Different observation and modelling approaches are used in understanding and predicting spatial dynamics at individual, population and species level. Although the relative importance of the basic determinants of species distributions vary across scales (Hortal et al. 2010
), the basic concepts of niche (relationship between environmental factors and fitness), spatial variation in environments and dispersal underlie spatial ecology at all levels (Holt 2003
). Links are also given by underlying ecological-evolutionary theory. The interaction of population processes, dispersal and evolutionary change determine the dynamics of species' ranges. Linking spatial distributions to processes at the individual, population and metapopulation level provides insights into their potential response to environmental change.
Cross-level analysis is critical to understanding processes that determine the spatial behaviour of individuals and populations. Terrestrial studies, because of the relative ease of accurate census and detailed long-term observation, can reveal the consequences of individual behaviour for population trajectories and range dynamics at a level of detail that is difficult to achieve with organisms that are largely invisible. Studies on the spatial behaviour of fish populations provide insights into processes that lead to population-wide response to climate change, and ultimately, to global changes in biodiversity (Cheung et al. 2009
). They also hold the key to breaking the circularity immanent in many habitat–distribution modelling approaches where habitat suitability is inferred from observed distributions.
Marine and terrestrial scientists face similar challenges when dealing with large observational errors and uncertainty in underlying process dynamics. Linking levels and approaches will help in developing predictive models, using methods developed in both scientific realms to fill gaps in distribution maps and possibly allowing separation of process and observation uncertainties. State space models accomplish this feat. Widely used in fisheries science, they have recently been discovered for animal movement models (reviewed in Patterson et al. 2008
), and may fruitfully be applied at other levels and in other systems.
The species–habitat associations that we observe and model are continually re-shaped by dynamic ecological and evolutionary processes which can feedback across levels of organization. Anthropogenic and environmental changes are also occurring and driving changes at similar time scales. Improved connectivity of data and theory across all levels of organization will only help in our ability to understand and predict the consequences.