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Abstract In parts of Africa, greater honeyguides (Indicator indicator) lead people to bees' nests, after which people harvest the honey, and make beeswax and larvae accessible to the honeyguide. In scientific and popular literature, a similar cooperative relationship is frequently described between honeyguides and honey badgers (Mellivora capensis), yet the evidence that this occurs is unclear. Such a partnership may have implications for the origins of our own species' cooperation with honeyguides and for the ecology and conservation of both honey badgers and honeyguides. Here, we review the evidence that honey badgers and honeyguides cooperate to access bees' nests, drawing from the published literature, from our own observations whilst studying both species, and by conducting 394 interviews with honey‐hunters in 11 communities across nine African countries. We find that the scientific evidence relies on incomplete and second‐hand accounts and does not convincingly indicate that the two species cooperate. The majority of honey‐hunters we interviewed were similarly doubtful about the interaction, but many interviewees in the Hadzabe, Maasai, and mixed culture communities in Tanzania reported having seen honey badgers and honeyguides interact, and think that they do cooperate. This complementary approach suggests that the most likely scenario is that the interaction does occur but is highly localized or extremely difficult to observe, or both. With substantial uncertainty remaining, we outline empirical studies that would clarify whether and where honeyguides and honey badgers cooperate, and emphasize the value of integrating scientific and cultural knowledge in ecology. 

Coronavirus genomes have evolutionary histories shaped extensively by recombination. Yet, how often recombination occurs at a cellular level, or the factors that regulate recombination rates, are poorly understood. Utilizing experimental co-infections with pairs of genetically distinct coronaviruses, we found that recombination is both frequent and rare during coinfection. Recombination occurred in every instance of co-infection yet resulted in relatively few recombinant RNAs. By integrating a discrete-time Susceptible-Infected-Removed (SIR) model, we found that rates of recombination are determined primarily by rates of cellular co-infection, rather than other possible barriers such as RNA compartmentalization. By staggering the order and timing of infection with each virus we also found that rates of co-infection are themselves heavily influenced by genetic and ecological mechanisms, including superinfection exclusion and the relative fitness of competing viruses. Our study highlights recombination as a potent yet regulated force: frequent enough to ensure a steady influx of genetic variation but also infrequent enough to maintain genomic integrity. As recombination is thought to be an important driver of host-switching and disease emergence, our study provides new insights into the factors that regulate coronavirus recombination and evolution more broadly. 

Human evolutionary ecology stands to benefit by integrating theory and methods developed in movement ecology, and in turn, to make contributions to the broader field of movement ecology by leveraging our species' distinct attributes. In this paper, we review data and evolutionary models suggesting that major changes in socio-spatial behavior accompanied the evolution of language. To illustrate and explore these issues, we present a comparison of GPS measures of the socio-spatial behavior of Hadza hunter-gatherers of northern Tanzania to those of olive baboons (Papio anubis), a comparatively small-brained primate that is also savanna-adapted. While standard spatial metrics show modest differences, measures of spatial diversity, landscape exploration, and spatiotemporal displacement between individuals differ markedly. Groups of Hadza foragers rapidly accumulate a vast, diverse knowledge pool about places and things over the horizon, contrasting with the baboon's narrower and more homogeneous pool of ecological information. The larger and more complex socio-spatial world illustrated by the Hadza is one where heightened cognitive abilities for spatial and episodic memory, navigation, perspective taking, and communication about things beyond the here and now all have clear value. 

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields. 

Species interactions that vary across environments can create geographical mosaics of genetic coevolution. However, traits mediating species interactions are sometimes culturally inherited. Here we show that traditions of interspecies communication between people and wild birds vary in a culturally determined geographical mosaic. Honey hunters in different parts of Africa use different calls to communicate with greater honeyguides (Indicator indicator) that lead them to bees’ nests. We show experimentally that honeyguides in Tanzania and Mozambique discriminate among honey hunters’ calls, responding more readily to local than to foreign calls. This was not explained by variation in sound transmission and instead suggests that honeyguides learn local human signals. We discuss the forces stabilizing and diversifying interspecies communication traditions, and the potential for cultural coevolution between species. 

Among mammals, post-reproductive life spans are currently documented only in humans and a few species of toothed whales. Here we show that a post-reproductive life span exists among wild chimpanzees in the Ngogo community of Kibale National Park, Uganda. Post-reproductive representation was 0.195, indicating that a female who reached adulthood could expect to live about one-fifth of her adult life in a post-reproductive state, around half as long as human hunter-gatherers. Post-reproductive females exhibited hormonal signatures of menopause, including sharply increasing gonadotropins after age 50. We discuss whether post-reproductive life spans in wild chimpanzees occur only rarely, as a short-term response to favorable ecological conditions, or instead are an evolved species-typical trait as well as the implications of these alternatives for our understanding of the evolution of post-reproductive life spans. 

Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.