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Extensive hydrodynamic model validation
To provide a comprehensive validation of the modeled surge-tide-wave dynamics during Sandy, this study made use of all available field data from an extensive network of coastal water level gauges and wave buoys in the study region, as well as hundreds of HWMs and many rapid deployment surge sensors (SI Fig. S1). The good agreements between simulated and observed water levels and waves were quantified in terms of the root-mean-square error (RMSE) and correlation coefficient (CORR).
Storm tide
Available data included those from hundreds of permanent and temporary surge sensors from NOAA (SI Fig. S2), Hudson River Environmental Conditions Observing System (HRECOS), and USGS. Statistics (SI Table S1) showed excellent agreement between the time series of simulated and measured storm tide data. The averaged RMSE at USGS temporary sites was 0.20 m and the averaged CORR was 0.94. CH3D-SSMS successfully reproduced the surge and tides with high confidence in not only the open ocean but also the complex estuarine system during Sandy (SI Fig. S3) with a maximum coastal storm tide at NY Bight of 3.36 m and NJ coast of 3.27 m.
High water marks and inundation
A total of 526 out of 653 independent HWM locations were located within the study region, most of which clustered in NJ and NY coastal areas. 83.7% (440 out of 526) of HWMs were captured by the CH3D-SSMS model grid and compared with the surveyed values. The model results had 0.33 m RMSE and 0.87 CORR (SI Fig. S4). When only “good” and “excellent” HWMs (rated by USGS) were used, the RMSE dropped to 0.30 m, and CORR increased to 0.90. The noticeable data disagreements (Fig. SI 4) were caused by the inconsistency of surveyed data.
Wave
During Sandy, four wave-buoys within the domain recorded the wave data: two at the apex of NY Bight, and another two in Long Island Sound (LIS). The significant wave height (H_{sig}) and peak wave period (T_{p}) simulated by the Simulating Waves Nearshore (SWAN) model in the CH3D-SSMS were compared with measured data and Wave Watch III (WW3) operational run results (SI Fig. S5)25,26. SWAN more accurately captured the evolution of (H_{sig}) in NY Bight and LIS. Maximum significant wave height over land was as high as 2.17 m at NY Bight and 2.60 m along NJ coast.
Impact of wetland on surge and wave during super storm sandy
To estimate the value of the coastal wetlands in reducing flood, we calculated the following four metrics for inundation: the Average Inundation Height ((AIH)), the Maximum Inundation Height ((MIH)), the Total Inundation Area ((TIA)), and the Total Inundation Volume ((TIV)) with and without wetlands. The (TIA) and (TIV) are defined as

$$ TIA = iint_{{{text{Landward}};{text{area}}}} {dxdy} , $$
(1)

$$ TIV = iint_{{{text{Landward}};{text{area}}}} [H_{max} left( {x,y} right) – H_{0} left( {x,y} right)]dxdy, $$
(2)

where (H_{max} left( {x,y} right)) and (H_{0} left( {x,y} right)) are the maximum water level and the land elevation at land cells (left( {x,y} right)), respectively11,12. The wave analysis was carried out by calculating the average wave height ((AWH)), the maximum wave height ((MWH)), and the total wave energy ((TWE)) which is defined as

$$ TWE = iint_{{{text{Landward}};{text{area}}}} [frac{1}{8}rho_{w} g{ }left( {H_{{rms,max{ }}} } right)^{2} ]dxdy, $$
(3)

where (H_{rms max}) is the maximum root-mean-square wave height over the flooded land. The wave energy, instead of wave height, is more directly related to the wave-induced structure loss.
We define the relative inundation reduction ((RIR)) as the difference in (TIV) (value without wetland minus value with wetland), divided by the (TIV) with wetland. The relative wave reduction ((RWR)) is defined accordingly using the (TWE). The inundation and wave analysis were carried out at the regional level (SI Table S2) and zip-code resolution (Figs. 1, S6, S7).
Figure 1

Zip-code resolution wetland’s effect on (TIV) and (TWE) during Sandy in 2012. Map showing zip-code resolution avoidance in (A) (TIV) and (B) (TWE) during Sandy without wetlands, as a percentage of those for the with-wetland scenario. Dark red values show zip-code with the most wetland benefit while dark blue areas have the least wetland benefit. Negative values indicate that the presence of wetland would increase (TIV)/(TWE) and positive values indicate that wetland would lower (TIV)/(TWE). The map is produced using ESRI ArcGIS Pro 2.7 (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

