Philippa Kaur

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DNA concentration in core sediments
qPCR analyses for each core (Experiment (a) in Fig. 1) showed that the mean DNA copies for anchovy ranged from 233 ± 215 copies g−1 dry sediment (hereafter, copies g−1, mean ± 1 SD) to 3075 ± 781 copies g−1 (Supplementary Table 1). For all data, anchovy had 1067 ± 968 mean DNA copies g−1. Anchovy DNA copies were detected in all samples, except three (13 cm depth in BG17-1, 41 and 45 cm in BMC18-6). For most samples, DNA was detected in more than three out of four replicates. In each core, the mean DNA copies for sardines ranged from 1.7 ± 4.0 to 12.0 ± 29.2 copies g−1. For all data, sardine had 5.1 ± 3.8 mean DNA copies g−1. This concentration was 0.5% that of anchovy. Sardine DNA copies were not detected in many of the samples from each core. One or two replicates were detected in most samples, while a few samples had more than three out of four replicates for sardines. Jack mackerel DNA copies were also not detected for many samples from each core. Mean DNA copies for each core ranged from 0 to 55.9 ± 127.2 copies g−1. For all data, Jack mackerel had 14.8 ± 19.1 mean DNA copies g−1. This concentration was 1.4% that of anchovy. Few samples had more than three out of four replicates, with most samples having one or two replicates.
For the negative control for sectioning, subsampling, DNA extraction, and PCR processes (Experiment (a) in Fig. 1), DNA of the three marine species was not detected in any of the core sediment samples from Lake Biwa (LBHR18-1) (Supplementary Table 1). DNA was not detected on the PCR blanks either (Supplementary Table 1). Thus, contamination was not an issue during sampling, extraction, purification, and the PCR processes. Also, through the direct sequencing of PCR amplicons by the qPCR assay of Japanese sardine, we confirmed that only the DNA was amplified.
We performed spike test to evaluate the effect of PCR inhibition (Experiment (a) in Fig. 1). All ΔCt values were less than three (ΔCt: −2.4–2.9) (Supplementary Table 2), providing no evidence of inhibition31.
Down-core changes in DNA concentration
Down-core changes in DNA concentration for anchovy showed different patterns between each core (Supplementary Figs. 2 and 3) (Experiment (a) in Supplementary Fig. 1). For 50-cm-long core samples, peaks in DNA occurred at around 5 and 20 cm in BMC18-6, but occurred at around 0 cm in BMC17 S1-7 (Supplementary Fig. 2). There was no noticeable peak in BMC17 S1-10 (Supplementary Fig. 2). For 110-cm-long core samples, there was no consistent vertical pattern, except for the uppermost layers, with the highest values being detected for BG18-6W and BG18-8A (Supplementary Fig. 3). For sardine and jack mackerel, there were no consistent vertical patterns in DNA for the short cores. In contrast, the 1.1-m-long cores had peaks centered at around 16 and 57 cm deep for sardine and at around 20 cm for jack mackerel. Comparison between short and long cores (Supplementary Figs. 2 and 3) showed that anchovy and jack mackerel had the highest DNA concentrations in the uppermost layers in the short cores. The long cores did not show a similar trend, due to loss of surface layers (approximately 20 cm) during core collection. In contrast, the highest DNA concentrations for sardine occurred at 57–58 cm depth in the long cores, not in the uppermost layers of the short cores.
There was no clear evidence that DNA concentrations were higher in core samples that were instantly frozen after core collection (core BMC17 S1-7) compared to samples that were frozen 6 days or 4 weeks after core collection (Supplementary Figs. 2 and 3) (Experiment (a) in Fig. 1). Thus, chilled storage for 4 weeks only caused minor degradation of DNA in core samples.
Temporal changes in DNA concentration
General additive models (GAMs) showed that the decadal–centennial dynamics of the inter-core, seven-year averaged, and sedDNA concentrations for the last 300 years significantly varied non-linearly (Japanese anchovy, s = 7.22, P = 2.96 × 10−7; Japanese sardine, s = 12.61, P = 1.10 × 10−4; jack mackerel, s = 8.831, P = 2.84 × 10−9, Fig. 2). DNA concentrations for Japanese anchovy were high after 2010 CE (BMC18-6, BMC17 S1-7, and BMC17 S1-10) (Fig. 3). While there was no consistent pattern in the time series of the cores before 2000 CE, one or two of the time series showed high values around the 1960s CE and the 2000 CE. These periods with high values showed large scatters between the cores, indicating spatial heterogeneity in DNA deposition.
Fig. 2: The results of general additive models (GAM) from inter-core, seven-year averaged sedDNA concentrations.

