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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Effective ocean management and conservation of highly migratory species depends onresolving overlap between animal movements and distributions, and fishing effort.However, this information is lacking at a global scale. Here we show, using a big-data approach that combines satellite-tracked movements of pelagic sharks and global fishing fleets, that 24% of the mean monthly space used by sharks falls under the footprint of pelagic longline fisheries. Space-use hotspots of commercially valuable sharks and of internationally protected species had the highest overlap with longlines (up to 76% and 64%, respectively), and were also associated with significant increases in fishing effort.We conclude that pelagic sharks have limited spatial refuge from current levels of fishing effort in marine areas beyond national jurisdictions (the high seas). Our results demonstrate an urgent need for conservation and management measures at high-seas hotspots of shark space use, and highlight the potential of simultaneous satellite surveillance of megafauna and fishers as a tool for near-real-time, dynamic management.Industrialised fishing is a major source of mortality for large marine animals (marine megafauna) 1-6 . Humans have hunted megafauna in the open ocean for at least 42,000 years 7 , but international fishing fleets targeting large, epipelagic fishes did not spread into the high seas (areas beyond national jurisdiction) until the 1950s 8 . Prior to this, the high seas constituted a spatial refuge largely free from exploitation as fishing pressure was concentrated on continental shelves 3,8 . Pelagic sharks are among the widest ranging vertebrates, with some species exhibiting annual ocean-basin-scale migrations 9 , long term trans-ocean movements 10 , and/or fine-scale site fidelity to preferred shelf and open ocean areas 5,9,11 . These behaviours could cause extensive spatial overlap with different fisheries from coastal areas to the deep ocean. On average, large pelagic sharks account for 52% of all identified shark catch worldwide in target fisheries or as bycatch 12 . Regional declines in abundance of pelagic sharks have been reported 13,14 , but it is unclear whether exposure to high fishing effort extends across ocean-wide population ranges and overlaps areas in the high seas where sharks are most abundant 5,13 .Conservation of pelagic sharkswhich currently have limited high seas management 12,15,16would benefit greatly from a clearer understanding of the spatial relationships between sharks' habitats and active fishing zones. However, obtaining unbiased estimates of shark and fisher distributions is complicated by the fact that most data on pelagic sharks come from catch records and other fishery-dependent sources 4,15,16 .Here, we provide the first global estimate of the extent of space use overlap of sharks with industrial fisheries. This is based on the analysis of the movements of pelagic sharks tagged with satellite transmitters in the Atlantic, Indian and Pacific oceans, together with fishing vessel movements m...

After decades of extensive surveying, knowledge of the global distribution of species still remains inadequate for many purposes. In the short to medium term, such knowledge is unlikely to improve greatly given the often prohibitive costs of surveying and the typically limited resources available. By forecasting biodiversity patterns in time and space, predictive models can help fill critical knowledge gaps and prioritise research to support better conservation and management. The ability of a model to predict biodiversity metrics in novel environments is termed “transferability,” and models with high transferability will be the most useful in this context. Despite their potentially broad utility, little guidance exists on what confers high transferability to biodiversity models. We synthesise recent advances in biodiversity model transfers to facilitate increased understanding of what underpins successful model transferability, demonstrating that a consistent approach has so far been lacking but is essential for achieving high levels of repeatability, transparency and accountability of model transfers. We provide a set of guidelines to support efficient learning and the improvement of model transferability.

Aim  Predicting distribution patterns of whale sharks (Rhincodon typus, Smith 1828) in the open ocean remains elusive owing to few pelagic records. We developed multivariate distribution models of seasonally variant whale shark distributions derived from tuna purse‐seine fishery data. We tested the hypotheses that whale sharks use a narrow temperature range, are more abundant in productive waters and select sites closer to continents than the open ocean. Location  Indian Ocean. Methods  We compared a 17‐year time series of observations of whale sharks associated with tuna purse‐seine sets with chlorophyll a concentration and sea surface temperature data extracted from satellite images. Different sets of pseudo‐absences based on random distributions, distance to shark locations and tuna catch were generated to account for spatiotemporal variation in sampling effort and probability of detection. We applied generalized linear, spatial mixed‐effects and Maximum Entropy models to predict seasonal variation in habitat suitability and produced maps of distribution. Results  The saturated generalized linear models including bathymetric slope, depth, distance to shore, the quadratic of mean sea surface temperature, sea surface temperature variance and chlorophyll a had the highest relative statistical support, with the highest percent deviance explained when using random pseudo‐absences with fixed effect‐only models and the tuna pseudo‐absences with mixed‐effects models (e.g. 58% and 26% in autumn, respectively). Maximum Entropy results suggested that whale sharks responded mainly to variation in depth, chlorophyll a and temperature in all seasons. Bathymetric slope had only a minor influence on the presence. Main conclusions  Whale shark habitat suitability in the Indian Ocean is mainly correlated with spatial variation in sea surface temperature. The relative influence of this predictor provides a basis for predicting habitat suitability in the open ocean, possibly giving insights into the migratory behaviour of the world’s largest fish. Our results also provide a baseline for temperature‐dependent predictions of distributional changes in the future.

Ten years have passed since the last synopsis of whale shark Rhincodon typus biogeography. While a recent review of the species' biology and ecology summarized the vast data collected since then, it is clear that information on population geographic connectivity, migration and demography of R. typus is still limited and scattered. Understanding R. typus migratory behaviour is central to its conservation management considering the genetic evidence suggesting local aggregations are connected at the generational scale over entire ocean basins. By collating available data on sightings, tracked movements and distribution information, this review provides evidence for the hypothesis of broad-scale connectivity among populations, and generates a model describing how the world's R. typus are part of a single, global meta-population. Rhincodon typus occurrence timings and distribution patterns make possible a connection between several aggregation sites in the Indian Ocean. The present conceptual model and validating data lend support to the hypothesis that R. typus are able to move among the three largest ocean basins with a minimum total travelling time of around 2-4 years. The model provides a worldwide perspective of possible R. typus migration routes, and suggests a modified focus for additional research to test its predictions. The framework can be used to trim the hypotheses for R. typus movements and aggregation timings, thereby isolating possible mating and breeding areas that are currently unknown. This will assist endeavours to predict the longer-term response of the species to ocean warming and changing patterns of human-induced mortality.

Animal telemetry is a powerful tool for observing marine animals and the physical environments that they inhabit, from coastal and continental shelf ecosystems to polar seas and open oceans. Satellite-linked biologgers and networks of acoustic receivers allow animals to be reliably monitored over scales of tens of meters to thousands of kilometers, giving insight into their habitat use, home range size, the phenology of migratory patterns and the biotic and abiotic factors that drive their distributions. Furthermore, physical environmental variables can be collected using animals as autonomous sampling platforms, increasing spatial and temporal coverage of global oceanographic observation systems. The use of animal telemetry, therefore, has the capacity to provide measures from a suite of essential ocean variables (EOVs) for improved monitoring of Earth's oceans. Here we outline the design features of animal telemetry systems, describe current applications and their benefits and challenges, and discuss future directions. We describe new analytical techniques that improve our ability to not only quantify animal movements but to also provide a powerful framework for comparative studies across taxa. We discuss the application of animal telemetry and its capacity to collect biotic and abiotic data, how the data collected can be incorporated into ocean observing systems, and the role these data can play in improved ocean management.