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Cone characteristics: the entire set and individual variantsCones used in all the test variants did not differ from each other in terms of height (coefficient of variance in the Student t-test–F = 1.33 at p = 0.23), diameter (F = 1.77 at p = 0.08), or initial weight (F = 0.86 at p = 0.55). Analysis of variance revealed a significant difference for cone humidity (F = 2.52 at p ˂ 0.05).Cone parameters such as height, diameter and initial weight are factors that can determine the course of the extraction process. Therefore, the relationship between diameter and height for all cones used in the study was described using a linear regression equation ((y=0.2794x+8.3195)), which means that cone diameter increased by 0.28 mm per 1 mm of cone height, ((R=0.778 >0.104-{R}_{kr})).The initial weight of cones may be associated with their harvest time or storage conditions. A linear regression equation was also used to describe the relationship between the height and initial weight of the examined cones (y = 0.238x–3.918), which means that initial weight increased on average by 0.238 g per 1 mm of height, (R = 0.795  > 0.104).Table 2 shows means with standard deviations, the minimum and maximum values of the measured parameters, the range of variance, the coefficient of variation and the standard error for the entire set of studied cones and seeds. The Shapiro–Wilk test showed that the examined characteristics had a normal distribution.Table 2 Cone and seed parameters for the entire study set.Full size tableThe cones used in the study had a height of 21.4–44.1 mm and a diameter of 12.5–24.3 mm. The mean height of a cone was 33.8 (± 3.4) mm and the mean diameter was 17.8 (± 1.6) mm. The initial weight of cones ranged from 2.137 to 9.111 g, with a mean of 4.144 (± 1.019) g. The initial moisture content of cones was from 27.6 to 57.1%, with a mean of 40.4 (± 4.5)%. Analysis was performed for individual extraction variants. The mean values of cone height h, diameter d, initial weight m01, and moisture content W were calculated (Table 3).Table 3 Mean parameter values and standard deviations for the nine process variants.Full size tableThe HSD Tukey test revealed one homogeneous group for cone height encompassing all variants and two homogeneous groups for diameter. The first group consisted of all variants except 7, and the second group included all variants except 2. One homogeneous group was obtained for initial weight. Two homogeneous groups were found for moisture content, one consisting of all variants except 7, and the other one containing variants 1, 4, 5, 6, 7, 8, and 9.Seed extraction results for the studied stepsSeed extraction conditions and timeThe change in cone weight in each step of the extraction process depended on its duration, temperature and humidity conditions in the extraction cabinet, as well as on the initial moisture content of the cones.Humidity inside the drying chamber decreased to an average of 30% after 2 h of the process in each step as a result of increasing temperature. Over the subsequent 4 h of the process, after increasing the temperature, the humidity inside the chamber declined significantly, and then (over 2 and 4 h) it decreased further only slightly, stabilizing at approx. 5% for the 10 h variants, 6% for the 8 h variants, and 8% for 6 h variants on average.Moisture content changes in cones during the seed extraction processThe initial moisture content (u01) of the studied cones was much greater than 0.20 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), which means that special care must be taken during seed extraction, which should be conducted at a temperature of up to 50 °C8.The relatively high moisture content of the cones could be attributed to the absence of preliminary drying in airy storage places prior to seed extraction (which is typically the case in commercial practice) and the early date of cone harvest, at the beginning of the extraction season. The initial (u0x) and final (ukx) moisture content of cones used in each process variant is given with standard deviation in Table 4.Table 4 Initial and final moisture content of cones used in each process variant.Full size tableThe initial moisture content of cones (u0x) in most variants increased with each extraction step due to immersion. In most variants, the final moisture content (ukx) was the highest in the first extraction step and decreased or remained at the same level with each subsequent step.The mean initial moisture content for the three process variants with 10 h of drying was 0.411 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 10 h of drying, the mean moisture content decreased to 0.130 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). The mean initial moisture content in the fifth extraction step was 0.437 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.071 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 10 h reached on average 7% moisture content after extraction steps 4 and 5.The mean initial moisture content for the three process variants with 8 h of drying was 0.412 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 8 h of drying, the mean moisture content decreased to 0.128 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . The mean initial moisture content in the fifth extraction step was 0.440 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.064 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 8 h reached on average 7.1% moisture content after extraction step IV and 6.4% after step V.The mean initial moisture content for the three process variants with 6 h of drying was 0.389 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 6 h of drying, the mean moisture content decreased to 0.129 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . The mean initial moisture content in the fifth extraction step was 0.415 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.084 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 6 h reached on average 8.9% moisture content after extraction step IV and 8.4% moisture content after step V, which means that their final moisture content was higher than that of cones dried for 8 h and 10 h.The cones with the longest immersion time (15 min) were characterized by the highest initial moisture content in each extraction step as compared to the other two variants (immersion of 5 min and 10 min) with the same drying time. The final moisture content in a given extraction step differed between cones with different immersion times. Cones with an immersion time of 15 min were characterized by the highest final moisture content in individual extraction steps, and those with 5 min immersion revealed the lowest final moisture content.The Tukey HSD test revealed homogeneous groups in terms of initial moisture content (u01, u02, u03, u04, u05) and final moisture content (uk1, uk2, uk3, uk4, uk5) in each step, as shown in Table 4. For instance, four homogeneous groups were found for the final moisture content after extraction step V (uk5): the first one consisted of all variants except for 7, 8, and 9, the second one