a Engraulis japonicus (Japanese anchovy); b Sardinops melanostictus (Japanese sardine); c Tranchurus japonicus (jack mackerel). Blue line denotes a regression line of GAM with the 95% confidence interval (gray zone).

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Fig. 3: Temporal changes in mean DNA concentrations for all cores.

a Japanese anchovy; b Japanese sardine; c jack mackerel. Error bar of each data point denotes 1 SD (n = 4 or 8). The horizontal bar in panel b represents historical good (solid) and poor (gray) catch periods (open: no data). Translucent colored plots denote each data point in qPCR replicates.

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These high values were not obtained in the anchovy fish scale concentrations during the same periods17 (Supplementary Fig. 4). There was also no significant relationship in the Type II regression model for the inter-core, seven-year averaged, concentrations in DNA (Fig. 4a) with those of fish scale concentrations (Fig. 4c) for the 300 years (R2 = 0.0157, P = 0.429, n = 42, Fig. 5a, also see Supplementary Fig. 5 for the log-transformed model). In contrast, the two DNA peaks in the 1960s and 2000 were temporally consistent with those of the catch record in Japan (Statistics of Agriculture, Forestry and Fisheries, with the landing data being updated from previous studies32,33) (Fig. 4b). This result was supported by a Type II regression model, with a significant correlation existing between inter-core, seven-year averaged concentrations in DNA (Fig. 4a) and seven-year averaged catches in Japan (Fig. 4b) (R2 = 0.255, P = 0.0459, n = 16, Fig. 6a, also see Supplementary Fig. 6a for the log-transformed model). Anchovy sedDNA and landings in Japan before 1990 showed a positive-phase relationship with the Bungo Channel (Supplementary Fig. 7, see Supplementary Fig. 1a for the location), but a negative-phase relationship with the central Seto Inland Sea (Supplementary Fig. 7c). A decadal peak around 2000, as shown by the sedDNA and landings in Japan, was not obtained in the landings from the Bungo Channel and Beppu Bay, Iyo-nada, and Suo-nada (Supplementary Fig 7a, b, see Supplementary Fig. 1 for the locations and see Supplementary Discussion for the reasons). An abnormally high value in 2014–2017 was not found in the landing records (Supplementary Fig 7a, b). This inconsistency indicates the presence of enriched DNA in the surface layer that is susceptible to rapid decomposition due to early diagenesis in a few years.
Fig. 4: Comparison between temporal changes in sedDNA concentrations, landings, and fish scales.

a, d, and g: inter-core, seven-year averaged concentrations of DNA for anchovy (left), sardine (middle), and jack mackerel (right). b, e, and h: total landings in Japan. c and f: fish scales. Of note, the landings of Caranginae (jack mackerel plus amberstripe scad, Decapterus muroadsi) consist mostly of those of jack mackerel. Error bar of each data point denotes 1 SD. Translucent colored plots denote annual data points for each core.

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Fig. 5: Relationships between sedDNA and fish scale concentrations.

a: Japanese anchovy; and b: Japanese sardine. Inter-core, 7-year average data were used for the models. Red line denotes a regression line of Gaussian Type II regression model with the 95% confidence interval (gray zone).

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Fig. 6: Relationships between sedDNA concentrations and the total landings in Japan.

a: Japanese anchovy; b: Japanese sardine; c: jack mackerel; and d: Caranginae (jack mackerel and amberstripe scad). Inter-core, 7-year average data for eDNA and 7-year average data for landing were used in the models. Red line denotes a regression line of Gaussian Type II regression model with the 95% confidence interval (gray zone).

