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The pervasive influence of human induced global environmental change affects biodiversity across the globe, and there is great uncertainty as to how the biosphere will react on short and longer time scales. To adapt to what the future holds and to manage the impacts of global change, scientists need to predict the expected effects with some confidence and communicate these predictions to policy makers. However, recent reviews found that we currently lack a clear understanding of how predictable ecology is, with views seeing it as mostly unpredictable to potentially predictable, at least over short time frames. However, in applied, ecology-related fields predictions are more commonly formulated and reported, as well as evaluated in hindsight, potentially allowing one to define baselines of predictive proficiency in these fields. We searched the literature for representative case studies in these fields and collected information about modeling approaches, target variables of prediction, predictive proficiency achieved, as well as the availability of data to parameterize predictive models. We find that some fields such as epidemiology achieve high predictive proficiency, but even in the more predictive fields proficiency is evaluated in different ways. Both phenomenological and mechanistic approaches are used in most fields, but differences are often small, with no clear superiority of one approach over the other. Data availability is limiting in most fields, with long-term studies being rare and detailed data for parameterizing mechanistic models being in short supply. We suggest that ecologists adopt a more rigorous approach to report and assess predictive proficiency, and embrace the challenges of real world decision making to strengthen the practice of prediction in ecology.

Plato’s well-known allegory of the cave describes an observer chained in a cave facing a blank wall on which shadows are projected of objects that are outside the cave. Only by breaking free from the chains can the observer submerge from the cave to see what the objects really look like. Ecological model features compare to the objects outside the cave in this allegory. By performing model analysis light is shed on these features, creating projections that researchers can see. Model analysis methodologies like bifurcation analysis and sensitivity analysis each focus on particular model features and thus allow researchers to uncover only part of the model behaviour. By combining methodologies for model analysis possibilities arise for unravelling more of the model’s behaviour, allowing researchers to ‘break free’. In this paper benefits and issues of combining model analysis methodologies are discussed using a case study. The case study involves three representations of the well-known Rosenzweig-MacArthur predator-prey model, namely the usual one where state variables and parameters have dimensions, a dimensionless representation, and a generalized representation. Based on the results we argue that researchers should combine bifurcation and sensitivity analysis methodologies when analyzing ecological models.

Dominance of free-floating plants poses a threat to biodiversity in many freshwater ecosystems. Here we propose a theoretical framework to understand this dominance, by modeling the competition for light and nutrients in a layered community of floating and submerged plants. The model shows that at high supply of light and nutrients, floating plants always dominate due to their primacy for light, even when submerged plants have lower minimal resource requirements. The model also shows that floating-plant dominance cannot be an alternative stable state in light-limited environments but only in nutrient-limited environments, depending on the plants’ resource consumption traits. Compared to unlayered communities, the asymmetry in competition for light—coincident with symmetry in competition for nutrients—leads to fundamentally different results: competition outcomes can no longer be predicted from species traits such as minimal resource requirements (R* rule) and resource consumption. Also, the same two species can, depending on the environment, coexist or be alternative stable states. When applied to two common plant species in temperate regions, both the model and field data suggest that floating-plant dominance is unlikely to be an alternative stable state.