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If the wetlands were absent during Sandy, the (RIR) of the entire model domain would have been 4% and the (RWR) would have been 19%. Breaking the entire model domain into 6 regions (Table S2): South NJ (SNJ), Central NJ (CNJ), North NJ (NNJ), CT, Long Island (LI), and Mainland NY (MNY), it was found that the (RIR) during sandy was low ( 50%) (RWR), while LI. CNJ and SNJ had significant (~ 25–50%) (RWR). The regions with the highest (RIR) and (RWR) were closest to the storm landfall location.
Benefit of wetland on surge and wave for the 1% annual chance flood
The maximum 1% annual chance maximum flood elevation (Fig. S9) was 5.15 m for NY Bight while 5.34 m for the NJ coast. The 1% annual chance maximum wave height (Fig. S9) overland is 2.24 m at NY Bight and 1.90 m at NJ coast. All the regions had moderate (RIR) except for SNJ which had a significant (RIR) due to highest (25.4%) wetland cover (see Table S2) and NNJ experienced significant (RWR) while the remaining regions had moderate values. CT ranked 2nd in (RIR ) although it has less wetland cover than CNJ because the mostly woody wetlands in CT are more effective in buffering storm surge than the marshes in NJ and NY. On the other hand, CT ranked 3rd in (RWR) due to the blocking of offshore wave energy by LI. (RIR) and (RWR) are found to be functions of storm characteristics, wetland type, and cover, and local conditions. Fig. S10 shows the percent wetland cover, RIR (relative TIV reduction), and RWR (relative wave energy reduction) in six regions (New York, North New Jersey, Long Island, Connecticut, Central New Jersey, and South New Jersey) during 1% annual chance events. As the wetland cover increases from less than 5% (NY, NNJ, and LI) to more than 10% in CNJ and SNJ, RIR and RWR generally increase, showing the increasing role of wetland in reducing inundation and wave. Relative reduction in inundation and wave energy are modest, between 10 and 30%. NNJ has properties behind the relatively sparse marsh, followed by woody wetland which protects properties behind them. Connecticut has less wetland than Central Jersey, but the mostly woody wetland is more effective in reducing flood and wave.
Benefit of wetlands on reducing residential structure loss
The monetary loss of residential structures was estimated using the simulated inundation and wave results while employing damage functions from the United States Army Corps of Engineer (USACE) North Atlantic Comprehensive Coastal Study (NACCS) and was validated using the NFIP building loss payouts aggregated by zip-code21,24. Direct simulation of wave-induced damage requires understanding and calculation of wave loads on structures using a depth- and phase-resolving model, which is beyond the capability of the models used in this study. Therefore, in this paper, we did not directly simulate wave-induced damage, but are accounting for wave-induced change in total water level which results in increase in estimated damage based on depth-damage functions. Overall, 96 coastal zip-codes in the state of NJ were used to validate the estimated loss. The model showed a correlation coefficient (CORR = 0.69) between simulated structure losses and NFIP payouts (Fig. 2). In NJ, as of 2019, the total NFIP payout was $3.9 billion USD, in comparison to the estimated total structure loss of $3.6 billion USD (SI Table S3), with an absolute error of 7.7%. This good agreement, plus the good agreement between the simulated and observed surge and wave reported earlier, confirms the validity of our “dynamics-based” loss assessment.
Figure 2

Economic model validation at zip-code resolution. Simulated losses during Sandy in NJ using the USACE damage functions versus FEMA NFIP payouts. Results were aggregated by zip-code and the corresponding correlation coefficient is 0.69 (R2 = 0.47). Validation use transformed structure loss ((PL_{T})) instead of the structure loss ((PL)). The figure is produced using MATLAB R2020 (https://www.mathworks.com/).