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DNA concentrations for Japanese sardine showed large scatters of contemporary values between the cores (Fig. 3b), indicating spatial heterogeneity in DNA depositions. The concentrations were high ( >20 copies g−1) during the 1840s to 1850s and 1970s to 1980s for the time series of the three cores, and were low (0.25 g−1) recorded for sardine fish scale concentrations (Supplementary Fig. 4b, f); however, there was no noticeable peak during the 1920s and 1930s (Figs. 3b and 4b), despite a peak occurring in the fish scale record (Fig. 4f and Supplementary Fig. 4b). The peak in DNA during the1970s to 1980s corresponded to a distinct peak in sardine catches during the twentieth century (Fig. 4e). The DNA peak in the 1840s to 1850s was consistent with good catch periods recorded by historical documents in and around the Bungo Channel34,35 (Fig. 3b). Sardine DNA was detected during ~1700 CE (14 copies g−1), which was consistent with a good catch period recorded in the historical documents (Fig. 3b), and a minor peak in the fish scale record (Fig. 4f and Supplementary Fig. 4b). Type II regression for sardine showed a significant correlation between inter-core, seven-year averaged concentrations of DNA (Fig. 4d) and fish scale concentrations (Fig. 4f) for the last 300 years (R2 = 0.436, P = 1.93 × 10−6, n = 42, Fig. 5b, also see Supplementary Fig. 5b for the log-transformed model). It also showed a significant correlation between inter-core, seven-year averaged concentrations of DNA and seven-year averaged catches in Japan (R2 = 0.269, P = 0.0395, n = 16, Fig. 6b, also see Supplementary Fig. 6b for the log-transformed model). sedDNA and landings in Japan showed a positive-phase relationship with Bungo Channel. However, a clear relationship was not detected with the landings in Beppu Bay, Iyo-nada, and Suo-nada or the central Seto Inland Sea (Supplementary Fig. 8, see Supplementary Discussion for the reasons). There was a negative phase relationship between sardine and anchovy in sedDNA after the 1950 CE (Fig. 2a, b).
Jack mackerel DNA concentrations for each core (Fig. 3c) were high, exceeding 50 copies g−1 around 1970 and 1990 for the two core time series, and exceeding 100 copies g−1 after 2005, with low values ( 0.05, Supplementary Table 3). In the lower massive layers (Supplementary Figs. 11 and 12), anchovy DNA showed a significant positive correlation with TOC (r = −0.50, P = 0.029) and biogenic opal (r = 0.47, P = 0.044), and a negative correlation with C/N (r = −0.48, P = 0.040). It showed no correlation with Ti and sedimentation rate.
Source materials of DNA in marine sediments
The DNA in the pore water of each sample (Experiment (c) in Fig. 1) was not detected by qPCR assays for any of the species (Table 1). In contrast, anchovy DNA was detected in the residual bulk sediments of all samples (range: 367–6423 copies g−1, mean: 2704 ± 2233 copies g−1), while sardine DNA was detected in two samples (range: 12.4–283.5 copies g−1, mean: 51.6 ± 113 copies g−1) and jack mackerel was detected in one sample (84.6 copies g−1) (Table 1, Experiment (c) in Fig. 1). DNA was only detected in the fish scales of anchovy (1.5 ± 4.2 copies scale−1) (Table 2) (Experiment (b) in Fig. 1). DNA from bones was not detected in any of the species (Table 2) (Experiment (b) in Fig. 1). DNA was detected in the 63–180 μm size fractions of one sample for sardine (0.9 ± 1.7 copies g−1 dry sediment before sieved) and jack mackerel (0.3 ± 0.6 copies g−1 dry sediment before sieved), but was not detected for anchovy (Table 2). DNA was not detected in the 180–500 μm fractions for any of the species (Table 2).
Table 1 DNA copies for each species for pore water and pore water-free sediment samples.
Full size table

Table 2 DNA copies for each species for fish scales, bones, and fine (63–180 μm) and coarse (180–500 μm) particle size fraction of sediment samples.
Full size table More