included variants 1, 2, 3, and 7, the third one comprised of variants 7 and 8, while the fourth one was constituted by variant 9 alone.Using Eq. (1), changes in moisture content were described for each of the tested cones over all five steps of each variant. The equation included the initial and final values of moisture content and the b coefficient for individual cones. The average values of the b coefficient and standard deviations for each extraction step are presented in Table 5 for individual extraction variants.Table 5 Mean values of the b coefficient and standard deviations for the five steps of the studied process variants.Full size tableThe lowest value of the b coefficient was recorded for the first step of the 10h_15min variant (b1 = 0.34), while the highest value was obtained for the fifth step of the 8 h_15 min variant (b5 = 0.60). In the process variants involving 10 and 8 h of drying , the b coefficient increased with each extraction step until the third one; in the fourth step it slightly decreased and in the fifth step it remained constant. In the variants with 6 h of drying the b coefficient almost peaked in the second extraction step and remained at a similar level until the fifth step. In the first steps of the variants with 6 h of drying, the mean value of the b coefficient was 0.54 and did not differ significantly from the coefficients obtained during the other steps. It was noted that in the 8 h_15 min variant, the b coefficients increased over successive steps.Figures 2–3 show examples of curves of actual and model changes in moisture content and the rate of extraction for sample cones, one each for variants 10 h_15 min and 8 h_15 min.Figure 2Diagrams: (a) actual and predicted changes in cone moisture content, (b) extraction rate in five extraction steps for larch cone no. 32 in the 10 h_15 min variant throughout effective extraction.Full size imageFigure 3Diagrams: (a) actual and predicted changes in cone moisture content, (b) extraction rate in five extraction steps for larch cone no. 17 in the 8 h_15 min variant throughout effective extraction.Full size imageEquations for changes in moisture content and extraction rate in consecutive extraction steps are given below for the graphically for the cone shown in Fig. 2 (no. 32 in the 10 h_15 min variant):Step I: ({u}_{1}=0.264cdot {mathrm{e }}^{left(-0.38 cdot {tau }_{i}right)}+0.107) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.100cdot {mathrm{e }}^{(-0.38 cdot {tau }_{i})})Step II: ({u}_{2}=0.372cdot {mathrm{e }}^{left(-0.44 cdot {tau }_{i}right)}+0.095) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.164cdot {mathrm{e }}^{(-0.44 cdot {tau }_{i})})Step III: ({u}_{3}=0.397cdot {mathrm{e }}^{left(-0.49 cdot {tau }_{i}right)}+0.086) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.195cdot {mathrm{e }}^{(-0.49 cdot {tau }_{i})})Step IV: ({u}_{4}=0.536cdot {mathrm{e }}^{left(-0.44 cdot {tau }_{i}right)}+0.080) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.236cdot {mathrm{e }}^{(-0.44 cdot {tau }_{i})})Step V: ({u}_{5}=0.485cdot {mathrm{e }}^{left(-0.46 cdot {tau }_{i}right)}+0.076) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.223cdot {mathrm{e }}^{(-0.46 cdot {tau }_{i})})Equations for changes (Fig. 3) in moisture content and extraction rate in consecutive extraction steps are also given for this cone (no. 17 in the 8 h_15 min variant):Step I: ({u}_{1}=0.304cdot {mathrm{e }}^{left(-0.53 cdot {tau }_{i}right)}+0.113) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.161cdot {mathrm{e }}^{(-0.53 cdot {tau }_{i})})Step II: ({u}_{2}=0.292cdot {mathrm{e }}^{left(-0.55 cdot {tau }_{i}right)}+0.085) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.161cdot {mathrm{e }}^{(-0.55 cdot {tau }_{i})})Step III: ({u}_{3}=0.369cdot {mathrm{e }}^{left(-0.70 cdot {tau }_{i}right)}+0.077) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.258cdot {mathrm{e }}^{(-0.70 cdot {tau }_{i})})Step IV: ({u}_{4}=0.379cdot {mathrm{e }}^{left(-0.71 cdot {tau }_{i}right)}+0.059) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.269cdot {mathrm{e }}^{(-0.71 cdot {tau }_{i})})Step V: ({u}_{5}=0.428cdot {mathrm{e }}^{left(-0.77 cdot {tau }_{i}right)}+0.060) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.330cdot {mathrm{e }}^{(-0.77 cdot {tau }_{i})})Finally, equations for changes in moisture content and extraction rate in consecutive extraction steps are given for cone no. 5 in the 6 h_15 min variant:Step I: ({u}_{1}=0.308cdot {mathrm{e }}^{left(-0.58 cdot {tau }_{i}right)}+0.0904) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.179cdot {mathrm{e }}^{(-0.58 cdot {tau }_{i})})Step II: ({u}_{2}=0.346cdot {mathrm{e }}^{left(-0.63 cdot {tau }_{i}right)}+0.1070) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.218cdot {mathrm{e }}^{(-0.63 cdot {tau }_{i})})Step III: ({u}_{3}=0.368cdot {mathrm{e }}^{left(-0.63 cdot {tau }_{i}right)}+0.0837) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.232cdot {mathrm{e }}^{(-0.63 cdot {tau }_{i})})Step IV: ({u}_{4}=0.387cdot {mathrm{e }}^{left(-0.68 cdot {tau }_{i}right)}+0.0838) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.263cdot {mathrm{e }}^{(-0.68 cdot {tau }_{i})})Step V: ({u}_{5}=0.396cdot {mathrm{e }}^{left(-0.65 cdot {tau }_{i}right)}+0.0743) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.257cdot {mathrm{e }}^{(-0.65 cdot {tau }_{i})})Figures 2a, 3a show the curves of actual changes in the moisture content of three sample cones subjected to different drying times (10 and 8 h) but the same immersion time (15 min); the curves were fitted to a model which is widely used in descriptions of drying at constant temperature (mostly for vegetables). The present study used variable temperature, which may have influenced the fit of the model, in addition to the input variables (drying and immersion times). The best fit was found for the cone subjected to the variant with 8 h of drying (Fig. 3), with a slight deviation in the first three extraction steps, and with a very good fit in the fourth and fifth steps. The lowest fit was found for the cone subjected to 6 h drying, which may be caused by insufficient drying time (the cone was exposed to 35 °C for 2 h, and to 50 °C for only 4 h).Figures 2b, 3b show diagrams for cone extraction rates at different drying times (10 h and 8 h) at the same immersion times (15 min). As can be seen, extraction rates decreased in the very beginning, which is characteristic of the so-called second period of solid drying (Pabis44).Seed extraction dynamicsTable 2 presents data on the number of scales and seeds for the studied cones. There were from 33 to 70 open scales per cone, with an average of 48 (± 6). From 1 to 76 seeds were extracted per cone, with an average of 36 (± 18). Finally, each cone contained from 5 to 97 seeds, with an average of 52 (± 19). The weight of the extracted seeds ranged from 0.001 g to 0.651 g, on average 0.193 (± 0.109) g.Cones obtained from different process variants did not differ in terms of the number of seeds extracted (F = 0.862 at p = 0.55) or their weight (F = 0.720 at p = 0.674). However, ANOVA did reveal significant differences in the number