FIGURE 1 (a) Map of the Karibetsu
and Sarufutsu Rivers in Hokkaido, Japan. The grey rectangle denotes the
area shown in (b). (b) Locations of fish sampling (L) and passive
integrated transponder (PIT) detection systems (L and T1–T4) in the
Karibetsu River.
2.2 Repeatability in migration
timing
We calculated Spearman’s rank
correlation between migration
timings in 2018 and 2019 for individuals that returned consecutively to
the Karibetsu River. The calculation was made on both seasonal (date and
time) and diel (time only) scales, separately for each sex at each of
the following migration stages: arrival at Site L from the estuary
(migration stage [MS]1), arrival at tributary sites from Site L
(MS2), departure from tributary sites for Site L (MS3), and departure
from Site L for the estuary (MS4). Seasonal-scale correlation was also
calculated between MS1 and MS4 migration timings of the same individuals
in 2019 (i.e. before and after spawning within a season). Circular
statistical analyses were performed to test for the uniformity of the
diel migration timing using the Rayleigh test, and for differences in
the timing between sexes and between upstream and downstream migrations
using the Watson–Wheeler test.
2.3 Modeling migration timing with biological and behavioral
variables
Seasonal migration timing was modeled with linear mixed-effects models
(LMMs) separately for each migration stage. The PIT code was used as a
random effect to account for between-year correlation of the same
individuals. Sex, standardized FL, whether migration occurred during
daytime (0420–1837 on 1 May, the
approximate midpoint of the run, in Sarufutsu Village) or night-time
(DN), and the first (First) and total number of tributaries (Tributary)
each fish entered for spawning each year were included as candidate
fixed effects. Sex and FL were unique to each fish across years and MS;
First and Tributary could be different across years but constant across
MS; and DN could be different across years and MS. To account for
possible sex-related influences, we created
a full model including
interaction terms between Sex and the other fixed effects. The original
response variable of timing in date and time was standardized to have
mean 0 and standard deviation 1 for each year and MS. Water temperature
and water level variables were not included in this modelling to avoid
spurious correlations described earlier.
2.4 Testing the environmental control
hypothesis
To determine whether Sakhalin taimen exhibited individual-specific
responsiveness to environmental signals, and when the signals became
critical, we used a sliding window approach. We defined a critical time
window as a period when an environmental cue perceived by the same
individuals between years yielded the highest correlation, based on the
premise that the cue to initiate movement should be similar within
individuals across years if responsiveness to the signal is
idiosyncratic. As such, we calculated a series of Pearson correlations
between 2018 and 2019 for each of the two variables (water temperature
and water level), averaged over time windows relative to each spawner’s
arrival timing at the first tributary each year
(i.e. MS2). The window size was
varied from 8 to 240 h at 4-h increments in both time lag (time to the
window’s start) and duration (window width). The maximum correlation
coefficient (r obs) and the corresponding critical
time window were recorded for each sex and variable. The significance ofr obs was determined with a randomization test, in
which Pearson correlations were calculated between randomly selected
individuals from the two years by varying the window size to find a
critical window with a maximum correlation
(r sim). This process was iterated 999 times, and
the proportion of r sim as large as or larger thanr obs was considered a measure of significance by
applying a one-sided test (van de Pol et al., 2016).
2.5 Testing the social interaction
hypothesis
We evaluated whether Sakhalin taimen employed social navigation
strategies that could lead to coordinated movements and repeatable
migration timing by asking two questions: 1) did comigrating groups of
pre-spawners ascending through Site L (MS1) tend to enter the same
spawning tributaries, diverge into different tributaries, or enter
tributaries at random, and 2) did comigrating groups of post-spawners
descending through Site L (MS4) tend to consist of individuals that
spawned in and departed from the same tributaries, different
tributaries, or any tributary at random? If two or more tagged
individuals passed Site L over an elapsed time of less than 1 h, the
individuals were considered ‘comigrants’ according to a time-series
analysis of fish passage in an earlier study (Rand & Fukushima, 2014).
Given the disparity in migratory behavior between sexes (see Results),
we considered sexes separately in this analysis.
To answer the questions above, we counted the observed total number of
comigrating pairs during MS1 or MS4 that subsequently entered, or
previously departed from, the same tributaries
(n obs) based on the 2019 dataset. Individual
migrants were then randomly assigned to a spawning tributary while
preserving their actual MS1 (and MS4) migration timings and the total
numbers of migrants accommodated by each tributary. Comigrating pairs
that were assigned the same tributaries during each randomization trial
were counted (n sim). This process was iterated
9999 times to generate a distribution of n sim to
compare with n obs. If n obswas found to be significantly high relative to the distribution ofn sim, this would provide evidence that
individuals exhibit conspecific attraction during the pre-spawn (i.e.
transition from MS1 to MS2) or post-spawn (i.e. transition from MS3 to
MS4) periods. Conversely, if n obs was found to be
lower than expected by chance, this provided evidence that individuals
exhibit conspecific repulsion during the aforementioned migration
stages. Thus, the social interaction hypothesis was tested with
two-sided tests.
All statistical analyses were performed using R version 3.4.3 (R
Development Core Team, 2017). The R package ‘lme4’ (Bates et al., 2015)
was used to perform LMM, and ‘lmerTest’ (Kuznetsova et al., 2020) was
used to select the best models via sequential backward elimination of
non-significant effects, and to calculate P values based on
Satterthwaite’s approximation. Circular statistical analyses used the R
package ‘circular’ (Agostinelli & Lund, 2017). Statistical significance
was defined at α= 0.05.