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Vertical migration
We performed two separate vertical migration experiments (Fig. 1A,B). In the first one (Fig. 1A) a group of 45 krill was exposed to LD for 48 h. In the second one (Fig. 1B) a group of 41 krill was exposed to LD for 24 h followed by constant darkness (DD) for 48 h. During LD, lights were turned on at 6:00 (corresponding to Zeitgeber Time 0 or ZT0). Light intensity gradually increased until 12:00 (ZT6) and then decreased gradually until 18:00 (ZT12), when the lights were turned off. During DD, lights were turned off at all times. In both experiments, krill could move freely within a vertical column tank (200 cm height × 50 cm diameter) filled with chilled and filtered seawater (Supplementary Fig. S1). No food was offered at any time during the experiments and an acclimation period of three days (LD, no food) was provided before each experiment. We used an infrared (IR) camera system to monitor DVM during light and dark phases and estimated the variation in mean krill depth over time. We tested the presence of rhythmic patterns using the RAIN algorithm, which allows the detection of rhythms of any period and waveform23.
Figure 1

Vertical migration patterns of krill exposed to LD and LD-DD. (A) Vertical migration patterns of krill exposed to LD for 48 h. X-axis indicate the Zeitgeber Time (ZT) given in hours from each event of lights-on (6:00), corresponding to ZT0. Y-axis indicate the mean krill depth in cm (n = 45). Error bars represent SEMs. White and grey rectangles represent alternation of light and dark phases. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). (B) Vertical migration patterns of krill exposed to LD for 24 h followed by 48 h in DD. X-axis represent the time in hours from the beginning of the run. For the first 24 h, time is given in ZT, with ZT0 corresponding to lights-on (6:00). For the following 48 h, time is given in Circadian Time or CT, with CT0 corresponding to subjective lights-on in DD (6:00). Y-axis indicate the mean krill depth in cm (n = 41). Error bars represent SEMs. White, grey and light grey rectangles represent alternation of light, dark and subjective light phases respectively. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

