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Nesting recordsDipogon nests were created singly per cane, because there were no examples in which wasps of two species emerged from the same cane in the study site. Thus, we designate “utilized canes” as “nests”.In the four years, in pine forests in Takarazuka, Hyogo, Japan, we collected a total of 419 nests with 1033 cells from which species of Dipogon emerged (Fig. 1; Table 1; Supplementary Table S1). The numbers of nests and cells and the average and SD of the number of cells per nest for each species are shown in Table 1. Other wasps, bees, and parasitic wasps and flies also emerged from our trap nests (Supplementary Table S2), but we did not consider their nesting in the following analyses. Among 1033 cells, D. sperconsus emerged from 623 cells, D. inconspersus from 26 cells, and D. bifasciatus (Geoffroy) from 4 cells, while rearing failure occurred in 380 cells (Table 1), the owners of which we designate as “unknown Dipogon spp.” Based on the total cells of Dipogon, the proportion of cells constructed by D. sperconsus was 60.3% (623/1033*100), that of D. inconspersus was 2.5% (26/1033*100), and that of D. bifasciatus was 0.39% (4/1033*100). Based on the cells of the identified species, the proportion of cells constructed by D. sperconsus was 95.4% (623/(623 + 26 + 4)*100), that of D. inconspersus was 4.0% (26/(623 + 26 + 4)*100), and that of D. bifasciatus was 0.6% (4/(623 + 26 + 4)*100). From these proportions, we can estimate the number of cells constructed by the three species of Dipogon in the total 1033 Dipogon cells as ca. 985.5 cells (1033*0.954) by D. sperconsus, ca. 41.3 cells (1033*0.04) by D inconspersus, and ca. 6.2 cells (1033*0.006) by D. bifasciatus.Figure 1The study site in Kirihata, Takarazuka City, Hyogo Pref., Japan, and trap nests. (a) An old pine forest in which trap nests were installed. (b) A set of trap nests (cane bundle), 15 mixed-size bamboo canes bound vertically with vinyl-covered wires like a screen, attached to a tree trunk approximately 1.5 m above the ground. (c) A nest of D. sperconsus; this cane was installed in Shibutani, Takarazuka, Hyoto Pref. about 1 km southeast of the present study site on 29 July 2007 and was withdrawn on 6 August 2007. (d) A nest (6–5-5–1) of D. sperconsus; this cane was installed in Kirihata, Takarazuka, Hyoto Pref. about 500 m west-southwest of the present study site on 25 August 2010 and was withdrawn on 27 August 2010 (prey spider, Agelena limbata Thorell). (e) A nest of D. sperconsus; this cane was installed in Najio, Nishinomiya, Hyoto Pref. about 10 km southwest of the present study site on 15 July 2007 and was withdrawn on 25 July 2007. The minimum grid in the background graph paper of (c)–(e) is 1 mm. All photos taken by Y. Nishimoto.Full size imageTable 1 The numbers of the collected nests and brood cells, and the mean number of cells per nest in three species of Dipogon (Deuteragenia).Full size tableBecause multiple cells were often constructed in a single nest, the number of nests was much smaller than the number of constructed cells. Among the 419 nests, 221 nests belonged to D. sperconsus, 7 nests belonged to D. inconspersus, and a single nest belonged to D. bifasciatus, but the remaining 190 nests could not be identified because of rearing failure (Table 1). The proportions of the nests in the three Dipogon species were calculated as follows: 96.5% (221/(221 + 7 + 1)*100) in D. sperconsus, 3.1% (7/(221 + 7 + 1)*100) in D inconspersus, and 0.4% (1/(221 + 7 + 1)*100) in D. bifasciatus. Thus, the estimated number of nests in each species was ca. 404.3 (419*0.965) in D. sperconsus, ca. 13.0 (419*0.031) in D inconspersus, and ca. 1.7 (419*0.004) in D. bifasciatus.Next, we considered whether the cane bundles were used randomly. Based on the yearly frequency distributions of nests (Supplementary Tables S3–S6), we developed a null hypothesis assuming the nests are randomly distributed over bundles, where a negative binomial distribution is expected (Supplementary Tables S7–S8). Our yearly data indicate that the null hypothesis was rejected and that nests were more or less aggregated in a few bundles (Supplementary Figure S1; test statistics, Supplementary Table S8). This aggregation tendency (e.g., no nests in some bundles) may imply that some selected sites for bundles are not appropriate for D. sperconsus, for some unknown behavioral reasons. Further studies are needed to verify the habitat use of