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We define the structural loss reduction ((SLR)) as the structural loss without wetland minus the structural loss with wetland, and the relative structural loss reduction ((RSLR)) as the ratio between (SLR) and the loss with wetland. A state-level analysis of structure loss in NJ showed a (RSLR) of 8.5%, 26.0%, and 52.3% for Sandy, BS storm, and 1% chance flood/wave, respectively (Table S3). Analysis of losses due to flood and wave indicated that for Sandy and the 1% event, most of the loss came from flood, while most of the loss in the BS storm came from waves. Avoided wave-induced loss was comparable to the avoided flood-induced loss during the BS storm and the 1% event, but much higher during Sandy, suggesting that NJ wetlands are more effective in reducing wave-induced loss vs. flood-induced loss. Results from the zip-code scale analysis during Sandy (Fig. 3) showed less variability: for most north NJ, (RSLR) ranged from 10 to 100% except for those along the Hudson River that had small increased loss (negative avoided loss). Most zip-codes in SNJ had (RSLR) between 5% and more than 100%. (RSLR) during the BS storm (Fig. S11) was more notable: ~ 50–100% for north NJ, 0–100% for central NJ. The 1% flood event (Fig. 4) showed an average RSLR  > 25% for NJ.
Figure 3

Percent structural loss reduction during Sandy. Map showing zip-code resolution avoided loss (difference in loss without wetlands and loss with wetlands) during Sandy, as a percentage of the loss of the with-wetland scenario. Dark red values show zip-code with the highest wetland benefit, while dark blue areas have the least benefit. Negative values indicate that the presence of wetland would increase structural losses and positive values indicate that wetland would lower the structural losses. The results are shown for the NJ coastal zip-codes affected by Sandy. This study shows that the percent avoided loss in this figure does not always represent the actual wetland value for loss reduction because areas with few structures and losses could give misleadingly high values of percent avoided loss, as shown in south NJ. The primary purpose is for comparison with a similar figure in NAR17. The map is produced using ESRI ArcGIS Pro 2.7 (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

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Figure 4

Effect of wetlands on structural losses over zip code scale during the 1% annual event. Map showing zip code resolution difference in losses if the wetlands were absent, as a percentage of the wetland present scenario. Dark red values show zip code with the highest benefit of having wetlands while dark blue areas show the least benefited area. Negative values indicate that the presence of wetland would increase structural losses and positive values indicate that wetland would lower the structural losses. The map is produced using ESRI ArcGIS Pro 2.7 (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).

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The above results showed that the value of coastal wetlands for flood/wave protection varies significantly with the storm. While wetlands may be more effective in reducing wave loss in some storms but flood loss in other storms, they may be ineffective in extreme storms. The 1% annual chance flood and wave event, which resulted from an ensemble of many less extreme but more frequent storms28, provides a more reasonable integrated scenario for the loss analysis. This is similar to the preferred use of the 1% flood map, instead of the flood map associated with a single design storm, for assessing the flood risk in any coastal region.
As shown in Fig. 5, in zip-codes with larger wetland coverage area in SNJ, wetlands could only prevent  $60,000 annual loss. On the other hand, wetlands in north NJ zip-codes with smaller wetland coverage area would increase the loss. The annual loss in most of the zip-codes is  More

A new biodiversity decade is about to start and hopefully will achieve just progress for both people and the planet.

