Philippa Kaur

Zhao, B. H. et al. Spatial distribution of soil organic carbon and its influencing factors under the condition of ecological construction in a hilly-gully watershed of the Loess Plateau China. Geoderma 296, 10–17 (2017).ADS 
CAS 
Article 

Google Scholar 
Shi, P. et al. Soil respiration and response of carbon source changes to vegetation restoration in the Loess Plateau China. Sci. Total Environ. 707, 135507 (2019).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Zhang, Y. et al. Effects of farmland conversion on the stoichiometry of carbon, nitrogen, and phosphorus in soil aggregates on the Loess Plateau of China. Geoderma 351, 188–196 (2019).ADS 
CAS 
Article 

Google Scholar 
Chang, E. H. et al. Using water isotopes to analyze water uptake during vegetation succession on abandoned cropland on the Loess Plateau China. CATENA 181, 104095 (2019).Article 

Google Scholar 
Chang, E. H. et al. The impact of vegetation successional status on slope runoff erosion in the Loess Plateau of China. Water 11, 2614 (2019).CAS 
Article 

Google Scholar 
Sun, L. Y., Zhou, J. L., Cai, Q. G., Liu, S. X. & Xiao, J. G. Comparing surface erosion processes in four soils from the Loess Plateau under extreme rainfall events. Int. Soil Water Conse. 9, 520–531 (2021).Article 

Google Scholar 
Wang, R. et al. Effects of gully head height and soil texture on gully headcut erosion in the Loess Plateau of China. CATENA 207, 105674 (2021).Article 

Google Scholar 
Wei, H., Zhao, W. W. & Wang, H. Effects of vegetation restoration on soil erosion on the Loess Plateau: A case study in the Ansai watershed. Int. J. Environ. Res. Pub He. 18, 6266 (2021).Article 

Google Scholar 
Zhang, X., Li, P., Li, Z. B., Yu, G. Q. & Li, C. Effects of precipitation and different distributions of grass strips on runoff and sediment in the loess convex hillslope. CATENA 162, 130–140 (2018).Article 

Google Scholar 
Foster, G. R., Huggins, L. F. & Meyer, L. D. A laboratory study of rill hydraulics: II Shear Stress Relationships. T Asabe. 27, 797–804 (1984).Article 

Google Scholar 
Zhu, B. B., Zhou, Z. C. & Li, Z. B. Soil erosion and controls in the slope-gully system of the Loess Plateau of China: A review. Front. Environ. Sci. 9, 657030 (2021).Article 

Google Scholar 
Wang, H., Wang, J. & Zhang, G. H. Impact of landscape positions on soil erodibility indices in typical vegetation-restored slope-gully systems on the Loess Plateau of China. CATENA 201, 105235 (2021).Article 

Google Scholar 
Chang, X. G. et al. Determining the contributions of vegetation and climate change to ecosystem WUE variation over the last two decades on the Loess Plateau China. Forests 12, 1442 (2021).Article 

Google Scholar 
Li, B. B. et al. Deep soil moisture limits the sustainable vegetation restoration in arid and semi-arid Loess Plateau. Geoderma 399, 115122 (2021).ADS 
Article 

Google Scholar 
Dong, L. B. et al. Effects of vegetation restoration types on soil nutrients and soil erodibility regulated by slope positions on the Loess Plateau. J. Environ. Manage. 302, 113985 (2022).CAS 
PubMed 
Article 

Google Scholar 
Shi, P. et al. Effects of grass vegetation coverage and position on runoff and sediment yields on the slope of Loess Plateau China. Agric. Water Manage. 259, 107231 (2022).Article 

Google Scholar 
Xia, L. et al. Soil moisture response to land use and topography across a semi-arid watershed: Implications for vegetation restoration on the Chinese Loess Plateau. J. Mt Sci. 19, 103–120 (2022).Article 

