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Study sitesWe conducted this study in three protected areas in Sabah, Malaysian Borneo: Danum Valley Conservation Area (DVCA), the Lower Kinabatangan Wildlife Sanctuary (LKWS), and Tabin Wildlife Reserve (TWR) (Fig. 4). The minimum and maximum daily temperatures and annual precipitation among the three study sites did not differ significantly (annual temperature: 22–33 ℃, annual precipitation 2400–3100 mm; Mitchell37; Matsuda et al.39; South East Asia Rainforest Research Partnership Unpublished data. https://www.searrp.org/) although there is no recent precise climate data of TWR.Figure 4Location of the three study sites in Borneo.Full size imageThe DVCA (4° 50′–5° 05′ N, 117° 30′–117° 48′ E) is a Class I Protection Forest Reserve established by the Sabah state government in 1996 and managed by the Sabah Foundation (Yayasan Sabah Group) covering 438 km2. Approximately 90% of the area is comprised of mature lowland evergreen dipterocarp forests34. The study area is an old-growth forest surrounding the Borneo Rainforest Lodge (5° 01′ N, 117° 44′ E), a tourist lodging facility.The LKWS (5° 10′–5° 50′ N, 117° 40′–118° 30′ E), is located along the Kinabatangan River, which is the longest river flowing to the east coast, reaching 560 km inland and with a catchment area of 16,800 km2. Designated as a wildlife sanctuary and gazetted in 2005, the LKWS consists of ten forest blocks totaling 270 km2, comprised of seasonal and tidal swamp forests, permanent freshwater swamps, mangrove forests, and lowland dipterocarp forests35,36. The southern area of the Menanggul River is extensively covered by secondary forest. However, the northern area has been deforested for oil palm (Elaeis guineensis) plantations, except for a protected zone along the river. The TWR (5° 05′–5° 22′ N, 118° 30′–118° 55′ E) is located approximately 50 km northeast of Lahad Datu, eastern Sabah, and covers approximately 1225 km2.The TWR is exclusively surrounded by large oil palm plantations. Most parts of the TWR were heavily logged in the 1970s and the 1980s, leaving mainly regenerating mixed dipterocarp tropical rainforests dominated by pioneer species such as Neolamarckia cadamba and Macaranga bancana37,38. The study area was near the Sabah Wildlife Department base camp located on the western boundary of the TWR (5° 11′ N, 118° 30′ E). The study area includes heavily logged secondary forests and a small patchy old forest (0.74 km2).Data collectionWe set up 15, 30, and 28 infrared-triggered sensor cameras (Bushnell, Trophy Cam TM) in the DVCA (July 2010–August 2011 and May 2014–December 2016), LKWS (July 2010–December 2014) and TWR (May 2010–June 2012), respectively. As a result, the cumulative number of camera operation days in DVCA, LKWS, and TWR were 14,134, 18,265, and 4980, for a total of 37,379 days. Although it was impossible to record the animals during certain months because of adverse weather conditions, such as heavy rain, flooding, battery failure, other malfunctions mainly caused by insects nesting inside the cameras, or logistical problems, the cameras remained continuously activated. Due to these reasons, camera operating days differed among the cameras in each site. In this study, we used photos of animals, and we did not handle animals directly. All cameras were placed at heights of 30–50 cm above the forest floor and were tied to tree trunks using fabric belts to reduce damage to the trees.Because the terrain and level of regulations to conduct this study differed by the study site, we employed different layouts of camera stations at each study site. In the DVCA, T. K. and three trained assistants placed 15 cameras along six forest trails totaling 9000 m, which were established and maintained by the tourist lodging facility. Because it was prohibited to establish new trails and to place cameras at sites where tourism activity would be disturbed in the study area; therefore, the trails that were longer than 1 km and relatively easily accessible were selected as camera locations to maintain consistency of trail characteristics. Cameras were placed on each trail at 50 m intervals, alternating right