Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

Ljunggren, L., Sandström, A., Bergström, U., Mattila, J., Lappalainen, A., Johansson, G., Sundblad, G., Casini, M., Kaljuste, O., and Eriksson, B. K. 2010. Recruitment failure of coastal predatory fish in the Baltic Sea coincident with an offshore ecosystem regime shift. – ICES Journal of Marine Science, 67: 1587–1595. The dominant coastal predatory fish in the southwestern Baltic Sea, perch and pike, have decreased markedly in abundance during the past decade. An investigation into their recruitment at 135 coastal sites showed that both species suffered from recruitment failures, mainly in open coastal areas. A detailed study of 15 sites showed that areas with recruitment problems were also notable for mortality of early-stage larvae at the onset of exogenous food-intake. At those sites, zooplankton abundance predicted 83 and 34% of the variation in young of the year perch and pike, respectively, suggesting that the declines were caused by recruitment failure attributable to zooplankton food limitation. Incidences of recruitment failure match in time an offshore trophic cascade that generated massive increases in planktivorous sprat and decreases in zooplankton biomass in the early 1990s. Therefore, sprat biomass explained 53% of the variation in perch recruitment from 1994 to 2007 at an open coastal site, where three-spined stickleback also increased exponentially after 2002. The results indicate that the dramatic change in the offshore ecosystem may have propagated to the coast causing declines of the dominating coastal predators perch and pike followed by an increase in the abundance of small-bodied fish.

Habitat protection is a strategy often proposed in fisheries management to help maintain viable populations of exploited species. Yet, quantifying the importance of habitat availability for population sizes is difficult, as the precise distribution of essential habitats is poorly known. To quantify the contribution from coastal nursery habitats to exploited fish population sizes, we related adult density to the amount of nursery habitat available for 12 populations of the two dominant predatory fish species in a 40 000-km2 archipelago area of the Baltic Sea. Habitat distribution was mapped using three conceptually different techniques, Maxent, generalized additive models, and random forest, using spawning and 0-group point samples. Adult densities were estimated from gillnet surveys. Regressions demonstrated no evident effect from fishing, whereas habitat availability had a positive effect, explaining almost half of the variation in population sizes of both species. This result shows that a substantial proportion of the potential production of adult fish can be estimated by mapping essential nursery habitats distribution. Responses were non-linear, indicating that habitat protection has largest effects where there is little available habitat. By demonstrating the importance of habitat limitation of two exploited fish species, we provide quantitative support to the benefits of habitat protection for fisheries.

Trophic cascades occur in many ecosystems, but the factors regulating them are still elusive. We suggest that an overlooked factor is that trophic interactions (TIs) are often scale-dependent and possibly interact across spatial scales. To explore the role of spatial scale for trophic cascades, and particularly the occurrence of cross-scale interactions (CSIs), we collected and analysed food-web data from 139 stations across 32 bays in the Baltic Sea. We found evidence of a four-level trophic cascade linking TIs across two spatial scales: at bay scale, piscivores (perch and pike) controlled mesopredators (three-spined stickleback), which in turn negatively affected epifaunal grazers. At station scale (within bays), grazers on average suppressed epiphytic algae, and indirectly benefitted habitat-forming vegetation. Moreover, the direction and strength of the grazer-algae relationship at station scale depended on the piscivore biomass at bay scale, indicating a cross-scale interaction effect, potentially caused by a shift in grazer assemblage composition. In summary, the trophic cascade from piscivores to algae appears to involve TIs that occur at, but also interact across, different spatial scales. Considering scale-dependence in general, and CSIs in particular, could therefore enhance our understanding of trophic cascades.