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...

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Bythotrephes longimanus is an invasive pelagic crustacean, which first arrived in North America from Europe in early 1980s and can now be found throughout the Great Lakes and in many inland lakes and waterways. Determining the suitability of lakes to Bythotrephes establishment is an important step in quantifying its potential habitat range and environmental risk. Lake environmental conditions, planktivorous fishes, sport fishes and Bythotrephes occurrence data from 179 south-central Ontario lakes were used in this study to model lake characteristics suitable for its establishment. The performance of principal component analysis and different predictive models was used to determine the habitats that are suitable for the survival of Bythotrephes and the factors that may regulate its spread. Four modeling approaches were employed: linear discriminant analysis; multiple logistic regression; random forests; and, artificial neural networks. Ensemble prediction based on the four modeling approaches was also used as an indicator for predicting Bythotrephes occurrence. Bythotrephes appears to establish more readily in larger, deeper lakes with lower elevation, that have more sport fishes. Bythotrephes occurrence can be best predicted by artificial neural networks when including the measures of fish data, in addition to lake environmental data. Lake elevation, surface area and sport fish occurrence were ranked as the most important predictors of Bythotrephes invasion. The inclusion of biotic variables (occurrence or diversity of sport or planktivorous fishes) enhanced cross-validated models relative to analyses based on environmental data alone.

Understanding how ocean conditions influence fish distributions is critical for elucidating the role of climate in ecosystem change and forecasting how fish may be distributed in the future. Traditional species distribution models are often applied to scientific‐survey data, which include species presence‐absence information, to predict distributions. Maximum entropy (MaxEnt) models are promising tools as they can be applied to presence‐only data (e.g., data collected from fishermen targeting a specific species or from observers in citizen‐science programs). We used MaxEnt models to relate occurrence records of three marine pelagic fish (Atlantic herring, Atlantic mackerel, and butterfish) in fishery‐dependent data to environmental conditions (sea surface temperature (SST) and chlorophyll‐a concentration from satellite remote sensing, bathymetry, and climate indices), and evaluated model performance by both cross‐validation and validation using fishery‐independent data. We developed monthly habitat suitability maps for these fish in the Northwest Atlantic Shelf area, and assessed the relative influence of environmental factors on their distributions. Across months, their suitable habitat areas varied with each species exhibiting inshore‐offshore and north‐south movements in response to changing environmental conditions. Overall, SST and chlorophyll‐a concentration had the greatest influence on the distributions of these fish, with bathymetry having moderate influence and climate indices having little influence. Our application of MaxEnt models enabled us to integrate presence‐only data and high resolution environmental data from satellite remote sensing to describe spatiotemporal distributions of marine pelagic fish. These models were used to hindcast species occurrence in relation to historical environmental conditions to evaluate their predictive performance, and have the potential to provide nowcasts in relation to current conditions or forecasts of species future distributions.