and left to avoid bias of photo-capture frequency caused by terrain differences. Each station was at least 25 m away from each other on the different trails (Fig. 5a). The operating days differed among the 15 cameras, i.e., mean = 942.2; SD = 152.0; range = 682–1229.Figure 5Maps of camera locations at each study site. (a) Trails and camera stations at DVCA; (b1) trails and camera stations and (b2) trail locations at LKWS; (c) a trail and camera stations at TWR.Full size imageIn the LKWS, I. M. and two trained assistants had planned to install 30 cameras, but a maximum of only 27 cameras were in operation during the study period in the LKWS, probably owing to malfunctions caused by high humidity and rain in the tropical rainforest. All cameras were placed on the trails in the riverine forest along the Menanggul River. As part of a project on the primates of the riverine forests along the Menanggul River and to assist their observation and tracking in the swampy habitat in the LKWS39, trails 200–500 m long and 1 m wide were established at 500 m intervals on both sides of the river. Of the 16 trails, we selected ten trails that were all 500 m long and placed three cameras at the points from the riverbank to the inland forest in each trail, that is, 10 m, 250 m and 500 m from the riverbank (Fig. 5b1); cameras were set up 50 m away from the trails (Fig. 5b2). Consequently, the number of operating days differed among 30 cameras, i.e., mean = 608.8; SD = 531.4; range = 28–1315.In the TWR, M. N. and A. M placed 28 and three cameras on camera stations created by overlaying a 750 × 500 m grid in May and August 2010, respectively. Cameras were placed at each grid point at 250 m intervals (Fig. 5c). The operating days differed significantly among the 28 cameras, that is, mean = 177.9; SD = 123.2; range = 26–539.Temporal activity analysisWe defined non-independent photo capture events as consecutive photos of the same or different individuals of the same species taken within a 30-min interval and removed these photos from the analysis. We plotted the activity patterns of each species using a von Mises kernel40,41 using the package activity42 in R version 4.0.243. We estimated the activity level of animals with more than ten independent photo-capture events as indicated in the previous studies26,44. For our analysis, we pooled the images from all study sites if the photo number of a species was less than 10 in any study locations. If that was not the case, we used the package activity42 to compare species activity levels across the three research sites using a Wald test with Bonferroni correction for multiple pairwise comparisons. When there were significant differences, we separately estimated activity levels by the study sites. When there were no significant differences among the sites, we pooled the photo numbers to estimate activity levels.We divided a day into three periods: nighttime (19:00–04:59 h local time (GMT + 8)); daytime (07:00–16:59 h); and twilight (05:00–06:59 h and 17:00–18:59 h). During the study period, twilight hours essentially corresponded to 1 h between sunset and sunrise, at 5:54–6:25 and 17:50–18:25 in DVCA, 5:51–6:23 and 17:47–18:25 in LKWS, and 5:50–6:21 and 17:46–18:22 in TWR (data from https://www.timeanddate.com). After converting the time data of each photo-capture event into radians, we fitted a circular kernel density distribution estimated by 10,000 bootstrap resampling to radian time data, and we estimated the percentage of active time in each period. We then categorized the activity patterns of photo-captured carnivore species into four categories: nocturnal (active at night); crepuscular (active during twilight periods); diurnal (active during daytime); and cathemeral (active in all periods). We defined the activity pattern of the species as showing a statistically higher proportion of photo-captures at nighttime, daytime, and twilight periods than at other periods, such as nocturnal, diurnal, and crepuscular, respectively. When photo-capture proportions showed no differences among the three periods, we defined the activity pattern as cathemeral. For species with substantial sample size (50  More