Full size image

Clear rhythmic oscillations were detected in both experiments. When krill were exposed to LD, they displayed rhythmic vertical migration with a period of about 24 h. Peak upward migration occurred towards the second half of the light phase. In the first experiment (Fig. 1A), RAIN detected a period of oscillation of 24 h (p-value = 3.5e−07) in day 1 and a period of 23 h (p-value = 5.4e−08) in day 2, with peak upward migration at ZT9 in both days. In the second experiment (Fig. 1B), RAIN detected a period of 24 h (p-value = 4.2e−04) in day 1, when krill were exposed to LD, with peak upward migration at ZT8.
Krill DVM in the field displays mostly a nocturnal migratory pattern, characterized by upward migration during the night and downward migration during the day6,11. Therefore, we were at first surprised to observe the opposite DVM pattern in the lab, with upward migration during the day (i.e. light phase) and downward migration during the night (i.e. dark phase). However, previous studies on Drosophila already revealed that circadian behaviors might differ significantly between wild and laboratory populations of the same species24. Physical constriction, exposure to artificial light regimes and feeding schedules and deprivation of ecological interactions may have a profound impact on the behavior of captured animals25. In the aquarium, krill are fed mostly during the day. This, in association with the absence of predators, might have led to the reversal of the DVM rhythm. In addition, in order to avoid interaction with rhythmic cues related to food, krill were briefly starved before and during the experiment. Previous studies suggested that starved zooplankton might become more attracted towards light sources26,27. This may have contributed to stimulate the upward movement observed during the light phases. Additional observations of DVM with (A) krill accustomed to different feeding schedules (e.g. krill fed during the night) and (B) starved krill exposed to illuminated dark phases (DL or LL) might help to clarify these points.
After we switched to DD conditions (Fig. 1B), during the first day we did not observe any significant rhythm. This might not necessarily imply that no rhythmic migration occurred. In fact, individual krill might have been still rhythmic, but they might have not been synchronized with each other due to the absence of the LD entraining cue or Zeitgeber. A similar phenomenon has already been reported for wild zooplankton in the Arctic and Antarctic during periods of continuous illumination in summer (i.e. during midnight sun)28,29. At that time of the year, in the Arctic, the local populations of Calanus finmarchicus and C. glacialis did not display any net DVM as revealed by backscatter data, but unsynchronized individual migrations were registered instead29. Implementation of individual tracking methods to apply in the lab during DVM monitoring under extreme photoperiodic conditions including constant darkness (DD) and constant light (LL) would allow further insight into this aspect.
During the second day of DD exposure, RAIN detected a period of 12 h (p-value = 2.3e−02), with a first peak of migration in the early subjective light phase (CT29) and a second peak in the early dark phase (CT41). This suggested that in free-running conditions an endogenous rhythm of vertical migration might emerge with a period of about 12 h. This is in agreement with previous studies of swimming activity, oxygen consumption, enzyme activity and transcription in krill exposed to DD16,18,22 and strongly suggests that the endogenous clock in krill is characterized by a free-running period significantly shorter than 24 h.
Oxygen consumption
An endogenous rhythm of activity occurring at the organismic level might contribute to the occurrence of DVM in zooplankton19,30. This might apply to krill as well, since they are known to display daily rhythms in metabolism and physiology15,16. Therefore, we monitored daily rhythms of oxygen consumption in krill exposed to LD and DD and associated them with the observed DVM patterns. Due to technical limitations, we could not monitor oxygen consumption directly within the vertical migration tank. We used individual krill incubated in 2 L Schott bottles filled with oxygen-saturated chilled and filtered seawater instead. We followed the decrease in pO2 over a period of 48 h and tested the presence of rhythmic consumption patterns using the RAIN algorithm. Clear rhythmic oscillations were detected in both conditions.
In LD, two out of six tested animals (33%) displayed rhythmic oxygen consumption (Fig. 2A,B). Both individuals displayed clear oscillations in the circadian range (i.e., approx. 24 h period): the first one (krill#4, Fig. 2A) displayed a period of 21 h in day 1 (p-value = 1.9e−04) and 24 h in day 2 (p-value = 3.7e−02), while the second one (krill#6, Fig. 2B) displayed a period of 24 h in both days (day 1, p-value = 5.3e−11; day 2, p-value = 3.9e−11). The oscillation was always strongly synchronized to the LD cycle, with peak oxygen consumption occurring at ZT11 (day 1) and ZT12 (day 2) in krill#4 (Fig. 2A) and ZT12 (day 1) and ZT13 (day 2) in krill#6 (Fig. 2B), corresponding to the light/dark transitions. Considering that the animals were incubated within a small volume of water (2 L) and their ability of swim freely might have been severely reduced, changes in swimming activity might not have greatly contributed to the oxygen uptake oscillation. Therefore, this could be interpreted as the result of an internal metabolic oscillation instead. This would support the hypothesis that an internal rhythm of activity might contribute to DVM in krill, even if the mechanisms still remain unknown. In the Calanoid copepod Calanus finmarchicus similar oscillations of oxygen consumption were interpreted as a metabolic anticipation of DVM19.
Figure 2

Oxygen consumption patterns of krill exposed to LD. (A,B) In both panels, X-axis represents time given as Zeitgeber Time or ZT measured in hours. ZT0 corresponds to each event of lights-on (6:00). Y-axis represents residual oxygen consumption expressed in mg/L. Positive values indicate increase of oxygen consumption, whereas negative values indicate decrease of oxygen consumption. Black points represent raw data points. Black solid line represents the model fit obtained by applying a GAM to the residual oxygen consumption over time. Red-shaded areas represent the 95% confidence interval around the GAM model’s fit. White and grey rectangles represent alternation of light and dark phases. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