this species.Yearly frequency distributions of the number of cells show that the range of cells constructed by D. sperconsus and unknown D. spp. combined were 1–10 cells, and the median was 2 cells (Supplementary Table S3–S6, Supplementary Figure S2). Most of the nests included 1–3 cells, and five or more cells were very rare. Most of the nests with many cells (e.g., 7–10 cells) were likely to be constructed by a single wasp because these wasps avoid interactions with other spider wasps. The average number of D. sperconsus cells per nest was 2.82 for four years, varying from 2.21 (2014) to 3.16 (2016) (Table 1), and the yearly differences were significant (Kruskal–Wallis test, (chi ^{2} = 7.70), df = 3, p = 0.05). In contrast, the average number of cells per nest of D. sayi sayi was slightly greater than that of D. sperconsus: 3.2 (1–6, SD = 1.47, n = 41) in the first generation and 4.7 (1–13, SD = 2.52, n = 107) in the second generation in Wisconsin, USA8; and 6.2 (1961), 4.0 (1962) and 3.0 (1963) in the summer generation and 7.5 (1961) and 3.2 (1962) in the overwintering generation in Northwestern Ontario9.Life history of Dipogon sperconsusDevelopmental periodThe developmental period of reared wasps was estimated in the summer and overwintering generations separately (Table 2, Supplementary Figure S3, Supplementary Tables S9–S12). In the summer generation, both females and males developed from egg to adult over approximately three weeks (23.1 days for females and 21.6 days for males; Table 2). There was no significant difference between sexes (t-test, after adjustment by Bonferroni method: p  > 0.05). In the overwintering generation, approximately eight months were required from egg to adult (246 days for females and 247 days for males). There was also no significant difference between sexes (t-test, after adjustment by Bonferroni method: p  > 0.05). In females, all developmental periods were significantly longer in the overwintering generation than in the summer generation (t-test, after adjustment by Bonferroni method: p  0.05 for egg and larval periods; p  0.1). Among the 40 coelotid female spiders, the sex ratio of wasp eggs was even: 20 female wasp eggs and 20 males. However, the female spiders on which female wasp eggs were laid were significantly greater in cephalothorax width than those on which male eggs were laid (t = 3.98, p  More

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Russia has been reporting almost no changes in forested area, growing stock volume (GSV) and biomass to the United Nations Framework Convention on Climate Change (UNFCCC)1 and the Food and Agriculture Organization of the United Nations (FAO) Forest Resources Assessment (FRA)2 since the collapse of the USSR and the decline in the Soviet Forest Inventory and Planning (FIP) system. According to the State Forest Register (SFR)3, which is the main repository of forest information, and national reporting to the FAO FRA2, the GSV and the above ground biomass (AGB) increased by 1.1% and 0.6% (Table S1), respectively, during 1990–2015, yet studies using remote sensing (RS) indicate increased vegetation productivity4, tree cover (annual rate + 0.417% over 1982–2016)5, increased AGB (+ 329 Tg C yr−1 over 2000–20076), total biomass (annual rate + 0.44% or + 153 Tg C yr−1 over 1990–20077), and forest ecosystem carbon pools (ca + 470 Tg C yr−1 over 2001–20198). This inconsistency in estimates can be explained by an information gap that appeared when Russia decided to move from the FIP to another system for the collection of forest information at the national scale – the National Forest Inventory (NFI).The FIP involves revisiting every forest stand (on the ground for managed forests or using RS techniques for remote non-commercial forests) on a 10–15-year interval, with the measurement of forest parameters combined with the formulation of forest management directives. After the collapse of the USSR, the inventory within the FIP system slowed down substantially. For example, more than 50% of the forest area was surveyed by the FIP more than 25 years ago9. For these reasons, the reliability of information on forests in Russia has deteriorated since 1988, which is the year when FIP-based reporting10 involved the largest inventory efforts in recent decades. According to this report10, the total GSV of Russian forests was 81.7 × 109 m3 (without shrubland, bias corrected11). This value is used here as a reference to quantify biomass stock changes in Russia