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l-cry mutants show higher spawning synchrony than wild-type animals under non-natural light conditionsIn order to test for a functional involvement of L-Cry in monthly oscillator function, we generated two l-cry mutant alleles (Δ34 and Δ11bp) (Fig. 1a) using TALENs28. In parallel, we generated a monoclonal antibody against Platynereis L-Cry. By testing mutant versus wildtype worms with the anti-L-Cry antibody in Western blots (Fig. 1b) and immunohistochemistry (Fig. 1e–j), we verified the absence of L-Cry protein in mutants. Furthermore, we confirmed that the staining of the antibody in wildtype worms (Fig. 1e–h) matches the regions where l-cry mRNA is expressed (Fig. 1d). These tests confirmed that the engineered l-cry mutations result in loss-of-function alleles. In turn, they validate the specificity of the raised anti-L-Cry antibody.Fig. 1: l-cry–/– mutants are loss-of-function alleles.a Overview of the l-cry genomic locus for wt and mutants. Both mutant alleles result in an early frameshift and premature stop codons. The Δ34 allele has an additional 9 bp deletion in exon 3. b Western Blots of P. dumerilii heads probed with anti-L-Cry antibody. In the context of further investigations such Western blots of mutant versus wild types have been performed more than 10 times with highly consistent results. Also see further analyses in this manuscript and ref. 36. c overview of P. dumerilii. d whole mount in situ hybridization against l-cry mRNA on worm head. ae, anterior eye; pe, posterior eye. e–j Immunohistochemistry of premature wild-type (e–h) and mutant (i, j) worm heads sampled at zt19/20 using anti-L-Cry antibody (green) and Hoechst staining (magenta), dorsal views, anterior up. e, f: z-stack images (maximal projections of 50 layers, 1.28 µm each) in the area highlighted by the rectangle in (d), whereas (g–j) are single layer images of the area highlighted by the white rectangles in (e, f). In the context of further investigations such stainings of mutant versus wild types have been performed more than 10 times with highly consistent results. Also see further analyses in this manuscript and ref. 36.Full size imageWe next assessed the circalunar maturation timing of wild types and l-cry mutant populations in conventional culture conditions, i.e. worms grown under typical indoor room lighting (named here artificial sun- and moonlight, Supplementary Fig. 1b).We expected either no phenotype (if L-Cry was not involved in circalunar clock entrainment) or a decreased spawning precision (if L-Cry was functioning as moonlight receptor in circalunar clock entrainment). Instead we observed an increased precision of the entrained worm population:We analyzed the maturation data using two statistical approaches, linear and circular statistics. We used the classical linear plots5 and statistics to compare the monthly spawning data distribution (Fig. 2a–c, i). This revealed a clear difference between mutant animals, which exhibited a stronger spawning peak at the beginning of the NM phase, compared to their wildtype and heterozygous counterparts (Fig. 2a–c, Kolmogorov–Smirnov test on overall data distribution, Fig. 2i).Fig. 2: L-Cry shields the circalunar clock from light that is not naturalistic moonlight.a–d, j Spawning of l-cry +/+ (a), l-cry +/– (Δ34) (b) and l-cry −/−(Δ34/ Δ34) (c) animals over the lunar month in the lab with 8 nights of artificial moonlight (a–c), under natural conditions in the sea (d, replotted from ref. 34,50,) and in the lab using naturalistic sun- and moonlight (j, 8 nights moonlight). e–h, k Data as in (a–d, j) as circular plot. 360° correspond to 30 days of the lunar month. The arrow represents the mean vector, characterized by the direction angle µ and r (length of µ). r indicates phase coherence (measure of population synchrony). p-values inside the plots: result of Rayleigh Tests. Significance indicates non-random distribution of data points. The inner circle represents the Rayleigh critical value (p = 0.05). i–l Results of two-sided multisample statistics on spawning data shown in (a–h, j, k). The phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageWe then analyzed the same data using circular statistics (as the monthly cycle is repeating, see details in Methods section), which allowed us to describe the data with the mean vector (defined by the direction angle µ and its length r, shown as arrows in Fig. 2e–g). The phase coherence r (ranging from 0 to 1) serves as a measure for synchrony of the population data. As expected for entrained populations, all genotypes distributed their spawning across a lunar month significantly different from random (Fig. 2e–g, p values in circles, Rayleigh’s Uniformity test29). In line with the observed higher spawning peak of the l-cry−/− mutants in the linear plots, the circular analysis revealed a significant difference in spawning distribution (Mardia–Watson-Wheeler test, for details see Methods section) and higher spawning synchrony of mutants (r = 0.614) than in wild types and heterozygotes (r = 0.295 and r = 0.222) (Fig. 2i). The specificity of this phenotype of higher spawning precision for l-cry homozygous mutants was confirmed by analyses on trans-heterozygous l-cry (Δ34/Δ11) mutants (Supplementary Fig. 2), and by the fact that such a phenotype is not detectable in any other light receptor mutant available in Platynereis (r-opsin130: Supplementary Fig. 3a, b, e, f, i; c-opsin131: Supplementary Fig. 3c, d, g, h, i, Go-opsin: refs. 32, 33).The higher spawning synchrony of l-cry mutants under artificial light mimics the spawning precision of wild-type at its natural habitatThis increased spawning precision of l-cry mutants under artificial (but conventional indoor) laboratory light conditions let us wonder about the actual population synchrony of the worms under truly natural conditions. The lunar spawning synchrony of P. dumerilii at the Bay of Naples (the origin of our lab culture) has been worked on for more than 100 y. This allowed us to re-investigate very detailed spawning data records from the worms’ natural habitat published prior to environmental/light pollution. For better accessibility and comparability we combined all months and replotted the data published in 192934 (Fig. 2d, h, I; see details in Methods section; r = 0.631). This analysis revealed that the higher spawning synchrony in l-cry–/– worms mimics the actual spawning synchrony of P. dumerillii populations in their natural habitat34 (compare Fig. 2c, g with 2d, h.)Given that recent, non-inbred isolates from the same habitat as our lab inbred strains (which is the same habitat as the data collected in ref. 34) exhibit a broad spawning distribution under standard worm culture light conditions (which includes the bright artificial moonlight)35, we hypothesized that the difference in spawning synchrony between wildtype laboratory cultures and populations in their natural habitat is caused by the rather bright nocturnal light stimulus typically used for the standard laboratory culture (Supplementary Fig. 1a vs. b).Lunar spawning precision of wild-type animals depends on naturalistic moonlight conditionsWe next