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doi: https://doi.org/10.1038/d41586-021-02715-z

References1.Anderson, D. M. et al. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2107387118 (2021).Article 

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Inverted paradoxNeutral theory can reproduce properties of terrestrial biodiversity observed at local (e.g., an island) or metacommunity (i.e., a set of interacting communities linked by dispersal of species) scales, particularly ranked species abundance curves (i.e., histograms of species abundance ordered along the x-axis from most to least common) [14]. Central to neutral theory is the interplay between ‘stochastic exclusion’ and either immigration or speciation. Stochastic exclusion is the reduction in biodiversity caused by random deaths and abundance-dependent replacement and, if not countered by other processes, ultimately leads to only a single remaining species [14]. Immigration of species into a local community or speciation within the metacommunity offset stochastic exclusion and maintain biodiversity [14]. This relationship is illustrated in Fig. 1 by simulated time-series of phytoplankton diversity for three populations at steady-state with 10,000, 100,000, and 1,000,000 total individuals and an initial condition of 10,000 species each (Fig. 1) (Methods). Subjection of these populations to 50% random mortality per generation and replacement in proportion to the relative abundance of remaining species results in an eventual rate of decrease in diversity that is equivalent across population sizes (Fig. 1; dashed black lines), eventually yielding the expected final equilibrium of a single species. When a small rate of immigration is added to this simulation (here, 0.03% or 0.3% per generation), complete stochastic exclusion is replaced by steady-state diversities that vary in direct proportion to population size and immigration rate (Fig. 1; colored dashed and dotted lines). Similar considerations led Hubbell [14] to earlier propose in his “Unified Neutral Theory” a fundamental biodiversity number, θ, controlling both species richness and relative abundance:$$theta ,=, 2Jupsilon$$
(1)
where J is the total number of individuals in the community and υ the rate of immigration (local) or speciation (metacommunity).Fig. 1: Phytoplankton biodiversity following purely stochastic processes.Red, blue, and green = phytoplankton populations (J) of 10,000, 100,000, and 1,000,000 individuals, respectively (Methods). Colored solid lines = species richness in the absence of immigration (υ). Colored dashed and dotted lines = species richness for υ values of 0.03% and 0.3% per generation. Black dashed line = mean rate of decline for the primary phase of stochastic exclusion (slope of this line is the same for all three populations). Blue and green downturned triangles = threshold for the two larger populations where diversity begins to decline rapidly because a sufficient number of species have been reduced to an abundance where extinction within a generation becomes likely.Full size imageIn addition to illustrating the balance between stochastic exclusion and immigration into a local phytoplankton community, Fig. 1 shows that significant decreases in species richness only ensue after a subpopulation of species within a community has been sufficiently decimated in number that their remaining individuals might be lost through random mortality within a generation. In our simulations, this threshold is demarked by the downturn in species richness for the populations of 100,000 and 1,000,000 individuals (Fig. 1; blue and green triangles). The significance of stochastic exclusion is thus dependent on the relation between extant species number and size of the physically-homogenized community. With respect to the latter property, typical horizontal eddy diffusion values for the upper ocean are O(103 m2 s−1), implying that the length scale for mixing in 1 day is O(1000 m). Typical number concentrations for phytoplankton of different species in the ocean range from More

