Current evaluations of farmland biodiversity are often limited to indirect broad-scale multispecies assessments of terrestrial vertebrates or observations of farmland birds and butterfly populations. While some monitoring data do exist there is no consistent information on the status of more common arthropod species, despite the fact that these are usually providing ecosystem services or are the cause of crop damage and yield loss.
In 2012, European researchers developed a generic set of farmland biodiversity indicators which capture species and habitat diversity at the farm scale (Targetti et al. 2014, https://doi.org/10.1016/j.ecolind.2014.04.050). Their suggested approach to determine diversity requires the identification of a wide range of species that represent different trophic levels in the local ecosystem. Despite the importance of such information, the model seems neither feasible nor scalable when using conventional approaches to species identification which require impractically extensive time and cost commitments. In addition, information on traits associated with trophic guilds and behavior are sparse, so measuring ecosystem services, estimating potential for crop damage, and assessing other kinds of biodiversity function are not simple, even once we have species monitoring data.
This project works under the premise to make farmland biodiversity estimates and assessments cost effective and scalable, by leveraging existing metabarcoding technology, biodiversity knowledge, and research e-infrastructure. Prospective students could work on the following aspects of the project:
- Automated data mining (crawler and scraper technology) of distributed digitized primary literature.
- Help to build the analytical backbone that delivers near real-time information on both the presence/absence of species and the functional diversity indicators that reflect the productive capacity of agrosystems.