tested the resulting prediction that naturalistic moonlight should increase the spawning precision of the wildtype population, using naturalistic sun- and moonlight devices we specifically designed based on light measurements at the natural habitat of P. dumerilii31 (Supplementary Fig. 1a, c). We assessed the impact of the naturalistic sun- and moonlight (Supplementary Fig. 1a, c) on wildtype animals, maintaining the temporal aspects of the lab light regime (i.e. 8 nights of “full moon”). Indeed, merely adjusting the light intensity to naturalistic conditions increased the precision and phase coherence of population-wide reproduction: After several months under naturalistic sun- and moonlight, wildtype worms spawned with a major peak highly comparable to the wildtype precision reported at its natural habitat (Fig. 2d, h vs. j, k), and also exhibited an increased population synchrony (r = 0.398 compared to r = 0.295 under standard worm room light conditions). This increased similarity to the spawning distribution at the natural habitat (“Sea”) is confirmed by statistical analyses (Fig. 2l): The phase difference (angle between the two mean vectors) is only one day (corresponding to 12°). In contrast, the spawning distribution of wild types under standard worm room light versus naturalistic light conditions is highly significantly different in linear and circular statistical tests and has a phase difference of 7.7 days (Fig. 2l).These findings show that it is the naturalistic light that is critical for a highly precise entrainment of the monthly clock of wild-type worms. Given that l-cry–/– animals reach this high precision with the artificial light (i.e. standard lab light) implies that in wildtype L-Cry blocks artificial, but not naturalistic full-moonlight from efficiently synchronizing the circalunar clock. This block is removed in l-cry–/– animals, leading to a better synchronization of the l-cry–/– population. This finding suggests that L-Cry’s major role could be that of a gatekeeper controlling which ambient light is interpreted as full-moonlight stimulus for circalunar clock entrainment.
l-cry functions as a light signal gatekeeper for circalunar clock entrainmentA prediction of this hypothesis is that mutants should entrain better to an artificial full-moonlight stimulus provided out-of-phase than their wild type counterparts (in which L-Cry should block the “wrong” moonlight at least partially from re-entraining the circalunar oscillator).We thus compared the spawning rhythms of l-cry+/+ and l-cry–/– worms under a re-entrainment paradigm, where we provided our bright artificial culture full-moonlight at the time of the subjective new moon phase (Fig. 3a). In order to compare the spawning data distribution relative to the initial full moon (FM) stimulus, as well as to the new full moon stimulus (i.e. new FM), we used two nomenclatures for the months: months with numbers are analyzed relative to the initial nocturnal light stimulus (i.e. FM), whereas months with letters are analyzed relative to the new (phase-shifted) nocturnal light stimulus (i.e. new FM, Fig. 3a). When the nocturnal light stimulus is omitted (to test for the oscillator function) we then refer to ‘free-running FM’ (FR-FM) or ‘new free-running FM’ (new FR-FM), respectively (Fig. 3a). Using these definitions, the efficiency of circalunar clock re-entrainment will be reflected in the similarity of spawning data distributions between month 1 and month D, i.e. the more similar the distribution, the more the population has shifted to the new phase.Fig. 3: l-cry−/− mutants entrain the circalunar clock faster than wt to a high-intensity artificial moonlight stimulus.a Nocturnal moonlight exposure protocol of lunar phase shift (entrained by 8 nights, phased shifted by 6 nights of artificial culture moon, light green). b, c Number of mature animals (percent per month, rolling mean with a window of 3 days) of l-cry wild-type (b) and homozygous mutant (c) animals. p-values indicate results of Kolomogorov–Smirnov tests. Dark blue arrowheads- old FM phase: wt show a spawning minimum, indicative that the worms are not properly phase shifted. Mutants spawn in high numbers, but don’t spawn at the old NM indicated by light blue arrowhead. Also compare to initial FM and NM in months 1,2. d, e Circular plots of the data shown in (b) and (c). Each circle represents one lunar month. Each dot represents one mature worm. The arrow represents the mean vector characterized by the direction angle µ and r. r (length of µ) indicates phase coherence (measure of population synchrony). The inner circle represents the Rayleigh critical value (p = 0.05). f, g Results of two-sided multisample statistics of data in (d, e). Phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageWhen using the artificial nocturnal light conditions, the re-entrainment of l-cry–/– animals was both faster and more complete than for their wildtype relatives, as predicted from our gate keeper hypothesis. This is evident from the linear data analysis and Kolmogorov–Smirnov tests when comparing the month before the entrainment (month 1) with two months that should be shifted after the entrainment (months C,D, Fig. 3b, c, f, g).Most notably, while l-cry−/− worms were fully shifted in month D (Fig. 3c: compare boxes and see complete lack of spawning at the light blue arrowhead indicating the old NM/new FR-FM phase versus massive spawning at new NM phase around dark blue arrowhead), wildtype animals were still mostly spawning according to the initial lunar phase (Fig. 3b: compare boxes and see spawning at the light blue arrowhead versus almost lack of spawning at dark blue arrowhead). The faster re-entrainment of l-cry–/–, compared to l-cry+/+ animals is also confirmed by the Mardia–Watson-Wheeler test (see Methods section for details). For l-cry+/+ animals, the comparisons of the spawning distributions before and after re-entrainment show a 1000-fold (months 1 versus C) and tenfold (months 1 versus D) higher statistical significance difference than the corresponding comparisons for l-cry−/− worms (Fig. 3f, g). Consistently, the phase differences in days calculated from the angle between the two mean vectors from the circular analysis is smaller in the mutants than in the wild types when comparing the phase of the month before the entrainment (month 1) with two months after the entrainment (months C, D) (Fig. 3d–g). The fact that there are still differences in the mutant population before and after entrainment is likely due to the fact that even the mutants are not fully re-entrained. However, they have shifted more robustly in response to an artificial nocturnal light stimulus than the wild types. This provides further evidence that in wildtype worms L-Cry indeed blocks the “wrong” light from entering into the circalunar clock and thus functions as a light gatekeeper.L-Cry functions mainly as light interpreter, while its contribution as direct moonlight entraining photoreceptor is (at best) minorWe next tested to which extent L-Cry is itself a sensor for the re-entrainment signal under naturalistic light conditions. Based on the finding that l-cry−/− worms can still re-entrain