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In DD, four out of seven tested animals (57%) displayed rhythmic oxygen consumption (Fig. 3A–D). Three of them (krill#3, #4 and #7, Fig. 3B–D) showed a period of about 24 h. The peak of oxygen uptake was advanced compared to LD and occurred during the subjective light phase. Krill#3 (Fig. 3B) displayed a period of 23 h in day 1 (p-value = 1.0e−07) and 24 h in day 2 (p-value = 4.2e−06), with peak oxygen consumption at CT11 and CT34. Krill#4 (Fig. 3C) displayed a period of 21 h in day 1 (p-value = 2.0e−05) and 24 h in day 2 (p-value = 1.6e−06), with peak consumption at CT9 and CT28. Krill#7 (Fig. 3D) displayed a period of 24 h in day 2 (p-value = 2.2e−07), with peak consumption at CT28. A fourth individual (krill#2, Fig. 3A) displayed a period of 12 h in day 1 (p-value = 1.7e−03), with a first peak of consumption at CT3 during the early subjective light phase and a second peak 12 h later (CT15) during the early dark phase. The general advance of the oxygen uptake peak observed in krill#3, 4 and 7 together with the 12 h rhythm displayed by krill#2 suggested that in free-running DD conditions a process of period shortening similar to the one observed for DVM might occur also for oxygen consumption. Previous studies suggested that in krill similar 12 h free-running endogenous rhythms might occur also at the level of metabolic gene expression and enzymatic activity16. This, together with the recent analysis of krill circadian transcriptome revealing a significant 12 h free-running oscillation at the molecular level, strongly suggests that a short endogenous period of oscillation might characterize the circadian clock of Antarctic krill.
Figure 3

Oxygen consumption patterns of krill exposed to DD. (A–D) In all panels, X-axis represents time given as Circadian Time or CT measured in hours. CT0 corresponds to first subjective lights-on event (6:00). Y-axis represents residual oxygen consumption expressed in mg/L. Positive values indicate increase of oxygen consumption, whereas negative values indicate decrease of oxygen consumption. Black points represent raw data points. Black solid line represents the model fit obtained by applying a GAM to the residual oxygen consumption over time. Red-shaded areas represent the 95% confidence interval around the GAM model’s fit. Light grey and grey rectangles represent alternation of subjective light and dark phases respectively. For each experimental day, results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

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The relatively small percentages (33% in LD and 57% in DD) of individual krill showing significant rhythmic oxygen consumption might have been related to the small sample size in our experiment (n = 6 in LD and n = 7 in DD). However, additional measurements of krill oxygen consumption performed using the same methods in LD and DD on larger samples (n = 10), on board of RV Polarstern during Antarctic expedition PS 112 (March to May 2018), displayed similar percentages of rhythmic individuals (3 out of 10 or 30% in LD, and 4 out of 10 or 40% in DD) (Supplementary Figs. S2A–C and S3A–D). Interestingly, the on-board analyses revealed the same tendency to develop shorter periods of oscillation in DD (krill#7: T = 12 h, p-value = 8.5e−04; krill#9: T = 17 h, p-value = 8.3e−08) (Supplementary Fig. S3C,D). Also, on-board measurements in LD displayed almost a reversed phase of oscillation compared to those in the lab, with peak oxygen consumption occurring at the beginning of the light phase, around ZT4 (Supplementary Fig. S2A–C). It is tempting to speculate that this might be related to the reverse DVM pattern observed in the lab. Unfortunately, we do not have the corresponding DVM measurements from the field to support this hypothesis. Additional field observations of krill rhythms of oxygen consumption in association with DVM would help to clarify how these two phenomena might be related to each other.
Dissection of putative circadian oscillators in krill brain and eyestalks
It might be possible that the DVM rhythm and the metabolic oscillation observed in krill were under the influence of separated circadian oscillators. This was suggested by the different responses displayed by DVM and oxygen consumption in DD. While DVM showed a complete shift to a 12 h rhythm, oxygen consumption only showed an advance in the peaking time.
Previous studies on the circadian system of Crustaceans identified multiple oscillators located in the head and along the body31. In particular, a model has been proposed where the circadian oscillators in the head are situated in the brain, in the eyestalks and in the retinae of the compound eye (Supplementary Fig. S4)31. To investigate this hypothesis we compared, for the first time, daily patterns of clock genes expression in brain and eyestalks tissue of krill exposed to LD and DD. Krill were sampled within a 72-h time-series, 9 animals were collected every four hours. During the first 24 h (ZT0-24), krill were exposed to LD, while during the remaining 48 h (CT0-48) krill were exposed to DD. We dissected brain and eyestalks tissue (Supplementary Fig. S5) and measured relative changes in clock-related mRNAs over time during the first day in LD (ZT0-24) and during the second day in DD (CT24-48) (Supplementary Table S1). We used RAIN to check for rhythmic oscillations within tissue.
Clear rhythms of oscillation were found in both tissues. In LD, the core clock components in the brain displayed an antiphase relationship between positive (clk-cyc) and negative (per-tim) regulators, with clk-cyc peaking around ZT16-20 (clk: T = 24 h, p-value = 4.86e−07; cyc: T = 24 h, p-value = 2.28e−12) and per-tim peaking around ZT4 (per: T = 24 h, p-value = 2.99e−05; tim: T = 24 h, p-value = 1.31e−11) (Fig. 4A). In the eyestalks such antiphase relationship was not present and most clock genes showed upregulation during the dark phase (ZT16-24) (Fig. 4B). The antiphase relationship is a typical feature of the circadian clock and is well-known from studies on model organisms like Drosophila and mouse2. So far, previous studies on krill failed to demonstrate a clear antiphase relationship between positive and negative regulators21,22. This was discussed as a non-canonical feature of krill circadian clock, as observed before in other Crustacean species32,33. However, our results suggest that this might be related to the specific tissue analyzed. In fact, previous works focused mostly on the eyestalks or on the full head21,22, possibly failing to detect the antiphase oscillation in the brain. This indicates that separate oscillators might be present in the brain and the eyestalks of krill, as already suggested for other Crustaceans31, and suggests that the central pacemaker might be located in the brain. The oscillator in the eyestalks, where all clock genes displayed upregulation during the dark phase, seems to be mostly influenced by the external photoperiod and might be considered as a peripheral oscillator.
Figure 4

