A5083163931- Environmental DNA in Biodiversity Studies
- Species Distribution and Climate Change
- Microbial Community Ecology and Physiology
- Genomics and Phylogenetic Studies
- Identification and Quantification in Food
- Evolution and Genetic Dynamics
- Ecology and Vegetation Dynamics Studies
- Evolution and Paleontology Studies
- Geology and Paleoclimatology Research
- Physiological and biochemical adaptations
- Earth Systems and Cosmic Evolution
- Sustainability and Ecological Systems Analysis
- Paleontology and Stratigraphy of Fossils
Swiss Federal Institute for Forest, Snow and Landscape Research
2020-2023
ETH Zurich
2021-2023
Understanding the origins of biodiversity has been an aspiration since days early naturalists. The immense complexity ecological, evolutionary, and spatial processes, however, made this goal elusive to day. Computer models serve progress in many scientific fields, but fields macroecology macroevolution, eco-evolutionary are comparatively less developed. We present a general, spatially explicit, engine with modular implementation that enables modeling multiple macroecological...
Abstract Through the development of environmental DNA (eDNA) metabarcoding, in situ monitoring organisms is becoming easier and promises a revolution our approaches to detect changes biodiversity over space time. A cornerstone eDNA approach primer pairs that allow amplifying specific taxonomic groups, which then used link sequence identification. Here, we propose framework for comparing regarding (a) their capacity bind amplify broad coverage species within target clade using silico PCR, (b)...
High-throughput DNA sequencing is becoming an increasingly important tool to monitor and better understand biodiversity responses environmental changes in a standardized reproducible way. Environmental (eDNA) from organisms can be captured ecosystem samples sequenced using metabarcoding, but processing large volumes of eDNA data annotating sequences recognized taxa remains computationally expensive. Speed accuracy are two major bottlenecks this critical step. Here, we evaluated the ability...
Abstract Understanding the origins of biodiversity has been an aspiration since days early naturalists. The immense complexity ecological, evolutionary and spatial processes, however, made this goal elusive to day. Computer models serve progress in many scientific fields, but fields macroecology macroevolution, eco-evolutionary are comparatively less developed. We present a general, spatially-explicit, engine with modular implementation that enables modelling multiple macroecological...
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 analyzing all the relevant ecological information they contain, new may provide better dimensionality reduction clustering. Here we present two deep learning-based that combine different types neural networks (NNs) to ordinate eDNA samples visualize ecosystem properties a...
<p>Explaining the origin of large-scale biodiversity gradients has been a key aspiration early naturalists such as Wegener, Darwin and Humboldt; who looked at natural processes in an integrated way. Early on, these acknowledged role plate tectonics climate variations shaping modern day patterns.<span> </span></p><p>As science advanced, complexity ecological, evolutionary, geological climatological became...
1 Abstract The intensification of anthropogenic pressures have increased consequences on biodiversity and ultimately the functioning ecosystems. To monitor better understand responses to environmental changes using standardized reproducible methods, novel high-throughput DNA sequencing is becoming a major tool. Indeed, organisms shed traces in their environment this “environmental DNA” (eDNA) can be collected sequenced eDNA metabarcoding. processing large volumes metabarcoding data remains...
1. Metabarcoding of environmental DNA (eDNA) has recently improved our understanding biodiversity patterns in marine and terrestrial ecosystems. However, the complexity these data prevents current methods to extract analyze all relevant ecological information they contain. Therefore, modeling could greatly benefit from new providing better dimensionality reduction clustering. 2. Here we present two deep learning-based that combine different types neural networks ordinate eDNA samples...