I am broadly interested in global change ecology, with a focus on using big data to understand ecological and biological patterns and processes in space and time ranging from local to global spatial scales. My work tends to focus on urban ecology, asking fundamental questions such as how species traits influence a species’ ability to persist in urban ecosystems as well as applied questions such as how to better preserve biodiversity in urban greenspaces. While I am partial to birds, my research cuts across different taxa including birds, amphibians, butterflies, and multi-taxa compilations. In addition, I am interested in how we can better use and improve citizen science data, and how people interact with nature through citizen science.
Because people tend to collect data where they live, biodiversity data are traditionally biased towards urban areas. While this may be a problem for some ecological questions, urban areas represent model systems for many other questions, for instance, about population and community responses to land-use intensity. This has been the conceptual underpinning for much of my research to date. My work transcends geographic scales, from analyses in small cities to continental or global analyses, as well as taxonomic scales, from individuals to populations and species to communities. During my PhD, for example, I derived a novel framework for quantifying continuous responses to urbanization at species and community levels (Callaghan et al. 2019). This framework has recently been applied to birds (Callaghan et al. 2019), plants (Callaghan et al. 2020), frogs (Liu et al. 2021), and butterflies (Callaghan et al. 2021). Collectively, this body of work has highlighted that specialist species are significantly at risk of increasing urbanization.
Ongoing work is extending this research in both a theoretical and an applied sense. First, in a theoretical sense, are there unifying macroevolutionary rules of how organisms respond to urbanization across taxa? Which ecological traits are the best predictor of a species’ ability to tolerate urban environments? Do populations respond to urbanization similarly at different spatial scales? While these questions have been addressed previously to some extent, they are often limited in geographic and/or taxonomic coverage. I am currently working on cross-taxa comparisons, incorporating under-studied groups such as insects. My goal is to develop and test general theory about which components of biodiversity can be maintained in our increasingly anthropogenically-modified world. Second, from an applied sense, conservation of urban biodiversity is increasingly recognized as being important, with increased efforts aimed at restoration work in urban areas (e.g., rewilding urban parks, wetland mitigation, urban meadows). But monitoring the success or failure of such restorations is difficult in practice, and often funding is not available. This is where I see citizen science data as a valuable tool to quantify and monitor urban biodiversity (see Callaghan et al. 2019).
In 2021, iNaturalist averaged about 81,000 observations per day. I believe that big biodiversity data (e.g., eBird, iNaturalist, and aggregations like GBIF) will be fundamental to the future of research in ecology and evolution, enabling ecologists to ask questions about biodiversity patterns at scales that were previously impossible. This is why one part of my research has focused on understanding how these big datasets can be most useful for biodiversity research. For example, I have (1) quantified how citizen science can be used to estimate species richness (Callaghan et al. 2020); (2) developed a novel adaptive sampling framework aimed at collecting better data for biodiversity monitoring (Callaghan et al. 2019); and (3) quantified biases in citizen science data that should be accounted for in modelling (Callaghan et al. 2021).
My interest in citizen science began from recognizing the importance of the data to answer ecological questions. But each observation represents not only a data point about biodiversity, but a data point about when, and how, someone interacted with nature. I am increasingly interested in this line of work, quantifying how people interact and engage with the natural environment through citizen science. As such, I have a number of ongoing projects and collaborations aimed at understanding why, and how, people contribute to biodiversity citizen science datasets (e.g., Bowler et al. 2022). Most of this research is with the aim of understanding how to improve modelling of citizen science data for better understanding biodiversity change.