Implications for Conservation & Management
Accurate predictions of species distributions across multiple time scales, i.e. nowcasts to long term projections, are vital to support climate-ready and -resilient conservation and resource management (Lindgren et al. 2011, Crear et al. 2021). Prolonged MHWs in the NEP have been linked to widespread ecosystem changes that have exacerbated human-wildlife conflicts (e.g. whale entanglements and sea turtle bycatch; Santora et al. 2020) and intensified socio-economic stress on fishing communities (e.g., decreased catch or shifting fishing grounds; (Fisher et al. 2020, Smith et al. 2021, Free et al. 2023, Farchadi et al. 2024). With increased uncertainty about how future, extreme climate events will affect the displacement of species and fisheries, the ability to quickly adapt to such novel conditions poses considerable challenges for marine conservation and management (Fisher et al. 2020, Samhouri et al. 2021). Therefore, there is a critical need for skillful ecological forecasts that provide advanced warnings on relevant timescales for decision-making, enabling a more proactive management framework capable of keeping pace with MHWs (Brodie et al. 2023). For example, in the case of transboundary fisheries like albacore tuna, which have exhibited cross-jurisdictional shifts under MHWs (Welch et al. 2023), the ability to leverage data across geographical and political regions can yield more accurate predictions of how albacore may redistribute during future extreme climate events. Here, we demonstrate the utility of model-based data integration in ecological forecasting and offer insights on best practices for integrating diverse data sources when predicting into uncertain and variable future conditions. Although extremely novel environmental conditions may always pose challenges (Pinsky and Mantua 2014, Pinsky et al. 2021), iSDMs are well-positioned to readily integrate disparate sources in a way that retains the strengths of each and can better inform potential ecological impacts of extreme events (Isaac et al. 2020, Chevalier et al. 2021).
Our study adds to the growing body of literature that indicates the utility of iSDMs (Isaac et al. 2020) and echoes calls to continue exploring their performance under different applications through retrospective skill testing (Thorson 2018, Barnes et al. 2022). For example, operational forecasts of SST in the California Current, when configured for specific management applications, have demonstrated skillful predictions up to 12 months in advance (Brodie et al. 2023). Incorporating such forecasting tools into an iSDM framework could enhance near-term seasonal forecasts of species distributions, though further studies are needed to evaluate their contribution and determine at what lead times do forecasts remain skillful (Thorson 2018, Brodie et al. 2023).Furthermore, while our study demonstrated that broader species-environment response curves may help buffer prediction skill against environmental novelty, previous studies have suggested that non-stationarity could impact model performance (Yates et al. 2018, Ward et al. 2022) – highlighting the need for additional research to understand how non-stationarity in environmental relationships can be accounted for when forecasting (Yates et al. 2018). As our social-ecological systems face increasingly novel conditions under climate change, enhancing our capacity to leverage growing, diverse datasets will be essential for developing robust models that support conservation and management decisions