of scales (F = 3.561 at p ˂0.05) and the total number of seeds per cone (F = 2.93601 at p = 0.003645). Table 6 gives mean scale and seed numbers per larch cone (with standard deviations) for the various extraction variants and homogeneous groups.Table 6 Mean numbers of cone scales and seeds for each process variant.Full size tableOn average, 70% of the seeds were extracted from cones used in all nine study variants, with 30% of the seeds remaining in the cones. Table 7 shows the number of seeds extracted in individual variants and the number of seeds remaining in the cones, expressed as a percentage.Table 7 Number of seeds extracted from and remaining in the cones for each process variant.Full size tableThe greatest number of seeds was obtained in process variants 2–73%, closely followed by variants 3, 1, and 7 (72%), and 8 (70%). The lowest seed yield was obtained from variant 4 (65%).In all study variants, some of the seeds were obtained in the process of extraction in the chamber and some in the process of shaking in the drum (Table 7). The highest number of seeds in the chamber was obtained in variant 2 (69%), and the lowest in variant 9 (56%). On average, the largest quantity of seeds was obtained in the chamber in the 10 h variants, and the lowest quantity in the 6 h variants. Comparing different process variants of the same drying duration, the greatest number of seeds in the chamber were obtained in variants 2, 5, and 7 (and also in variant 8—only 1% fewer). The greatest quantity of seeds extracted by shaking in the drum was obtained in variant 9 (44%), and the lowest in variant 2 (31%). On average, 38% of seeds extracted in all variants were obtained by shaking in the drum.It can be seen that in each of the variants and their individual steps, the highest number of seeds was obtained after 6 h of the process. Figure 4a–c shows the percentage of seeds obtained during the effective extraction time, where the number of seeds extracted at a given step was added cumulatively to those from the previous steps.Figure 4Percentage seed yield dynamics for each step of a five-step extraction process: (a) 10 h of drying, (b) 8 h of drying, (c) 6 h of drying.Full size imageThe diagrams in Fig. 4 show the percentage of seeds obtained throughout the entire process. Each step consists of drying, shaking, immersion, and soaking, except for step V, which involved only drying and shaking without immersion or soaking. Analysis of seed yield over 10 h of drying (Fig. 4a) shows that on average 37% of all extracted seeds were obtained in the first step, 26% in the second step, approx. 20% in the third step, 11% in the fourth step, and about 6% in the fifth step.As regards the 8 h process (Fig. 4b), on average 30% of all extracted seeds were obtained in the in the first extraction step in the 8 h_5 min and 8 h_15 min variants, and as much as 53% in the 8 h_10 min variant. An average of 27% of all seeds were extracted in the second step, 15% in the third step, about 11% in the fourth step, and approx. 5% in the fifth step. The 8 h_10 min variant was characterized by the highest seed yield, beginning in the first step of the process (as compared to the 8 h_5 min and 8 h_15 min variants).As far as the variant with 6 h of drying is concerned (Fig. 4c), on average approx. 46% of all extracted seeds were obtained in the first step, 24% in the second step, 15% in the third step, approx. 11% in the fourth step, about 4% in the fifth step.When extracting seeds from larch cones, scale deflection and the number of obtained seeds are not assessed during the process, as is the case with pine and spruce cones due to the difficulties caused by the aforementioned morphology of larch cones (Tyszkiewicz, 1949). The presented diagrams show that a satisfactory seed yield (60%) was obtained in variants with 8 and 6 h of drying already after 10 h of effective extraction time.The seed yield coefficient, α (3), and the cone mass yield coefficient, β (4), for each extraction variant are presented in Table 8.Table 8 Seed yield coefficient and cone mass yield coefficient for each process variants.Full size tableThe seed yield coefficient was the highest for variants 2 (0.73) and 3 (0.72), and the lowest for variants 4 (0.65) and 6 and 9 (0.67). The cone mass yield coefficient was the highest for variant 5, and the lowest for variant 9.Seed viabilityTable 9 presents germination energy (E) and capacity (Z) for the control seeds as well as for seeds obtained from the various steps of the nine process variants, as well as their corresponding quality classes.Table 9 Germination energy and capacity for the control seeds as well as for seeds obtained from the various extraction process variants.Full size tableGermination energy and capacity for the control sample were 45% and 57%, respectively, meaning that naturally released seeds, not subjected to any thermal or mechanical treatments, were classified in quality class I18. Importantly, seeds obtained from all the studied process variants were also placed in the same class; their germination energy ranged from 30 to 59%, and their germination capacity from 35 to 61%. When analyzing each extraction step separately, no correlation was found between decreasing germination energy and successive steps. However, the average germination energy was 46% for seeds obtained in the first extraction step of all nine variants, 45% for those from the second and third steps, 41% for seeds from the fourth step and 40% for those from the fifth one. Thus, in each subsequent step the average germination energy of seeds was equal or lower than in the previous step, which is consistent with literature reports that prolonged drying may reduce the quality of seeds8. This is also corroborated by the fact that the highest germination energy and capacity was revealed by seeds from variants with 6 h of drying while the lowest germination indicators characterized seeds from the 10 h variants. Furthermore, seeds from variant 1 exhibited the lowest germination energy and capacity and seeds from variant 8–the highest.Another reason for the higher quality of seeds from variants with 6 h of drying may be the lower initial moisture content of the cones due to the longer time they were kept at room temperature immediately before the test (u01 = 0.391 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{d.mathrm{w}.}^{-1}) as compared to u01 = 0.411 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{d.mathrm{w}.}^{-1}) for seeds from variants with 8 and 10 h of drying). These results are in line with the study of Tyszkiewicz8, who noted that under the same temperature and humidity conditions, the quality of seeds from cones with a lower moisture content did not deteriorate, in contrast to the quality of seeds obtained from cones with a higher moisture content.The germination capacity of seeds calculated from the mean capacity of seeds obtained from the same extraction steps of all process variants was similar at 45% for each of the steps.In summary, in the study the authors investigated a five-step process of extracting seeds from larch cones involving immersion and heat treatment to maximize seed yield. It was found that the two-step process widely used in extractories is insufficient, while a four-step process does not lead to a significantly higher number of obtained seeds. Thus, a three-step process appears to be optimal. More