Temporal variation and dynamic analysis of eucalyptus forestsData from the 1st-9th NFIs suggested that the total area of eucalyptus plantations had started to increase since 1973 (Table 1). In 1973–1976, Eucalyptus plantations only existed in Guangxi and Guangdong (including Hainan) in China with a total area of 23.0 × 104 hectares, taking up 0.38% of total forest area in China. The stock volume was 372.0 × 104 m3, about 0.04% of that in China. In 2014–2018, the eucalyptus plantation area increased to 546.74 × 104 hectares, about 24 times of that in 1973–1976. The growing stock has increased to 21,562.90 × 104 m3 in 2014–2018 which increased about 58 times from 1973–1976.Table 1 Eucalyptus plantation area and stand volume in different time periods by province.Full size tableThe stock volume per unit area of eucalyptus plantations did not increase significantly from 1973–2008, ranging from 14–30 m3/hectares, but it increased rapidly from 2009 to 2018, reaching 39.43 m3/hectares. This increase occurred because China started to focus and value the development of eucalyptus plantations. As a. result, plantations expanded rapidly, and the need for eucalyptus with greater trunk radius increased. The extended harvest cycle of eucalyptus plantations, not the rise of eucalyptus productivity, caused the increase in stock volume per unit area29,30,31.Based on CFLDM, the distribution of eucalyptus plantations in 2003 and 2016 are mapped (Fig. 1a,b). It suggests that the distribution of eucalyptus plantations extended from Leizhou Peninsula, Guangdong and Hainan Province to the north (Guangxi, Hunan, and Guizhou provinces), east (Fujian and Jiangxi provinces), and west (Yunan and Sichuan provinces). This is consistent with data from the NFIs. The widespread expansion of eucalyptus also leads to several regions with clustered plantations.Figure 1Distribution of eucalyptus in the south of China [(a) 2003; (b) 2016]. This figure was created by spatially overlaying spatial sample plots data from National Forest Inventory (NFI) and patch vectors data from China Forest-Land Database Map (CFLDM), (a) shows that the point data are from 6th NFIs eucalyptus sample plots and the polygon vector data are from the 2003 CFLDM; (b) shows that the point data are from 9th NFIs eucalyptus sample plots and the polygon vector data are from the 2016 CFLDM. The extents of eucalyptus plantations is mainly concerned with 11 provinces (e.g. Zhejiang, Fujian, Guangdong, Guangxi) in Southern China.Full size imageChanges in spatial distributionBased on the database of sample plots (including climate and elevation data) and sampled eucalyptus, we analyze the distribution of eucalyptus plantations, and how it is affected by elevation and climate conditions. It is found that most eucalyptus plantations are within the region of 110°23′–120°5′E and 18°21′–30°39′N. The annual mean temperature within this region ranges from 11 to 25 °C with an average of 19.5 °C, and the annual precipitation ranges from 600 to 2000 mm with an average of 1455 mm. Elevation in this region is 0–2500 m with an average of 338 m.To find out the most suitable conditions for eucalyptus growth and its plantation management, we classify this region based on their elevation and climate conditions. The classification is done separately and independently for each factor (i.e., elevation, temperature, and precipitation). In terms of elevation, the region is assigned to seven grades from below 300 m to 2100 m with an interval of 300 m in between (i.e., below 300 m, 300–600 m, 600–900 m, 900–1200 m, 1200–1500 m, 1500–1800 m, 1800–2100 m). Land with elevation above 2100 mm has limited eucalyptus plantations, and thus is not taken into consideration. Similar criterion is applied to the classification based on annual mean temperature and annual precipitation. The grades are from 11 to 25 °C within an interval of 2 °C for temperature and 600–2000 mm with an interval of 200 mm for precipitation.We examine how the eucalyptus forest area changes with these factors. This is done by plotting the eucalyptus plantation area within a certain group against the corresponding grade number (Fig. 2). Eucalyptus is mostly distributed below 300 m, reaching an area of 301.1 × 104 hectares and counting for 67.58% of the total eucalyptus plantation area in China. Eucalyptus occurs rarely in areas with elevation above 900 m.Figure 2Area of eucalyptus plantations in China based on grades defined in text.Full size