (Altman, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The trained model predicted palaeoclimate classes for each fossil site, and accuracy was evaluated internally using scikit-learn metrics (Pedregosa et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Predicted sites were visualized in two-dimensional feature space with matplotlib (Hunter, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), using a predefined RGB palette for K\u0026ouml;ppen codes and highlighting test sites with enlarged red markers. This allowed direct comparison between training climate classes and predicted palaeoclimates within ecological and archaeological contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Selection of fossil mammals for analysis\u003c/h2\u003e \u003cp\u003eIn this study, fossil mammal taxa were limited to species that are extant or have closely related modern counterparts (e.g., \u003cem\u003eBison priscus\u003c/em\u003e \u0026rarr; \u003cem\u003eBison bonasus\u003c/em\u003e; \u003cem\u003eBos primigenius\u003c/em\u003e \u0026rarr; domestic cattle) to allow meaningful inferences about their potential as zoonotic reservoirs. Extinct taxa lacking close modern analogues, particularly large-bodied megafauna, were excluded. This approach focuses on plausible reservoirs, especially small- to medium-sized mammals such as rodents, lagomorphs, and carnivores, while potentially underrepresenting pathogen diversity linked to extinct hosts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Data S1.xlsx\u003c/b\u003e provides the zoonotic potential of mammals and the fossil assemblages for Neanderthal site\u0026ndash;stratum pairs, including only taxa of zoonotic relevance in a binary presence\u0026ndash;absence format.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Host and vector prediction\u003c/h2\u003e \u003cp\u003eA k-nearest neighbour (k-NN) approach was used to predict potential hosts, vectors, and pathogens associated with fossil Neanderthal and faunal assemblages. The training dataset comprised global occurrences of hosts and vectors linked to climate and environmental variables, while the test dataset included fossil sites with Neanderthal and mammalian specimens (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBoth datasets were pre-processed by imputing missing or infinite values with column-wise means, extracting feature matrices, and standardizing variables to improve classification accuracy (Elmore \u0026amp; Richman, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Occurrence records were sourced from GBIF (Luo et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to the large number of known zoonotic pathogens, the analysis focused on representative taxa, including ticks, insects, freshwater snails and crustaceans, terrestrial vertebrates, unicellular eukaryotic pathogens, and viruses.\u003c/p\u003e \u003cp\u003eThe k-NN classifier used 20 neighbours with Minkowski distance (p\u0026thinsp;=\u0026thinsp;1) and distance-based weighting. Predictions for each fossil site were based on the nearest neighbours, with the 10 closest neighbours also examined to identify alternative plausible taxa. Training and test data were visualized in two-dimensional feature space, with test sites highlighted and annotated by predicted taxa.\u003c/p\u003e \u003cp\u003eThis integration of palaeoclimate reconstruction with host\u0026ndash;vector prediction provides a quantitative and interpretable framework for assessing the ecological context of Neanderthals and associated mammalian assemblages. Only taxa with established host\u0026ndash;pathogen relationships or close phylogenetic analogues were retained (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/b\u003e). All computations were conducted using standard Python scientific libraries (pandas, numpy, scikit-learn, matplotlib) to ensure reproducibility.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Diversity metrics and software\u003c/h2\u003e \u003cp\u003eTo quantify patterns of ecological and epidemiological complexity across sites, Shannon and Simpson diversity indices were calculated for mammalian host assemblages and associated pathogens. These indices capture both richness and evenness and were used to compare site-level differences in inferred pathogen exposure and host diversity. All analyses were implemented in Python (Harris et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and executed using Python 3.10 (64-bit). Spatial analysis and geoprocessing operations were performed in QGIS 3.31.11 using GRASS GIS 8.4.