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Systematic reviewWe searched relevant publications through Web of Science (all databases), Google Scholar, and the China National Knowledge Infrastructure Database between 1945 and March 2021 with the following combinations of keywords: (drain* OR lower* water table OR standing water depth OR ground water table level drawdown OR decline OR drought OR dry*) with (peatland* OR mire* OR fen OR bog OR swamp OR marsh*) with (soil respiration OR heterotrophic respiration OR microbial respiration OR soil CO2 OR soil carbon decompos* OR soil carbon minerali* or peat subsidence). Using these search terms, we initially identified 2120 different publications. To reliably evaluate WT decline impacts on SR and peat subsidence-associated soil CO2 emissions, the following further criteria were applied:1) Only paired studies with pristine peatland (i.e., undrained, near-natural peatland without direct drainage history) as a control and pristine peatland with direct WT decline (due to drainage and land use or climate-induced drying) as a treatment were included by carefully checking the descriptions of field conditions from the publications. For the pristine peatlands, we included the peatland only if the peat soil had at least 30% dry organic matter, a peat depth of >40 cm1, and did not have any direct drainage history2. We acknowledge that few, if any, untouched and completely pristine peatlands currently exist, particularly in Europe.2) WT decline in peatlands referred to only the WT depth lowered by drainage or climate-induced drying and/or additional management practices related to C or N input (e.g., manure/N fertilizers); treatments in which WT decline was combined with manipulated warming, elevated CO2, N deposition, etc., were excluded, while individual treatments (i.e., peatlands affected by WT decline without additional warming, elevated CO2, N deposition treatments, etc.) were included, as the primary objective of this study was to evaluate the responses of peatland C decomposition to WT decline.3) Each individual study included SR or at least one of its components (HR and AR), and the measurement intervals were at least monthly. The in situ measurements of SR or its components (HR and AR) covered at least the growing or nongrowing season in temperate/boreal climate zones and the whole wet or dry season in (sub)tropical climate zones.4) Both in situ and soil core/microcosm/mesocosm measurements of SR or its components (HR and AR) were included. SR and its components were exclusively measured using the chamber method. The results of the latter group were used to test the results of the former.Finally, 386 paired in situ and 21 paired soil core incubation measurements of SR or its components (HR and AR) were extracted from 63 in situ studies and 9 soil core studies, respectively (see Supplementary Data A). Furthermore, to estimate HR emissions from global drained peatlands, the in situ measured paired peat subsidence rate (Rps, cm yr–1) and drainage duration (i.e., years since first drainage) and the proportion of peat subsidence rate attributed to oxidation (Po, %) and drainage duration, as well as the soil (0–30 cm) organic C and bulk density in pristine peatlands, were extracted from peer-reviewed publications. In drained boreal and temperate peatlands, most studies measured the total subsidence (in meter) during a certain drainage period, therefore the average Rps was calculated as the ratio of total subsidence and drainage years. It was assumed that the Rps was faster at the beginning and lower at the end of drainage duration, so the average subsidence rate is the rate for the middle year of the drainage duration41. The remaining studies directly showed the in situ measured Rps at the ith year of drainage. A similar procedure was applied for the Po in the ith year of drainage. In sum, 230 paired Rps–drainage duration observations and 49 paired Po–drainage duration observations, as well as 76 SOC and 63 BD in pristine peatlands, were taken from 80, 25, 58, and 44 studies, respectively (see Supplementary Data B).Data compilationTo systematically evaluate the impacts of WT decline on SR in pristine peatlands and clarify the underlying mechanisms, we obtained data related to SR and its components (HR and AR) together with environmental variables such as the mean annual temperature [MAT], mean annual precipitation [MAP], peat depth [PD], WT depth [WTD], soil water content [SWC], soil temperature [Ts], soil redox potential [Eh], soil air oxygen level [O2], soil bulk density [BD], soil pH [pH], soil organic carbon [SOC], soil total nitrogen [TN], soil total phosphorus [TP], soil ammonium [({{{{rm{NH}}}}}_{4}^{+})], soil nitrate [({{{{rm{NO}}}}}_{3}^{-})], soil dissolved organic carbon [DOC], microbial biomass carbon [MBC], microbial biomass nitrogen [MBN], dissolved total phosphorus [DTP], belowground biomass [BGB], iron [Fe3+, Fe2+] and sulfate [({{{{rm{SO}}}}}_{4}^{2-})] when possible. If available, other important information, such as geographic location (latitude, longitude), climate and WT decline driver and duration, intensity, peatland type, Rps, Po, nutrient type, inundated condition, microtopography, and plant functional types, was recorded. For WT decline intensity, net WT declines greater and less than 30 cm were defined as deep and shallow declines, respectively, according to the IPCC wetland report42. The abovementioned information about pristine peatlands and peatlands affected by WT decline is compiled in Supplementary Data A and B.We subsequently extracted the mean ((bar{X})), standard deviation (SD) and replicates (n) from different publications. If studies reported standard error (SE) rather than SD, then SD was calculated by SE (sqrt{n}). If studies reported only the median, maximum, minimum, and 25th and 75th percentiles, then the mean and SD were estimated following the mathematical equations recommended by ref. 60. If neither SD nor SE was reported, then the missing SD was estimated by multiplying the reported mean by the average coefficient of variation (CV) obtained from the remaining observations, resulting in both the mean and SD being reported61. The data were either obtained directly from tables and texts or extracted by digitizing graphs using Getdata Graph Digitizer software (version 2.26, Russia).The final database consisted of 250 paired SR, 101 paired HR and 35 paired AR in situ observations. Only 35 paired observations simultaneously reported SR, HR, and AR. Twenty-one paired SR soil core incubation measurements were also collected to test the results of the in situ measurements. The dataset mainly originated from Europe, North America, and Southeast Asia, and most studies ( >70%) were conducted in temperate and boreal peatlands in the Northern Hemisphere (Fig. 1a). Moreover, 230 paired Rps–drainage duration