In October 2010, the Convention on Biological Diversity (CBD) signed up to a promising 10-year framework, the Strategic Plan for Biodiversity 2011–2020, which represented a major step to ensure that by 2050 biodiversity would be ‘valued, conserved, restored and wisely used’ in a world where people live in harmony with nature, according to the vision stated in the strategic plan. Underpinning the plan were the so-called 20 Aichi targets aiming to articulate the specific areas of intervention to achieve a set of broad strategic goals — addressing the causes of biodiversity loss and promoting its sustainable use, safeguarding ecosystems, ensuring all would benefit from biodiversity and promoting implementation through participatory planning. The CBD referred to the targets as ambitious but achievable. A decade later, in October 2020, signatories to the CBD were due to discuss progress towards the Aichi targets at the fifteenth Conference of the Parties (COP 15) in Kunming, China, but the meeting was postponed until May 2021 due to the impacts of the COVID-19 pandemic. In the lead up to the meeting, research efforts and wider debates about the Aichi targets have intensified and have all sent a strong and unequivocal message: the state of biodiversity is now worrying more than ever; the Aichi targets have largely not been met. What went wrong? Some feel the problem lays with the ever-lasting challenge of measuring living nature, others refer to inadequate national implementation strategies. Answering the question for CBD members is of critical importance. As COP 15 to the CBD is also the gathering where the parties will agree on a post-2020 biodiversity framework, it is crucial for them to learn from past mistakes. Indeed, the parties have been working to define a new framework and a new set of targets, this time for 2030. Back in October, in preparation for the COP, a revised draft of the new set of biodiversity targets was released. Waiting for the final deliberations in China, some experts have already raised concerns about the effectiveness of the proposed new framework. More specifically, some point to the lack of recognition of the role of agro-ecosystems in conservation, reflecting on the challenges of protecting more land in regions under pressure to feed a growing population (for example, Sub-Saharan Africa). Aichi target 11 required that by 2020 at least 17% of terrestrial areas and 10% of marine areas across the world, especially those of critical importance for biodiversity, should be conserved and equitably managed. Alarmed by lack of progress towards the target, prominent conservationists proposed to ramp up the ambition and protect half of the Earth’s surface to halt biodiversity decline. A study by Mehrabi and colleagues assessed the food production costs of the proposal and found that globally about 23–25% of non-food calories and 3–29% of food calories could be lost. Another study by Schleicher and co-authors found that in the event of including all ecoregions in the proposal there would be substantial socio-economic impacts as over one billion people currently live in areas that would be protected under Half Earth. Both articles, part of a joint collection with Nature Ecology & Evolution, shed light on the challenges of reconciling development needs with biodiversity protection. Some possible solutions to this challenge are controversial; for example, biodiversity offsetting, which refers to actions intended to deliver biodiversity gains as a way of compensating for development impacts. In another study in the collection, Damiens et al. analysed policy documents produced between 1958 and 2019 and showed that biodiversity offsetting was historically promoted through approaches encouraging economic growth with no consideration of biocultural limits. They showed also that, recently, more transformative approaches include offsetting as a tool to transition towards economic systems respectful of planetary boundaries, but their success hinges on realizing quite challenging governance changes.

Credit: Ines Porada / Alamy Stock Photo

Realizing the CBD vision by 2050 requires acknowledging the need of transformative change. What does it mean in practice? It means understanding as best we can the complexity of human–nature interactions. But it also means accepting that there is a lot we don’t know and most probably won’t have enough time to know if we don’t act now. Against this backdrop, the biodiversity community can influence the debate about what should happen in order to sustainably manage living nature. How? Perhaps, as Wyborn and colleagues suggest in their paper, also part of the collection, they could use some imagination to visualize how the future will unfold from the choices made today. There are multiple possible paths to set the world on course to live in harmony with nature — none is perfect, all are possible. This variety reflects diverse values and relations with biodiversity that in turn drive choices. Rather than shying away from it, the biodiversity community should embrace the challenge fully in order to define new priorities for both research and action. More

