Influence of scale sampling method
Our dataset comprised a mix of SI values measured from whole scales and the last annulus of the scale. We integrated SST over different time ranges to evaluate the best match with the potential integration time of SI values from these different methodologies. The time period from January to June of the return year gave the best results for the two scale sampling methods. This suggested a negligible influence of scale material laid down during the freshwater phase (Hutchinson & Trueman, 2006) and furthermore that the whole scale SI value was dominated by the last growing season. This can be explained by the largest increase in size and weight of the fish occurring during the last year at sea (Ishida et al., 1998). The mass of collagen produced during this time period largely overwhelms the rest of the scale mass. Our results suggest that using the whole scale did not affect time series comparison, as whole scale and last annulus have very similar SI values (Finney, unpublished data). Studies focusing on the freshwater phase or early marine phase of salmon should however be careful in their interpretations when using scales from maturing fish, as it seems inevitable that the recent layers deposited will strongly influence the SI values (Hutchinson & Trueman, 2006).
We demonstrated that when analyzing time series of > 20 data points with yearly resolution, using the original un-treated data gave the most significant results. These data retain the influence of both low and high frequency variations but require correction of the number of degrees of freedom to adjust the p-value (Pyper & Peterman, 1998). Some stocks with geographically close spawning grounds showed δ13C variations driven by different factors. For example, Chilkoot salmon δ13C data demonstrated a largely increasing trend from low values in earlier years to high values in later years, while Chilkat did not show a clear long term trend but rather greater interannual variability (Figure S1.2). The Suess effect correction parameter, by changing the overall inclination of the δ13C trend, could affect the part of the correlation driven by the low frequency (long term trend). We tested this using data for the Chilkoot and Chilkat stocks and additionally the Okanagan stock for which the data covers more recent years. The Suess effect parameter was set at -0.015 ‰ yr-1 and -0.025 ‰ yr-1. This resulted in similar distributions and very little change in correlation significance (Table S1.1). Furthermore, for the large majority of the stocks for which we found significant correlations, the three types of series generated the same distribution. Among the series with yearly resolution, only Red Lake displayed different high correlation areas between the original series and the smoothed series. The detrended series, however gave a similar, but non-significant, distribution to the original one. Overall, using the three approaches enabled improved evaluation of the ocean area most likely used by the salmon, and dismissal of the spots which were only driven by interannual variability or long term trend.
Series with coarser or irregular resolution, usually the smaller datasets (< 20), still allowed feeding grounds estimates. Best results were obtained after smoothing the SST series. We tested the effect of sampling resolution by downgrading the resolution of the Okanagan series using data from every 3rd year and obtained comparable distributions to the full resolution data set (Figure S1.3). We do not encourage the use of such small datasets, as it is often hard to estimate if the series meets the basic assumptions for correlation test, and there is an increased chance of spurious correlations. Nonetheless, datasets of approximately ten points appear sufficient to provide a meaningful preliminary indication of salmon distribution. We recommend that these are subsequently validated with longer time series.