| dc.description.abstract | While human-made elements often overshadow urban ecosystems, they have recently shown to be able to support and harbour significant biological diversity, which is critical yet challenging to monitor effectively. Given the fact that 68% present of the world’s population is expected to reside in cities by 2050, these systems are under great pressure by rapid urbanisation and understanding how these systems function becomes increasingly important. There is an urgent need for the creation of novel, more comprehensive monitoring techniques, that overcome the disadvantages from labour and time intensive, invasive and biased traditional methods and to enable more nuanced and faster conservations efforts. This study proposes a novel methodological framework to harmonize different data sets, reduce biases and enhance comparability across biodiversity studies, addressing gaps in standardized methods and data collection.
We propose an integrated multimethod approach that combines sensors based on Artificial Intelligence (AI), environmental DNA (eDNA), and conventional ecological methods, to enhance accuracy, efficiency and scalability of urban biodiversity monitoring. With the use of AI-based sensors, large volumes of data and species can be analysed and determined rapidly in real time, while eDNA offers a way to assess a broad taxa spectrum from environmental samples. Combining and ground truthing these methods with traditional observational methods, we aim to provide a more comprehensive understanding of urban biodiversity.
Our project will result in a biodiversity monitoring toolkit designed for use by scientists, citizen and policymakers, which will engage a broader community in conservation efforts. By leveraging the strengths of AI, eDNA and traditional methods, this toolkit will advance urban ecological research and impact sustainable urban planning and policymakers. | |