imageEucalyptus is sensitive to temperature, and its distribution is limited within areas with annual mean temperature below 19 °C. Eucalyptus is mostly distributed within areas with annual mean temperature of 19–21 °C. Areas with annual mean temperature within this range have a total of 291.49 × 104 hectares eucalyptus plantations, approximately 65.43% of all eucalyptus plantations in China. This result is slightly different from previous studies3,5, which suggests that eucalyptus prefers areas with mean annual temperature above 20 °C.Eucalyptus has a high tolerance to annual precipitation. Eucalyptus plantations can be found in areas with annual precipitation ranging from 600–2000 mm. It should be noted that this is related to irrigation conditions in production and management. However, in areas with annual precipitation below 600 mm, Eucalyptus plantations are extremely rare. Areas with annual precipitation of 1400–1600 mm (and without considering other factors) have the largest portion of eucalyptus plantations, whose total area reaches 146.49 × 104 hectares. This accounts for about 32.94% of total eucalyptus plantations in China.Productivity analysis of eucalyptusVariability in mean productivityEucalyptus annual productivity for each province based on the 5th to 9th NFIs (no digitized data for the 1th to 4th NFIs) is calculated, which includes 3564 sample plots (Table 2). Among which, 769 sample plots had been harvested at the time of the survey, and to remove the influence of these 769 sample plots, their data were removed during the productivity of the eucalyptus age-productivity relationship graph (see Fig. 3), which shows that the period of maximum productivity for eucalyptus lasts for approximately 2–3 years. Its productivity declines rapidly after 10 years of growth. Therefore, the harvest cycle of eucalyptus is normally 4–5 years. After coppicing and growing for another 4–5 years, Eucalyptus will be harvested again, which will be followed by its replanting.Table 2 Basic sample plot statistics (quantity, mean and maximum annual productivity) of eucalyptus plantations by province from the 5th to 9th NFIs.Full size tableFigure 3Relationship between age and productivity of eucalyptus sample plots.Full size imageVariability in eucalyptus productivityEucalyptus productivity for each province based on the 5th to 9th NFIs is calculated and shown in Table 2. It can be seen that from 1994 to 2018, mean and maximum productivities of eucalyptus plantations have increased. This is especially show for Guangxi and Fujian Provinces during 2009–2018. The averaged productivity of eucalyptus plantations in China increased from 4.14 to 8.57 m3 hm−2 a−1 from 1994–1998 to 2014–2018, which can be explained by the improved management of eucalyptus plantations (e.g., high soil fertility for newly cultivated lands and improved ability for irrigation) and their expansion.Data from the 5th to 9th NFIs suggest that a lot of sampled plots were no longer used for growing eucalyptus before the next inventory (Table 3). There were 226, 273, 687, and 109 eucalyptus plots in the 5th, 6th, 7th, 8th inventories, respectively, and in the corresponding next NFI (6th, 7th, 8th, 9th), only 150, 179, 544, and 848 of these sample plots were left unabandoned. This suggests that 33.63%, 34.43%, 20.82%, and 22.63% of the plots were abandoned before the next inventory. New plots have been included in each inventory, but large portions of these plots were abandoned as well. There are 123, 508, and 552 new eucclyptus plots in the 6th, 7th, 8th inventories, and 76, 413, and 433 of them were left unabandoned in the next inventories. The land abandonment rates for them are 38.21%, 18.70%, 21.56%, respectively.Table 3 Quantity of newly-cultivated, retained, and abandoned sample plots during different NFIs.Full size tableWe examine how the productivity changes with time for eucalyptus plantations that have been operated for more than 20 years. From the 5th to 9th NFIs, we find 55 and 38 such (operating for more than 25 and 20 years, respectively; Table 4). It is found that the productivity of eucalyptus is relatively low in the first 5 years of its growing. The productivity increases in the 5th–10th years, and reaches its peak after 10–15 years of growing eucalyptus. For land that have been continuously growing eucalytptus for 15–25 years or more, the productivity decreases significantly. This is due to the decrease in soil fertility.Table 4 Relationship between