observations and 49 paired Po–drainage duration observations (Fig. 5a, b) and an additional 485 drainage year (Supplementary Fig. 9) observations classified by climate zone (i.e., boreal, temperate and tropical) and land use (i.e., agriculture, forestry, and grassland) were collected. A total of 76 SOC and 63 BD measurements from pristine peatlands categorized by climate zone (i.e., boreal, temperate, and tropical) were extracted to estimate Rps by oxidation and associated soil HR from global pristine peatlands due to drainage activities (Supplementary Fig. 10 and Supplementary Data B). In this study, we were unable to estimate climate drying-induced net CO2 emissions through soil HR, as the areas of pristine peatlands affected by climate drying currently remain unknown.Meta-analysisTo assess the relative changes in SR and its components (HR and AR), as well as environmental variables (e.g., SOC, BD, Ts, etc.) due to WT decline, the log-transformed response ratio (RR) was used:62$${{{mathrm{ln}}}}({{{rm{RR}}}})=,{{{mathrm{ln}}}}({X}_{{{{rm{t}}}}}/{X}_{{{{rm{c}}}}})$$
(1)
The results are presented as the percent change ((elnRR  – 1) × 100). The variance (v) of RR was estimated using the following equation:$$v=frac{{{{{rm{SD}}}}}_{{{{rm{t}}}}}^{2}}{{n}_{{{{rm{t}}}}},{X}_{{{{rm{t}}}}}^{2}}+frac{{{{{rm{SD}}}}}_{{{{rm{c}}}}}^{2}}{{n}_{{{{rm{c}}}}},{X}_{{{{rm{c}}}}}^{2}}$$
(2)
where Xt and Xc indicate the means of the treatment and control, SDt and SDc indicate the SDs of the treatment and control and nt and nc indicate the numbers of replicates in the treatment and control, respectively.However, in our study, approximately 60% of the WTD and Eh observations for the peatlands in pristine condition (control) and affected by WT decline (treatment) showed opposite signs; e.g., the pristine peatlands generally exhibited positive WTDs (higher than the peat surface) and negative Eh values, while those affected by WT decline exhibited negative WTDs (lower than the peat surface) and positive Eh values. Since it is impossible to calculate the logarithm of negative values, we introduced a new study index (net changes) for these two variables in our meta-analysis according to ref. 63:$$D={X}_{{{{rm{t}}}}}-{X}_{{{{rm{c}}}}}$$
(3)
where Xt and Xc indicate the paired annual mean WTD and Eh for the treatment and control, respectively, and D indicates the difference between the treatment and control.The SD and variance (v) of D were estimated using the following equation:$${{{rm{SD}}}}=sqrt{frac{({n}_{{{{rm{c}}}}}-1);{{{{rm{SD}}}}}_{{{{rm{c}}}}}^{2}+({n}_{{{{rm{t}}}}}-1);{{{{rm{SD}}}}}_{{{{rm{t}}}}}^{2}}{{n}_{{{{rm{c}}}}}+{n}_{{{{rm{t}}}}}-2}}$$
(4)
$$v=frac{{{{{rm{SD}}}}}_{{{{rm{t}}}}}^{2}}{{n}_{{{{rm{t}}}}}}+frac{{{{{rm{SD}}}}}_{{{{rm{c}}}}}^{2}}{{n}_{{{{rm{c}}}}}}$$
(5)
where SDt and SDc indicate the SD of the treatment and control and nt and nc indicate the number of replicates for the treatment and control, respectively.The weighted mean RR or D was calculated by individual RR or D with bias-corrected 95% confidence intervals (CIs) using the rma.mv function in the metafor package in R software (R core team, 2019)64, in which the variable “study” was regarded as a random effect to account for the dependence of observations derived from the same study. The impact of WT decline on a response variable was considered significant if the 95% CI did not overlap 065. Differences between subgroups (e.g., WT decline driver, climate zone, drainage duration) were considered significant if the 95% CIs did not overlap each other65. The frequency distribution of RR was calculated to test variability among individual studies using the Gaussian function (i.e., normal distribution)66.Estimation of peat subsidence rate by oxidation and associated HR rateDrainage has induced widespread peat subsidence and associated large CO2 release through soil HR and consequently reduced the sustainable utilization of drained peatlands and contributed to global warming11,12. In this study, we estimated the spatial patterns of Rps by oxidation and associated soil HR from global drained peatlands. Using the 230 paired Rps and drainage duration observations synthesized in this study, we first constructed empirical models between Rps and drainage duration for drained peatlands categorized by climate zone (boreal, temperate and tropical climate) and land use (i.e., agriculture, forestry and grassland) (Fig. 5a, b). The values of Rps for certain groups classified by climate zone and land use could be estimated by using the corresponding empirical models established in this study and reported drainage durations that were extracted from the literature. The empirical models categorized by climate zone and land use are listed below (Fig. 5a, b):$${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{Agr}}}}=13.95,{{{{rm{Dur}}}}}^{-0.58},,n=48,,{R}_{{{{rm{adj}}}}.}^{2}=0.85,,p; < ; 0.0001$$ (6) $${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{For}}}}=5.36,{{{{rm{Dur}}}}}^{-0.83},,n=21,,{R}_{{{{rm{adj}}}}.}^{2}=0.92,,p; < ; 0.0001$$ (7) $${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{Gra}}}}=5.55,{{{{rm{Dur}}}}}^{-0.36},,n=40,,{R}_{{{{rm{adj}}}}.}^{2}=0.61,,p; < ; 0.0001$$ (8) $${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Tro}}}}{mbox{-}}{{{rm{Agr}}}}{mbox{-}}{{{rm{For}}}}{mbox{-}}{{{rm{Gra}}}}=6.63,{{{{rm{Dur}}}}}^{-0.37},,n=121,,{R}_{{{{rm{adj}}}}.}^{2}=0.55,,p; < ; 0.0001$$ (9) where Rps indicates the peat subsidence rate (cm yr–1), Dur is the drainage duration, and the numbers indicate coefficients for the established empirical models. Bor, Tem, and Tro indicate boreal, temperate, and tropical climate zones, respectively. Agr, For, and Gra represent agriculture, forestry, and grassland land uses, respectively. We note that it was not possible to further distinguish these models between boreal and temperate climate zones and among agriculture, forestry, or grassland land use in tropical climates, as there is currently a lack of sufficient measurements, which warrants more research.However, the Rps is triggered by a combination of processes such as physical compaction by heavy equipment or livestock trampling and shrinkage through contraction of organic fibers when drying, consolidation by loss of water from pores in the peat and oxidation owing to the breakdown of peat organic matter10,11,12. Therefore, to reliably estimate the soil HR rate from Rps due to oxidation, the proportion of Rps attributed to oxidation (Po, in %) should be considered12. Using the 49 paired Po and drainage duration observations synthesized in this study, we then constructed empirical models between Po and drainage duration for drained peatlands that were also categorized by climate zone (boreal, temperate, and tropical climate) and land use (agriculture, forestry, and grassland) (Fig. 5c, d). Similarly, the Po values of certain groups classified by climate zone and land use could be estimated by using the corresponding empirical models established in this study and reported drainage durations that were extracted from the literature. The empirical models categorized by climate zone and land use are shown below (Fig. 5c, d):$$ {P}_{{{{rm{o}}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Agr}}}}{mbox{-}}{{{rm{For}}}}{mbox{-}}{{{rm{Gra}}}}=12.05,{{{mathrm{Ln}}}}({{{rm{Dur}}}})+2.15,,n=30,\ {R}_{{{{rm{adj}}}}.