Aktuelle Entdeckungen aus dem «helvetischen» Bodenarchiv

Unser Bild von der Besiedlung des Schweizer Mittellandes durch die keltischen Helvetier im späteren 2. und im 1. Jh. v. Chr. war und ist von Kenntnissen über die Zentralorte geprägt. Aktuelle Entdeckungen aus verschiedenen Kantonen beleuchten das Leben der damaligen Bevölkerung im ländlichen Umland und insbesondere auch in den Randregionen ihres Siedlungsgebiets. Actualités des découvertes en terre «helvète» À la fin du 2e et au 1er siècles avant notre ère (époque de La Tène finale), une grande partie du plateau suisse était habitée par un peuple celte, les Helvètes. Notre connaissance de la vie d’alors repose essentiellement sur les résultats des fouilles archéologiques – en plus des sources écrites, dont les récits de César. Quatre brèves contributions choisies, portant sur des découvertes de la partie germanophone de la Confoedera- tio Helvetica constituée en 1848, illustrent les découvertes actuelles, les questions en suspens ou encore les stratégies de recherche. Nuove scoperte dal territorio degli Elvezi Alla fine del II e nel I sec. a.C. (ossia alla fine dell'epoca di La Tène), la maggior parte dell’Altopiano svizzero era occupata dalla popolazione celtica degli Elvezi. Le nostre conoscenze sulla vita di allora si basano in primo luogo sui risultati delle ricerche archeologiche, integrate da testimonianze provenienti dalle fonti scritte, in particolare dal resoconto di Cesare. Quattro brevi contributi scelti dalla regione di lingua tedesca della Confoederatio Helvetica, costituita nel 1848, ci offrono un quadro variegato delle nuove scoperte, delle questioni della ricerca e delle strategie d’indagine

gen3sis : A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species’ abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, β, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth’s Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth’s biodiversity

New deep learning‐based methods for visualizing ecosystem properties using environmental DNA metabarcoding data

Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant ecological information they contain, and new methods may provide better dimensionality reduction and clustering. Here we present two new deep learning‐based methods that combine different types of neural networks (NNs) to ordinate eDNA samples and visualize ecosystem properties in a two‐dimensional space: the first is based on variational autoencoders and the second on deep metric learning. The strength of our new methods lies in the combination of two inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a two‐dimensional latent space: MOTU richness per sample, sequence α‐diversity per sample, Jaccard's and sequence β‐diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t‐distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring

Applying convolutional neural networks to speed up environmental DNA annotation in a highly diverse ecosystem

Abstract High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses to environmental changes in a standardized and reproducible way. Environmental DNA (eDNA) from organisms can be captured in ecosystem samples and sequenced using metabarcoding, but processing large volumes of eDNA data and annotating sequences to recognized taxa remains computationally expensive. Speed and accuracy are two major bottlenecks in this critical step. Here, we evaluated the ability of convolutional neural networks (CNNs) to process short eDNA sequences and associate them with taxonomic labels. Using a unique eDNA data set collected in highly diverse Tropical South America, we compared the speed and accuracy of CNNs with that of a well-known bioinformatic pipeline (OBITools) in processing a small region (60 bp) of the 12S ribosomal DNA targeting freshwater fishes. We found that the taxonomic labels from the CNNs were comparable to those from OBITools, with high correlation levels for the composition of the regional fish fauna. The CNNs enabled the processing of raw fastq files at a rate of approximately 1 million sequences per minute, which was about 150 times faster than with OBITools. Given the good performance of CNNs in the highly diverse ecosystem considered here, the development of more elaborate CNNs promises fast deployment for future biodiversity inventories using eDNA

Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems

Through the development of environmental DNA (eDNA) metabarcoding, in situ monitoring of organisms is becoming easier and promises a revolution in our approaches to detect changes in biodiversity over space and time. A cornerstone of eDNA approach is the development of primer pairs that allow amplifying the DNA of specific taxonomic groups, which is then used to link the DNA sequence to taxonomic identification. Here, we propose a framework for comparing primer pairs regarding (a) their capacity to bind and amplify a broad coverage of species within the target clade using in silico PCR, (b) their capacity to not only discriminate between species but also genera or families, and (c) their in situ specificity and efficiency across a variety of environments. As a case study, we focus on two mitochondrial 12S primer pairs, MiFish-U and teleo, which were designed to amplify fishes. We found that the performance of in silico PCRs were high for both primer pairs, but teleo amplified more genera across Actinopterygii, Chondrichthyes, and Petromyzontomorphi than MiFish-U. In contrast, the discriminatory power for species, genera, and families were higher for MiFish-U than teleo, likely associated with the greater length of the amplified DNA fragments. The evaluation of their in situ efficiency showed a higher recovered species richness of teleo compared to MiFish-U in tropical and temperate freshwater environments, but that generally both teleo and MiFish-U primers pairs perform well to monitor fish species. Since more species were detected when used together, those primer pairs are best used in combination to increase the ability of species detection.ISSN:2637-494