Continuous planting time and Mean productivity of reserved and newly-cultivated eucalyptus sample plots during different NFIs (unit: m3 hm−2 a−1).Full size tableMost (~ 90%) of the sample plots have mean annual productivity below 10 m3 hm−2 a−1 (Fig. 4). The productivity reaches its peak after 10–15 years of growing eucalyptus (mean annual average: 7.0175 m3 hm−2 a−1), and starts to decrease afterwards. Statistical model (Table 5) is established between productivity and age of eucalyptus plots. The results suggest that eucalyptus productivity follows a consistent pattern: it increases with time until a peak and then decrease (Fig. 5), and this applies to the old, newly-included plots and their average. The statistical model also agrees to the observed data, suggesting that the productivity peak is reached 10–15 years after the planting of eucalyptus, and the productivity reaches its minimum or even zero after 50 years of growing eucalyptus continuously.Figure 4Distribution of mean annual productivity for sample plot from different NFIs.Full size imageTable 5 Productivity prediction model of multi-stage reserved and increase eucalyptus sample plots.Full size tableFigure 5Statistical model showing how mean annual productivity of eucalyptus sample plots changes with time.Full size imageSoil fertility variation of eucalyptus plantationsHow eucalyptus affects soil fertility is not well-studied. Here, based on 948 sample points from Tang32, which includes monitoring of soil fertility of eucalyptus plantations from 1993 to 2018, we report and study the temporal soil fertility variation for eucalyptus plantations. After 25 years of growing eucalyptus, acidification of the corresponding lands persists. The pH value changed to 4.63 in 2018, a 4.14% decrease compared to that in1993. The organic content within the soil reached its minimum of 17.98 g/kg in 2018, a decrease of 23.19% compared to 1993. Total nitrogen content of the investigated samples changed from 2.11 to 1.98 g/kg, and total phosphorus content decreased from 1.12 to 0.75 g/kg. The temporal variation of potassium does not change in a consistent pattern with time. Alkaline hydrolysis of nitrogen and available potassium content in 2018 are significantly lower than those in 1993. From more to less, the rank of soil fertility indicator affiliation polygon area is 1993  > 1998  > 2003  > 2013  > 2018  > 2008. The rank of soil fertility index is 1993  > 1998  > 2018  > 2003  > 2013  > 2008. It decreased first, and then increased. The minimum soil fertility (0.475) was reached in 2008 (22.51), which is smaller than that in 1993. The soil fertility decreases at the greatest rate after 15 years of growing eucalyptus. This argument from Tang32 is consistent with this work (Table 6). Soil fertility generally decreases with the age of eucalyptus plantations.Table 6 Evolutionary characteristics of soil chemical indicators in eucalyptus plantation forests.Full size tableIn addition, Parfitt et al.33 studied the variation of soil fertility of pine plantations in New Zealand for a period of 20 years, and found that long-term successive rotations lead to an increase of the soil C/N ratio. Carbon is lost at a speed much greater than nitrogen. Successive rotations of eucalyptus lead to environmental issues such as decrease in soil fertility and ecological diversity and soil erosion. These would limit the sustainable management of eucalyptus plantations34,35,36,37,38,39,40.Abandonment of sample plotsWe find that many sample plots were not used for growing eucalyptus anymore after each inventory. The abandonment rate is high, ranging from 18.7 to 38.21%. The 226 eucalyptus sample plots in the 5th inventory decreased to 103 (the others are abandoned) during the 7th inventory, and the land abandonment rate was 31.33%. In the 8th and 9th inventories, the abandonment rates are 30.10% and 23.61%, respectively. The cumulative land abandonment rates are 33.63%, 54.43%, 68.15%, and 75.66% after 5, 10, 15, and 20 years of growing eucalyptus, respectively (Table 7).Table 7 Quantity (rates) of retained and abandoned sample plots after certain periods of plantation management.Full size tableThere are a total of 1843 eucalyptus plots from the 5th to 9th NFIs. In the last NFI, there are 1282 sampled plots still growing eucalyptus, and the rest 561 plots are abandoned. The averaged land abandonment rates of these plots every 5 years are 23.92%, 24.26% (43.52% cumulatively), 32.10% (68.48% cumulatively), and 