18 research outputs found
Community assembly and diversification in a species-rich radiation of island weevils (Coleoptera: Cratopini)
Barriers to gene flow in the deepest ocean ecosystems: Evidence from global population genomics of a cosmopolitan amphipod
Merging DNA metabarcoding and ecological network analysis to understand and build resilient terrestrial ecosystems
Convergent evolution and possible constraint in the posterodorsal retraction of the external nares in pelagic crocodylomorphs
Nested metabarcode tagging: a robust tool for studying species interactions in ecology and evolution
Detecting host–parasitoid interactions in an invasive Lepidopteran using nested tagging DNA metabarcoding
Construction, validation, and application of nocturnal pollen transport networks in an agro‐ecosystem: a comparison using light microscopy and DNA metabarcoding
The predator problem and PCR primers in molecular dietary analysis: swamped or silenced; depth or breadth?
Biomonitoring for the 21st Century: Integrating Next-Generation Sequencing Into Ecological Network Analysis
Ecological network analysis (ENA) provides a mechanistic framework for describing complex species interactions, quantifying ecosystem services, and examining the impacts of environmental change on ecosystems. In this chapter, we highlight the importance and potential of ENA in future biomonitoring programs, as current biomonitoring indicators (e.g. species richness, population abundances of targeted species) are mostly descriptive and unable to characterize the mechanisms that underpin ecosystem functioning. Measuring the robustness of multilayer networks in the long term is one way of integrating ecological metrics more generally into biomonitoring schemes to better measure biodiversity and ecosystem functioning. Ecological networks are nevertheless difficult and labour-intensive to construct using conventional approaches, especially when building multilayer networks in poorly studied ecosystems (i.e. many tropical regions). Next-generation sequencing (NGS) provides unprecedented opportunities to rapidly build highly resolved species interaction networks across multiple trophic levels, but are yet to be fully exploited. We highlight the impediments to ecologists wishing to build DNA-based ecological networks and discuss some possible solutions. Machine learning and better data sharing between ecologists represent very important areas for advances in NGS-based networks. The future of network ecology is very exciting as all the tools necessary to build highly resolved multilayer networks are now within ecologists reach