One of the most pressing challenges facing biodiversity science is to make and deliver local, regional, and global forecasts across scales of ecological organisation. Such forecasts can assist decision making, and drive fundamental research into the patterns and processes structuring biodiversity. Altered environmental conditions, include those driven by global change, further motivate biodiversity forecasting research. This project will use diverse data sources, where possible across URPP test sites and infrastructures, and analytical tools to explore and advance the current limits of biodiversity forecasting through time, space, and environmental conditions. Data from already conducted experiments as well as from specifically designed future experiments will help disentangle the factors that affect our current forecasting skills. The results will support the improvement of forecasting tools, will inform about which ecological scales are more or less amenable to forecasting, and will provide insights about data requirements for effective forecasting.
Environments, ecosystems and the biodiversity within them are facing changes at unprecedented rates. This not only poses current conservation challenges, but creates uncertainty about the future of biodiversity and of the availability of ecosystems services. Developing effective tools to forecast temporal and spatial changes in biodiversity and its components is more necessary than ever. Yet opinion is divided as to the prospects of accurate and useful biodiversity forecasts. Complexity, nonlinearity, evolution, multiple sources of uncertainty, and lack of appropriate information, are often cited as barriers to such forecasting. Increases in quantity and quality of data, advances in analytical methods, and demonstrations of successful forecasts of complex systems suggest such barriers may not be too great or pervasive. However, there are very few systematic studies of the challenges of ecological forecasting, and the opportunities available to address them.
A major challenge for biodiversity forecasting is the availability of adequate data. Long and consistent time series are scarce and usually are only about a focal species at one locality, neglecting abiotic factors, spatial complexity and the community in which that species is embedded. Long, multivariate time series, with low observation error and regular sampling are required to test the latest forecasting approaches under ideal data conditions, with the possibility to test approaches with artificially degraded data. The second challenge is the development and thorough testing of analytical approaches that can transform the available data into useful forecasts. There are many available approaches, from very phenomenological to very mechanistic, and very few studies of their relative forecasting performance under a variety of abiotic and biotic contexts.
We believe that the URPP GCB is in a strong position to meet these challenges. It has a wide array of available data both from a range of ecosystems across the globe as well as from a series of controlled laboratory experiments manipulating spatial or environmental drivers. The URPP GCB has infrastructure for intensively monitoring and even experimenting on small scale and moderately complex communities, to which manipulative treatments can be applied. Furthermore, we have and are developing the expertise required to apply the latest numerical modelling and statistical methods for making forecasts and comparing them with observations.
1. Review data availability across URPP test sites, and prioritise inclusion in this project, and provide recommendations for future data acquisition. Criteria for prioritization would include but not be limited to: length and temporal resolution of time series, extent and resolution of spatial series, number and diversity of variables observed, and relevance for decision making.
2. Test the ability of a battery of forecasting methods, from phenomenological time series forecasting, to, where available, mechanistic models of ecological processes and interactions, to forecast responses to environmental change.
3. Establish an experimental study of a moderately complex microbial community, including environmental change treatments, to act as test bed for assessing and developing forecast models and tools. Critical for this experiment is intensive and thorough observation of the organisms and environmental conditions, so as to provide high quality and large quantity of data. This experiment will have a combination of temporal and spatial extent, and environmental change treatments.
4. Develop novel experimental and analytical methods for exploring community dynamics in a continuous spatial setting with changing environmental gradients. This could involve a new and relatively untested experimental apparatus of a “temperature gradient bar” onto which ecological communities can be placed, and which then experience continuous variation in temperature, across which individuals can disperse and adapt. This is a relatively high risk objective, since the required methods are not completely developed.