Comparison of daily patterns of clock genes expression in LD between brain and eyestalk tissues. (A, B) Heatmaps representing up- (in yellow) and down- (in blue) regulation of clock genes expression over time in the brain (A) and eyestalks (B) of krill exposed to LD. For gene names abbreviations please see Supplementary Table S1. Zeitgeber Time (ZT) given in hour from time of lights-on (6:00), corresponding to ZT0, is indicated below each column of the heatmaps. Heatmaps were generated using the heatmap.2 function in the “gplots” package (version 3.0.4, https://github.com/talgalili/gplots) in R (RStudio version 1.0.136, RStudio Team 2016).

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As in LD, also in DD different patterns of regulations were observed in the brain and in the eyestalks (Supplementary Fig. S6A,B). In the brain, clk, cyc and per, three of the core clock components, displayed a marked tendency towards a shortening of the oscillation period (Fig. 5A–C). Clk shifted from 24 to 12 h (p-value = 1.31e−16), cyc from 24 to 16 h (p-value = 2.48e−10) and per from 24 to 12 h (p-value = 4.05e−07). In the eyestalks, only per showed a similar tendency, shifting from 24 h (p-value = 3.69e−06) to 16 h (p-value = 2.36e−13) (Fig. 5D). This again suggested that separated oscillators might be present in krill brain and eyestalks. The shortening of clock gene oscillation period observed in DD in the brain and, to a lesser extent, in the eyestalks, together with the short (12–16 h) oscillation period registered for cry2 across all tissues and conditions (brain LD: T = 12 h, p-value = 2.4e−08; brain DD: T = 12 h, p-value = 1.4e−04; eyestalk LD: T = 12 h, p-value = 3.2e−05; eyestalk DD: T = 16 h, p-value = 6.1e−13) (Supplementary Fig. S7A,B), is in agreement with previous observations of krill clock gene expression in DD22 and strongly indicates that krill endogenous circadian period might be significantly shorter than 24 h also at the molecular level.
Figure 5