Google Scholar 
Chen, Y. X. et al. Soil enzyme activities of typical plant communities after vegetation restoration on the Loess Plateau China. Appl. Soil Ecol. 170, 104292 (2022).Article 

Google Scholar 
Qiu, L. J. et al. Quantifying spatiotemporal variations in soil moisture driven by vegetation restoration on the Loess Plateau of China. J. Hydrol. 600, 126580 (2021).Article 

Google Scholar 
Fang, H. Y., Li, Q. Y. & Cai, Q. G. A study on the vegetation recovery and crop pattern adjustment on the Loess Plateau of China. Afr. J. Microbiol. Res. 5, 1414–1419 (2011).Article 

Google Scholar 
Hu, C. J., Fu, B. J., Liu, G. H., Jin, T. T. & Guo, L. Vegetation patterns influence on soil microbial biomass and functional diversity in a hilly area of the Loess Plateau China. J. Soil Sedim. 10, 1082–1091 (2010).CAS 
Article 

Google Scholar 
Sun, C. L., Chai, Z. Z., Liu, G. B. & Xue, S. Changes in species diversity patterns and spatial heterogeneity during the secondary succession of grassland vegetation on the Loess Plateau China. Front. Plant Sci. 8, 1465 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, J. X. Threholds in vegetation-precipitation relationship and the implications in restoration of vegetation on the Loesee Plateau China. Acta Ecol. Sin. 25, 1233–1239 (2005).
Google Scholar 
Yang, X., Shao, M. A., Li, T. C. G, M. & Chen, M. Y. Community characteristics and distribution patterns of soil fauna after vegetation restoration in the northern Loess Plateau. Ecol. Indic. 122, 107236 (2021).Bullock, M. S., Nelson, S. D. & Kemper, W. D. Soil cohesion as affected by freezing, water content, time and tillage. Soil Sci. Soc. Am. J. 52, 70–776 (1988).Article 

Google Scholar 
Wang, T. et al. Effects of freeze-thaw on soil erosion processes and sediment selectivity under simulated rainfall. J. Arid Land. 9, 34–243 (2017).
Google Scholar 
Su, Y. Y., Li, P., Ren, Z. P., Xiao, L. & Zhang, H. Freeze–thaw effects on erosion process in loess slope under simulated rainfall. J. Arid Land. 12, 937–949 (2020).Article 

Google Scholar 
Slattery, M. C. & Burt, T, P. Particle size characteristics of suspended sediment in hillslope runoff and stream flow. Earth Surf. Proc. Land. 22, 705–719 (1997).Wu, F. Z., Shi, Z. H., Yue, B. J. & Wang, L. Particle characteristics of sediment in erosion on hillslope. Acta Pedol. Sin. 49, 1235–1240 (2012).
Google Scholar 
Issa, O. M., Bissonnais, Y. L. & Planchon, O. Soil detachment and transport on field-and laboratory-scale interrill areas: Erosion processes and the size-selectivity of eroded sediment. Earth Surf. Proc. Land. 31, 929–939 (2006).ADS 
Article 

Google Scholar 
Shi, Z. H. et al. Soil erosion processes and sediment sorting associated with transport mechanisms on steep slopes. J. Hydrol. 454–455, 123–130 (2012).Article 

Google Scholar 
Koiter, A. J., Owens, P. N. & Petticrew, E. L. The behavioural characteristics of sediment properties and their implications for sediment fingerprinting as an approach for identifying sediment sources in river basins. Earth Sci. Rev. 125, 24–42 (2013).ADS 
CAS 
Article 

Google Scholar 
Pan, C. Z. & Shang, G. Z. P. Runoff hydraulic characteristics and sediment generation in sloped grassplots under simulated rainfall conditions. J. Hydrol. 331, 178–185 (2006).ADS 
Article 

