Fraser River sockeye salmon: data synthesis and cumulative impacts

To address potential skepticism over the Kalman filter and Ricker Larkin models, other datawere also analyzed. Raw data for log e (recruits per spawner) show the same pattern as Kalmanfilter analyses, with southern and Fraser sockeye stocks (except Pitt and Harrison) decreasingover time, and Alaska stocks increasing. The pattern for BC non-Fraser stocks was similar to thatfor the Fraser, but additional patterns at smaller geographical scales indicate there may also besome underlying freshwater patterns too.Conclusion #3: Alaskan productivity patterns are the opposite of those for the Fraser and othersouthern stocks. This is likely due to a shared large-scale driver within each region, possiblyrelating to climate.Overall Conclusions: There were three periods of extended decreases in productivity: Onestarting in the mid-1960s affected Early Stuart and Early Summer stocks like Bowron, Fennell,Nadina, Pitt, Seymour. A second from the late 1980s to around 1990 affected the Summers(Chilko, Late Stuart, Stellako), Birkenhead, and the Barkley Sound, Central Coast, Skeena andNass stocks. A third period starting in the late 1990s affected most BC and Lake Washingtonstocks except the Harrison and Pitt stocks, which have been increasing. This has happened at thesame time as higher SST.The emergent hypothesis is that widespread shared trends since the 1990s and 2000s are likelydue to large-scale forcing in the ocean, plus possibly some shared forcing in freshwater but in thepost-juvenile estimation stage.DiscussionRoutledge: Regarding the high proportion of the change explained by the principal components –perhaps this is because it was smoothed data. If you used the raw data, would that be lower?Peterman: Yes.McKinnell: This relies on two different time series – one based on observations of spawners andone on observed returns. It’s not clear how the signal and noise relating to the observation ofspawners is captured in the model.Peterman: There is some overlap since recruits consist of spawners plus catch. To get totalreturns you have to use expansion factors. I’m not sure it would have a huge influencebut we are sensitive to the issue. Recruits per spawner show the same pattern but theKalman filter turned out to provide the best method for tracking it. This is not how natureworks – all models are wrong of course – but they are useful nonetheless.Cox: What is the error range around the estimates?Peterman: The trends are still prominent.Routledge: I understand the concern but it’s an over-simplification. This is about smoothingthe rough points, not an issue of trying to capture true biological processes.Peterman: The raw data show a very similar pattern – with shared spatial variation – it’s allin the report’s appendices.Cox: Did you try instead letting b be free instead of maintaining a fixed b value?31