}^{2}=0.89,,p; < ;0.0001$$ (10) $$ {P}_{{{{rm{o}}}}}{mbox{-}}{{{rm{Tro}}}}{mbox{-}}{{{rm{Agr}}}}{mbox{-}}{{{rm{For}}}}{mbox{-}}{{{rm{Gra}}}}=14.36,{{{mathrm{Ln}}}}({{{rm{Dur}}}})+37.05,,n=19,\ {R}_{{{{rm{adj}}}}.}^{2}=0.81,,p; < ;0.0001$$ (11) where Po indicates the proportion of Rps attributable to oxidation, Dur is the drainage duration, and the numbers indicate coefficients for the established empirical models. The abbreviations Bor, Tem, Tro, Agr, For, and Gra have been described previously. We note that the different land uses shared the same models across temperate and boreal climates and tropical climate due to a lack of sufficient global observations. This will also induce some uncertainties in our analysis.Furthermore, the soil HR (FHR, Mt C yr−1) due to peat oxidation induced by drainage was estimated using the following equation according to ref. 11:$${F}_{{{{rm{HR}}}}}=sum {R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i}times {A}_{i,j}$$ (12) where SOC (g kg–1) and BD (g cm–3) indicate the soil (0–30 cm) organic C concentration and bulk density of pristine peatlands, respectively; A (×103 km2) indicates the drained peatland area; i indicates the climate zone (boreal, temperate or tropical); j indicates the land use (agriculture, forestry or grassland); and Rps (cm yr–1) and Po (%) are described in Eqs. (6–11). Datasets of the SOC concentration and BD and Rps due to oxidation were systematically reviewed and bootstrapped and categorized by climate zones and land uses (see Supplementary Fig. 10 and Supplementary Data B). Regarding the large uncertainties for areas of drained peatlands, we combined two previously published datasets (72, 61, 22, 37, 43, 26, 94, 109, and 39 × 103 km2 by ref. 18, and 37, 55, 4, 109, 63, 58, 96, 72, and 1 × 103 km2 by ref. 20. for agriculture-, forestry- and grassland-drained peatlands in boreal, temperate and tropical climate zones, respectively) and obtained their mean values with 95% CIs (for details, see bootstrapping procedure in Data analysis). Uncertainties (i.e., 95% CI) in total HR (δFHR) were propagated according to the Gaussian random error propagation principle as follows:$${{{rm{delta }}}}{F}_{{{{rm{HR}}}}}=sqrt{sum sqrt{begin{array}{c}{(delta {R}_{{{{rm{ps}}}},i,j})}^{2}times {({P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {P}_{{{{rm{o}}}},i,j})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {{{{rm{SOC}}}}}_{i})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{BD}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {{{{rm{BD}}}}}_{i})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {A}_{i,j})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i})}^{2}end{array}}}$$ (13) where δFHR, δRps, δPo, δSOC, δBD, and δA indicate the 95% CIs of total soil HR, Rps, Po, SOC, and BD and drained peatland area, respectively, and i and j indicate the climate zone (boreal, temperate, tropical) and land use (agriculture, forestry, or grassland), respectively.To further estimate the total SR (FSR, Mt C yr−1) and its uncertainty (δFSR) from global drained peatlands, the following equations were used:$${F}_{{{{rm{SR}}}}}=sum frac{{F}_{{{{rm{HR}}}},i,j}}{{C}_{{{{rm{HR}}}},i,j}}$$ (14) $$delta {F}_{{{{rm{SR}}}}}=sqrt{sum sqrt{{(frac{1}{{C}_{{{{rm{HR}}}},i,j}})}^{2}times delta {F}_{{{{rm{HR}}}},i,j}^{2}+{(-frac{{F}_{{{{rm{HR}}}},i,j}}{{C}_{{{{rm{HR}}}},i,j}^{2}})}^{2}times delta {C}_{{{{rm{HR}}}},i,j}^{2}}}$$ (15) where CHR (%) indicates the mean relative contribution of HR to SR from simultaneously measured SR, HR, and AR from our meta-analysis (see Supplementary Fig. 11 and Supplementary Data A) and i and j indicate the climate zone (boreal, temperate, tropical) and land use (agriculture, forestry, or grassland), respectively. FHR and δFHR are given in Eqs. (12, 13). We note that the CHR could be classified only by climate zone, as there is a lack of sufficient measurements of land use; that is, the different land uses under the same climate shared the same CHR value, which may induce uncertainties in estimating the total SR from global drained peatlands.Regarding the abovementioned lack of sufficient measurements for distinguishing between boreal and temperate drained peatlands, we also used another method to estimate the annual total HR and SR from global drained peatlands. Specifically, we obtained the mean values of Rps by oxidation across boreal and temperate drained peatlands for each land use (i.e., climate zones were classified as boreal+temperate or tropical) (Supplementary Fig. 14 and Supplementary Table 1). The estimation process was the same as that previously described. The different estimation methods were likely to provide results with greater convergence.Data analysisSignificant differences in observed variables were tested by performing nonparametric analysis. Specifically, tests with two independent samples (i.e., Mann–Whitney U test) were used for only two variables (i.e., to compare the contribution of HR to SR between pristine and drained peatlands), and tests with two or more independent samples (i.e., Kruskal–Wallis test and pairwise comparisons) were used if there were three or more variables (i.e., SOC, BD and Rps due to oxidation in the boreal, temperate and tropical climate zones or different land uses). Linear or nonlinear regression analysis was performed to examine the relationships between the responses of SR and its components with environmental variables or the peat subsidence rate with drainage duration.To reliably estimate the uncertainties in Rps by oxidation, SOC, BD, drained peatland area, and relative contribution of HR to SR, bootstrap resampling with 10000 iterations was conducted using the boot package, and 95% CIs were calculated using the “basic” type. The ggplot 2 package in R software (R core team, 2019) was used for statistical analysis. Data were expressed as the means with their 95% CIs, and significance of the regression analyses was indicated at the level of p  More

