NovelEco | Societal Attitudes to Urban Novel Ecosystems

Uploaded by RRI Tools on 20 May 2022



From 01/07/2021 to 30/06/2026

NovelEco is a citizen science project that will measure, for the first time, societal attitudes towards urban wild spaces (novel ecosystems) by working with citizens to study them and generate data on urban ecosystems.

NovelEco will take a citizen science approach to measure, for the first time, societal attitudes to urban wild spaces by asking citizens to study them. This will address a major gap in understanding whether human contact with nature, especially ‘wild’ nature, can have beneficial social and personal effects.

NovelEco will thus create a new awareness of the transformative potential of urban wild spaces while advancing frontier science in the fields of urban novel ecosystems and social-ecological systems science.

The aim of NovelEco is to explore novel ecosystem theory as a bridging concept and a conduit for rewilding urban society.

NovelEco will engage citizens in co-creating an online instrument to enable ecological data collection within urban novel ecosystems. During data collection, the citizen scientists will record their attitudes to novel ecosystems and reveal whether engagement with them alters their values and perhaps even their environmental behaviour. Comparing these data with the wider community, this project will be the first to quantify the social and ecological values of novel ecosystems. 

The project will be looking for volunteers to:

  • Co-create research questions around what ecological characteristics are interesting to you
  • Co-produce an app for gathering data and communicating with other citizen scientists t establish the values of wild spaces
  • Systematically gather data on what species can be found in your local wild spaces over several years
  • Co-produce papers, videos, blogs, etc.
The NovelEco project team consists of a core research team at Trinity College Dublin, School of Natural Sciences and the SFI ADAPT Centre.
The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research & Innovation programme (Grant Agreement No. 101002440).

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