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Ecological forecasts are potentially of great value for managing fisheries and for stakeholders dependent on their long-term sustainability. Yet, most forecasting approaches are data-intensive, requiring information not just on the focal species but also on ecological interactions and the physical environment. Empirical dynamic modeling (EDM) is an equation-free approach to forecasting species' abundance using only data on past abundance, but the time series required for this approach must be long enough to reconstruct the dynamics of the system. This requirement is rarely met, especially for long-lived species. Here we used simulations and empirical data to demonstrate that incorporating time series from multiple age classes can improve our ability to forecast abundance compared to a single age class or index of total abundance. Including data from multiple age classes produced the greatest gains in forecast accuracy when time series were the shortest. Overall, our results show that the incorporation of age structure could allow EDM to be applied to many species for which relatively short time series would have previously been a limiting factor. [ABSTRACT FROM AUTHOR]

Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fishery-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of relevant habitat. Here, we demonstrated a method for integrating presence–absence data across surveys and gear types that allows us to predict the coastwide distributions of 65 groundfish species in British Columbia. Our model leverages data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in survey catchability. We find that this method has two main benefits: (1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on accuracy when data are already sufficient, and (2) it reduces uncertainty, resulting in tighter confidence intervals on predicted occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish surveys. [ABSTRACT FROM AUTHOR]

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