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...

Despite the enormous economic and ecological importance of marine organisms, the spatial scales of adaptation and biocomplexity remain largely unknown. Yet, the preservation of local stocks that possess adaptive diversity is critical to the long-term maintenance of productive stable fisheries and ecosystems. Here, we document genomic evidence of range-wide adaptive differentiation in a broadcast spawning marine fish, Atlantic cod (Gadus morhua), using a genome survey of single nucleotide polymorphisms. Of 1641 gene-associated polymorphisms examined, 70 (4.2%) tested positive for signatures of selection using a Bayesian approach. We identify a subset of these loci (n ¼ 40) for which allele frequencies show parallel temperature-associated clines (p , 0.001, r 2 ¼ 0.89) in the eastern and western north Atlantic. Temperature associations were robust to the statistical removal of geographic distance or latitude effects, and contrasted 'neutral' loci, which displayed no temperature association. Allele frequencies at temperature-associated loci were significantly correlated, spanned three linkage groups and several were successfully annotated supporting the involvement of multiple independent genes. Our results are consistent with the evolution and/or selective sweep of multiple genes in response to ocean temperature, and support the possibility of a new conservation paradigm for non-model marine organisms based on genomic approaches to resolving functional and adaptive diversity.

Estuaries and coastal wetlands are critical transition zones (CTZs) that link land, freshwater habitats, and the sea. CTZs provide essential ecological functions, including decomposition, nutrient cycling, and nutrient production, as well as regulation of fluxes of nutrients, water, particles, and organisms to and from land, rivers, and the ocean. Sedimentassociated biota are integral to these functions. Functional groups considered essential to CTZ processes include heterotrophic bacteria and fungi, as well as many benthic invertebrates. Key invertebrate functions include shredding, which breaks down and recycles organic matter; suspension feeding, which collects and transports sediments across the sediment-water interface; and bioturbating, which moves sediment into or out of the seabed. In addition, macrophytes regulate many aspects of nutrient, particle, and organism dynamics above-and belowground. Animals moving within or through CTZs are vectors that transport nutrients and organic matter across terrestrial, freshwater, and marine interfaces. Significant threats to biodiversity within CTZs are posed by anthropogenic influences; eutrophication, nonnutrient pollutants, species invasions, overfishing, habitat alteration, and climate change affect species richness or composition in many coastal environments. Because biotic diversity in marine CTZ sediments is inherently low whereas their functional significance is great, shifts in diversity are likely to be particularly important. Species introductions (from invasion) or loss (from overfishing or habitat alteration) provide evidence that single-species changes can have overt, sweeping effects on CTZ structure and function. Certain species may be critically important to the maintenance of ecosystem functions in CTZs even though at present there is limited empirical evidence that the number of species in CTZ sediments is critical. We hypothesized that diversity is indeed important to ecosystem function in marine CTZs because high diversity maintains positive interactions among species (facilitation and mutualism), promoting stability and resistance to invasion or other forms of disturbance. The complexity of interactions among species and feedbacks with ecosystem functions suggests that comparative (mensurative) and ma- 430nipulative approaches will be required to elucidate the role of diversity in sustaining CTZ functions.

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We examine estimates of dispersal in a broad range of marine species through an analysis of published values, and evaluate how well these values represent global patterns through a comparison with correlates of dispersal. Our analysis indicates a historical focus in dispersal studies on low-dispersal/low-latitude species, and we hypothesize that these studies are not generally applicable and representative of global patterns. Large-scale patterns in dispersal were examined using a database of correlates of dispersal such as planktonic larval duration (PLD, 318 species) and genetic differentiation (F ST , 246 species). We observed significant differences in F ST ( p!0.001) and PLD ( p!0.001) between taxonomic groups (e.g. fishes, cnidarians, etc.). Within marine fishes (more than 50% of datasets), the prevalence of demersal eggs was negatively associated with PLD (R 2 Z0.80, p!0.001) and positively associated with genetic structure (R 2 Z0.74, p!0.001). Furthermore, dispersal within marine fishes (i.e. PLD and F ST ) increased with latitude, adult body size and water depth. Of these variables, multiple regression identified latitude and body size as persistent predictors across taxonomic levels. These global patterns of dispersal represent a first step towards understanding and predicting species-level and regional differences in dispersal, and will be improved as more comprehensive data become available.