Google Scholar 
Pan, C. Z. & Shang, G. Z. P. The effects of ryegrass roots and shoots on loess erosion under simulated rainfall. CATENA 2007(70), 350–355 (2007).
Google Scholar 
Zheng, M. G., Cai, Q. G., Wang, C. F. & Liu, J. G. Effect of vegetation and other measures for soil and water conservation on runoff-sediment relationship in watershed scale. J. Hydraul. Eng. 38, 47–53 (2007).
Google Scholar 
Wei, X. et al. Flow characteristics of convex composite slopes of loess under vegetation cover. Trans. Chin. Soc. Agric. Eng. 30, 147–154 (2014).CAS 

Google Scholar 
Wang, L. et al. Rainfall kinetic energy controlling erosion processes and sediment sorting on steep hillslopes: A case study of clay loam soil from the Loess Plateau China. J. Hydrol. 512, 168–176 (2014).ADS 
Article 

Google Scholar 
Li, M., Yao, W. Y., Ding, W. F., Yang, J. F. & Chen, J. N. Effect of grass coverage on sediment yield in the hillslope-gully side erosion system. J. Geogr. Sci. 19, 321–330 (2009).Article 

Google Scholar 
Benito, E., Santiago, J. L., Blas, E. D. & Varela, M. E. Deforestation of water-repellent soils in Galicia (NW Spain): Effects on surface runoff and erosion under simulated rainfall. Earth Surf. Proc. Land. 28, 145–155 (2003).ADS 
Article 

Google Scholar 
Han, P. & Li, X. X. Study on soil erosion and vegetation effect on soil conservation in the Yellow River Basin. J. Basic Sci. Eng. 16, 181–190 (2008).
Google Scholar 
Bissonnais, Y. L. Aggregate stability and assessment of soil crustability and erodibility: I. Theory and methodology. Eur. J. Soil Sci. 47, 425–437 (1996).Zhang, X., Yu, G. Q., Li, Z. B. & Li, P. Experimental study on slope runoff, erosion and sediment under different vegetation types. Water Resour. Manag. 28, 2415–2433 (2014).Article 

Google Scholar 
Xu, G. C. et al. Temporal and spatial characteristics of soil water content in diverse soil layers on land terraces of the Loess Plateau China. CATENA 158, 20–29 (2017).Article 

Google Scholar 
Yu, Y. et al. Land preparation and vegetation type jointly determine soil conditions after long-term land stabilization measures in a typical hilly catchment, Loess Plateau of China. J. Soil Sedim. 17, 144–156 (2017).CAS 
Article 

Google Scholar 
Dou, Y. X., Yang, Y., An, S. S. & Zhu, Z. L. Effects of different vegetation restoration measures on soil aggregate stability and erodibility on the Loess Plateau China. CATENA 185, 104294 (2020).CAS 
Article 

Google Scholar 
He, J., Shi, X. Y. & Fu, Y. J. Identifying vegetation restoration effectiveness and driving factors on different micro-topographic types of hilly Loess Plateau: From the perspective of ecological resilience. J. Environ. Manage. 289, 112562 (2021).PubMed 
Article 

Google Scholar 
Qiu, D. X., Gao, P., Mu, X. M. & Zhao, B. L. Vertical variations and transport mechanism of soil moisture in response to vegetation restoration on the Loess Plateau of China. Hydrol. Process. 35, e14397 (2021).
Google Scholar 
Zhang, G. H., Liu, G. B., Wang, G. L. & Wang, Y. X. Effects of Vegetation cover and rainfall intensity on sediment-bound nutrient loss, size composition and volume fractal dimension of sediment particles. Pedosphere 21, 676–684 (2011).CAS 
Article 

Google Scholar 
Gu, Z. J. et al. Estimating the effect of Pinus massoniana Lamb plots on soil and water conservation during rainfall events using vegetation fractional coverage. CATENA 109, 225–233 (2013).Article 

