Introduction
Climate variability and change is impacting marine ecosystems and the services they provide to the communities, businesses, and fisheries that rely on them (Doney et al. 2012, Scheffers et al. 2016, Bryndum‐Buchholz et al. 2019). Globally, oceans are experiencing gradual climate trends (e.g., long-term warming) as well as climate-induced extreme events (e.g., heatwaves, cold snaps) that can push ecosystems into novel environmental states, straining social-ecological systems (Samhouri et al. 2021, Schlegel et al. 2021, Free et al. 2023). Over the past decade, episodic ocean warming events, known as marine heatwaves (MHWs), have increased in frequency, intensity, and size compared to a historical baseline (Oliver et al. 2018, 2021), leading to significant ecological, socio-economic, and human health impacts globally (Smith et al. 2021). Recent work has focused on characterizing the spatial and temporal redistribution of species in response to MHWs (Welch et al. 2023); however, species’ responses to each MHW event can vary significantly (Welch et al. 2023, Farchadi et al. 2024), challenging traditional ocean management and spatial planning approaches (Samhouri et al. 2021, Welch et al. 2024).
Species distribution models (SDMs) are a popular tool to understand and predict how species’ spatiotemporal distributions or abundances change in response to environmental conditions (Hazen et al. 2018, Milanesi et al. 2020, Barnes et al. 2022, Braun et al. 2023a). Recent studies have used SDMs to evaluate MHW-driven redistribution of marine predators (Welch et al. 2023) and pelagic fishing fleets (Farchadi et al. 2024), providing critical insights into the ecological and socioeconomic impacts of MHWs. Despite the utility of SDMs in supporting ocean management and conservation efforts (Robinson et al. 2017, Hazen et al. 2018), their performance can decline under extreme climate events (Muhling et al. 2020, Karp et al. 2023, Allyn et al. 2024), a challenge that is exacerbated by the increasing prevalence of novel environmental conditions (Smith et al. 2022). Model performance often depends on how well the training data captures the full gradient of a species’ environmental preferences and whether novel conditions created by extreme events generate new areas of suitable habitat (Thuiller et al. 2004, Yates et al. 2018). Model inaccuracies may also stem from inherent biases in the training data, such as sampling process, observer bias, and analytical errors, which, if ignored, can generate misleading species-environmental relationships and lead to erroneous predictions of species distributions (Fletcher et al. 2016, 2019).
In marine fisheries, SDMs have largely relied on single data type approaches, typically derived from either a fishery-independent source, such as standardized survey efforts and electronic tags (Kleisner et al. 2017, Lezama-Ochoa et al. 2023), or fishery-dependent data, like vessel catch logbooks and observer programs (Crear et al. 2021, Karp et al. 2023). Although fishery-independent data is generally considered a less biased representation of a species’ distribution (Braun et al. 2023b), associated high costs and logistical challenges often limit the spatiotemporal coverage of these datasets (Dennis et al. 2015). Fishery-independent data can be particularly sparse in a given area for highly migratory pelagic species, which tend to have extensive and dynamic ranges (Block et al. 2011) and spend considerable time at depth (Braun et al. 2023c). In contrast, fishery-dependent data can provide greater spatial and temporal coverage and is common for many important fishery species. Fishery-dependent data have been used to predict how species may interact with a fishery (Crear et al. 2021), identify target and bycatch hotspots (Hazen et al. 2018), assess climate-induced fleet displacements (Farchadi et al. 2024), and analyze spatiotemporal dynamics of catch-per-unit-effort (Muhling et al. 2019). However, these data can be biased due to preferential sampling by fishers, which can mask underlying drivers of species distributions and are more prone to reporting errors (Karp et al. 2023).
Fisheries-independent and -dependent data together may offer complementary information on species distributions. Recent studies have shown that integrated SDMs (iSDMs), which simultaneously model different data sources while explicitly accounting for the differences in the sampling processes (Isaac et al. 2020), can yield more accurate predictions of species distributions (Paradinas et al. 2023) with greater predictive skill compared to models fitted to a single data source (Simmonds et al. 2020, Ahmad Suhaimi et al. 2021). While various approaches to combine data types have been shown to yield robust SDMs, most studies have assessed these methods under constant environmental conditions (e.g., k-fold cross-validation; Simmonds et al. 2020, Ahmad Suhaimi et al. 2021, Braun et al. 2023b). Thus, it remains unknown whether leveraging diverse data types enhances model performance under novel environmental conditions such as during MHWs.
There is an increasing body of literature examining the performance of SDMs under climate change and variability (Muhling et al. 2020, Barnes et al. 2022, Brodie et al. 2022, Karp et al. 2023). As marine systems continue to face increasingly novel and uncertain conditions, quantitative comparisons of model forecasting performance are crucial to provide accurate and forward-looking information for marine conservation and management (Thorson 2018). Here, we compare three modeling approaches that vary in their approaches to leverage multiple data types and spatial dependence treatments for an important fishery species, albacore tuna (Thunnus alalunga ), during a period of unprecedented MHWs in the northeast Pacific Ocean (NEP) to assess each model’s capacity to accurately forecast albacore distributions under MHW conditions. We discuss our results in the context of current SDM techniques, highlighting how to combine diverse data sources to advance species distribution modelling in a changing climate.