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Environmental background\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Predicted palaeoclimates\u003c/h2\u003e \u003cp\u003eKNN-based palaeoclimatic reconstruction shows that Neanderthal-bearing stratigraphic units consistently fall within a restricted set of K\u0026ouml;ppen\u0026ndash;Geiger climate regimes. These are dominated by Mediterranean temperate (Csa), oceanic temperate (Cfb), and continental climates (Dfb\u0026ndash;Dsb), while steppe and boreal conditions are comparatively rare (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMediterranean hot-summer temperate environments (K\u0026ouml;ppen Csa) are primarily associated with Levantine and southeastern European sites, including Dederiyeh Cave (Layers 1 and 3), Ras el-Kelb (Tunnel Trench Unit K), Ksar Akil (Layer XXV), Amud Cave (Layers B1, 2, and 4), Tabun C, and Kalamakia (Unit IV). In contrast, many Western European assemblages are linked to warm, humid temperate oceanic climates (K\u0026ouml;ppen Cfb), notably at Saint-C\u0026eacute;saire I, Le Moustier, La Ferrassie, Combe-Grenal, Hortus, Grotta Guattari, and El Sidr\u0026oacute;n.\u003c/p\u003e \u003cp\u003eContinental climates without a pronounced dry season are widely represented across Central and Eastern Europe. Cold-winter, hot-summer continental conditions (K\u0026ouml;ppen Dfa) were inferred for Vindija Cave (Units G1\u0026thinsp;+\u0026thinsp;2 and G3), whereas cold-winter, warm-summer continental climates (K\u0026ouml;ppen Dfb) characterize a broad range of sites, including Subalyuk, Krapina, Bordu Mare Cave, Fumane, Riparo Tagliente, Ciota Ciara, Spy, Feldhofer Grotte, Kulna, Zaskalnaya VI, Sakajia Cave, Rozhok I, and Scladina. More arid continental settings with dry summers (K\u0026ouml;ppen Dsb) are reconstructed for Shanidar (Layer D) and Teshik-Tash (Occupation Layers I\u0026ndash;V).\u003c/p\u003e \u003cp\u003eClimatic extremes are less common but clearly defined. Cold, arid steppe conditions (K\u0026ouml;ppen BSk) are predicted for Cova Negra (Level 2\u0026ndash;2B) and Cova del Gegant (Episode 3a), whereas boreal environments with cold winters and humid summers (K\u0026ouml;ppen Dfc) characterize the Neanderthal-bearing deposits of Chagyrskaya Cave (Stratum 6a and c/1) and Denisova Cave (East Chamber, Layer 12.3).\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that Neanderthal occupations spanned a wide yet non-random climatic range, with a strong emphasis on temperate and continental environments and only limited expansion into ecological extremes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Predicted invertebrate host and vector environment\u003c/h2\u003e \u003cp\u003eUsing K-nearest neighbour predictions, Neanderthal-associated sites and faunal assemblages were linked to likely vector taxa, revealing climate-dependent patterns across Eurasia. As it was already mentioned, these predictions are based on modern vector distributions and ecological associations; while they provide plausible reconstructions for the Pleistocene, some vector\u0026ndash;pathogen combinations may have differed from the actual Neanderthal-era communities.\u003c/p\u003e \u003cp\u003eIn Mediterranean and Levantine contexts (Sites 3\u0026ndash;8), sand flies (\u003cem\u003ePhlebotomus\u003c/em\u003e), the established vector of human-pathogenic \u003cem\u003eLeishmania\u003c/em\u003e, frequently rank as top-1 or top-2 predicted vectors, with \u003cem\u003eChrysomyia\u003c/em\u003e or \u003cem\u003eHyalomma\u003c/em\u003e often appearing in the complementary position. Cold, arid steppe sites (Sites 1\u0026ndash;2: Cova Negra, Cova del Gegant) show \u003cem\u003eHyalomma\u003c/em\u003e dominating top-1 predictions, while \u003cem\u003ePhlebotomus\u003c/em\u003e appears as a secondary candidate, indicating a shift in vector composition under steppe conditions. Western European temperate sites display mixed top-1 predictions: \u003cem\u003ePhlebotomus\u003c/em\u003e is often present, but \u003cem\u003eDermacentor\u003c/em\u003e and \u003cem\u003eIxodes\u003c/em\u003e occasionally dominate, reflecting local variability. Continental and boreal sites (Dfb\u0026ndash;Dsb), including