Google Scholar 
Comprehensive analysis of relationship between vegetation attributes and soil erosion on hillslopes in the Loess Plateau of China. Environ Earth Sci. 72, 1721–1731 (2014).Zhao, G. J., Mu, X. M., Wen, Z. M., Wang, F. & Gao, P. Soil erosion, conservation, and eco-environment changes in the loess plateau of China. Land Degrad. Dev. 24, 499–510 (2013).Article 

Google Scholar 
Zhang, L., Wang, J. M., Bai, Z. K. & Lv, C. J. Effects of vegetation on runoff and soil erosion on reclaimed land in an opencast coal-mine dump in a loess area. CATENA 128, 44–53 (2015).Article 

Google Scholar 
Wei, W., Pan, D. L. & Feng, J. Tradeoffs between soil conservation and soil-water retention: The role of vegetation pattern and density. Land Degrad. Dev. 33, 18–27 (2021).Article 

Google Scholar 
Asadi, H., Ghadiri, H., Rose, C. W., Yu, B. & Hussein, J. An investigation of flow-driven soil erosion processes at low streampowers. J. Hydrol. 342, 134–142 (2007).ADS 
Article 

Google Scholar 
Shi, Z. H., Yan, F. L., Li, L., Li, Z. X. & Cai, C. F. Interrill erosion from disturbed and undisturbed samples in relation to topsoil aggregate stability in red soils from subtropical China. CATENA 81, 240–248 (2010).Article 

Google Scholar 
Zhou, J. et al. Effects of precipitation and restoration vegetation on soil erosion in a semi-arid environment in the Loess Plateau China. CATENA 137, 1–11 (2016).Article 

Google Scholar 
Han, Z. M. et al. Effects of vegetation restoration on groundwater drought in the Loess Plateau China. J. Hydrol. 591, 125566 (2020).Article 

Google Scholar 
Liang, Y., Jiao, J. Y., Tang, B. Z., Cao, B. T. & Li, H. Response of runoff and soil erosion to erosive rainstorm events and vegetation restoration on abandoned slope farmland in the Loess Plateau region China. J. Hydrol. 584, 124694 (2020).Article 

