Send message to Authors

Poster Short Abstract

To inform our design of a new wildlife tracking project in Trondheim, we explored spatial bias, sampling effort and detection via a simulation framework that mimics citizen science data collection. Results support improved design of future citizen science projects.

Poster Abstract

Citizen science data related to wildlife observations is often affected by participatory bias – linked to where, when, and how participants make and register observations. To explore and mitigate these challenges, we developed a simulation framework that mimics citizen science data collection under varying conditions and scenarios.

We developed this framework to support our design of a new structured citizen science program conducted in Trondheim, Norway, where participants follow a standardized protocol to collect photos of animal tracks. The study area is a developed urban landscape centered around a green corridor, and the research objective is to assess whether the number of wildlife observations can be explained by corridor status (corridor vs. non-corridor), in combination with other landscape variables.

Data collected through the program combined with publicly available citizen science data from GBIF is used to model a baseline scenario, describing the expected distribution of animal species. To evaluate the potential and the data requirements of the program, we conduct a range of simulations on scenarios of participant behavior and spatial sampling patterns. The scenarios are based on insights from the citizen science program and other existing literature on realistic participatory bias.

The simulations allow us to estimate the data volume and participant numbers required to confidently address the research objectives under different conditions typical of citizen science projects. Ultimately, this framework can support the design and evaluation of future citizen science projects by helping anticipate data needs and optimize study design.