the circalunar oscillator (see above), it is clear that even if L-Cry also directly contributed to the entrainment, it cannot be the only moonlight receptor mediating entrainment. With the experiments below, we aimed to test if L-Cry has any role as an entraining photoreceptor to the monthly oscillator.Thus, we tested how the circalunar clock is shifted in response to a re-entrainment with naturalistic moonlight in Platynereis wt versus l-cry−/− worms. For this, animals initially raised and entrained under standard worm room light conditions of artificial sun- and moonlight (Supplementary Fig. 1b, e) were challenged by a deviating FM stimulus of 8 nights of naturalistic moonlight (Fig. 4a, Supplementary Fig. 1c, e). This re-entraining stimulus was repeated for three consecutive months (Fig. 4a).Fig. 4: l-cry has a minor contribution as entraining photoreceptor to circalunar clock entrainment.a Nocturnal moonlight exposure protocol of lunar phase shift with 8 nights of naturalistic moonlight (dark green). Number of mature animals (percent per month, rolling mean with a window of 3 days) of l-cry wild-type (b) and mutant (c) animals. p-values: Kolomogorov–Smirnov tests. Black arrowheads indicate spawning-free intervals of the wildtype, which shifted to the position of the new FM (under free-running conditions: FR-FM). d, e Data as in (b, c) plotted as circular data. 360° correspond to 30 days of the lunar month. The arrow represents the mean vector characterized by the direction angle µ and r. r (length of µ) indicates phase coherence (measure of population synchrony). p values are results of Rayleigh Tests: Significance indicates non-random distribution of data points. The inner circle represents the Rayleigh critical value (p = 0.05). f, g Results of two-sided multisample statistics on spawning data shown in (a–e). Phase differences in days can be calculated from the angle between the two mean vectors (i.e. 12°= 1 day).Full size imageThe resulting spawning distribution was analyzed for the efficacy of the naturalistic moonlight to phase-shift the circalunar oscillator. In order to test if the animals had shifted their spawning to the new phase, we again compared the spawning pattern before the exposure to the new full moon stimulus (months with numbers: data distribution analyzed relative to the initial/old FM, see Fig. 4a for an overview) to the spawning pattern after the exposure to the new full moon stimulus (months with letters: data distribution analyzed relative to the new FM, Fig. 4a). The more similar the data distributions of month 1 is to the months C, D, the more the population was shifted to the new phase.The first re-entraining full moon stimulus (Fig. 4b, first dark green box) is given in the middle of the main spawning period. The nocturnal light itself does not cause immediate effects on the number of spawning worms (Fig. 4b, see also Fig. 2b, c), but the repeated exposure resulted in a noticeable shift of the spawning distribution indicating a phase shift of the monthly oscillator in wildtype. Already at the third re-entraining full moon stimulus, wildtype animals exhibited a completely shifted spawning pattern (Fig. 4b, d-d″, month 1, 2 vs. month C). This is supported by statistical analyses: When comparing the months 1 and 2 (relative to the old FM before the shift) to the month C (relative to the new FM after the shift), both the Kolmogorov–Smirnov test (Fig. 4b: gray rectangles, 4f) and the Mardia–Watson–Wheeler test of the same data were non-significant (Fig. 4f), indicative of the population shifting to the new phase. Consistently, the direction angle (µ) of the mean vectors before and after the shift was highly similar, resulting in a phase difference of only 0.2 days between months 1 and C and 0.5 days between month 2 and month C (Fig. 4f, for details see methods). The month under circalunar free-running conditions (month D) supports this observation, albeit with lower statistical support (Fig. 4b, d″, f).Of note, wild-type worms would eventually reach the high spawning precision found under naturalistic moonlight only after several more months based on independent experiments (Fig. 2j, k).When we analyzed the spawning distribution of l-cry mutants in the same way as the wild types, we found that the data distribution exhibited significant differences in the linear Kolmogorov–Smirnov test when comparing months 1 and 2 before the shift to the months C and D after the shift (Fig. 4c: gray rectangles, Fig. 4g); as well as in the phase distribution in the circular analyses when comparing the months before the shift (months 1 and 2) with the last months of the shift (months C,D) (Fig. 4e, e′ versus e″, e‴, g). The populations also exhibited a noticeable phase difference of ≥3.5 days (Fig. 4g).Based on the statistical significant difference in the re-entrainment of l-cry–/–, but not wild-type populations under a naturalistic sun- and moonlight regime, we conclude that L-Cry also likely contributes to circalunar entrainment as a photoreceptor. However, as these differences are rather minor, compared to the much stronger differences seen under artificial light regime, we conclude that its major role is the light gatekeeping function.In an independent study that focused on the impact of moonlight on daily timing, we identified r-Opsin1 as a lunar light receptor that mediates moonlight effects on the worms’ ~24 h clock36. We tested if r-opsin1 is similarly important for mediating the moonlight effects on the monthly oscillator of the worm, analyzed here. This is not the case. r-opsin1–/– animals re-entrain as well as wildtype worms under naturalistic light conditions (Supplementary Fig. 4). This adds to and is also consistent with our above observation that the spawning distribution is un-altered between r-opsin1–/– and wildtype animals under artificial light conditions (Supplementary Fig. 3a, b, e, f). This finding also further enforces the notion that monthly and daily oscillators use distinct mechanisms, but both require L-Cry as light interpreter.L-Cry discriminates between naturalistic sun- and moonlight by forming differently photoreduced statesGiven that the phenotype of l-cry–/– animals suggests a role of L-Cry as light gatekeeper, i.e. only allowing the ‘right’ light to most efficiently impact on the circalunar oscillator, we next investigated how this could function on the biochemical and cell biological level.While we have previously shown that Pdu-L-Cry is degraded upon light exposure in S2 cell culture15, it has remained unclear if L-Cry has the spectral properties and sensitivity to sense moonlight and whether this would differ from sunlight sensation. To test this, we purified full length L-Cry from insect cells (Supplementary Fig. 5a–c). Multi-angle light scattering (SEC-MALS) analyses of purified dark-state L-Cry revealed a molar mass of about 130 kDa, consistent with the formation of an L-Cry homodimer (theoretical molar mass of L-Cry monomer is 65.6 kDa) (Fig. 5a). Furthermore, purified L-Cry binds Flavin Adenine Dinucleotide (FAD) as its chromophore (Supplementary Fig. 5d, e). We then used UV/Vis absorption spectroscopy to analyze the FAD photoreaction of