Google Scholar  More

Data collectionVirus-host data was collated from various sources. Major sources for the association databases included data shared by Olival et al4., Pandit et al.3, and Johnson et al.13. In data provided by Olival et al (assessed September 2019), host-virus associations have been assigned a score, based on detection methods and tests that are specific and more reliable. We used associations that have been identified as the most reliable (stringent data) from Olival et al4. In addition, a query in GenBank was run to parse out hosts reported for each GenBank submission for viruses presented in each of these three databases. Initially, for each virus name, taxonomic ID was identified using entrez.esearch function in biopython package. The taxonomic ID helped linked to the GenBank databases, identify the ICTV lineage and associated data in PubMed20,21. NCBI TaxID closely follows the ICTV database, but some recent changes in ICTV might not always be reflected in NCBI, so we manually checked names to ensure matching. This included virus genus and family information along with a standard virus name. Host data were aggregated based on the taxonomic ID and associated standard name. Finally, for each virus, a search was completed in PubMed to compile the number of hits related to the virus and their vertebrate hosts using the search terms below. The number of PubMed hits (PMH1) were used as a proxy for sampling bias3,13. The virus-host association data source is presented in supplementary code and data files (https://zenodo.org/record/5899054).$$ searchterm= (+virus_name+,[Title/Abstract])\ ANDleft(host,OR,hosts,OR,reservoir,OR,reservoirs,OR right.\ wild,OR,wildlife,OR,domestic,OR,animal,OR,animals,OR\ mammal,OR,bird,OR,birds,OR,aves,OR,avian,OR,avians\ left. OR,vertebrate,OR,vertebrates,OR,surveillance,OR,sylvaticright)$$Along with the PubMed terms we also queried the nucleotide database on PubMed using the taxonomic ID to find the number of GenBank entries for these viruses (PMH2). A correlation analysis between the PMH1 and PMH2 of well-recognized known viruses showed a high correlation with each other for us to safely use GenBank hits for novel viruses during the prediction stage of the model (Fig. S32).Development of ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})
a. Centrality measures of observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}}))To test if centrality measures (degree centrality, betweenness centrality, eigenvector centrality, clustering coefficient) for viral nodes in the observed network (({G}_{c})) vary significantly between viral families, we firstly used the Kolmogorov-Smirnov (KS) test. KS test is routinely used to identify distances between cumulative distribution functions of two probability distributions and is largely used to compare degree distributions of networks22,23. For each viral family, distributions of centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) and clustering coefficient within the observed network (({G}_{c})) were compared with the distribution of all nodes in the network using the two-tailed KS test. Secondly, a linear regression model with virus family as a categorical variable and the number of PubMed hits as a covariate to adjust for sampling bias were fitted to understand associations of viral families with centrality measures.$${centrality},{measure}={beta }_{0}{intercept}+{{beta }_{1}{Viral}{family}}_{{categorical}}+{beta }_{2}{PubMed},{hits}$$After fitting the model, node-level permutations were implemented. For each random permutation, the output variable was randomly assigned to covariate values and the model was re-fitted. Finally, a p-value was calculated by comparing the distribution of coefficients from permutations with the original model coefficient.Network topology feature selectionUsing the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})), multiple network topological features for all node (virus) pairs were calculated. The following are topological network features calculated. Features data type, definition and methods to calculate these features are presented in Table S3.1. The Jaccard coefficient: a commonly used similarity metric between nodes in information retrieval, is also called an intersection of over the union for two nodes in the network. In the unipartite network generated here, it represents the proportion of common neighbor viruses from the union of neighbor viruses for two nodes. Neighbor viruses are defined as viruses with which the virus shares at least a single host.2. Adamic/Adar (Frequency-Weighted Common Neighbors): Is the sum of inverse logarithmic degree centrality of the neighbors shared by two nodes in the network24. The concept of Adamic Adar index is a weighted common neighbors for viruses in the network. Within network prediction, the index assumes that viruses with large neighborhoods have a less significant impact while predicting a connection between two viruses compared with smaller neighborhoods.Both Jaccard and Adamic Adar coefficients have been routinely used for generalized network prediction and have shown high accuracy in predicting missing links in networks, specifically bipartite networks25, the information flowing through neighborhoods formed by two nodes might not always be enough to have similar predictive power in an unipartite network. This warrants use of other topology features along with neighborhood-based features.3. Resource allocation: Similarity score of two nodes defined by the weights of common neighbors of two nodes. Resource allocation is another measure to quantify the closeness of two nodes in the network