The Internal, External and Extended Microbiomes of Hominins

The social structure of primates has recently been shown to influence the composition of their microbiomes. What is less clear is how primate microbiomes might in turn influence their social behavior, either in general or with particular reference to hominins. Here we use a comparative approach to understand how microbiomes of hominins have, or might have, changed since the last common ancestor (LCA) of chimpanzees and humans, roughly six million years ago. We focus on microbiomes associated with social evolution, namely those hosted or influenced by stomachs, intestines, armpits, and food fermentation. In doing so, we highlight the potential influence of microbiomes in hominin evolution while also offering a series of hypotheses and questions with regard to evolution of human stomach acidity, the factors structuring gut microbiomes, the functional consequences of changes in armpit ecology, and whether Homo erectus was engaged in fermentation. We conclude by briefly considering the possibility that hominin social behavior was influenced by prosocial microbes whose fitness was favored by social interactions among individual hominins

The Use of Swear Words by Junior High School Students 1 at Kotabaru Karawang West Java

Dalam setiap bahasa terdapat kata-kata yang dianggap tidak sopan untuk dikatakan kepada lawan bicara. Kata-kata tersebut mengacu kepada kata-kata yang mengandung makian dan sumpah serapah, serta memiliki konotasi kasar dan tabu untuk diucapkan dalam situasi formal. Dalam era globalisasi ini, banyak remaja khususnya remaja Sekolah menengah Pertama yang menggunakan umpatan dalam komunikasi mereka sehari-hari. Oleh karena itu, penulis tergugah untuk menganalisis fenomena penggunaan kata umpatan oleh siswa SMPN 1 di wilayah Kotabaru Karawang. Tujuan dari penelitian ini adalah untuk menunjukkan dan menganalisis penggunaan kata umpatan yang digunakan oleh siswa SMP mengacu kepada teori Sosiolinguistik yang dikemukakan oleh Janet Holmes. Kata-kata umpatan yang diproduksi oleh siswa merupakan penelitian yang bersifat deskriptif dengan pendekatan kualitatif. Penulis mengambil data dengan teknik purposive sampling sehingga penulis mendapat data sebanyak 25 kata umpatan yang dalam pengumpulan datanya dibagi menjadi 2 tahap yaitu wawancara dan observasi. Penulis juga menggunakan teknik Simak Bebas Libat Cakap dan teknik catat ketika mengobservasi tuturan umpatan yang digunakan oleh siswa. Dalam mewawancarai siswa, penulis menggunakan teknik rekam untuk menjaga keaslian data. Dari hasil pengumpulan data, penulis mendapatkan 16 tuturan umpatan yang diperoleh dengan cara mewawancarai siswa dan 9 tuturan umpatan yang diperoleh dengan cara observasi di sekolah. Sebagian besar kata – kata umpatan dituturkan oleh siswa laki – laki. Namun ada beberapa tuturan umpatan yang diproduksi oleh siswa perempuan ketika mengumpat dengan teman sebayanya. Dari hasil observasi, penulis menemukan kata – kata umpatan yang digunakan siswa kepada temannya hanya pada latar informal. Walaupun ada seorang murid yang mengaku bahwa dirinya pernah mengumpat pada saat terdapat guru di dalam kelas, hal itu tidak lebih dari sekedar lelucon belaka. Secara garis besar, topik ketika siswa mengumpat kepada temannya hanya sebagai bahan lelucon. Meskipun ada beberapa tuturan umpatan yang mempunyai topik kemarahan, sebagian besar umpatan yang digunakan siswa mempunyai fungsi ekspresif

Human impact erodes chimpanzee behavioral diversity

Chimpanzees possess a large number of behavioral and cultural traits among non-human species. The ‘disturbance hypothesis’ predicts that human impact depletes resources and disrupts social learning processes necessary for behavioral and cultural transmission. We used an unprecedented data set of 144 chimpanzee communities, with information on 31 behaviors, to show that chimpanzees inhabiting areas with high human impact have a mean probability of occurrence reduced by 88%, across all behaviors, compared to low impact areas. This behavioral diversity loss was evident irrespective of the grouping or categorization of behaviors. Therefore, human impact may not only be associated with the loss of populations and genetic diversity, but also affects how animals behave. Our results support the view that ‘culturally significant units’ should be integrated into wildlife conservation.Additional co-authors: Mattia Bessone, Gregory Brazzola, Rebecca Chancellor, Heather Cohen, Charlotte Coupland, Emmanuel Danquah, Tobias Deschner, Orume Diotoh, Dervla Dowd, Andrew Dunn, Villard Ebot Egbe, Henk Eshuis, Rumen Fernandez, Yisa Ginath, Annemarie Goedmakers, Anne-Céline Granjon, Josephine Head, Daniela Hedwig, Veerle Hermans, Inaoyom Imong, Sorrel Jones, Jessica Junker, Parag Kadam, Mbangi Kambere, Mohamed Kambi, Ivonne Kienast, Deo Kujirakwinja, Kevin Langergraber, Juan Lapuente, Bradley Larson, Kevin Lee, Vera Leinert, Manuel Llana, Giovanna Maretti, Sergio Marrocoli, Tanyi Julius Mbi, Amelia C. Meier, David Morgan, Felix Mulindahabi, Mizuki Murai, Emily Neil, Protais Niyigaba, Lucy Jayne Ormsby, Liliana Pacheco, Alex Piel, Jodie Preece, Sebastien Regnaut, Aaron Rundus, Crickette Sanz, Joost van Schijndel, Volker Sommer, Fiona Stewart, Nikki Tagg, Elleni Vendras, Virginie Vergnes, Adam Welsh, Erin G. Wessling, Jacob Willie, Roman M. Wittig, Kyle Yurkiw, Klaus Zuberbuehler, Ammie K. Kala