23.61% (75.66% cumulatively) over 5, 10, 15, and 20 years, respectively.These data suggest that the abandonment rate of eucalyptus plantations reaches its peak (about one third) after 15 years of operation. For other time intervals (i.e., 5, 10, and 20 years), the rate remains at around 25%. This is related to the management of eucalyptus plantations in the south of China: the first eucalyptus harvest cycle is about 6 years. The second generation of eucalyptus reproduces by division propagation (sprout naturally) with 4 years of harvest cycle, and the third generation follows the same pattern. These amount to 15–16 years-long period for plantation management. Eucalyptus requires stubble-cleaning after twice of division propagations (sprout reproduction), and needs to be re-planted. This is consistent with the timing of abandonment rate peak as stated above. It is highly likely that the eucalyptus plantations are abandoned due to the low soil fertility, and plantation managers or land owners decide to stop growing eucalyptus as a result.A simple statistical model (second-order polynomial) is established between eucalyptus plantations abandonment rate and time (Fig. 6), which suggests that all plantations will stop growing eucalyptus after 50 years, and the corresponding lands will be used for other purposes. The expansion of eucalyptus plantations relies on sustained cultivation of new lands (land reclamation). The total area of eucalyptus plantations reached 5,647,400 hectares in the 9th NFI, but only 4.29% of them (that) have been continuously growing eucalyptus since the 5th NFL (i.e., 24.34% of the plots from the 5th NFI are kept).Figure 6Statistical model showing how abandonment and replanting rates of eucalyptus plantations change with time.Full size imageThere are two main reasons to explain the loss of eucalyptus plantations. The land might be taken over for non-agricultural use (e.g., infrastructure and building construction), or they could be used for growing other crops. The latter is help for soil fertility restoration and soil microorganism readjustment. As most eucalyptus plantations in China are cultivated on lands with poor growing conditions, most of them were abandoned voluntarily by the land owner or plantation manager as stated earlier.After harvest, eucalyptus plantations could be reused for the continuation of eucalyptus growing or used for other purposes (e.g., growing other crops). The plots that were temporarily not used for growing eucalyptus could be used for re-growing it under certain conditions after a certain time period. We investigate the replanting rate of the 561 abandoned eucalyptus plantations, and study whether the abandoned plantations are used for growing other outcrops, and, if so, the corresponding tree species (Tables 8, 9, 10, 11). The 6th NFI data suggests that there are 76 plots abandoned after the 5th NFI. Their replanting rates are 2.63%, 7.89% (10.53% cumulatively), 0.00% (10.53% cumulatively), and 5.26% (15.79% cumulatively) within every 5 years, and after 5, 10, and 15 years of abandonment. For all the 561 abandoned plots, replanting rates are 9.09%, 5.53% (cumulatively 14.62% within 10 years), 0.53% (cumulatively 15.15% within 15 years), 5.26% (cumulatively 15.86% within 20 years) within every 5 years, and after 5, 10, and 15 years of abandonment. These suggest that about five sixth of the abandoned plots had not replanted eucalyptus for at least 20 years since abandonment.Table 8 Land use of eucalyptus plantation sample plots during different NFIs.Full size tableTable 9 Temporal change of tree species planted in sample plots.Full size tableTable 10 Replanting rate of eucalyptus plantations.Full size tableTable 11 Productivity of plantations that have replanted eucalyptus.Full size tableA simple statistical model is established between replanting rate and time (second-order polynomial) based on the current data (Fig. 5). It suggests that the replanting rates after 30 and 50 years are around 20% and 30%, respectively. These suggest that if the plantation management does not improve significantly, it would be difficult to maintain the current supply of eucalyptus and areal distribution of its plantations in the long term. It is necessary to rely on both land rotation and cultivation of new lands to maintain the current supply of eucalyptus.The NFI data suggests that very few eucalyputs plots are turned to non-plantation purposes. The exception is from the 6th