and hence to understand the similarity of hosts they infect.4. Preferential attachment coefficients: The mechanism of preferential attachment can be used to generate evolving scale-free networks, where the probability that a new link is connected to node x is proportional to k26.5. Betweenness centrality: For a node in the network betweenness centrality is the sum of the fraction of all-pairs shortest paths that pass through it. The feature that we used for training the supervised learning model was the absolute difference between of betweenness centralities of two nodes. The difference between the betweenness centrality represents the difference in the sharing observed by two viruses in the pair.6. Degree centrality: The degree centrality for a node v is the fraction of nodes it is connected to. The feature that we used for training the supervised learning model was the absolute difference between degree centralities of two nodes. Unlike the difference in the betweenness centrality, the difference in degree centrality only looks at the difference in the number of observed host sharing.7. Network clustering: All nodes were classified into community clusters using Louvain methods27. A binary feature variable was generated to describe if both the nodes in the pair were part of the same cluster or not. If both viruses are from the same cluster, it represents a similar host predilection than when both viruses are not from the same cluster hence accounting for the evolutionary predilection of viruses (or virus families) to infect a certain type of host.These topological network characteristics come with certain limitations when it comes to the unipartite network of viruses with links formed due to shared hosts and might not truly represent the flow of information between nodes as compared to a bipartite network. Therefore, to account for these limitations, we use multiple network features as weak learners in our model building characteristics summarizing the network through the use of several quantitative metrics. In addition to this, we estimated the feature importance of these metrics in predicting missing links between viruses to quantify the information pasting through these links.Pearson’s correlation coefficients were calculated to identify highly correlated features and for choosing features for model training (Fig. S33). Virological features included in model training were categorical variables describing the virus family of both the nodes in the pair, followed by a binary variable if both the viruses belong to the same virus family. During the model development, PubMed hits generated three predictive features for each pair of viruses on which model training and predictions were conducted. These included two features representing PubMed hits for the two viruses in the pair (PubMedV1, PubMedV2) and the absolute difference between PubMedV1 and PubMedV2 to account for differences in sampling bias between the two viruses.Cross-validation and fitting generalized boosting machine (GBMs) modelsA nested-cross-validation was implemented for the binary model while simple cross-validation was implemented for the multiclass model (multiple output categories). The parameters of the binary model were first hyper-tuned using a cross-validated grid-search method. Values were tested using a grid search to find the best-performing model parameters that showed the highest sensitivity (recall). The parameters tested for hypertuning and their performance are provided in the supplementary material (supplementary results and Table S5). For further cross-validation of the overall binary model, all the viruses were randomly assigned to five groups. For each fold, the viruses assigned to a group were dropped from the data, and a temporary training network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}{{{{{boldsymbol{)}}}}}}) was constructed, assuming that this represented the current observed status of the virus-host community. For all possible pairs in ({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{t}}}}}}}) (both that sharing and not sharing any hosts) ten topological and viral characteristics were calculated as training features (Table S4). Categorical features were one-hot-encoded and numeric features were scaled. An XGBClassifier model with binary: logistic family was trained using the feature dataset to predict if virus pairs share hosts (1,0 encoded output). The cross-validation was also used to determine the optimum decision threshold for determining binary classification (Fig. S6) and a precision-recall curve was used to identify positive predictive value and sensitivity at the optimum threshold (Fig. S8).The multiclass model was implemented in the same way, creating an observed network (({G}_{c})) based on species-level sharing of hosts and randomly dropping viruses to generate a training network (({G}_{t})) to train the XGboost model. The output variables were generated based on the taxonomical orders of shared hosts. A pair of viruses can share multiple hosts, hence we trained a multioutput-multiclass model. Humans were considered an independent category of taxonomical order (label) and were given a separate label from primates. For fine-tuning the multiclass model, we started with the best performing parameters of the binary model and manually tested 5 combinations of model parameters by adjusting values of the learning rate, number of estimators, maximum depth, and minimum child weight (Supplementary code and results).We used three methods to estimate the importance of features for our binary model. Specifically, improvement in accuracy brought by branching