Vindija, Subalyuk, Krapina, and Chagyrskaya, are frequently dominated by ixodid ticks (\u003cem\u003eIxodes\u003c/em\u003e, \u003cem\u003eDermacentor\u003c/em\u003e) in top-1 predictions, with \u003cem\u003ePhlebotomus\u003c/em\u003e or \u003cem\u003eHyalomma\u003c/em\u003e appearing as secondary vectors. Overall, while \u003cem\u003ePhlebotomus\u003c/em\u003e occurs widely across sites, vector rankings vary regionally and by climate type, with ticks prevailing in northern and continental zones and \u003cem\u003eHyalomma\u003c/em\u003e in steppe environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Complete top-1 and top-2 predictions for all 42 sites are provided in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Potential host and reservoir competence of mammal assemblages\u003c/h2\u003e \u003cp\u003eMammalian assemblages associated with Neanderthal sites exhibit pronounced variation in potential pathogen load, host susceptibility, and site-level diversity. In the following sections, we examine patterns of pathogen distribution among host taxa, site-level richness, and diversity indices, revealing how ecological context and host composition shaped the structuring of pathogen communities across the fossil record. All inferred host-pathogen associations are based on extant or closely related taxa. Pathogen exposure mediated by extinct species without living analogues, especially megafauna, is not captured and may have contributed additional epidemiological complexity.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Host mammal and potential pathogen diversities\u003c/h2\u003e \u003cp\u003eFollowing the selection criteria outlined in Methods 2.3, a subset of representative pathogens was retained for analysis, focusing on taxa with ecological plausibility and Pleistocene relevance. Within this retained subset, pathogen spectra across Neanderthal-associated sites are dominated by generalist zoonoses. \u003cem\u003eToxoplasma gondii\u003c/em\u003e emerges as the most frequent, associated with 45 host species, followed by \u003cem\u003eFrancisella tularensis\u003c/em\u003e (40), \u003cem\u003eListeria monocytogenes\u003c/em\u003e (32), and \u003cem\u003eYersinia pseudotuberculosis\u003c/em\u003e (30). Other recurrent pathogens include \u003cem\u003eLeptospira grippotyphosa\u003c/em\u003e, \u003cem\u003eCoxiella burnetii\u003c/em\u003e, tick-borne encephalitis virus (Flavivirus TBE), \u003cem\u003eErysipelothrix rhusiopathiae\u003c/em\u003e, \u003cem\u003eSalmonella enterica\u003c/em\u003e, and \u003cem\u003eLyssavirus\u003c/em\u003e s.s. (rabies group). Overall, this retained dataset captures a broad diversity of vector-borne bacteria and viruses, as well as directly transmitted protozoan and fungal agents (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). By limiting analysis to pathogens meeting the predefined criteria, these results provide a tractable and ecologically meaningful representation of potential zoonotic exposure in Neanderthal-associated mammalian assemblages.\u003c/p\u003e \u003cp\u003ePathogen burden is unevenly distributed among host taxa. Rodents, particularly the \u003cem\u003eApodemus flavicollis\u0026ndash;A. sylvaticus\u003c/em\u003e complex (31 pathogens) and \u003cem\u003eMicrotus arvalis\u003c/em\u003e (27), carry the highest loads, followed by larger mammals such as \u003cem\u003eBos primigenius\u003c/em\u003e (25) and \u003cem\u003eLepus europaeus\u003c/em\u003e (23). Other heavily burdened hosts include wild suids (\u003cem\u003eSus scrofa\u003c/em\u003e), voles (\u003cem\u003eClethrionomys glareolus\u003c/em\u003e), and a range of medium-sized carnivores and small ungulates, such as \u003cem\u003eArvicola sapidus\u003c/em\u003e, \u003cem\u003eSorex minutus\u003c/em\u003e, \u003cem\u003eSorex araneus\u003c/em\u003e, and \u003cem\u003eArvicola amphibius\u003c/em\u003e (16\u0026ndash;22 pathogens each). These patterns indicate that a combination of rodents, lagomorphs, suids, ungulates, and small carnivores functioned as key pathogen reservoirs in Neanderthal-associated ecosystems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Site-level patterns of species richness and pathogen load\u003c/h2\u003e \u003cp\u003eSpecies richness and mean pathogen burden per host species varied substantially across Neanderthal-associated sites. The highest richness was observed at Chagyrskaya S6a (35 species), Ciota Ciara (31), and Scladina (27), while the most depauperate assemblages occurred at Feldhofer Grotte (2 species). Notably, high species richness did not always translate into elevated pathogen loads. For instance, Chagyrskaya S6a, despite its 35 host species, exhibited only a moderate mean pathogen burden (6.91), whereas Cova del Gegant, with just three host species, supported the highest mean burden (20.33).