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Pot experiment of AG planting
As shown in Fig. 1, compared to CS, the survival rate of 10-year rotation AG decreased, indicating that 2-year-old AG survival rate in RS was lower than that of AG in CS. This confirmed the continued existence of AG continuous cropping obstacles in RS.
The decrease of physicochemical properties and enzyme activity
Plant growth requires water and nutrients. Because soil physicochemical properties influence water and nutrient availability, changes in soil physicochemical properties directly affect AG growth. In the present study, the water content of RS was significantly higher than that of CS under the same management conditions (Table 1). Shu et al.29 found that high soil water content induced root rot disease in AG when sandy loam water content exceeded 30% or that of clay exceeded 50%. Similarly, according to Wang et al.30, the incidence of rust rot positively correlated with soil moisture and rainfall. Therefore, high soil water content, caused by changes in soil physicochemical properties, may negatively affect AG replanting. Furthermore, the pH of RS was significantly lower than that of CS (Table 1). According to Rahman and Punja24, root rot severity at soil pH 5.05 was greater than that at pH 7.0, indicating that acidic conditions can negatively affect AG health. In addition, the available K content in RS was lower than that in CS (Table 1). Sun31 found that AG should be fertilized (N, P, K fertilizer) from emergence to early flowering, when its demand for potassium fertilizer is the highest, suggesting that AG has a high potassium requirement. The levels of ammonium N, nitrate N, available P, and available K, but not of total N and total C, were generally lower in RS than in CS (Table 1), indicating that the cultivation of AG may have long-term negative effects on these soil nutrients. The same trend was observed for soil enzyme activity. Urease, a nickel-containing enzyme, catalyzes the hydrolysis of urea into carbonate and ammonia. Here, urease activity was significantly higher in CS than in RS. Average phosphatase and sucrase activities were also higher in CS than those in RS, although these differences were not significant (Table 2). Yang32 also found that the activities of sucrase, urease, and phosphatase decreased during AG cultivation. In summary, compared to that of CS, RS had lower fertility, but higher soil water content and lower pH, two conditions which are conducive to AG disease, and that may, therefore, present obstacles to AG replanting.
The dual effects of phenolic acids
The results showed that the content of salicylic acid in RS was significantly higher than that in CS. Yang16 found that among the various phenolic acids tested, salicylic acid had the strongest inhibitory effect on AG radicle growth. In our study, higher salicylic acid content in RS may have posed direct autotoxicity to AG. As a major defense hormone, salicylic acid has the function of enhancing immune signals and reprogramming defense transcriptomes33. After planting AG, the soil salicylic acid content increased, which indicated that AG might release more salicylic acid in the growth process to improve immune response to the surrounding environment. Therefore, the role of salicylic acid in the continuous cropping obstacles to AG cultivation deserves further study.
In addition, we found that the content of most phenolic acids, such as p-coumaric, p-hydroxybenzoic, vanillic, caffeic, and cinnamic acid, decreased after AG cultivation, and had not returned to the levels in CS even after 10 years of subsequent crop rotation. AG requires a suitable environment for growth. Before germination in spring, the ginseng farmers’ association uses wheat straw to cover the soil, which not only maintains soil temperature and retains soil moisture, but also improves soil quality and promotes the growth of AG seedlings. Jia et al.34 detected the increase in ferulic, vanillic, cinnamic, and p-hydroxybenzoic acid in a wheat-corn rotation area. In addition, Zheng et al.35 found that straw return, a common method for soil improvement, also increased the concentration of phenolic acids in soil. In our study, the increased phenolic acid content in CS relative to RS may have been beneficial to the growth of AG. Similar to our research results, Jiao et al.36 also found that the content of phenolic acid substances such as syringic, vanillic, p-coumaric, and ferulic acid decreased by 49.1–81% after adding AG root residues (simulating the seasonal AG leaf and fibrous root senescence). Therefore, decreases in the soil contents of some phenolic acids after planting AG may underlie the decline of other soil properties, which is not conducive to the subsequent growth of AG.
As described above, some phenolic acids may be beneficial to the growth of AG; if so, by what mechanism do these beneficial phenolic acids exert their role? Phenolic acids are produced by plants under external stress37,38,39,40. They do have many beneficial functions, such as antibacterial, antioxidant and so on, which can alleviate the stress of plants41. However, with the increase of phenolic acid secretion, some phenolic acids will penetrate into the soil and affect the soil microorganisms. Li et al.42 found that cinnamic acid inhibits Cylindrocarpon destructans (a pathogen of ginseng) growth at high concentrations, while promoting it at low concentrations. Yang et al.43 found that vanillic acid promoted the growth of the pathogens Rhizoctonia solani and Fusarium solani at low concentrations, but inhibited it at high concentrations; many phenolic acid compounds can inhibit the proliferation of Phytophthora cactorum (a pathogenic bacterium that causes AG phytophthora disease) at high concentrations. In addition, Yuan et al.44 found that p-coumaric acid strongly suppressed the in vitro growth of fungi, significantly reducing the decay caused by Alternaria alternata. Therefore, it can be seen that phenolic acids have inhibitory effects on pathogens at higher concentrations. With a decrease in soil phenolic acid content, this inhibitory effect on pathogenic bacteria will be weakened, resulting in an imbalance in the soil microbial composition that affects AG growth performance. Overall, soil phenolic acid content may indirectly affect AG growth performance by affecting soil microorganisms.
