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.