\u003c/p\u003e \u003cp\u003eSites of intermediate richness often displayed a combination of moderate diversity and pathogen load, such as Vindija G1 (14 species, 10.87 mean pathogens), Vindija G3 (17 species, 9.50), and Fumane UA3 (14 species, 11.50). Conversely, some assemblages showed very low pathogen values despite moderate richness, including Teshik Tash (9 species, 3.11). Together, these patterns indicate that pathogen diversity was shaped by host composition and ecological context, rather than species richness alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Spatial and taxonomic variation in pathogen-host associations\u003c/h2\u003e \u003cp\u003eAnalysis of mammalian host associations across pathogens revealed clear spatial and taxonomic structuring across Europe, the Near East, Central Asia, and Southern Siberia (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u0026ndash;l). While some pathogens were widespread, others exhibited geographically restricted distributions, reflecting their ecological specificity and host requirements. For example, \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e s.l. and \u003cem\u003eCoxiella burnetii\u003c/em\u003e showed broad geographic coverage, with moderate to high host richness across central and Eastern Europe and the Levant, consistent with their reliance on diverse rodent and ungulate reservoirs. In contrast, \u003cem\u003eErysipelothrix rhusiopathiae\u003c/em\u003e and \u003cem\u003eLeishmania tropica\u003c/em\u003e were more spatially limited, occurring sporadically in Europe or largely confined to the Eastern Mediterranean, highlighting their dependency on particular rodent or canine hosts.\u003c/p\u003e \u003cp\u003eOther pathogens displayed intermediate or variable distributions. Tick-borne encephalitis virus (Flavivirus TBE) and \u003cem\u003eFrancisella tularensis\u003c/em\u003e were concentrated in central and Eastern Europe, reflecting local rodent and insectivore communities, while \u003cem\u003eLeptospira grippotyphosa\u003c/em\u003e and \u003cem\u003eListeria monocytogenes\u003c/em\u003e appeared widely across much of Europe but with lower to moderate host richness at individual sites, consistent with opportunistic colonization patterns.\u003c/p\u003e \u003cp\u003eSome pathogens were consistently generalist, such as \u003cem\u003eSalmonella enterica\u003c/em\u003e and \u003cem\u003eToxoplasma gondii\u003c/em\u003e, which exhibited high relative host richness across nearly all sampled regions, from Western Europe to Southern Siberia. By contrast, \u003cem\u003eYersinia pseudotuberculosis\u003c/em\u003e showed a narrower geographic and host range, with elevated associations in Eastern Europe and Western Asia. Collectively, these patterns indicate that pathogen distributions were shaped by both host availability and ecological constraints, producing clear regional and taxonomic variation in Neanderthal-associated ecosystems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. Diversity indices (Shannon and Simpson)\u003c/h2\u003e \u003cp\u003eShannon and Simpson diversity indices further highlight variation in pathogen-host assemblages across sites. The highest diversity scores were observed at Kalamakia (Shannon 3.76), Scladina (3.75), Bordu Mare (3.75), and Ciota Ciara (3.70), reflecting combinations of moderate-to-high species richness with relatively balanced pathogen distributions.\u003c/p\u003e \u003cp\u003eCova del Gegant presents an interesting contrast: despite an extremely high mean pathogen load per species, it exhibits a high Shannon index (3.57) but a relatively low Simpson value (0.54). This reflects a combination of moderate-to-high richness (captured by Shannon) alongside strong dominance by a few highly prevalent pathogens (captured by Simpson), highlighting how the two indices emphasize different aspects of diversity. At the lower extreme, Shukbah LD (Shannon 0.69, Simpson 0.00) and Amud Cave LB4 (Shannon 1.39, Simpson 0.50) exhibited minimal diversity, consistent with their restricted faunal assemblages. El Sidr\u0026oacute;n represents an intermediate case of note, combining moderate richness (8 species) with a very high mean pathogen load (12.13) and correspondingly high diversity (Shannon 3.45, Simpson 0.81), suggesting unusually complex pathogen-host networks at this site (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u0026ndash;b).