EcoScan SIGNED

Project "EcoScan" data sheet

The following table provides information about the project.

 Partnership

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 Project objective

Climate change is arguably the most pressing challenge facing all life on earth, threatening human health, ecosystem integrity and the survival of vulnerable species and habitats. Monitoring ecosystem health is critical for the early detection of imbalances but current approaches to do so measure the end-state, not the mechanisms driving patterns of change. Plants form the base of ecosystem functioning and accumulate increased viral- and fungal load in response to climate-related stress. These viruses are ingested along with plant matter by insect herbivores who similarly have microorganisms and may be eaten by a secondary consumer such as a bat. In the current proposal, we take advantage of this natural aggregation through trophic levels by collecting bat guano which contains a snap-shot sample of the environment. Directly sampling the individual taxa in multi-trophic assemblages would be unfeasible both financially and logistically. We will then use the latest in high-throughput sequencing protocols in metabarcoding and viral metagenomics to retrieve plant viruses and fungi, in addition to insect, fungi and bat DNA, viruses and fungi. Temporal sampling across climatic zones and habitats will ensure that complex and seasonal ecosystem processes are captured. We will use novel, sophisticated methods in ecological analyses to explore taxon diversity, abundance and associations, and how these change over time. By constructing weighted and multi-layer networks we will illuminate the processes affecting seasonal ecosystem health in relation to habitat and climate. As such we will be able to distinguish between localised phenomena and effects which cascade across layers. Our proposal will substantially increase knowledge of biodiverse but understudied groups (non-pathogenic fungi and viruses), shed light on ecosystem processes and how they respond to climate change, and ultimately provide proof of concept results for ‘aggregation’ as a means to survey ecosystem health.