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