\u003c/p\u003e \u003cp\u003eOverall, these indices reveal that both species richness and evenness of pathogen distributions contribute to site-level diversity, and that high pathogen loads do not necessarily coincide with maximal diversity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Paleoclimatic reconstruction of Neanderthal sites\u003c/h2\u003e \u003cp\u003eUsing the K-nearest neighbour (KNN) algorithm, Neanderthal-bearing deposits were assigned to K\u0026ouml;ppen climate types, largely supported by faunal evidence. Cold, arid steppe conditions (BSk) are reflected by large mammals such as \u003cem\u003eEquus caballus\u003c/em\u003e and \u003cem\u003eCapra pyrenaica\u003c/em\u003e, exemplified at Cova Negra and Cova del Gegant (Fosse et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Warm, dry Mediterranean-type conditions (Csa) are indicated by taxa like \u003cem\u003eGazella gazella\u003c/em\u003e, \u003cem\u003eCamelus\u003c/em\u003e sp., and cervids such as \u003cem\u003eCervus elaphus\u003c/em\u003e, seen at Dederiyeh Cave, Tabun Cave, and Ksar Akil (Bar-Yosef, 1998). Transitional continental climates (Dfa\u0026ndash;Dfb) are supported by \u003cem\u003eBison priscus\u003c/em\u003e, \u003cem\u003eBos primigenius\u003c/em\u003e, and \u003cem\u003eCapra ibex\u003c/em\u003e, reflecting open grasslands, parklands, and rugged terrain; dental microwear evidence confirms mixed foraging and habitat use (Hofman-Kamińska et al., 2024). Overall, KNN-based climate assignments align closely with faunal patterns, validating the paleoclimatic reconstruction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Host\u0026ndash;pathogen patterns in Neanderthal sites\u003c/h2\u003e \u003cp\u003eSeveral patterns emerge from the analysis of potential zoonotic reservoirs. A central finding is the apparent decoupling of mammalian host species richness from potential pathogen burden in Neanderthal remains-bearing strata. For example, Cova del Gegant exhibited extraordinarily high mean pathogen loads despite very low mammalian richness, whereas faunally rich sites such as Chagyrskaya S6a contained only moderate pathogen burdens. Site-level comparisons of host richness, mean pathogen burden, and diversity indices indicated that high host richness did not necessarily translate into high pathogen load. Instead, evenness and community composition, as captured by Shannon and Simpson indices, appeared to be more important determinants of pathogen structure.\u003c/p\u003e \u003cp\u003eSimilarity metrics revealed that a limited set of generalist pathogens persisted across most sites, producing recurrent host\u0026ndash;pathogen associations dominated by geographically widespread taxa. This pattern aligns with theoretical and empirical work showing that host\u0026ndash;pathogen systems are often structured around ecologically flexible pathogens capable of exploiting diverse hosts (Altizer et al., 2003; Dobson \u0026amp; Foufopoulos, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Keesing et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These findings indicate that zoonotic pressure in Neanderthal habitats was not governed solely by overall biodiversity, but also by the presence and relative abundance of specific reservoir hosts. This mechanism parallels dynamics documented in modern disease ecology, including the \u0026ldquo;dilution effect\u0026rdquo; and the role of \u0026ldquo;amplifier hosts\u0026rdquo; (Levi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In such systems, pathogen prevalence is shaped less by total host richness than by the dominance of highly competent reservoir species. Classic examples include \u003cem\u003ePeromyscus leucopus\u003c/em\u003e amplifying \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e in low-diversity North American forests (Ostfeld \u0026amp; Keesing, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and hantavirus prevalence