Éco-Anthropologie

Abstract Tropical forests are rich in biodiversity but face the rapid loss of their wildlife due to increasing anthropogenic pressure, underscoring the urgent need for effective monitoring. Remote‐sensing tools such as camera traps offer faster, less invasive alternatives to human observations. These technologies can provide complementary insights into elusive species, especially in habitats that are difficult to access through direct observation. However, analysing the large volumes of data they produce is labour‐intensive, often leaving datasets underutilised due to limited human resources. Deep learning algorithms can automate aspects of data analysis, but their value to research and conservation efforts depends on their ability to reliably identify target species and be easily deployed in field conditions. To improve wildlife monitoring in African forests using camera trap data, we develop DeepForestVision, the first deep learning algorithm tailored to these challenging habitats that can be used in the field to process both photographs and videos. DeepForestVision was trained on an unprecedented dataset of 2,775,671 photographs and 221,982 videos gathered from camera traps from more than 63 research sites across 11 African countries. It identifies 33 non‐human vertebrate taxa, including 31 mammal taxa, from the most common to the most threatened ones observed on ground‐level camera traps, as well as humans, vehicles and blank photographs or videos. Classification tests demonstrate that DeepForestVision achieves an accuracy of 87.7% on the video test set with 23 taxa. It outperforms the three existing species identification algorithms applicable to these environments: Zamba by 13.1%, Mbaza by 45.0% and SpeciesNet by 37.7%. We provide the model weights for researchers and developers and offer DeepForestVision through a free offline interface. The interface is designed to function in the field in a low‐resource setting and requires no programming expertise. Solution : DeepForestVision is a reliable field tool for monitoring species observed on camera trap photos and videos in African tropical forests. Used by research, conservation, private or political actors, it can guide conservation strategies inside and outside of protected areas, and thus contribute to reducing the loss of biodiversity in African tropical forests.

Many studies have attempted to gain insights into the kinship systems of past human populations using ancient DNA data. Several studies focusing on Neolithic and Bronze Age European sites reported a high male relatedness and a low Y chromosome diversity, and concluded that descent in these past societies was patrilineal and residence was patrilocal. Here, we used simulations to assess male and female relatedness as well as the uniparental genetic diversity expected under different types of descent and residence systems. We confirm that ancient DNA data from most of these sites are compatible with patrilocal residence; however, the claim that many Neolithic and Bronze Age European populations had patrilineal descent is not supported.

The ever-increasing availability of high-throughput DNA sequences and the development of numerous computational methods have led to considerable advances in our understanding of the evolutionary and demographic history of populations. Several demographic inference methods have been developed to take advantage of these massive genomic data. Simulation-based approaches, such as approximate Bayesian computation (ABC), have proved particularly efficient for complex demographic models. However, taking full advantage of the comprehensive information contained in massive genomic data remains a challenge for demographic inference methods, which generally rely on partial information from these data. Using advanced computational methods, such as machine learning, is valuable for efficiently integrating more comprehensive information. Here, we showed how simulation-based supervised machine learning methods applied to an extensive range of summary statistics are effective in inferring demographic parameters for connected populations. We compared three machine learning (ML) methods: a neural network, the multilayer perceptron (MLP), and two ensemble methods, random forest (RF) and the gradient boosting system XGBoost (XGB), to infer demographic parameters from genomic data under a standard isolation with migration model and a secondary contact model with varying population sizes. We showed that MLP outperformed the other two methods and that, on the basis of permutation feature importance, its predictions involved a larger combination of summary statistics. Moreover, they outperformed all three tested ABC algorithms. Finally, we demonstrated how a method called SHAP, from the field of explainable artificial intelligence, can be used to shed light on the contribution of summary statistics within the ML models.

Menopause is commonly viewed as a hormone deficiency and a risk factor for chronic illness, but it also represents a biological transition and profound social role change for women. This narrative review synthesises evidence on how women in sub-Saharan Africa conceptualise and experience menopause and ageing, and examines how research has captured these dimensions. Databases including SCOPUS, Science Direct, Mendeley Data, Web of Science, PubMed, Medline African Medical Index, and the National Library of Medicine were searched using the terms ‘menopause’, ‘ageing/aging’ and ‘sub-Saharan Africa’. Forty-two qualitative and quantitative studies in English and French were included. The review found that while many studies emphasise biological markers, symptoms, and the links between menopause and chronic disease, a significant number also highlight women’s own interpretations of menopause as a social transition, sometimes associated with enhanced status and autonomy. In some contexts, menopause is linked to stigma or loss of femininity, while in others, it enables greater autonomy and decision-making power. Few women report awareness or acceptance of biomedical interventions such as menopause hormone therapy, tending to view menopause as a natural process rather than a medical condition. These findings underscore the importance of culturally sensitive public health interventions and clinical strategies that address both the biomedical and sociocultural dimensions of menopause, to promote healthy ageing and reduce the burden of chronic disease among women in sub-Saharan Africa.