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All animal care and experimental protocols complied with local and federal laws and guidelines and were approved by the appropriate governing body in Berlin, Germany, the Landesamt fur Gesundheit und Soziales (LaGeSo G-0224/20).Experimental breeding and designThe all-female Amazon molly (Poecilia formosa) is a naturally clonal, live-bearing fish species that gives birth to broods of genetically identical offspring. Like all unisexual vertebrates, Amazon mollies are the result of inter-specific hybridization44,45. As such, this ‘frozen hybrid’ has a heterozygous genome from its ancestral P. mexicana mother and P. latipinna father alleviating concerns about reduced genetic variation and the resulting inbreeding depression often associated with artificially selected isogenic animals. Additionally, despite their clonal nature, the Amazon’s genome shows no evidence of increased mutation accumulation, genomic decay or transposable element activity suggesting the genomes of these animals are evolving in similar ways as sexual species46. They reproduce through gynogenesis where the meiotic process is disrupted so that the eggs contain a full maternal genome. The egg must be fused with a sperm from one of their ancestral species to stimulate embryogenesis, but this paternal DNA is not incorporated into the egg. This provides the opportunity to control when reproduction occurs by controlling the females’ access to male sperm donors.We placed adult females, as potential mothers of experimental fish, in individual (5-gallon) breeding tanks with two Atlantic molly (P. mexicana) males for one week to act as sperm donors. Amazon mollies give birth to broods of generally ~8-30 individuals. A brood is born at once (i.e. all individuals are born within minutes of each other) and birth generally happens early in the day close to dawn. These parental fish were lab-bred and themselves sisters, so of the same age and lineage, and were kept at similar social densities and under standardized environmental conditions throughout their lives to further minimize potential variation in maternal experience. Each breeding tank contained an artificial plant as refuge and was checked frequently each day for the presence of offspring, especially during the morning hours when births are most likely. Newborn mollies were always found in the morning and then singly netted by trained animal caretakers, into individual experimental tanks where their behavior was automatically recorded for the next 70 days (see below). Moving the fish from the maternal tank to the experimental tanks was done in a standardized manner (i.e. individual fish were netted and placed into small dishes of water and then placed in the tracking tanks to limit exposure to the air) by the same caretakers to minimize variation in experience among individual fish. Altogether, eight mothers provided offspring that completed the entire 10-week experiment (Supplementary Table 1).Experimental tanks (27 x 27 cm), made of white Perspex, consisted of four equally sized compartments, and were evenly lit from below using 6500K-LEDs. Environmental conditions were highly standardized across tanks: all tanks were on the same 11:13 (L:D) light schedule, water depth was maintained at 10 cm depth, temperature was maintained at 25 ± 1 °C by a room air conditioning system, and fish received a standardized amount of powdered flake fish food (TetraMin™) twice daily. Opaque blinds surrounded the tanks to further limit outside disturbances. All experimental tanks were connected to the same filtration system where water could mix in the sump tank, allowing chemical cues to be shared across all experimental fish. Previous work has shown exposure to just chemical cues of conspecifics is sufficient in preventing the developmental of pathological behavior that could be associated with development in complete isolation14. We initially placed a total of 40 newborn individuals into the tracking tanks. At the end of the 10-week experiment, we were able to achieve complete tracking data on 26 individuals; camera malfunctions prevented data collection on four individuals, two individuals jumped into neighboring tanks causing the loss of data of all four individuals as we could not verify their identity; four newborn individuals escaped through holes in the water outlet of the tanks; and four individuals died as newborns. All results in the manuscript are on these 26 animals, though including data from all 40 (e.g. patterns of individual variation on the first day post birth) did not change the results or their interpretation (see Supplementary Table 2).Behavioral trackingWe developed a custom recording system using Raspberry Pi computers, which are an upcoming low-cost, highly adaptable solution for many applications in the biological sciences25. Specifically, we created a local network of Raspberry Pi 3B + ’s, each connected to a Raspberry Pi camera positioned exactly above an experimental tank, commanded by a lab computer, and connected to the server on the institute network (Supplementary Fig. 1). We programmed the Raspberry Pi’s using pirecorder26 to take timestamped photos every 3 s across the daily light period, each day, for 10 weeks, and store them automatically in dedicated, automatically named folders on the server. Image settings and resolution were thereby optimized to minimize file size while assuring image quality. After the experimental period, we created videos of all the recorded images of each fish of each day. These videos were subsequently tracked with the Biotracker software27, using background subtraction, providing the x, y coordinates of each fish in each frame. We then processed the data, including scaling and converting the coordinates to mm, and, for each frame, computed fish’s swimming speed (cm/s) and distance from the tank walls (cm). We then summarized these variables both on an hourly and daily basis to compute fish’s median swimming speed, inter-quartile range of swimming speeds, activity (proportion of time spent moving >0.5 cm/s), and median border distance. To quantify fish’s body size over time, we randomly selected five photos per week of each compartment, making sure the fish was away from the compartment walls and did not show strong body curvature, and then used ImageJ software to measure total body length (mm) from the tip of the snout to the end of the body. By averaging the measurements of the five images, we acquired one body size measurement per week.Error checkingWe collected up to 924,000 photos on each individual throughout the experimental period resulting in a total of over 24 million data points collected on our experimental animals (N = 26 individuals). To ensure that our tracking software accurately captured the behavior of our fish, we checked for potential tracking errors in two ways. First, we estimated overall error rates. To do this, we selected at random a starting frame from within a day; then we manually checked each of the subsequent 200 frames and identified whether an error was made (fish was not properly located by BioTracker) or not (fish was properly located) by visual inspection of the videos. We estimated the error rate as the number of errors divided by the total number of checked frames. The overall median error rate over the entire observation period was estimated to be 7%. Error rates increased earlier in the observation period when the fish were smaller (Supplementary Note I). As such, as a second step, we manually went through and corrected all frames for the very first day of tracking (i.e. day 1 post-birth) for all fish (~13,200 frames per individual) as this is a critical time period for one of our research questions. This ensured that the resulting behavioral data were completely accurate for this day. This manual correction allowed us the additional opportunity to compare how well our automatically tracked (i.e. not manually corrected) data performed compared to the manually corrected data. We found that the automatically tracked data re-created near identical