depending on the density of particular rodent hosts (Jonsson et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Key mammalian contributors\u003c/h2\u003e \u003cp\u003eIn the present study, rodents, ungulates, and small carnivores repeatedly emerged as key contributors to potential pathogen diversity and persistence across sites. Their disproportionate representation among inferred pathogen hosts suggests that stable amplifier species were embedded in Pleistocene ecosystems, comparable to modern rodents and ungulates functioning as amplifying or diluting hosts for zoonotic agents such as \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e s.l. (Krawczyk et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consistent with this, potential mammal hosts of \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e s.l. and \u003cem\u003eLeptospira grippotyphosa\u003c/em\u003e occurred in 30 and 40 of the 42 studied Neanderthal-associated assemblages, respectively. It should be noted that while rodents, ungulates, and small carnivores dominate predicted pathogen networks, some large extinct taxa are absent from the analysis, which may slightly bias assessments of host contribution and pathogen richness.\u003c/p\u003e \u003cp\u003eZooarchaeological evidence provides a plausible behavioural context for exposure. Multiple Lower and Middle Palaeolithic Mediterranean sites document intensive exploitation of small mammals, particularly rabbits. Leporid assemblages from MIS 11\u0026ndash;3 contexts in southern France show cut marks and burning consistent with butchery and cooking (Morin et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while at Gabasa (Spain) rabbits dominate the faunal assemblage and exhibit anthropogenic modifications (Jaouen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Evidence from Abri du Maras further indicates Neanderthal exploitation of fast small game during MIS 4 (Hardy et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). During processing and consumption, Neanderthals would have come into direct contact with blood, urine, and other body fluids\u0026mdash;recognized transmission routes for pathogens such as \u003cem\u003eLeptospira\u003c/em\u003e spp. and \u003cem\u003eFrancisella tularensis\u003c/em\u003e (Lo et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLarge-bodied ungulates likely played important roles in pathogen transmission. Sites show tightly clustered associations between a few dominant herbivores\u0026mdash;such as \u003cem\u003eBos primigenius\u003c/em\u003e, \u003cem\u003eCervus elaphus\u003c/em\u003e, and \u003cem\u003eMicrotus arvalis\u003c/em\u003e\u0026mdash;and multiple inferred pathogens, suggesting transmission systems stabilized by a restricted group of core hosts (Cattadori et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Palaeopathological evidence indicates Neanderthals were affected by brucellosis and mycobacterial infections, consistent with frequent Brucella and \u003cem\u003eMycobacterium\u003c/em\u003e host taxa (Rothschild \u0026amp; Haeusler, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Potential hosts of \u003cem\u003eBrucella abortus\u003c/em\u003e and \u003cem\u003eMycobacterium bovis\u003c/em\u003e were present in most of the analysed site-age units.\u003c/p\u003e \u003cp\u003eCarnivores, including mustelids and felids, also represent relevant reservoirs. Zooarchaeological evidence indicates that Neanderthals processed and modified carnivore carcasses for pelts and other purposes (Russo et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Abrams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), entailing direct contact with tissues and secretions. In modern Eurasian ecosystems, mesocarnivores are established reservoirs for multiple zoonoses, including \u003cem\u003eEchinococcus multilocularis\u003c/em\u003e, \u003cem\u003eLeptospira\u003c/em\u003e spp., and rabies virus (Ionică et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Potential hosts of \u003cem\u003eE. multilocularis\u003c/em\u003e were present in key sites including Denisova Cave, Chagyrskaya, Kůlna, Zaskalnaya VI, and Scladina.