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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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The Santa Barbara Coastal Long Term Ecological Research program has monitored benthic communities in five kelp forests seasonally since 2008 using fixed transect diver surveys, and moored sensors at each reef have recorded bottom temperatures every 15 min. Blob-associated positive bottom temperature anomalies began in winter 2014 and persisted through autumn 2016 (Fig. 1a)18. Peak temperature anomalies occurred during the summer and autumn of 2014 and 2015 (Fig. 1a), and the average temperature anomaly in autumn 2015 was +3.1 °C, equivalent to an average daily temperature of 19.6 °C. In 2014 and 2015, 91 and 69% of autumn days, respectively, were classified as heatwave days as defined by Hobday et al.20. Seasonal chlorophyll-a concentration, a proxy for phytoplankton abundance, was obtained from satellite imagery at each of the five reefs over the 14-year period. The average chlorophyll-a concentration was anomalously low throughout the warming period, and exceptionally low during the springs of 2014 and 2015 (Fig. 1a), when upwelling-driven nutrient enrichment typically supports dense phytoplankton blooms.Fig. 1: Average seasonal bottom temperature anomaly, chlorophyll-a concentration anomaly, and percent cover and species richness of sessile invertebrates across five sites.The Blob, an anomalous warming period from spring of 2014 to winter of 2016, is highlighted in gray, coincident with (a) positive temperature anomalies (°C; solid line), negative chlorophyll-a anomalies (mg/m3; dashed line), and declines in (b) invertebrate cover (solid line) and species richness (number of unique species/taxa/80 contact points; dashed line). Seasons are denoted by Sp (Spring), Su (Summer), A (Autumn) and W (Winter).Full size imageMean sessile invertebrate cover averaged across all sites declined 71% during the Blob, reaching a 14-year minimum of 7% in autumn of 2015 (Fig. 1b and Supplementary Fig. 1). Species richness declined 69% during the same period (Fig. 1b and Supplementary Fig. 1). The responses of invertebrates to warming were not consistent across time even though the duration and intensity of warming was similar in 2014 and 2015, suggesting that extended periods of elevated seawater temperature were not solely responsible for the most severe loss of invertebrates. For ectotherms, increases in ambient seawater temperature should be met with increases in metabolic rate and food requirements to sustain metabolism21. Because of their sedentary lifestyle, sessile invertebrates cannot actively forage for food or seek spatial refuge from thermal extremes, and limitations in their planktonic food supply can result in metabolic stress over extended periods22,23. Anomalously low chlorophyll-a concentrations during the Blob (Fig. 1a), particularly in the spring of 2015, indicated that food limitation was a likely driver of invertebrate decline. Results from piecewise structural equation modeling (Fig. 2) that incorporated biological interactions with space competitors (understory macroalgae), predators (sea urchins), and foundation species (giant kelp) showed that the severity of warming had both a direct and indirect effect on the sessile invertebrate community. The proportion of heatwave days was a direct negative predictor of sessile invertebrate cover (−0.11) and species richness (−0.21). The proportion of heatwave days was an even stronger negative predictor of chlorophyll-a concentration (−0.26), yielding negative indirect effects on invertebrate cover (−0.07) and species richness (−0.05) due to the positive influence of chlorophyll-a concentration on sessile invertebrate cover (+0.26) and richness (+0.20).Fig. 2: Piecewise structural equation modeling (SEM) for sessile invertebrate cover and species richness.Arrows indicate directionality of effects on (a) invertebrate cover and (b) species richness. Red arrows show negative relationships; black arrows show positive relationships. R2 values are conditional R2. Arrow widths are proportional to effect sizes as measured by standardized regression coefficients (shown next to arrows). ***p  More