Integrating Multiple Data Types to Connect Ecological Theory and Data Among Levels

Ecological theories often encompass multiple levels of biological organisation, such as genes, individuals, populations, and communities. Despite substantial progress towards ecological theory spanning multiple levels, ecological data rarely are connected in this way. This is unfortunate because different types of ecological data often emerge from the same underlying processes and, therefore, are naturally connected among levels. Here, we present an approach to integrate data collected at multiple levels (e.g., individuals, populations) in a single statistical analysis. The resulting integrated models make full use of existing data and align statistical ecology with ecological theories that span multiple levels of organisation. Integrated models are increasingly feasible due to recent advances in computational statistics, which allow fast calculations of multiple likelihoods that depend on complex mechanistic models. We discuss recently developed integrated models and demonstrate their implementation and application using data on freshwater fishes in south-eastern Australia. Available data on freshwater fishes include population survey data, mark-recapture data, and individual growth trajectories. We use these data to estimate demographic vital rates (size-specific survival and reproduction) more accurately than previously possible. We show that integrating multiple data types enables parameter estimates that would otherwise be infeasible and argue that integrated models will strengthen the development of ecological theory in the face of limited data. Although integrated models remain conceptually and computationally challenging, integrating ecological data among levels is likely to be an important step towards unifying ecology among levels.