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Improving our understanding of biodiversity and ecosystem functioning and our capacity to inform ecosystem management requires an integrated framework for functional biodiversity research (FBR). However, adequate integration among empirical approaches (monitoring and experimental) and modelling has rarely been achieved in FBR. We offer an appraisal of the issues involved and chart a course towards enhanced integration. A major element of this path is the joint orientation towards the continuous refinement of a theoretical framework for FBR that links theory testing and generalization with applied research oriented towards the conservation of biodiversity and ecosystem functioning. We further emphasize existing decision-making frameworks as suitable instruments to practically merge these different aims of FBR and bring them into application. This integrated framework requires joint research planning, and should improve communication and stimulate collaboration between modellers and empiricists, thereby overcoming existing reservations and prejudices. The implementation of this integrative research agenda for FBR requires an adaptation in most national and international funding schemes in order to accommodate such joint teams and their more complex structures and data needs.
2. We present a hierarchical model that integrates observations from multiple sources to estimate spatio-temporal abundance trends. The model links annual population densities on a spatial grid to both long-term count data and to opportunistic occurrence records from a citizen science programme. Specific observation models for both data types explicitly account for differences in data structure and quality.
3. We test this novel method in a virtual study with simulated data and apply it to the estimation of abundance dynamics across the range of a butterfly species (Pyronia tithonus) in Great Britain between 1985 and 2004. The application to simulated and real data demonstrates how the hierarchical model structure accommodates various sources of uncertainty which occur at different stages of the link between observational data and the modelled abundance, thereby it accounts for these uncertainties in the inference of abundance variations.
4. We show that by using hierarchical observation models that integrate different types of commonly available data sources, we can improve the estimates of variation in species abundances across space and time. This will improve our ability to detect regional trends and can also enhance the empirical basis for understanding range dynamics.
Ecologists carry a well-stocked toolbox with a great variety of sampling methods, statistical analyses and modelling tools, and new methods are constantly appearing. Evaluation and optimisation of these methods is crucial to guide methodological choices. Simulating error-free data or taking high-quality data to qualify methods is common practice. Here, we emphasise the methodology of the 'virtual ecologist' (VE) approach where simulated data and observer models are used to mimic real species and how they are 'virtually' observed. This virtual data is then subjected to statistical analyses and modelling, and the results are evaluated against the 'true' simulated data. The VE approach is an intuitive and powerful evaluation framework that allows a quality assessment of sampling protocols, analyses and modelling tools. It works under controlled conditions as well as under consideration of confounding factors such as animal movement and biased observer behaviour. In this review, we promote the approach as a rigorous research tool, and demonstrate its capabilities and practical relevance. We explore past uses of VE in different ecological research fields, where it mainly has been used to test and improve sampling regimes as well as for testing and comparing models, for example species distribution models. We discuss its benefits as well as potential limitations, and provide some practical considerations for designing VE studies. Finally, research fields are identified for which the approach could be useful in the future. We conclude that VE could foster the integration of theoretical and empirical work and stimulate work that goes far beyond sampling methods, leading to new questions, theories, and better mechanistic understanding of ecological systems.