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(Finney, unpublished data).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.