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Vector ecology and climatic structuring\u003c/h2\u003e \u003cp\u003eVector predictions further highlight the climatic structuring of Neanderthal disease landscapes. Predicted vector assemblages rely on modern analogues and KNN-based climate matching, so these results represent likely scenarios rather than direct evidence. KNN models indicate that \u003cem\u003ePhlebotomus\u003c/em\u003e sand flies frequently rank among the top predicted vectors in Mediterranean, Levantine, and steppe contexts, while ixodid ticks (\u003cem\u003eIxodes\u003c/em\u003e, \u003cem\u003eDermacentor\u003c/em\u003e) dominate continental and boreal environments. Potential mammalian hosts of \u003cem\u003eLeishmania infantum\u003c/em\u003e were present in 25 Neanderthal-associated sites, supporting the plausibility of recurring leishmaniasis as a zoonotic risk (Tuon et al., 2008). In colder steppe and continental settings, vector composition included both sand flies and ticks, with \u003cem\u003eHyalomma\u003c/em\u003e prominent at Shanidar and Teshik-Tash, reflecting modern Central and Southwest Asian ecologies where these ticks transmit Crimean\u0026ndash;Congo haemorrhagic fever virus and \u003cem\u003eRickettsia\u003c/em\u003e (Estrada-Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Western European oceanic sites showed mixed vector assemblages, while boreal sites were dominated by ixodid ticks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Genetic and immunological implications\u003c/h2\u003e \u003cp\u003eThese ecological reconstructions are particularly relevant considering genetic evidence for introgressed Neanderthal immune alleles in modern humans. The persistence of Neanderthal-derived TLR6\u0026ndash;TLR1\u0026ndash;TLR10 haplotypes, associated with microbial recognition including \u003cem\u003eBorrelia\u003c/em\u003e antigens (Dannemann et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oosting et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Dedkov et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and OAS1\u0026ndash;3 haplotypes influencing antiviral responses (Sams et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Banday et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), aligns with long-term exposure to vector-borne bacteria and viruses. Associations between OAS polymorphisms and tick-borne encephalitis outcomes further support the adaptive relevance of these loci in Eurasian pathogen landscapes (Bogovic \u0026amp; Strle, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Limitations and scope of inferences\u003c/h2\u003e \u003cp\u003eThe analyses of this study are inherently constrained by the reliance on extant mammal species or those with close modern analogues, which allows for plausible inferences of zoonotic potential. This approach may underestimate total pathogen diversity, as extinct taxa without living relatives\u0026mdash;some of which could have served as key reservoirs\u0026mdash;are not represented. Consequently, certain Pleistocene host\u0026ndash;pathogen interactions might be overlooked, and reconstructed disease landscapes may emphasize generalist pathogens associated with surviving taxa. Nonetheless, most excluded extinct taxa are large-bodied megafauna (e.g., proboscideans, woolly rhinoceroses), typically representing only 1\u0026ndash;3 species per site and a minor fraction of overall host diversity. Contemporary evidence further suggests that known zoonotic reservoirs are disproportionately concentrated among rodents, bats, and primates, whereas large-bodied taxa such as elephants or rhinoceroses contribute minimally to human zoonotic disease burdens (Han et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eNeanderthal populations inhabited diverse host\u0026ndash;vector\u0026ndash;pathogen environments shaped by paleoclimate. \u003cem\u003ePhlebotomus\u003c/em\u003e sand flies dominated Mediterranean and temperate settings, whereas ixodid ticks prevailed in continental and boreal contexts. Generalist pathogens, including \u003cem\u003eToxoplasma gondii\u003c/em\u003e, \u003cem\u003eFrancisella tularensis\u003c/em\u003e, and \u003cem\u003eListeria monocytogenes\u003c/em\u003e, infected a wide range of mammals, while specialists were rare. Site-level analyses revealed variability in host\u0026ndash;pathogen composition, with the presence and abundance of key hosts strongly influencing local zoonotic risk. Ancient viral genomes in Neanderthal remains provide direct evidence of exposure to vector-borne agents. Overall, these findings demonstrate that Neanderthals were embedded in pathogen landscapes structured by paleoclimate, reservoir identity, and host community composition, highlighting the role of infectious diseases in their ecology and adaptive pressures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding number: bo/00896/24/5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrams, G., Auguste, P., Pirson, S., De Groote, I., Halbrucker, \u0026Eacute;., Di Modica, K., Pironneau, C., Dedrie, T., Meloro, C., Fischer, V., Bocherens, H., Vanbrabant, Y., \u0026amp; Bray, F. (2025). 