with respect to the current decade.In contrast, NFI is a state-of-the-art inventory system based on a statistical sampling method. It was initiated in 2007 and the first cycle was completed in 2020. The NFI data processing is ongoing, but the first official press release12 suggests that Russian forest accumulated 102 × 109 m3 over its lifespan until 2014. Once finalized, the NFI will be verified before adoption as the official source of information to the SFR and national reporting. The NFI has received some criticism13 because of the relatively sparse sampling employed and the stratification method used, which is partially based on outdated FIP data.In Russia, the long intervals between consecutive surveys and the difficulty in accessing very remote regions in a timely manner by an inventory system make satellite RS an essential tool for capturing forest dynamics and providing a comprehensive, wall-to-wall perspective on biomass distribution. However, observations from current RS sensors are not suited for producing accurate biomass estimates unless the estimation method is calibrated with a dense network of measurements from ground surveys14. Here we calibrated models relating two global RS biomass data products (GlobBiomass GSV15 and CCI Biomass GSV16) and additional RS data layers (forest cover mask9, the Copernicus Global Land Cover CGLS‐LC100 product17) with ca 10,000 ground plots (see Material and Methods) to reduce nuances in the individual input maps due to imperfections in the RS data and approximations in the retrieval procedure18,19. The combination of these two sources of information, i.e., ground measurements and RS, utilizes the advantages of both sources in terms of: (i) highly accurate ground measurements and (ii) the spatially comprehensive coverage of RS products and methods. The amount of ground plots currently available may be insufficient for providing an accurate estimate of GSV for the country when used alone, but they are the key to obtaining unbiased estimates when used to calibrate RS datasets20. The map merging procedure was preferred over a plot-aided direct estimation of GSV or AGB from the RS data because of the usually poor association between biomass measured at inventory plots and remote sensing observables21. In addition, models relating biomass and remote sensing observables that are trained with spatially inhomogeneous datasets (Figure S1) tend to be biased in regions not represented by the dataset of the reference biomass measurements.We estimate the total GSV of Russia for the year 2014 for the official forested area (713.1 × 106 ha) to be 111 ± 1.3 × 109 m3, which is 39% higher than the 79.9 × 109 m3 (excluding shrubland) figure reported in the SFR3 for the same year. An additional 7.1 × 109 m3 or 9% were found due to the larger forested area (+ 45.7 106 ha) recognized by RS9, following the expansion of forests to the north22, to higher elevations, in abandoned arable land23, as well as the inclusion of parks, gardens and other trees outside of forest, which were not counted as forest in the SFR. Based on cross-validation, our estimate at the regional level (81 regions of Russia – Table S2, Figure S2) is unbiased. The standard error varied from 0.6 to 17.6% depending on the region. The median error was 1.6%, while the area weighted error was 1.2%. The predicted GSV (Fig. 1) with associated uncertainties is available here (https://doi.org/10.5281/zenodo.3981198) as a GeoTiff at a spatial resolution of 3.2 arc sec. (ca 0.5 ha).Figure 1Predicted mean forest growing stock volume (m3 ha-1) for the year ca 2014 (Generated by Esri ArcGIS Desktop v.10.7, URL: https://desktop.arcgis.com/en/arcmap/).Full size imageHoughton et al.24 estimated forest biomass based on RS and FIP data in Russia for the year 2000. Average forest biomass density varied between 80.6 and 88.2 Mg ha-1 depending on which forest mask was used. Our estimate for the year 2014 of 107 Mg ha-1 (using the conversion factor of GSV to AGB from24 0.6859) is 21–33% higher than the one by Houghton et al., but this is consistent with expected biomass increases over time, i.e., 14 years after the Houghton et al. estimate.Assuming an unchanged total forest area (721.7 × 106 ha) in 1988 and 2014, we conclude that Russian forests have accumulated 1,163 × 106 m3 yr-1 or 407 Tg C yr-1 in live biomass of trees on average over 26 years. This gives an average GSV change rate of + 1.61 m3 ha-1 yr-1 or + 0.56 t C ha-1 yr-1. The sequestration rate