New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.

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Summary1. Rapid environmental changes are threatening biodiversity and exposing species to novel ecological and evolutionary pressures. The scientific community increasingly recognizes the need for dynamic models integrating sufficient complexity both to improve our understanding of species' responses to environmental changes and to inform effective management strategies. 2. Using three illustrative examples, we introduce a novel modelling platform, RangeShifter, which integrates complex population dynamics and dispersal behaviour, includes plastic and evolutionary processes and simulates scenarios on spatially explicit landscapes. The software provides functionality for a wide variety of modelling applications ranging from applied questions, where it can be parameterized for real landscapes and species to compare alternative potential management interventions, to purely theoretical studies of species' eco-evolutionary dynamics and responses to different environmental pressures. 3. RangeShifter provides an important tool for facilitating the advancement of ecological theory on species' spatial dynamics in response to environmental changes, and linking it directly to application in biodiversity conservation.

Summary 1.Understanding the causes and consequences of dispersal remains a central topic in ecology and evolution. However, a mismatch exists between our empirical understanding of the complexity of dispersal and our representation of dispersal in models. While the empirical literature is replete with examples of condition dependence at the emigration, movement and settlement phases, models rarely incorporate realism or complexity to this degree. Nor do models often include the different costs associated with dispersal, which can themselves be linked to one or more of the three key phases. 2. Here, we propose that by explicitly accounting for emigration, movement and settlement (and the multiple costs associated with each) we can substantially improve our understanding of both the dispersal process itself and how dispersal traits trade off against other life-history characteristics. We explore some of these issues conceptually, before presenting illustrative results gained from a flexible individual-based model which incorporates considerable dispersal complexity. 3. These results emphasise the nonlinear interplay between the different dispersal stages. For example, we find that investment in movement ability (at a cost to fecundity) depends upon the propensity to emigrate (and vice versa). However, owing to selection acting at the metapopulation level as well as at the individual level, the relationship between the two is not straightforward. Importantly, the shape of the trade-off between movement ability and reproductive potential can strongly influence the joint evolution of dispersal parameters controlling the degree of investment in safer movement, the probability of emigration and the straightness of movement. 4. Our results highlight that the joint evolution of dispersal characteristics can have major implications for spatial population dynamics and we argue that, in addition to increasing our fundamental biological understanding, a new generation of dispersal modelling, which exploits recent empirical advances, can substantially improve our ability to predict and manage the response of species to environmental change.