Citizen Science
Citizen Science as a tool in conservation biology
Citizen science (CS), the involvement of citizens from the non-scientific community in data collection, has become increasingly important in conservation science, as resources for monitoring fail to match the scale of the questions at hand. Networks of citizen scientists are engaging millions of individuals around the globe in collecting, categorizing, transcribing, or analyzing scientific data. Although their projects, objectives, activities and scope vary considerably among the CS projects, they share a common goal: the production of data that can be used for scientific purposes.
CS programs provide important data sets that allow to conduct research in ecology at multiple spatio-temporal scales. As such, CS is a major components of my research, and my most work relies on biodiversity data collected by citizens: Suivi Temporel des Oiseaux Communs (STOC -French Breeding Bird Survey), Pan-European Common Bird Monitoring Scheme (PECBMS), Project FeederWatch (PFW), Christmas Bird Count (CBC), Avian Mortality Botulism Lakeshore Events (AMBLE).
My research focuses on the analysis of these massive data sets to study topics such as examining climate-induced changes in bird community composition and population dynamics (e. g. Gaüzère, Princé & Devictor, 2016; Princé & Zuckerberg 2015), exploring the spatiotemporal characteristics of avian botulism mortality outbreaks (Princé et al. 2018), as well as assessing the potential impacts on biodiversity of future land use policies (e.g. Princé et al. 2013, 2015), or even assessing the effectiveness of measures for the conservation of species and spaces (e.g. Princé et al. 2012; Princé et al. 2021). All this work has been possible thanks to collaboration with CS project coordinators and managers: Vigie-Nature (Frédéric Jiguet), Cornell Lab of Ornithology (David Bonter), Audubon Society (Kathy Dale), European Breeding Bird Census Council (Petr Voříšek, Lluis Brotons), USGS National Wildlife Health Center (Jenny Chipault).
In addition to the scientific dimension, CS have a strong societal dimension. For example, participation in biodiversity monitoring programs can also lead to the emergence of behaviours that are favorable to biodiversity conservation.
As such, the study of the long-term behaviour of participants in the French Observatory of Garden Biodiversity (OBJ) has recently shown that participation in such programmes can lead to changes in the gardening practices of observers that are more favourable to biodiversity (Deguines, Princé, et al. 2020).
Although CS has become essential to address various environmental issues, the data generated by these programs may contain higher levels of variability (e.g. measurement error) or bias (e.g. spatiotemporal cluster), in comparison with the data collected by scientists or instruments measurement. However, new more sophisticated analytical approaches have recently emerged, allowing the analysis of such data. In particular, the recent development of new modelling methods based on a hierarchical framework that can integrate imperfect detection process, offers new opportunities. As the use of large citizen science database grows, more resources are needed for data management, training in techniques of analysis of data and development of macro-ecological theory. This propensity to "data intensive ecology" needs to develop research and training to new approaches to computing and statistical modelling to work with large sets of large spatial and temporal scales data. This also includes the development of appropriate tools for exploration, analysis and display of results.
My interest in citizen science as a research tool but also as a tool for scientific mediation, pushes me in this direction. I have been using some of these modelling methods (e.g. Bayesian statistics, hierarchical modelling) in my own research, and am interested in extending the methods to new applications and questions in ecology and conservation biology -in particular through interdisciplinary approaches to better understand the complexity of these large sets of ecological data and value. I led a symposium at the ICCB-ECCB 2015 conference on that topic (Princé & Hochachka, 2015). I am also one of the animator of the Citizen Science Theme within the French Statistical Ecology Research Group, and I participate to the CiSStats Network in France (Citizen Science Statistics).