The global 5G race: South Korea speeds ahead. IES Policy Brief Issue 2019/05, May 2019

South Korea has become the first country in the world to launch commercial 5G services on 3 April. 5G economic benefits are estimated to include worldwide revenues of €225 billion by 2025 and a wealth of job creation. The US, China, South Korea and the EU are economic powerhouses vying to lead the unfolding global 5G market. US and China are strongly positioned in the current telecom market, but their growing 5G competition is spilling over into geopolitical competition. Wary of being swept up in US-China rivalry, the Moon government is banking on building strong 5G market competitiveness and doubling down on the IT sector which represents a critical economic growth engine domestically

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

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

Structure of Chimpanzee Gut Microbiomes across Tropical Africa

Understanding variation in host-associated microbial communities is important given the relevance of microbiomes to host physiology and health. Using 560 fecal samples collected from wild chimpanzees (Pan troglodytes) across their range, we assessed how geography, genetics, climate, vegetation, and diet relate to gut microbial community structure (prokaryotes, eukaryotic parasites) at multiple spatial scales. We observed a high degree of regional specificity in the microbiome composition, which was associated with host genetics, available plant foods, and potentially with cultural differences in tool use, which affect diet. Genetic differences drove community composition at large scales, while vegetation and potentially tool use drove within-region differences, likely due to their influence on diet. Unlike industrialized human populations in the United States, where regional differences in the gut microbiome are undetectable, chimpanzee gut microbiomes are far more variable across space, suggesting that technological developments have decoupled humans from their local environments, obscuring regional differences that could have been important during human evolution.Additional co-authors: Heather Cohen, Charlotte Coupland, Tobias Deschner, Villard Ebot Egbe, Annemarie Goedmakers, Anne-Céline Granjon, Cyril C. Grueter, Josephine Head, R. Adriana Hernandez-Aguilar, Sorrel Jones, Parag Kadam, Michael Kaiser, Juan Lapuente, Bradley Larson, Sergio Marrocoli, David Morgan, Badru Mugerwa, Felix Mulindahabi, Emily Neil, Protais Niyigaba, Liliana Pacheco, Alex K. Piel, Martha M. Robbins, Aaron Rundus, Crickette M. Sanz, Lilah Sciaky, Douglas Sheil, Volker Sommer, Fiona A. Stewart, Els Ton, Joost van Schijndel, Virginie Vergnes, Erin G. Wessling, Roman M. Wittig, Yisa Ginath Yuh, Kyle Yurkiw, Klaus Zuberbühler, Jan F. Gogarten, Anna Heintz-Buschart, Alexandra N. Muellner-Riehl, Christophe Boesch, Hjalmar S. Kühl, Noah Fierer, Mimi Arandjelovic, Robert R. Dun

Persistent anthrax as a major driver of wildlife mortality in a tropical rainforest

Anthrax is a globally important animal disease and zoonosis. Despite this, our current knowledge of anthrax ecology is largely limited to arid ecosystems, where outbreaks are most commonly reported. Here we show that the dynamics of an anthrax-causing agent, Bacillus cereus biovar anthracis, in a tropical rainforest have severe consequences for local wildlife communities. Using data and samples collected over three decades, we show that rainforest anthrax is a persistent and widespread cause of death for a broad range of mammalian hosts. We predict that this pathogen will accelerate the decline and possibly result in the extirpation of local chimpanzee (Pan troglodytes verus) populations. We present the epidemiology of a cryptic pathogen and show that its presence has important implications for conservation

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