obtained, however, should be treated with caution because different methods have been applied in 1988 and 2014 (see “Caveats and Limitations” section). To provide some context for the magnitude of these numbers, one can compare the Russian forest gain to the net GSV losses in tropical forests over the period 1990–2015 according to FAO FRA25 (-1,033 × 106 m3 yr-1 in the regions with a negative trend: South and Central America, South and Southeast Asia, and Africa). A similar divergence in the carbon sink between Tropical and Boreal forest was recognized by Tagesson et al.26.In terms of carbon stock change, our estimates are substantially higher than those reported by Pan et al.7 for 1990–2007 (+ 153 Tg C yr-1) based on FIP data. The biomass carbon estimates by Liu et al.6 are instead in line with our results. There is an increase in the annual rate of AGB in Russia of + 329 Tg C yr−1 (annual variation from 214 to 400 Tg C yr−1) over 2000–20076. Interestingly, another boreal country – Canada – has demonstrated neutral or negative trends (from 0 to -14 Tg C yr−1) for the same time span using the same estimation method6.We can observe different spatial patterns in the change in the GSV density between 1988 (FIP10, bias corrected11) and 2014 (our estimate), which can be explained by climate change, CO2 fertilisation and changes in disturbance regimes (Fig. 2). The average linear trend in the annual temperature increase during 1976–2014 in Russia is + 0.45 °C per 10 years27. The temperature increase is statistically significant in every region except for western Siberia (Fig. 2–3). Significantly increased temperature extremes and an increase in the number of days without precipitation is observed in the south of European Russia, Baikal, Kamchatka, and Chukotka27 (Fig. 2–1). Some regions in the south of the European part of Russia are colored in dark blue, but they, as a rule, have a small share of forested area, which is often linked to water bodies and, therefore, suffers less from increased drought (Fig. 2–1). Central and eastern Siberia suffer from an increase in disturbances, which offsets the climate stimulation effect (Fig. 2–4). The forested area in the Nenets region (Fig. 2–2) is 4 times larger in 2014 based on the RS forest mask compared to the SFR in 1988 (where forest was accounted for up until a certain latitude at that time), where the increase in area resulted in a decrease in the average GSV.Figure 2Change in growing stock volume (m3 ha-1) from 1988 to 2014 (average over administrative regions) (Generated by Esri ArcGIS Desktop v.10.7, URL: https://desktop.arcgis.com/en/arcmap/). These changes can be categorized into: 1—significant increase in air temperature and drought; 2—substantially increased forest area, which lowers the average GSV density; 3—least (not significant) temperature increase; 4—increase of disturbances: wildfire and harvest (southern part), which offsets the climate stimulation effect.Full size imageFocusing specifically on national reporting of managed forest to the UNFCCC, 72% of forested area in Russia is considered to be managed1. We multiplied the GSV density by the managed forest area for each administrative region (Table S3). The difference in GSV estimation (between ours and the one from the SFR report) is 23.6 × 109 m3 (Table S3) or 33% higher. From the GSV of managed forests in 2014 and based on the same area in 1988, we can estimate the sequestration rate of live biomass of managed forests as 354 Tg C yr-1 , which is considerably higher than the figure of 230 Tg C yr-1 in the current report1.This proof of concept demonstrates the relevance of complementing recent NFI data with remote sensing map products. Our study demonstrates that the already considerable value of forest inventory data can be further enhanced in a forest resources mapping scenario. In addition, we seek to promote greater access to these data by opening up their access to the larger scientific community. Through the integration of RS estimates of GSV and forest inventory data from Russia, we confirm that carbon stocks increased substantially during the last few decades in contrast to the figures provided in official national reporting. Russian forests play an even more important global role in carbon sequestration than previously thought, where the increase in growing stock is of the same magnitude as the net losses in tropical forests over the same time period. More

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