purified L-Cry in presence of 1 mM TCEP to prevent protein oxidation. The absorption spectrum of dark-state L-Cry showed maxima at 450 nm and 475 nm, consistent with the presence of oxidized FAD (Supplementary Fig. 5f, black line). As basic starting point to analyze its photocycle, L-Cry was photoreduced using a LED (PerkinElmer ACULED Dyo) with a blue-light dominated spectrum and spectral peak at 450 nm (Supplementary Fig. 1d, d′, henceforth referred to as “blue-light”) for 110 s37. The light-activated spectrum showed that blue-light irradiation of L-Cry leads to the complete conversion of FADox into an anionic FAD radical (FADo-) with characteristic FADo- absorption maxima at 370 nm and 404 nm and reduced absorbance at 450 nm (Supplementary Fig. 5f, blue spectrum, black arrows). In darkness, L-Cry reverted back to the dark-state with time constants of 2 min (18 °C), 4 min (6 °C) and 4.7 min (ice) (Supplementary Fig. 5g–k).Fig. 5: L-Cry forms differently photoreduced sunlight- and moonlight states.a Multi-Angle Light Scattering (MALS) analyses of dark-state L-Cry fractionated by size exclusion chromatography (SEC). Black dashed line: normalized UV absorbance, solid line: normalized scattering signal. The molar mass of about 130 kDa derived from MALS (mass signal shown in red) corresponds to an L-Cry homodimer. b Absorption spectrum of L-Cry in darkness (black) and after sunlight exposure (orange). Additional timepoints: Supplementary Fig. 6a. c Dark recovery of L-Cry after 20 min sunlight on ice. Absorbance at 450 nm in Supplementary Fig. 6b. d, e Absorption spectra of L-Cry after exposure to naturalistic moonlight for different durations. f Full spectra of dark recovery after 6 h moonlight. Absorbance at 450 nm: Supplementary Fig. 6d. g Absorption spectrum of L-Cry after 6 h of moonlight followed by 20 min of sunlight. h Absorption spectrum of L-Cry after 20 min sunlight followed by moonlight first results in dark-state recovery. Absorbance at 450 nm: Supplementary Fig. 6e. i Absorption spectrum of L-Cry after 20 min sunlight followed by 4 h and 6 h moonlight builds up the moonlight state. j Model of L-Cry responses to sunlight (orange), moonlight (green) and darkness (black). Only transitions between stably accumulating states are shown. Absorbances in (b–i) were normalized when a shift in the baseline occurred between different measurements of the same measurement set, which is then indicated on the Y-axis as “normalized absorbance”.Full size imageWe then investigated the response of L-Cry to ecologically relevant light, i.e. sun- and moonlight using naturalistic sun- and moonlight devices that we designed based on light measurements at the natural habitat of P. dumerilii31 (Supplementary Fig. 1a, c, e). Upon naturalistic sunlight illumination, FAD was photoreduced to FADo-, but with slower kinetics than under the stronger blue-light source, likely due to the intensity differences between the two lights (Supplementary Fig. 1c–e).While blue-light illumination led to a complete photoreduction within 110 s (Supplementary Fig. 5f), sunlight induced photoreduction to FADo- was completed after 5–20 min (Fig. 5b) and did not further increase upon continued illumation for up to 2 h (Supplementary Fig. 6a). Dark recovery kinetics had time constants of 3.2 min (18 °C) and 5 min (ice) (Fig. 1c, Supplementary Fig. 6b, c).As the absorbance spectrum of L-Cry overlaps with that of moonlight at the Platynereis natural habitat (Supplementary Fig. 1a), L-Cry has the principle spectral prerequisite to sense moonlight. However, the most striking characteristic of moonlight is its very low intensity (5.8 × 1010 photons/cm2/s at −5m, Supplementary Fig. 1a–e). To test if Pdu-L-Cry is sensitive enough for moonlight, we illuminated purified L-Cry with our custom-built naturalistic moonlight, closely resembling full-moonlight intensity and spectrum at the Platynereis natural habitat (Supplementary Fig. 1a, c, e). Naturalistic moonlight exposure up to 2.75 h did not markedly photoreduce FAD, notably there was no difference between 1 h and 2.75 h (Fig. 5d). However, further continuous naturalistic moonlight illumination of 4 h and longer resulted in significant changes (Fig. 5d), whereby the spectrum transitioned towards the light activated state of FADo- (note peak changes at 404 nm and at 450 nm). This photoreduction progressed further until 6 h naturalistic moonlight exposure (Fig. 5d). No additional photoreduction could be observed after 9 h and 12 h of naturalistic moonlight exposure (Fig. 5e), indicating a distinct state induced by naturalistic moonlight that reaches its maximum after ~6 h, when about half of the L-Cry molecules are photoreduced. This time of ~6 h is remarkably consistent with classical work showing that a minimum of ~6 h of continuous nocturnal light is important for circalunar clock entrainment, irrespective of the preceding photoperiod5. The dark recovery of L-Cry after 6 h moonlight exposure occurred with a time constant of 6.7 min at 18 °C (Fig. 5f, Supplementary Fig. 6d). Given that both sunlight and moonlight cause FAD photoreduction, but with different kinetics and different final FADo- product/FADox educt ratios, we wondered how purified L-Cry would react to transitions between naturalistic sun- and moonlight (i.e. during “sunrise” and “sunset”).Mimicking the sunrise scenario, L-Cry was first illuminated with naturalistic moonlight for 6 h followed by 20 min of sunlight exposure. This resulted in an immediate enrichment of the FADo- state (Fig. 5g). Hence, naturalistic sunlight immediately photoreduces remaining oxidized flavin molecules, that are characteristic of moonlight activated L-Cry, to FADo-, to reach a distinct fully reduced sunlight state.In contrast, when we next mimicked the day-night transition (“sunset”) by first photoreducing with naturalistic sunlight (or strong blue-light) and subsequently exposed L-Cry to moonlight, L-Cry first returned to its full dark-state within about 30 min (naturalistic sunlight: τ = 7 min (ice), Fig. 5h, Supplementary Fig. 6e; blue-light: τ = 9 min (ice), Supplementary Fig. 6f–h), despite the continuous naturalistic moonlight illumination. Prolonged moonlight illumination then led to the conversion of dark-state L-Cry to the moonlight state (Fig. 5i, Supplementary Fig. 6f). Hence, fully photoreduced sunlight-state L-Cry first has to return to the dark-state before accumulating the moonlight state characterized by the stable presence of the partial FADo- product/FADox educt. In contrast to sunlight-state L-Cry, moonlight-state L-Cry does not return to the oxidized (dark) state under naturalistic moonlight (Fig. 5e), i.e. moonlight maintains the moonlight state, but not the sunlight state. We note, that a partially photoreduced L-Cry state may be formed transiently during dark-state recovery of the sunlight state under moonlight. However, this transiently occurring partially photoreduced L-Cry state would differ from the “true” moonlight state (e.g. by an allosteric change) preventing its accumulation (see discussion and Supplementary Fig. 6i).Given that L-Cry forms a homodimer and moonlight photoreduces about half of the FAD molecules, we propose