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(2023). \u003cem\u003eEchinococcus multilocularis\u003c/em\u003e and other zoonotic helminths in red foxes (\u003cem\u003eVulpes vulpes\u003c/em\u003e) from a southern German hotspot for human alveolar echinococcosis. \u003cem\u003eParasites \u0026amp; Vectors\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13071-023-06026-2\u003c/span\u003e\u003cspan address=\"10.1186/s13071-023-06026-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"human-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"huec","sideBox":"Learn more about [Human Ecology](http://link.springer.com/journal/10745)","snPcode":"10745","submissionUrl":"https://submission.nature.com/new-submission/10745/3","title":"Human Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neanderthals, palaeoclimate, mammal assemblages, zoonotic pathogens, vectors, Late Pleistocene, ecological networks","lastPublishedDoi":"10.21203/rs.3.rs-8718874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8718874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding Neanderthal interactions with Late Pleistocene ecosystems requires examining the dynamic interplay between climate, faunal communities, and potential zoonotic exposures. This study analyses 42 Neanderthal-bearing fossil assemblages across Eurasia, focusing on stratigraphic units directly associated with skeletal remains and considering only extant mammal taxa or their close relatives to infer plausible pathogen risks. By integrating palaeoclimate reconstructions with contemporary host\u0026ndash;pathogen data, we reconstruct site-specific environmental conditions, distributions of invertebrate vectors, and the presence of mammalian reservoir hosts. Rodents, ungulates, wild suids, and carnivores frequently emerge as potential pathogen carriers, while vector-borne transmission via ticks and sand flies appears climate-dependent. Substantial variation in species richness and inferred pathogen diversity reflects local ecological context and host composition. These results provide an ecologically grounded framework for understanding how environmental and climatic factors shaped Neanderthal exposures to zoonotic agents may have, highlighting the broader role of ecological interactions in hominin health and habitat use, and underscoring the need for further paleopathological evidence to validate predicted patterns.\u003c/p\u003e","manuscriptTitle":"Palaeoclimatic and ecological determinants of mammalian host–pathogen exposure in Neanderthal-associated sites across Eurasia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 09:28:27","doi":"10.21203/rs.3.rs-8718874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-20T14:52:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257976364642177822494959403847831058018","date":"2026-02-17T15:33:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T14:18:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T14:03:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T14:01:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Human Ecology","date":"2026-01-28T08:21:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"human-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"huec","sideBox":"Learn more about [Human Ecology](http://link.springer.com/journal/10745)","snPcode":"10745","submissionUrl":"https://submission.nature.com/new-submission/10745/3","title":"Human Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7b8e47ec-c487-45cb-8774-e9517bf4833d","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T09:28:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 09:28:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8718874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8718874","identity":"rs-8718874","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}