Ecological thresholds in forecast performance for key United States West Coast Chinook salmon stocks

Abstract

Preseason abundance forecasts drive management of US West Coast salmon fisheries, yet little is known about how environmental variability influences forecast performance. We compared forecasts of Chinook salmon (Oncorhynchus tshawytscha) against returns for (i) key California-Oregon ocean fishery stocks and (ii) high priority prey stocks for endangered Southern Resident Killer Whales (Orcinus orca) in Puget Sound, Washington. We explored how well environmental indices (at multiple locations and time lags) explained performance of forecasts based on different methods (i.e. sibling-based, production-based, environment-based, or recent averages), testing for nonlinear threshold dynamics. For the California stocks, no index tested explained >50% of the variation in forecast performance, but spring Pacific Decadal Oscillation and winter North Pacific Index during the year of return explained >40% of the variation for the sibling-based Sacramento Fall Chinook forecast, with nonlinearity and apparent thresholds. This suggests that oceanic conditions experienced by adults (after younger siblings returned) have the most impact on sibling-based forecasts. For Puget Sound stocks, we detected nonlinear/threshold relationships explaining >50% of the variation with multiple indices and lags. Environmental influences on preseason forecasts may create biases that render salmon fisheries management more or less conservative, and therefore could motivate the development of ecosystem-based risk assessments.

Quantifying a Novel Climate Through Changes in PDO‐Climate and PDO‐Salmon Relationships

“…Accordingly, SST and PDO values were smoothed with a 3‐year running mean prior to analysis. This smoothing is consistent with climate effects across multiple life history stages in salmon (Crozier et al, 2008; Malick, Cox, Peterman, et al, 2015; Satterthwaite et al, 2020). We assess the robustness of all presented results to this smoothing.…”

“…These variables showed strong statistical relationships with salmon productivity prior to 1988/1989 (Litzow et al, 2018), supporting hypothesized mechanistic relationships (Gargett, 1997; Malick, Cox, Mueter, & Peterman, 2015; Malick et al, 2016; Mueter et al, 2002). However, collinearity among the individual processes precludes an assessment of which are the most important drivers of survival (Dormann et al, 2013; Malick, Cox, Peterman, et al, 2015; Satterthwaite et al, 2020). The Papa advection index reflects winter patterns (December‐February), and we used winter‐spring (February‐April [FMA]) values of SSHA PC1, wind stress, and salinity, corresponding to the onset of stratification that sets up the spring bloom (Henson, 2007; Malick, Cox, Mueter, & Peterman, 2015).…”

Changes to the structure and function of an albacore fishery reveal shifting social‐ecological realities for Pacific Northwest fishermen

“…Alongside anomalous oceanographic conditions observed in 2004–2006 (Peterson et al., 2006) and 2014–2016 (Bond et al., 2015; Jacox et al., 2016), numerous changes to ecosystem structure and function have been reported (Cavole et al., 2016; Lindley et al., 2009; Sanford et al., 2019; Walker et al., 2020). Despite landings trending positively in the early 2000s, West Coast salmon fisheries were severely restricted during the 2008–2009 and 2016–2017 fishing seasons amidst poor run strength and increasingly variable escapement associated with drought, warm ocean temperatures and limited food availability (Richerson & Holland, 2017; Satterthwaite et al., 2019). While salmon runs have been inconsistent, groundfish stocks have recently begun to recover following several decades of depressed landings attributed to overcapacity and overfishing (PFMC, 2018).…”

Potential for ecological nonlinearities and thresholds to inform Pacific salmon management

“…Additionally, some forecasts are based on the assumption that returns will be proportional to jack (i.e., individuals one year younger than dominant returning age class) returns in the previous year. These forecasts risk‐marked inaccuracies if nonlinear ecosystem dynamics, such as poor ocean conditions, reduce survival in the final year at sea (sensu Satterthwaite et al 2020).…”

“…In fisheries management, forecasts of the expected abundance of adults returning to spawn (and also the target of fisheries) help determine annual harvest levels (PFMC 2016). Given recent work demonstrating that bias in these forecasts can change abruptly in relation to environmental thresholds (Satterthwaite et al 2020), there may be scenarios where state and federal managers choose precautionary harvest allocations suggested by effects of environmental conditions on forecast reliability. Because stronger stocks often co‐occur with weaker stocks, managers would also need to consider appropriate risk aversion in setting fisheries rules in mixed stock fisheries.…”