Fly-derived DNA and camera traps are complementary tools for assessing mammalian biodiversity

Background Metabarcoding of vertebrate DNA found in invertebrates (iDNA) represents a potentially powerful tool for monitoring biodiversity. Preliminary evidence suggests fly iDNA biodiversity assessments compare favorably with established approaches such as camera trapping or line transects. Aims and Methods To assess whether fly-derived iDNA is consistently useful for biodiversity monitoring across a diversity of ecosystems, we compared metabarcoding of the mitochondrial 16S gene of fly pool-derived iDNA (range = 49–105 flies/site, N = 784 flies) with camera traps (range = 198–1,654 videos of mammals identified to the species level/site) at eight sites, representing different habitat types in five countries across tropical Africa. Results We detected a similar number of mammal species using fly-derived iDNA (range = 8–15 species/site) and camera traps (range = 8–27 species/site). However, the two approaches detected mostly different species (range = 6%–43% of species detected/site were detected with both methods), with fly-derived iDNA detecting on average smaller-bodied species than camera traps. Despite addressing different phylogenetic components of local mammalian communities, both methods resulted in similar beta-diversity estimates across sites and habitats. Conclusion These results support a growing body of evidence that fly-derived iDNA is a cost- and time-efficient tool that complements camera trapping in assessing mammalian biodiversity. Fly-derived iDNA may facilitate biomonitoring in terrestrial ecosystems at broad spatial and temporal scales, in much the same way as water eDNA has improved biomonitoring across aquatic ecosystems.Peer Reviewe

Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

The manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable

The complex Y-chromosomal history of gorillas

Studies of the evolutionary relationships among gorilla populations using autosomal and mitochondrial sequences suggest that male-mediated gene flow may have been important in the past, but data on the Y-chromosomal relationships among the gorilla subspecies are limited. Here, we genotyped blood and noninvasively collected fecal samples from 12 captives and 257 wild male gorillas of known origin representing all four subspecies (Gorilla gorilla gorilla, G. g. diehli, G. beringei beringei, and G. b. graueri) at 10 Y-linked microsatellite loci resulting in 102 unique Y-haplotypes for 224 individuals. We found that western lowland gorilla (G. g. gorilla) haplotypes were consistently more diverse than any other subspecies for all measures of diversity and comprised several genetically distinct groups. However, these did not correspond to geographical proximity and some closely related haplotypes were found several hundred kilometers apart. Similarly, our broad sampling of eastern gorillas revealed that mountain (G. b. beringei) and Grauer's (G. b. graueri) gorilla Y-chromosomal haplotypes did not form distinct clusters. These observations suggest structure in the ancestral population with subsequent mixing of differentiated haplotypes by male dispersal for western lowland gorillas, and postisolation migration or incomplete lineage sorting due to short divergence times for eastern gorillas

Using nonhuman culture in conservation requires careful and concerted action

Discussions of how animal culture can aid the conservation crisis are burgeoning. As scientists and conservationists working to protect endangered species, we call for reflection on how the culture concept may be applied in practice. Here, we discuss both the potential benefits and potential shortcomings of applying the animal culture concept, and propose a set of achievable milestones that will help guide and ensure its effective integration existing conservation frameworks, such as Adaptive Management cycles or Open Standards

PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition

We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ~20,000 camera trap videos of chimpanzees and gorillas collected at 14 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts.Comment: Accepted at IJC

PanAf20K : a large video dataset for wild ape detection and behaviour recognition

The work that allowed for the collection of the dataset was funded by the Max Planck Society, Max Planck Society Innovation Fund, and Heinz L. Krekeler. This work was supported by the UKRI CDT in Interactive AI under grant EP/S022937/1.We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ∼20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts. The dataset and code are available from the project website: PanAf20KPeer reviewe

Author Correction: Environmental variability supports chimpanzee behavioural diversity

The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which three columns (L, M and P) had slightly different variable names from those written in the code. The HTML has been updated to include a corrected version of Supplementary Data 1; the correct version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction.Additional co-authors: Mattia Bessone, Gregory Brazzola, Valentine Ebua Buh, Rebecca Chancellor, Heather Cohen, Charlotte Coupland, Bryan Curran, Emmanuel Danquah, Tobias Deschner, Dervla Dowd, Manasseh Eno-Nku, J. Michael Fay, Annemarie Goedmakers, Anne-Céline Granjon, Josephine Head, Daniela Hedwig, Veerle Hermans, Sorrel Jones, Jessica Junker, Parag Kadam, Mohamed Kambi, Ivonne Kienast, Deo Kujirakwinja, Kevin E. Langergraber, Juan Lapuente, Bradley Larson, Kevin C. Lee, Vera Leinert, Manuel Llana, Sergio Marrocoli, Amelia C. Meier, David Morgan, Emily Neil, Sonia Nicholl, Emmanuelle Normand, Lucy Jayne Ormsby, Liliana Pacheco, Alex Piel, Jodie Preece, Martha M. Robbins, Aaron Rundus, Crickette Sanz, Volker Sommer, Fiona Stewart, Nikki Tagg, Claudio Tennie, Virginie Vergnes, Adam Welsh, Erin G. Wessling, Jacob Willie, Roman M. Wittig, Yisa Ginath Yuh, Klaus Zuberbühler & Hjalmar S. Küh