that the moonlight state corresponds to a half-reduced FADo- FADox dimer, where FAD is only photoreduced in one L-Cry monomer, whereas in the sunlight state both monomers are photoreduced (FADo- FADo-) (Fig. 5j). This implies that the quantum yield for FADox to FADo- photoreduction differs between the two L-Cry monomers. One monomer (referred to as “A” in Fig. 5j) acts as “very low intensity light sensor” with a high quantum yield ΦA. Hence, the very low photon number provided after 6 h of moonlight illumination is sufficient to photoreduce its flavin co-factor, resulting in the partially photoreduced FADo- FADox moonlight state (Fig. 5j).For direct comparison, our naturalistic moonlight’s emission (in the main absorbance range of L-Cry: 330 nm–510 nm) is 5.4 × 1010 photons/cm2/s (Supplementary Fig. 1e), which accumulates to ~1.2 × 1015 photons/cm2 in the 6 h required to reach the half-reduced moonlight state (Fig. 5d, e). For naturalistic sunlight, emitting ~7.5 × 1014 photons/cm2/s (330–510 nm), at least 5 min of sunlight illumination (i.e. > ~1.8 × 1017 photons/cm2) are required to photoreduce the flavin in both L-Cry monomers in order to reach the fully photoreduced FADo- FADo- sunlight state (Fig. 5b, j). Thus, the second L-Cry monomer (monomer “B” in Fig. 5j) has a significantly lower quantum yield ΦB for FAD photoreduction (ΦB  More

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Study subjectsSubjects in our study are individually recognized wild capuchins found in and around the Lomas Barbudal Biological Reserve in Guanacaste, Costa Rica. This population has been under observation since 1990 (Perry 2012; Perry et al. 2012), including near continuous observation from January 2002 through March 2020.Data collectionWe use proximity data on subjects collected during group scan sampling between January 2001 and March 2020 (Altmann 1974). Included in scans are the identity of the subject, and the identity of other individuals within approximately 4 meters of them. Scans have been collected on all individuals in study groups since 2002, and on all adults and subadults since 2001. Scans are taken opportunistically, without regard to time of day. At least 10 min separate consecutive scans of the same individual to reduce the non-independence of scans taken close in time.Data in this manuscript were collected by 124 observers, with an average of 7.1 data collectors per month. Observers typically work in teams of two to three and rotate across different groups to reduce potential observer bias. Observers also rotate across observer teams to avoid observer drift in coding, since observer teams could potentially start to code behaviors differently from each other in the absence of overlap in observer composition.Initial pedigree constructionOf the 376 individuals in our behavioral dataset, 280 (74.5%) were first seen within three months of their births, and we could confidently assign maternity to them based on demographic (pregnancies) and behavioral data (primary nursing) even prior to genotyping. Of the remaining individuals, 41 (10.9%) were males of unknown origin that immigrated into our study population, while the rest were natal to our study groups but were first seen as older infants ( >3 months), juveniles, or (sub)adults (14.6%) and required genotyping to assign/confirm maternity. Paternity was assigned based on genetic information when possible (but see Non-genotyped individuals).In total, 287 subjects (76.3%) had two assigned parents, 37 had one assigned parent (9.8%), and 52 (13.8%) had no assigned parent based on demographic, behavioral, and/or genetic parentage information. Most individuals with no assigned parents were immigrant males (78.9%).GenotypingInformation on genetic parentage assignment (at up to 18 microsatellite loci) in our study population is available from previously published work (1996–2005 (Muniz et al. 2006), 2005–2012 (Godoy et al. 2016b)). Partial genotypes (up to 14 loci) have been generated for individuals in this study which more recently entered the study population through birth or immigration (n = 91, 2012–2020) (See SI File 1). Briefly, DNA was extracted primarily from non-invasively collected fecal samples, and occasionally from tissue samples obtained from deceased individuals, then amplified at up to 18 autosomal tetranucleotide microsatellite loci (Muniz and Vigilant 2008) using either a 1-step or 2-step PCR protocol (Arandjelovic et al. 2009). There were no significant deviations from Hardy-Weinberg equilibrium, and no evidence of linkage disequilibrium between loci was found (Muniz 2008).DNA samples were run at a minimum in triplicate, but additional PCRs were performed on low quality samples (e.g., with low quantities of DNA). Genotypes at each of the loci were assigned to be heterozygous when each allele was seen at least twice in independent PCRs, and assigned as homozygous when the allele was seen in at least three independent PCRs in absence of a second allele.Amplicons were analyzed using an ABI PRISM3100 automated sequencer and GeneMapper Software (Applied Biosystems, Foster City, CA, USA). Likelihood-based parentage assignments were performed using CERVUS 2.0 or 3.0 (Marshall et al. 1998; Kalinowski et al. 2007). The average exclusionary power of the 18 microsatellites was 0.9888 for the first parent and 0.9998 for second parent (Muniz et al. 2006).Individuals with unknown parents (e.g., immigrant males, founders) were genotyped twice (i.e., using two independent DNA samples) following the procedures described above to guard against sample mix up. Known mother-offspring pairs were confirmed by ascertaining the absence of Mendelian mismatches across all loci for the pair, though one mismatch was allowed to account for null alleles, mutations, and genotyping errors. We detected one null allele in the population in 19 individuals and traced it back to a male who was either the father or grandfather of those individuals (Muniz et al. 2006; Godoy et al. 2016b).Candidate males for paternity assignment were chosen based on group membership around the time of an infant’s conception (typically 1–10 males). In cases when conceptions occurred prior to the habituation of a study group, we used the identities of all adult males present when the group was first observed. Candidate mothers were similarly chosen for individuals that were first seen as older infants, juveniles, or (sub)adults. For individuals born post-group habituation, CERVUS has always assigned paternity from the pool of potential candidate fathers. Parent-offspring pairs and trios were allowed one mismatch (excluding those at the locus with the known null allele).Pedigree updatingNon-genotyped individualsDuring stable tenures, alpha males in our population sire approximately 73% of infants born in their groups, including 90% of offspring born to unrelated females (Godoy et al. 2016a). There is strong evidence of inbreeding avoidance between alpha males and their female descendants, with relatedness to females as the primary factor constraining alpha male monopolization of paternity within groups (Muniz et al. 2006, 2010; Godoy et al. 2016a, 2016b; Wikberg et al. 2017, 2018). We