Abstract Which animals do people fear most, and why? Exploring animal fears in humans is crucial for understanding reactions in the face of danger, addressing both innate and learned determinants. Because of the central role they are thought to have played in primate evolution, most studies have focused on the fear of snakes. Other studies that have looked at a wider range of animals have either focused on a limited number of species and/or sampled participants from a narrow range of geographical locations. To overcome these shortcomings, we developed an immersive online survey based on animal images matches, during which participants had to choose the animal they feared most. With responses from 17,353 participants from all continents, we were able to rank 184 species (mammals, reptiles, birds, arthropods and amphibians) on a fear scale. Our results showed that images of dangerous animals elicited frequent and rapid fear responses. However, danger alone was not sufficient to explain fear, as harmless animals also reached high fear scores. Fear responses varied with participants' age, geographical region of residence and level of declared biophobia. The discrepancy between actual levels of danger and declared fears in humans may be due to social transmission and increasing disconnection from natural environments. This study highlights the need to consider a wide range of animal species to identify and understand people's fear of certain species, integrating the complex relationship between ecological danger and socio‐cultural influences. Read the free Plain Language Summary for this article on the Journal blog.

In modern human societies, social interactions and pro-social behaviours are associated with better individual and collective health, reduced mortality, and increased longevity. Conversely, social isolation is a predictor of shorter lifespan. The biological processes through which sociality affects the ageing process, as well as healthspan and lifespan, are still poorly understood. Unveiling the physiological, neurological, genomic, epigenomic, and evolutionary mechanisms underlying the association between sociality and longevity may open new perspectives to understand how lifespan is determined in a broader socio/evolutionary outlook. Here we summarize evidence showing how social dynamics can shape the evolution of life history traits through physiological and genetic processes directly or indirectly related to ageing and lifespan. We start by reviewing theories of ageing that incorporate social interactions into their model. Then, we address the link between sociality and lifespan from two separate points of view: (i) considering evidences from comparative evolutionary biology and bioanthropology that demonstrates how sociality contributes to natural variation in lifespan over the course of human evolution and among different human groups in both pre-industrial and post-industrial society, and (ii) discussing the main physiological, neurological, genetic, and epigenetic molecular processes at the interface between sociality and ageing. We highlight that the exposure to chronic social stressors deregulates neurophysiological and immunological pathways and promotes accelerated ageing and thereby reducing lifespan. In conclusion, we describe how sociality and social dynamics are intimately embedded in human biology, influencing healthy ageing and lifespan, and we highlight the need to foster interdisciplinary approaches including social sciences, biological anthropology, human ecology, physiology, and genetics.

This paper describes a feasibility study for the computational classification of traditional three-voiced Georgian vocal music, based on characteristic chord sequences extracted from digital scores. We demonstrate that for this purpose the differences between Western 5-line staff notation and a more appropriate heptatonic system for traditional Georgian music can be adjusted for by a simple transformation. A corpus of about 500 digital scores, consisting of labeled ‘song classes’, i.e. subsets of folk songs from different regions and of liturgical songs in different styles, served as a testbed for the development of a classification procedure based on a higher-order Markov model that - in addition to the classification - yields chord progression sequences for each ‘song class’. Their capacity for interpretation was tested by one hundred cross-validation runs, in which randomly selected subsets of ¾ of the size of each song class were used to train classifiers, which were then applied to the remaining ¼ subsets. The sizes of the intersections of the successfully classified songs in all cross-validations are interpreted as direct measures of the degree of representativeness of the songs for their respective ‘song classes’. Based on a second validation experiment, in which we split up the datasets into equally sized subsets of ½ and ¼ of the original subsets, respectively, we estimate that the smallest subset size for an interpretation of the observed chord progression patterns as properties of a ‘song class’ is of the order of 50 songs. Currently, in our corpus, this requirement is only met by the subsets from Svaneti and Shemokmedi.