The change in the relative abundance of key bacteria
Our results showed that there was no significant difference in bacterial α-diversity between 10-year post-ginseng RS and CS, but there were differences in β-diversity, which reflects community composition and structure, between CS and RS. In other words, there were significant differences in the relative abundance of key bacteria in the bacterial community, such as Chlamydiae (phylum level, RS: 0.28%, CS: 0.10%, P = 0.035), within this phylum, the c_Chlamydiae, o_Chlamydiales, f_Simkaniaceae, and g_uncultured; Acidothermus (genus level, RS: 2.40%, CS: 5.40%, P = 0.030); Sphingomonadales (order level, CS: 2.98%, RS: 1.68%, P = 0.002), Sphingomonadaceae (family level, CS: 2.88%, RS: 1.48%, P = 0.004), genera Novosphingobium (CS: 0.03%, RS: 0.20%, P = 0.035) and Sphingomonas (CS: 2.83%, RS: 1.10%, P = 0.000); Rhodanobacter (CS: 0.38%, RS: 3.45%, P = 0.050); Arthrobacter (CS: 0.03%, RS: 0.43%, P = 0.001); Mizugakiibacter (CS: 0.63%, RS: 2.28%, P = 0.048); Jatrophihabitans (CS: 1.15%, RS: 0.75%, P = 0.048); Pseudomonas (RS: 0.15%, CS: 0.03%, P = 0.029) among others (Fig. 4, see Supplementary Table S2).
There was no difference in soil bacterial α-diversity between RS and CS, which may be due to the recovery of soil bacterial diversity after 10 years of rotation. However, the results of the pot experiment showed that RS still presented continuous cropping obstacles, which indicated that restoring soil microbial α-diversity does not alleviate continuous cropping obstacles for AG. Instead, differences in microbial community composition (i.e., β-diversity), particularly the abundances of bacterial taxa that play key roles, may explain the persistence of AG continuous cropping obstacles in RS after 10 years.
Among the differences in microbial community composition, CS had higher relative abundances of some bacterial genera that may be beneficial bacteria. The genus Acidothermus had the highest abundance, and it contained a single species, A. cellulolyticus, which is thermophilic, acidophilic, and has the ability to produce many thermostable cellulose-degrading enzymes45. Therefore, higher cellulose-degrading capacity might exist in CS than that in RS. Sphingomonas, a bacterium with the ability to decompose mono- and polycyclic aromatic compounds, as well as heterocyclic compounds, was more abundant in CS than RS, suggesting that bacterial decay of recalcitrant plant compounds was also higher in CS than RS. In addition, Sphingomonas not only decomposes monoaromatic phenolic acids but also improves plant stress resistance, and it is considered a plant probiotic46. Similar to our results, Li and Jiang23 found that Jatrophihabitans relative abundance in soil used for AG for 4 years was significantly (P  root rot group  > control group; in addition, compared with CS, there was a higher abundance of Rhodanobacter in the soil in which Korean ginseng (Panax ginseng) was grown49. We also found that this genus might be increased by the influence of Panax plants, which warrants further study. Our results showed that Arthrobacter was higher in the RS group, and Jiang et al.48 also found that the relative abundance of Arthrobacter in the root rot group was higher than that in the healthy root group; therefore, we speculate that Arthrobacter might be a factor causing root rot of P. quinquefolius, leading to a continuous cropping obstacle to AG growth. Our results showed that the abundance of Pseudomonas sp. in RS was higher than that in CS (RS: 0.15%, CS: 0.03%, P = 0.029, see Supplementary Table S2). Tan et al.50 showed that Pseudomonas sp. was the main pathogen causing root rot disease in P. notoginseng. In addition, Jiang et al.48 also found that Pseudomonas is abundant in the rhizosphere soils of diseased ginseng roots. Therefore, it is necessary to further study the effects of Pseudomonas species on AG growth. To sum up, the relative abundances of a large number of bacteria that are either confirmed or potentially harmful to other plants increased in RS, which may be an important factor leading to the occurrence of continuous cropping obstacles in the 10-year post-ginseng rotation soil.
As shown in Fig. 6, there are many correlations among the three factors. The abundances of Acidothermus, Sphingomonas, Jatrophihabitans, and Actinospica were each positively correlated with that of available K, caffeic acid, and cinnamic acid, but negatively correlated with that of salicylic acid. Therefore, the interactions among phenolic acids, microorganisms, and soil nutrients evidenced possible “synergistic” or “antagonistic” effects within the microecosystem. Overall, these complex relationships are the main reason for AG continuous cropping obstacles, but it is still unknown which of these factors plays the primary role. Finally, Nitrobacter, Actinospica, Clostridium sensu stricto 1, Thermosporothrix, Holophaga, and Peptoclostridium, also showed significant differences in abundance between RS and CS (Fig. 4), which also should receive more attention. More