used this information to update our pedigree, filling missing father information with the identity of the alpha male around the time of a non-genotyped individual’s conception, but only if their mother was not the daughter or granddaughter of the alpha male (i.e., with inbreeding avoidance). This approach allowed us to assign presumed paternity to 21 non-genotyped individuals (5.6% of subjects) who were natal to our study groups.Individuals with missing or incomplete parentageOut of the original four study groups (from which fissions led to eight additional study groups), we lacked parentage information (i.e., neither parent was sampled) for 12 individuals first seen at the time of habituation. We had incomplete parentage on an additional 11 adults (i.e., only one parent was sampled). We used the software program COLONY version 2.0.6.7 to look for evidence of whether these individuals were related to each other at the level of full sibling (Jones and Wang 2010). We also looked for potential full sibling pairs among the non-natal immigrant males in the population, since co-migrant males are typically kin (Perry 2012; Wikberg et al. 2014, 2018). We assigned five full sibling pairs among co-migrant males, and four full sibling pairs among natal founders. For any remaining co-migrant males and natal founder pairs that were not assigned as full siblings, we assumed these to be either paternal (migrants) or maternal (natal) half siblings, as is typical in this study population (Perry 2012). These assignments are likely to have some error. However, based on what we know about kinship in capuchins, it would introduce more error to assume that these pairs are unrelated.We pruned our modified population pedigree using the R package pedantics version 1.01 (Morrissey and Wilson 2010), to include only individuals that were linked to the subjects in our behavioral dataset. The reduction in missing data can improve convergence and mixing of models (Hadfield 2010). The pruned pedigree contained 419 individuals, with 353 maternities, 354 paternities, 209 full sibships, 413 maternal half sibships, and 1496 paternal half sibships. Maximum pedigree depth was six generations (mean = 3.03).Sociality measures (response variables)We generated two related proximity-based measures of sociality—(1) whether an individual was seen in proximity of another monkey (within ~4 meters) during a scan (i.e., they were not alone), and (2) the number of partners an individual has nearby (within ~4 meters) during a scan. The former is measure of the propensity of an individual to be social versus alone, while the latter is more indicative of the gregariousness of an individual. These two phenotypes are not independent, as they are generated from the same data (Fig. 1a).Fig. 1: Distribution of sociality, sampling, group size, and alpha tenure length.The scatterplot in a shows the proportion of scans per individual per month where the subjects were recorded in proximity of others on the x-axis, and the average number of social partners per scan per month for subjects on the y-axis. The sizes of the circles in a are proportional to sample size (range: 5–317 scans per data point). The figure in b shows the number of calendar years of data sampling per subject (range: 1–20), c variation in group size, and d the number of calendar years represented by different alpha tenures in the dataset. Note that d does not represent the full diversity of alpha tenure lengths in the population, only within the dataset: some tenure lengths are left-truncated as data from 1990–2000 are not included in this dataset. Figure produced in R using ggplot2 version 3.3.5 (Wickham 2016) and cowplot version 1.1.1 (Wilke 2020). The capuchin image was generated in R using sketcher version 0.1.3 (Tsuda 2020) based on an image taken by Nicholas Schleissmann.Full size imageWe compiled the scans of individuals by month (mean: 31.9, range: 5–317 scans per month) so that we had counts of (1) the total number of scans where an individual was social and (2) the total number of partners an individual had. With these counts we could look at the (1) proportion of time spent social (versus alone) and (2) the average number of partners an individual had, while still preserving information about sampling density (number of scans).To be included in any month, subjects needed to have at least five scan samples in that period. As we are interested in the repeatability of our measures of social behavior, subjects had to have at least six months of data to be included.We excluded dependent infants (less than one year of age) as potential social partners of their mothers. We also excluded these dependent infants as subjects, since an infant is expected to be in close proximity of its mother, particularly during the first half of their first year of life (Godoy 2010; Perry 2012). Including data from infants would likely introduce upward bias to heritability estimates, because mothers and their dependent offspring (whom share high relatedness) would often be in close proximity of each other, and their measures of proximity to others would thus also be highly correlated.On average, subjects spent just over half of their sampled time within approximately four meters of another monkey (mean: 0.539, standard deviation: 0.193) and had approximately one social partner per scan (mean: 1.057, standard deviation: 0.619) (Fig. 1a). Our dataset consisted of 22,138 monthly sociality scores on 376 subjects generated from 641 140 scans (mean: 56.5 months per subject, range: 6–184 months per subject). Almost all subjects (99.7%, i.e., all but one) were represented by data across more than one calendar year (25, 50, 75% quantiles: 4, 7, 10 different years of data collection) (Fig. 1b).Fixed effectsWe included age (as a cubic function) and sex in our models, as well as their interaction to account for differences in how male and female capuchins sexually mature and age. Age in our dataset was right-skewed with higher representation at younger ages (mean: 9.3 years, standard deviation: 6.9) (Fig. 2). To put the ages in developmental context, mean age at first live birth is around 6.3 years for females in this population, though females can begin reproducing in their 5th year (Perry et al. 2012). Males as young as six years old have been known to sire offspring (Godoy et al. 2016b), but males tend to not reach full adult size until their 10th year (Jack et al. 2014).Fig. 2: Sociality as a function of age and sex.Circles represent individual monthly data. The sizes of the circles are proportional to sample size (range: 5–317 shows per data point). Circles in a represent the proportion of time individuals were seen in proximity of others (not alone) per month, while in b represent the average number of partners for individuals per month. Solid lines represent estimated sociality scores based on age and sex, with all other fixed effects set to the mean. The two x-axes represent age as z-scores and in years. Figure produced in R using ggplot2 version 3.3.5 (Wickham 2016).Full size imageSeasonal environmental changes, such as in food abundance, or temperature and rain, can lead to changes in how individuals