Technological advances in collecting ecological data have expanded rapidly in the last few years, and at a significantly faster rate than the associated and necessary statistical tools for analysing the data to obtain inference on the ecosystems under study. The availability of cheap technological devices suitable to collect the data has led to an explosion of new forms of data; quality of data (e.g. finer temporal/spatial resolution); and quantity of data. Each of these aspects provide new statistical challenges. Specific examples of the new forms or increased quality/quantity of data available include (but are not limited to): advanced geolocation tags; motion/acoustic detector trap arrays; and citizen science data. These lead to a mathematical demand for new statistical methodology that can take full account of the strengths and limitations of the data generated. Challenges that arise with these forms of data that need to be addressed include, for example, developing new finer resolution models (and continuous time/space models); accounting for interactions at both the individual and species level; dealing with computationally intensive likelihoods (and/or big data); dealing with different forms of missing (correlated) data; and efficient model-fitting tools. In addition to the individual challenges raised by the new data available, it is common for multiple different types of datasets be collected on the same ecosystem, leading to further challenges in integrating such data from different sources (of differing quality). However, the potential pay-off for being able to combine different forms of data, often of varying quality and quantity, has huge, and as yet largely untapped, potential.

The workshop focused on these emerging forms of data and the new statistical models and associated model-fitting tools for analysing such data. This included identifying the key issues that need to be addressed and discussing new potential approaches. The workshop also provided a platform for identifying new avenues related to the main statistical challenges for modern statistical ecology, build new collaborations and provide the opportunity to explore new interdisciplinary areas.