LD-DD shortening of the oscillation period in the core clock genes clk, cyc and per. (A–C) Line-plots representing changes in expression levels over time for the clock genes clk (A), cyc (B) and per (C) in LD (blue) and DD (red) in the brain, and for the clock gene per (D) in LD (blue) and DD (red) in the eyestalks. Time intervals are reported on the x-axis. For LD, Zeitgeber Time (ZT) is used, given in hours from the time of lights-on (6:00). For DD, Circadian Time (CT) is used, given in hours from the beginning of the subjective light phase (6:00). Mean gene expression levels (n = 7) are reported on the y-axis as mean Relative Quantities (RQ), indicating the average normalized expression levels of the target clock genes relative to the expression levels of the selected internal and external reference genes at each time interval. Error bars represents SEMs (n = 7). Results of the RAIN analysis test for the presence of rhythmic oscillations are reported (T = period of oscillation; p = p-value). A schematic representation of the light/dark cycle is given below the graphs. For LD, withe rectangles indicate light phases, grey rectangles indicate dark phases. For DD, light grey rectangles indicate subjective light phases, grey rectangles indicate dark phases. The figure was generated using the plot function in the “graphics” package (version 3.6.3, https://www.rdocumentation.org/packages/graphics) in R (RStudio version 1.0.136, RStudio Team 2016).

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The presence of separated oscillators in krill might help to coordinate different aspects of daily rhythmicity on different levels including physiology (e.g. oxygen consumption) and behavior (e.g. DVM). This might allow a flexible regulation of daily rhythms in response to local changes in environmental conditions driven by temporary and/or seasonal factors including photoperiod, food availability and presence/absence of predators among others.
Adaptive significance of the short endogenous free-running period in krill
The tendency in krill to display a 12 h endogenous free-running period might represent a circadian adaptation for living at high latitudes, where the photoperiodic signal displays strong seasonal variability16,22. Surveys from plants and insects along broad latitudinal gradients indicate that high-latitude species tend to have shorter endogenous periods compared to low-latitude ones34. The reasons why this happens are not fully understood yet, but it has been proposed that the short endogenous period might help the clock to entrain to a wider range of photoperiods34. Indeed, krill displayed rhythmic clock gene activity during the midnight sun in summer, suggesting that their clock can successfully entrain to extremely long photoperiods17,35. However, exposition to similar extreme photoperiods in the laboratory apparently caused the disruption of the clock, suggesting that in the field krill might switch to alternative Zeitgebers (i.e. entraining cues) to entrain the clock when photoperiod becomes extremely long/short (e.g. midsummer/midwinter)36.
From an ecological perspective, having a short endogenous period might help krill to adapt to different environmental scenarios. In the presence of overt day/night cycles, the clock would entrain to the photoperiod and promote daily rhythms with a period of approx. 24 h, like for example the nocturnal DVM pattern usually observed during spring and autumn. This would help krill to anticipate the day/night cycle and maximize the costs/benefits balance between time spent feeding at the surface and time spent hiding in deeper water layers. At the same time, the clock would promote 24 h oscillations in krill metabolism and physiology and coordinate phases of high and low activity with phases of upward and downward migration during DVM.
On the other hand, in the absence of overt day/night cues, for example during summer and winter, the clock would tend to shift towards the free-running period and promote daily rhythms with a period shorter than 24 h. Observations of krill DVM in the field during winter are scarce, due to the harsh weather conditions which characterize the Southern Ocean at that time of the year. Much of the rhythmic biology of krill during winter still remains unknown. At the same time, field observations of krill DVM and swimming activity during summer suggest that a de-synchronized, “around-the -clock”, individual migratory movement might be present instead18,28. According to some studies, frequent shallow individual migrations could occur during summer as a result of a hunger-satiation mechanism, triggered by the abundant primary production occurring in the surface layers37. To get further insight into these aspects, first we would need to perform additional laboratory studies and field observations of krill rhythmic functions (oxygen consumption, swimming activity, DVM) under extreme photoperiods (LL-DD in the lab, summer–winter in the field). Second, laboratory and on-board trials with krill exposed to different food concentrations (low-medium–high) and different photoperiods (LD-DD-LL) could be used to study the interaction between food and light cues in the emergence of “opportunistic” rhythmic responses. More

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