cluster near others, for example, because of how food resources become distributed in the environment. For example, in black-crested gibbons (Nomascus concolor), group averages of dyadic proximity have been documented to decrease from the dry season to wet season, with increased average group proximity during cold months and lowered proximity during warm months (Guan et al. 2013). We account for seasonal variation by modelling monthly changes as a sine wave, through inclusion of the sine and cosine functions of a transformed month variable (See SI File 1 for further details).Central America is a region of ENSO-related precipitation, where the El Niño-Southern Oscillation (ENSO) has an impact on large scale patterns of temperature and precipitation (Ropelewski and Halpert 1987). Bimonthly rainfall anomalies are linked with both the warm El Niño and cool La Niña phases in a neighboring tropical dry forest in Costa Rica, where long-term monitoring of wild white-faced capuchins has shown declines in reproductive output associated with El Niño-like conditions (Campos et al. 2015). To account for the large-scale influence of ENSO on group dynamics, we included a factor variable for three different ENSO phases (Average/Neutral, Cool/La Niña, and Warm/El Niño). We used the bi-monthly Multivariate El Niño/Southern Oscillation (ENSO) index (MEI.v2) obtained from the Physical Sciences Laboratory of the National Oceanic and Atmospheric Administration (https://psl.noaa.gov/enso/mei/, retrieved: 2021-11-06) to determine the different phases. MEI.v2 is a composite index of five different variables (sea level pressure, sea surface temperature, surface zonal winds, surface meridional winds, and Outgoing Longwave Radiation) used to create a time series of ENSO conditions from 1979 to present (Zhang et al. 2019). Warm phases correspond to MEI.v2 values of 0.5 or higher, while cool phases correspond to values of −0.5 or lower.Demographic differences between groups and within groups across time can also lead to variation in behavior. For example, group size has been found to correlate with the amount of time that individuals spend grooming in various primate species (Dunbar 1991; Lehmann et al. 2007). Group size is also associated with higher sociality measures such as both the number of strong and weak ties that individuals form in diverse clades of primates (Schülke et al. 2022). We attempt to account for variation that arises from such demographic differences by including group size (mean: 24.7, standard deviation: 7.9) (Fig. 1c) as a fixed effect.In our models, group size and cubic age were centered and scaled to a mean of zero and a standard deviation of one.Random effectsAll models include the identity of the subject (VID, n = 376) as a random factor, as well as subject identity nested within year (VID:Year, n = 3150), the identity of each subject’s mother (VM, n = 142), maternal identity nested within group of residence within year of data collection (VM:GroupAlpha:Year, n = 2085), and a special variable known as the animal term to account for additive genetic variance (VA). These components contribute to long- and/or short-term repeatability of individuals. All models also include year of data collection (VYear, n = 20), month nested within year (VMonth:Year, n = 224), and the identity of each subject’s group of residence both across years (VGroupAlpha, n = 56) and within years (VGroupAlpha:Year, n = 200).VID in the models (since the models also additionally estimate VM and VA) can be thought of as estimating the “permanent environment variance” (i.e., VPE) of an individual, which is the “individual-specific variation in environmental conditions that permanently affect the phenotype (e.g. early-life conditions)” (Dingemanse et al. 2010). VID:Year captures the variance explained by the repeated sampling of the same individuals within a particular year. We use it to estimate the proportion of the phenotypic variance due to similarity in the trait within individuals from data taken closer in time (within the same year). During such a relatively short period, individuals are more likely to be stable in important social traits such as kin availability, dominance rank for adults, and maternal dominance rank for infants and young juveniles.VM estimates the variance explained by maternal effects (m2), specifically similarity between maternal siblings. Maternal identities were not available for all subjects, namely 11 immigrant males of unknown origin who were not assigned by COLONY as having a full sibling. We created unique dummy codes for their maternal identities, so that no two of these individuals shared the same mother. We additionally nested maternal identities (VM:GroupAlpha:Year) to account for similarity between maternal siblings residing in the same group in the same year. Such a nested structure might capture potential upward biases on heritability due to maternal kin biases in spatial association among siblings residing in the same group.We estimate h2 in our models by fitting a random effects term (VA), referred to as the animal term, which in the R package MCMCglmm links to the identities of individuals in our population pedigree (Hadfield 2010; see below for details on the implementation of the models in MCMCglmm). Inclusion of the animal term provides our models with an additive genetic variance component based on the estimated coefficients of relatedness between individuals in our pedigree. In short, if animals that share more alleles are also more like each other in their behavior, then variation in the behavior may well be due to genetic variation in the population (under the assumption that phenotypic similarity is not due to a shared environment, or is adequately controlled for by fixed and random effects in the model).VYear and VMonth:Year were included in order to account for temporal variation in sociality scores not captured by the fixed effects of seasonality or ENSO phase. These could arise from, for example, observer drift in coding (i.e., measurement error) or prevailing environmental conditions (e.g., drought) that could lead to changes to how individuals cluster near others. There were 218 unique observer combinations across the 224 months represented in the dataset, so VMonth:Year should also capture variance due to any differences between observer teams, though we cannot separate out the unique influence of observers.VGroupAlpha represents variance arising from the different alpha tenures within groups in our study population. VGroupAlpha captures both variance due to group of residence effects and the additional influence of alpha tenures within those groups. In capuchins, alpha males are ‘keystone’ individuals, whose influence is disproportionate relative to that of others in the population, and thus play important roles in establishing group dynamics (Jack and Fedigan 2018). Including group of residence, as defined by alpha tenure, is also important because it helps to account for the higher relatedness within groups within alpha tenures which results from high male reproductive skew toward alpha males. At Lomas Barbudal, males can remain in their alpha position for upwards of 