NFI in which 34.21% of plots have been used for other purposes after harvest. This rate is below 20% for all other inventory data. A lot of abandoned eucalyptus plantations are still used as plantations, and they are for growing eucalyptus, and the rate of regrowing eucalyptus tends to remain low for a long period of time (below one sixth after 20 years based on current data). This is because eucalyptus grows fast with high productivity, and it has high demand for soil fertility and water. Land rotation is necessary after a few harvest cycles to restore the soil fertility, which would take relatively long period of time before the land becomes suitable to regrow euccalyptus. Among the 561 abandoned eucalyptus plots, broad-leaf and economic tree species are the most commonly planted species after stop growing eucalyptus (e.g., rubber tree and Lychee; 18.54% and 18.36%; Table 9). The greater variability of land use for the abandoned plots suggests greater management intensity. Afforestation with eucalyptus is dominated by short rotation period (harvest cycle). Frequently modifying tree species planted within plantations helps maintain a high productivity of the land.
Carbon storage and fixation of eucalyptus plantationsVariability in carbon storageBased on the 9th NFI data and Eqs. (2–8), we calculate the BEF of eucalyptus in each province (Table 12). The results suggest that the BEF ranges from 0.982–1.652 with a weighted average of 1.236 (weight determined by stock volume).Table 12 BEF of eucalyptus by province.Full size tableCalculation from Eqs. (9) and (10) suggests that the total carbon storage (excluding harvest volume) of eucalyptus in China is 2.40 TgC (1973–1976, 1Tg = 1012 g), 4.14 TgC (1977–1981), 2.73 TgC (1984–1988), 5.42 TgC (1989–1993), 9.73 TgC (1994–1998), 12.58 TgC (1999–2003), 28.90 TgC (2004–2008), 98.61 TgC (2009–2013), and 133.00 TgC (2014–2018) in different time periods in the past 45 years (Table 13).Table 13 Eucalyptus carbon density and storage by province.Full size tableThe carbon storage of eucalyptus increased rapidly in the past 45 years especially since the end of last century. This is due to the rapid expansion of eucalyptus plantations in China, and its carbon storage in 2014–2018 is 55.42 times of that in 1973–1976. The carbon density per square hectometer also increases from 5.22 MgC (1 Mg = 106 g) in 1973–1976 to 12.16 MgC in 2014–2018, about 2.33 times of the former.Carbon fixationThe mean annual productivity of eucalyptus is 8.57 m3 hm−2 a−1 in 2014–2018 based on the 9th NFI. This is a lot greater compared to other species widely planted in the same areas (Pinus massoniana Lamb.: 2.91 m3 hm−2 a−1; Cunninghamia lanceolata Lamb.: 3.93 m3 hm−2 a−1). Using the stock volume biomass method with BEF being 1.2336 (from previous calculation) and carbon storage coefficient of 0.5, the mean annual carbon fixed by eucalyptus is 5.29 t hm−2 a−1, which are about 2.95 and 2.18 times that of Pinus massoniana Lamb. (1.79 t hm−2 a−1) and Cunninghamia lanceolata Lamb. (2.42 t hm−2 a−1), respectively. This shows that eucalyptus is characterized by high biomass productivity and high carbon fixation capability. It thus plays an important role in maintaining the carbon balance in China. More

CORRESPONDENCE
05 October 2021

Fund natural-history museums, not de-extinction

Corrie S. Moreau

 ORCID: http://orcid.org/0000-0003-1139-5792

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Jessica L. Ware

 ORCID: http://orcid.org/0000-0002-4066-7681

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Corrie S. Moreau

Cornell University, Ithaca, New York, USA.

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Jessica L. Ware

American Museum of Natural History, New York City, New York, USA.

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The only way to study extinct species is by leveraging the irreplaceable collections of natural-history museums. It is unfortunate, then, that instead of supporting these often imperilled institutions, private investors are spending millions on attempts to resurrect species. For example, the US start-up firm Colossal Laboratories and Biosciences, co-founded by synthetic biologist George Church, is exploring such feats.

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Nature 598, 32 (2021)
doi: https://doi.org/10.1038/d41586-021-02710-4

Competing Interests
The authors declare no competing interests.

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