Atlantic salmon survival at sea: temporal changes that lack regional synchrony

Spatial and temporal synchrony in abundance or survival trends can be indicative of whether populations are affected by common environmental drivers. In Atlantic salmon (Salmo salar L.), return rates to natal rivers have generally been assumed to be affected primarily by shared oceanic conditions, leading to spatially synchronous trends in mortality. Here, we investigate the existence of parallel trends in salmon sea survival, using data on migrating smolts and returning adults from seven Canadian populations presumed to share feeding grounds. We analyse sea survival, using a Bayesian change-point model capable of detecting non-stationarity in time series data. Our results indicate that while salmon have experienced broadly comparable patterns in survival, finer-scale temporal shifts are not synchronous among populations. Our findings are not consistent with the hypothesis that salmon populations consistently share the same mortality-related stressors in the marine environment. Although populations may have shared greater synchrony in survival patterns in the past, this synchrony may be breaking down. It may be prudent to direct greater attention to smaller-scale regional and population-level correlates of survival

Methods
We analysed the time series of smolts and returning fish of Atlantic salmon populations in seven rivers in eastern Canada. In this, we used a Bayesian change-point model that has the Murphy's maturity schedule as the underlying predictive model.

The numbers of emigrating smolts were estimated from mark-recapture studies or directly recorded at counting fences during their seaward migration. The methods are fully cited by Pardo et al. (2021). The number of returning adults was based on direct counts of small (< 63 cm) and large (<= 63 cm) salmon. Age determination from scales was done for a subset of returns each year, which allowed for calculating the proportion of different age classes within the size groups, and subsequently estimate the total proportion of each age class among the returning fish (see Pardo et al, 2021).

In order to decrease bias in our results caused by measurement errors, we fitted our model using the medians of the posterior distributions of the true smolt abundances obtained by Pardo et al. (2021) as smolt abundance data. In addition, we utilized the estimated measurement errors by Pardo et al. (2021) in returning fish abundance data in our model.