18 years. Alpha tenures in this dataset spanned one to 14 years (Fig. 1d), so we additionally nested the identity of alpha males per group within years (VGroupAlpha:Year) so as to separate the within-year and across-year influences of group of residence.Statistical methodsWe ran analyses in R 4.1.2 (R Core Team 2021), using a Bayesian method with the R package MCMCglmm version 2.32 (Hadfield 2010). Data and code used to run all models is provided in the Supplementary Information.For our binary response variable (social versus alone), which was pooled into monthly units, we fit models with a binomial distribution and logit link function (family = “multinomial2”), with the number of scans each individual was documented social (‘successes’) versus the number of times alone (‘failures’).For our other response variable (number of partners), which was also pooled into monthly units, we fit models with a Poisson distribution (family = “poisson”), with the total number (sum) of partners per month. We included the natural log of the number of scans per month as a fixed effect to account for sampling effort. We set a strong prior for the log of sampling effort so that the rate at which events occurred was 1 (i.e., we could look at average number of partners per scan).We used a parameter-expanded prior (V = 1, nu = 1, alpha.mu = 0, alpha.V = 1000) and two inverse Wishart priors (V = 1, nu = 0.002; V = 1, nu = 0.02) for the G structures in our models (i.e., random effects variance components). The prior on the residual variance component was set to one for both the binomial and Poisson models. Estimates for variance components were robust against the choice of prior (SI Fig. 3). We therefore only report findings from models run with parameter-expanded priors in the main text.Pilot runs (thin = 10, burnin = 3000, nitt = 13,000) indicated that autocorrelation values would remain high for some variance components in models run with parameter-expanded priors, even with large thinning intervals. We therefore increased the number of iterations to guarantee effective sample sizes of at least 1000, but ideally closer to 4000. All models were run with a long burn-in period of at least 10,000 iterations.We ran multiple chains (n = 4) of each model and assessed convergence of the chains visually (SI Files 2a-b), as well as through the Gelman-Rubin criterion implemented via the ‘gelman.diag’ function from the coda package in R (version 0.19-4) (Plummer et al. 2006). Scale reduction factors were below 1.02, signifying good convergence. We used Heidelberger and Welch’s convergence diagnostic test for stationarity to check convergence of each chain using the ‘heidel.diag’ function from the coda package. Results are presented from the first chain of each model.Reduced modelsInclusion of fixed effects can potentially have an impact on the estimates of variance components in models because total phenotypic variance (VP) is estimated (and partitioned among the different random effects) after conditioning on the fixed effects. Heritability estimates, for example, can be higher because the variance explained by the fixed effects structure (VFE) is not included in VP, thus making the relative contribution of VA to VP larger compared to the same model without fixed effects (Wilson 2008). Conversely, not adequately controlling for relevant fixed effects that contribute to phenotypic variance among and within individuals may potentially lead to an underestimation of VA and associated heritability (h2).We ran multiple reduced versions of our models to look at the impact of fixed effects on our variance components. We began with an intercept-only version (i.e., no fixed effects), then built-up complexity by adding in versions with the properties of the individuals first (age, sex), then properties of the group (group size), and subsequently environmental properties (seasonality, ENSO phases). Outputs for these reduced models are provided in the Supplementary Information (SI Table 2, SI Table 3).We provide the deviance information criterion (DIC) values for models (automatically generated by the MCMCglmm package). DIC is a generalization for multi-level models of the Akaike Information Criterion (AIC); and as in AIC, lower DIC values indicate better fit.Transformations from unobserved latent scale to observed data scaleOutputs from our MCMCglmm models were on the unobserved latent scale. We used the R package QGglmm (version 0.7.4) to additionally compute parameters of interest on the observed data scale (de Villemereuil et al. 2016; de Villemereuil 2018). We used the functions ‘QCicc’ to compute Intra-Class Correlation (ICC) coefficients and ‘QGparams’ to compute additive genetic variance and thus narrow-sense heritability (h2) on the observed data scale. We implemented the ‘QGparams’ and ‘QGicc’ functions with parameters model = ‘binomN.logit’ and n.obs = 32 (the average number of scans per subject per month in our dataset) for the binomial model and model = ‘Poisson.log’ for the Poisson model. The choice of value for n.obs is somewhat arbitrary, and we show the consequences for changes in values of this parameter (i.e., higher estimates with increasing values of n.obs) in SI Fig. 4.Closed form solutions in QGglmm are not available for integrating over posterior distributions generated from binomial models with logit link functions (de Villemereuil 2016). Consequently, using the ‘QGicc’ function is particularly slow. We therefore estimate ICCs from our binomial models using a random subset of the posterior (n = 1000 iterations).The code used for transforming the MCMCglmm outputs from the latent scale to the original data scale are available online (see DATA AVAILABILITY).Repeatability and the proportion of variance explained by variance componentsTotal phenotypic variance (VP) was the sum of estimates from all variance components and residual variance in a model (VP = VID + VID:Year + VM + VM:GroupAlpha:Year + VA + VGroupAlpha + VGroupAlpha:Year + VMonth:Year + VYear + Vresidual). The proportion of variance explained by each variance component was calculated by including its estimate in the numerator while including total phenotypic variance in the denominator. So, for example the proportion of variance explained by year of data collection was calculated as (left( {frac{{V_{Year}}}{{V_P}}} right)).Long-term repeatability was calculated with the sum of VID, VM, and VA in the numerator. Short-term repeatability was calculated similarly but with inclusion of within-series variances (VID + VM + VA + VID:Year + VM:GroupAlpha:Year) in the numerator to capture additional consistency in among-individual differences resulting from greater environmental similarity within a time series (i.e., year).We report posterior modes and 95% Highest Posterior Density intervals (i.e., 95HPDI in square brackets). Unless mentioned otherwise, we present results on the unobserved latent scale, and without the variance from the fixed effects (VFE) incorporated into VP. For completeness, estimates with VFE included in VP and transformations to the observed data scale are also provided in SI Table 3. More

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