Post provided by Andrew R. Mahon
The use of molecular methods for monitoring and surveillance of organisms in aquatic and marine systems has become more and more common. We’ve since expanded this technology this through using both captured whole organisms and collecting/filtering environmental DNA (eDNA). These methods naturally migrated from single species, active surveillance methods towards using high throughput sequencing as a method of passive surveillance via metabarcoding.
In this virtual issue, the article “Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring” by Vasco Elbrecht et al. provides an excellent overview to the field. It also helps to clarify the work being done to provide interested groups, including management agencies, with the best practices for utilising these new methods for monitoring and surveillance. This work will help the field, particularly for those searching for rare species of organisms in aquatic systems.
I’d recommend this paper to all researchers and management groups interested in applying metabarcoding techniques to answer both experimental and applied questions. The design of this article will provide both experienced researchers and those new to the field with important information to further this rapidly expanding field.
To find out more about, read the full Methods in Ecology and Evolution article ‘Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring’
This article is part of ‘Practical Tools: A Field Methods Virtual Issue’. All articles in this Virtual Issue will be available for a limited time.
“I am an evolutionary ecologist with broad interests in behavioural and population ecology. My research has explored reproductive strategies and the evolution of male and female reproductive traits in mammals and insects and I have used a range of techniques to study the behaviour and welfare of wildlife. I have just started to explore interdisciplinary approaches with the aim to improve our understanding of the value and role of ecosystem services in human health, specifically for antimicrobial resistance.”
“Methods employing epigenetics, environmental DNA analysis or bioinformatics for ecological research are improving rapidly and have clear potential for future development. My research focuses on creating new methods in these areas and using them to study population biology and biodiversity. Epigenetic markers for physiological features such as biological age can be used to determine key features of population biology such as age class distribution. Environmental DNA can be used to measure species distributions; biodiversity in environmental samples; and animal diet composition. I am interested in the molecular biology and computational approaches that are required to implement these methods; as well as how they can be used to study specific ecological questions.”
“I am interested in the way biological and ecological phenomena change in space and over time. My special interest is animal movement ecology and the mechanisms behind the patterns of movement we observe. Most of the time I work on ecological questions about how animals use their environment and the resources in it, using data collected remotely with animal-attached instruments. Marine biology was my first love so I will always have a soft spot for marine systems, especially movement of large marine vertebrates, but I work on all sorts of tracking data and also some non-tracking data.”
“I am a computational biologist and my research focuses on (macro)evolution and the development of new probabilistic models to better understand it. I am interested in the implementation of Bayesian algorithms to model evolutionary processes such as phenotypic trait evolution and species diversification and extinction. I am also interested in historical biogeography and in particular in the estimation of dispersal rates and biotic connectivity between geographic areas. A lot of my work involves developing new models and algorithms and implementing them in computer programs. I have been using both phylogenetic data and fossil occurrences to infer deep time evolutionary dynamics and I am keen to see an improved integration between paleontological and neontological data in evolutionary research.”
