Predicting bycatch of Chinook salmon in the Pacific hake fishery using spatiotemporal models

Abstract

Fisheries bycatch is a global problem, and the ability to avoid incidental catch of non-target species is important to fishermen, managers, and conservationists. In areas with sufficient data, spatiotemporal models have been used to identify times and locations with high bycatch risk, potentially enabling fishing operations to shift their effort in response to the dynamic ocean landscape. Here, we use 18 years of observer data from the Pacific hake (Merluccius productus) fishery, the largest by tonnage on the US West Coast, to evaluate our ability to predict bycatch of the commercially, culturally, and ecologically important Chinook salmon (Oncorhynchus tshawytscha). Using multiple approaches (regression models, tree-based methods, and model averages), we tested our ability to predict bycatch at weekly and yearly timescales and found that spatiotemporal models can have good predictive ability. Gradient boosting trees (GBTs) and model averages typically had higher performance, while generalized linear models and generalized additive models (without interaction terms) did less well. Using a GBT model to remove 1% of hauls with the highest predicted bycatch reduced the bycatch-to-hake ratio by 20%. Our results indicate that spatiotemporal models may be a useful forecasting tool that can help fishing operations avoid bycatch while minimizing losses from target catches.

Warm oceans exacerbate Chinook salmon bycatch in the Pacific hake fishery driven by thermal and diel depth‐use behaviours

“…We add broader spatial and temporal evidence for Chinook salmon diel depth‐use behaviours of moving deeper at night, which increased overlap with deeper‐dwelling hake at the most common fishing depths of the hake fishery. This provides a previously unknown behavioural explanation for why Chinook salmon bycatch rates in the hake fishery are greater at night and suggests that DVM could explain diel patterns in fisheries bycatch in other systems (Orbesen et al, 2017; Shirk et al, 2022). Hake fishers voluntarily limit night fishing as a strategy to avoid bycatch of multiple species with only 25% of the observed hauls occurring at night (Holland & Martin, 2019).…”

“…Therefore, hake have a spatial distribution centred along the continental shelf‐break, whereas salmon bycatch occurs more often inshore, despite a wide spatial extent of fishing effort and bycatch (Figure 1a,b). See Shirk et al (2022) for a similar map of hake catches per unit effort. Along depth in the water column, the highest observed Chinook salmon bycatch rates occurred in the shallowest fishing depths (0–100 m), which was five times greater than bycatch rates at depths where hake were most commonly targeted (200–300 m; Figure 1d,e).…”

“…(3) The ultimate goal is to inform bycatch mitigation, which will require understanding the drivers of fisher behaviour, the effectiveness of various mitigation strategies, and the trade-offs between bycatch and hake catch. Shirk et al (2022) examined this exact trade-off and found that removing only the top 1% of hauls with the highest predicted Chinook salmon bycatch reduced the bycatch-to-hake ratio by 20%. Thus, there is potential for thermal and diel depth-use behaviours to inform adaptive management with minimal costs to hake catches (Pons et al, 2022).…”

“…In addition to temperature‐driven changes to vertical distributions, many marine species alter depth distributions across time of day (Behrenfeld et al, 2019; Brierley, 2014; Hays, 2003). Diel patterns in bycatch have also been observed and suggest that diel depth‐use behaviours might be a causative link; however, this is rarely examined explicitly (Orbesen et al, 2017; Shirk et al, 2022). Understanding the behavioural, environmental and temporal mechanisms underlying spatial overlap in the water column could provide actionable information for bycatch mitigation, allowing fishermen to further avoid locations, depths, and times with higher bycatch rates (Holt et al, 2013; Humphries et al, 2016).…”

Relationship between Resource Distribution and Vertical Structure of Water Temperature of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean Based on GAM and GBT Models

“…The GBT model is insensitive to outliers, extreme values, and missing values in the data [32]. Some scholars have applied the GBT model in fisheries research [33,34], but there have not been any reports on its application in the distribution study of cephalopod resources.…”