9 research outputs found
eDNA in subterranean ecosystems: Applications, technical aspects, and future prospects
Monitoring of biota is pivotal for the assessment and conservation of ecosystems. Environments worldwide are being continuously and increasingly exposed to multiple adverse impacts, and the accuracy and reliability of the biomonitoring tools that can be employed shape not only the present, but more importantly, the future of entire habitats. The analysis of environmental DNA (eDNA) metabarcoding data provides a quick, affordable, and reliable molecular approach for biodiversity assessments. However, while extensively employed in aquatic and terrestrial surface environments, eDNA-based studies targeting subterranean ecosystems are still uncommon due to the lack of accessibility and the cryptic nature of these environments and their species. Recent advances in genetic and genomic analyses have established a promising framework for shedding new light on subterranean biodiversity and ecology. To address current knowledge and the future use of eDNA methods in groundwaters and caves, this review explores conceptual and technical aspects of the application and its potential in subterranean systems. We briefly introduce subterranean biota and describe the most used traditional sampling techniques. Next, eDNA characteristics, application, and limitations in the subsurface environment are outlined. Last, we provide suggestions on how to overcome caveats and delineate some of the research avenues that will likely shape this field in the near future. We advocate that eDNA analyses, when carefully conducted and ideally combined with conventional sampling techniques, will substantially increase understanding and enable crucial expansion of subterranean community characterisation. Given the importance of groundwater and cave ecosystems for nature and humans, eDNA can bring to the surface essential insights, such as study of ecosystem assemblages and rare species detection, which are critical for the preservation of life below, as well as above, the ground.Mattia Saccò, Michelle T. Guzik, Mieke van der Heyde, Paul Nevill, Steven J.B. Cooper, Andrew D. Austin, Peterson J. Coates, Morten E. Allentoft, Nicole E. Whit
Thinning operations for modeling time series of counts—a survey
Thinning operations, Binomial thinning, Count time series, INAR(1) models,