estimates of among- and within-individual variance components and most importantly the among-individual correlation between the automatically tracked and manually corrected data was over 0.98 for our behavioral variables (Supplementary Note I). This strongly suggests that any errors introduced by our automated tracking software have minimal influence of our behavioral variables at best and do not affect our interpretation of the results.Statistical analysesWe used linear mixed, or hierarchical, models to partition the behavioral variation across different times periods into its among- and within-individual components. Throughout we focused our analysis on the 26 individuals for which we had complete data for the entire 10-week observation period to ensure comparable variation over time and across models.Our first question of interest was to test when individual differences in behavior first appeared over the course of the experiment. We started by investigating behavior on the first day post birth (Fig. 1A, Supplementary Table 2) and then planned to proceed in a day-by-day fashion until significant repeatability in behavior was apparent (Supplementary Table 3). We used hourly median swimming speed (11 observations for each of 26 individuals) as our response variable and included ‘hour’ and ‘total length (TL)’ as fixed effects and ‘individual’ was included as our random effect of interest. Including TL as a covariate allowed us to test whether behavior was related to an offspring’s body size on its first day of life. We set the first hour of the day as 0 and mean-centered TL as this would allow the among- (and within-) individual variance components to be estimated at these values (i.e. the earliest possible moment from when we could record behavior in the fish). We estimated the adjusted repeatability of median swimming speed as the variance attributable to individual identity over the total variance not explained by the fixed effects. We additionally estimated both marginal and conditional R-squared values which estimate the variance explained by the fixed effects only and the variance explained by the fixed and random effects combined, respectively. As our individual experimental fish came from different mothers, we first explored a number of different variance structures including random intercepts and slopes for both individual ID and maternal ID. This allowed us to test whether maternal identity explained variation in individual behavior. However, the most supported model included random intercepts and slopes for individual ID and not for mother ID, indicating that our methods to reduce variation among mothers were successful (Table 1). We used median swimming speed as our behavioral variable of interest throughout the main manuscript, as this behavior was tightly correlated with most of our other behavioral variables (Supplementary Fig. 2); though results using the other behavioral variables yielded the same interpretation (i.e. that significant individuality in (any) behavior was present on the very first day post-birth; Supplementary Table 2).Our second research question was to investigate how individual behavioral variance changed over the course of the entire observation period (70 days). Again, we first explored several different variance structures to test the importance of maternal identity and/or individual identity on behavioral variation. We found support for the inclusion of random slopes at the individual level, but not maternal level (Table 1). This indicates that levels of among- (and within-) individual variation may differ throughout the observation period. To investigate patterns of change in the variance components, we ran a series of models where we centered the observation covariate on different days. Individual intercepts are estimated when all covariates are set to zero, so this allowed us to ‘slice’ the data to estimate the among- and within-individual variance at different time points over the ten weeks. We ran 11 models as we chose to center the data every 7 days (first model was centered on observation 1; 11th model was centered on observation 70). The predicted individual intercepts (best linear unbiased predictors) and estimated variance components from each model are plotted in Fig. 3.We also closely investigated any potential influence of body size and/or growth rate differences on behavioral expression and individual behavioral variation in this entire 10-week data set. First, we estimated the repeatability of both weekly total length and weekly growth rates to determine if individuals consistently differed in these traits. Then, we ran a series of models with median weekly swimming speed as the response variable and included either weekly total length, weekly growth rate, and/or overall growth rate (estimated over the entire 10 weeks), as our fixed effects of interest. Each model also included the random effects of individual intercepts and slopes. Finally, because body size varies both among individuals (some individuals are on average larger than others) and within individuals (as they grow), we also performed within-individual centering of total length. In this fifth model, we included each individual’s average total length and their weekly deviation from their average length as the two fixed effects of interest. Individual identity and slopes were included as random effects. For all models, we estimated the variance explained by the fixed effects (marginal R2) and the fixed and random effects together (conditional R2). These results are reported in Table 2.For our third and final research question, we tested whether early-life behavior predicted later-life behavior. To test this, we estimated the among-individual correlation (including ‘individual ID’ as our random effect) in behavior using multivariate mixed models where the daily median swimming speeds in each week were the response variables (7 observations per week per individual; 10 weeks total; Fig. 4A). Then to investigate how the strength of these correlations may change over development, we used a linear model to test whether the correlation strength was predicted by the interaction between the first week included in the correlation and distance to the next week in the correlation (1, 2, 3, 4 or 5 weeks away in time; Fig. 4B).All models were performed using Markov Chain Monte Carlo estimation with the MCMCglmm package38 in R v3.6.139. We set our models to run 510,000 iterations with a 10,000 burn-in and thinning every 200 iterations. To ensure proper model mixing and convergence, we initially ran 5 independent chains and inspected posterior trace plots of parameter estimates (Supplementary Note II). In a preliminary analysis we tested three different prior settings (Supplementary Note II); results did not change with prior settings so we chose parameter-expanded priors for all models reported here as these are generally considered to be more robust. An R Markdown file with all the results presented here is included in Supplementary Note II.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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