based on the feature (gain), the percentage of times the feature appears in the XGboost tree model (weight), and the relative number of observations related to the specific feature (cover). Results for feature importance are shown in supplementary results (Fig. S10).Missing links for novel viruses, binary and multiclass predictionThe wildlife surveillance data represented a sampling of 99,379 animals (94,723 wildlife, 4656 domesticated animals) conducted in 34 countries around the world between 2009–2019 (Table S6)1. Specimens were tested using conventional Rt-PCR, Quantitative PCR, Sanger sequencing, and Next Generation Sequencing protocols to detect viruses from 28 virus families or taxonomic groups (Table S7). Testing resulted in 951 novel monophyletic clusters of virus sequences (referred to as novel viruses henceforth). Within 951 novel viruses, 944 novel viruses had vertebrate hosts that were identified with certainty based on barcoding methods and field identification. Host species identification was confirmed by cytochrome b (cytb) DNA barcoding using DNA extracted from the samples28. We predicted the shared host links between novel viruses and known viruses using binary and multiclass models in the following steps. Out of 944 novel viruses discovered in the last ten years, we were able to generate predictions for 531 novel viruses that were detected in species already classified as hosts within the network. The remaining 413 viruses were the first detection of any virus in that species and thus host associations could not be informed by the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{C}}}}}}})) data.1. A new node representing the novel virus was inserted in the observed network (({{{{{{boldsymbol{G}}}}}}}_{{{{{{boldsymbol{c}}}}}}})). Using the list of species in which the novel virus was detected, new edges were created with known viruses that are also known to be found in those hosts. This generated a temporary network for the novel virus (({{{{{{boldsymbol{G}}}}}}}_{{temp}})). If the novel virus was not able to generate any edges with known viruses, meaning the host in which they have been found was never found positive for any known virus, predictions were not performed.2. Using ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) feature values were calculated for the novel virus (betweenness centrality, clustering, and degree). For all possible pairs of the novel virus with known viruses that are not yet connected with each other through an edge in ({{{{{{boldsymbol{G}}}}}}}_{{temp}}) a feature dataset was generated (Jaccard coefficient(novel virus, known virus), the difference in betweenness centrality of the novel virus and known virus, if the novel virus and known virus were in the same cluster, the difference in degree centrality(novel virus, known virus), if the novel virus and known virus were from same virus family, the difference in PubMed hits(novel virus, known virus), PubMed hits for the novel virus, PubMed hits for the known virus). Studies and nucleotide sequences for novel viruses are expected to be published and shared on PubMed’s Nucleotide database and in various peer-reviewed publications. Data associated with GenBank accession numbers and nucleotide sequences for novel viruses are presented in Supplementary Data 3 and Supplementary Data 4 respectively. At the time of development of the model, data for all viruses was not shared in a format that would reflect on PubMed’s database, we decided to use the number of unique species the virus was detected in the last ten years of wildlife surveillance conducted by the USAID PREDICT project. These detections will be reflected in PubMed’s Nucleotide database and search term eventually, hence we considered them as a proxy for search terms conducted for known viruses. Currently, evaluation of the effects of this substitution of PubMed hits with the number of detections for novel viruses is not possible with limited data on novel viruses but needs to be reevaluated as more studies are published on these novel viruses. To further evaluate the association between PubMed hits through search term and Genbank hits, we ran a generalized linear regression model with PubMed hits as dependent variable and Genbank hits as intendent variable, accounting for virus families.$${{PubMed}}_{{Search}}left({log }right)={beta }_{0}{intercept}+{{beta }_{1}{Virus}{family}}_{{categorical}}+{beta }_{2}{Genbank},{hits},({log })$$The results indicated that Genbank hits had statistically significant predictive value in predicting PubMed hits (β = 0.72, p  More

Wiegand, T., Gunatilleke, S. & Gunatilleke, N. Species Associations in a Heterogeneous Sri Lankan Dipterocarp Forest. Am. Nat. 170, E77–E95. https://doi.org/10.1890/06-1350.1 (2007).Article 
PubMed 

Google Scholar 
Zhang, J. et al. Spatial patterns and associations of six congeneric species in an old-growth temperate forest. Acta Oecol. 11, 29–38. https://doi.org/10.1016/j.actao.2009.09.005 (2010).ADS 
Article 

Google Scholar 
Pretzsch, H. et al. Comparison between the productivity of pure and mixed stands of Norway spruce and European beech along an ecological gradient. Ann. For. Sci. 67, 712–712. https://doi.org/10.1051/forest/2010037 (2010).Article 

Google Scholar 
Zhu, J., Kang, H., Tan, H., Xu, M. & Wang, J. Natural regeneration characteristics ofPinus sylvestris var.mongolica forests on sandy land in Honghuaerji, China. J. For. Res. 16, 253–259. https://doi.org/10.1007/BF02858184 (2005).Felton, A., Felton, A. M., Wood, J. & Lindenmayer, D. B. Vegetation structure, phenology, and regeneration in the natural and anthropogenic tree-fall gaps of a reduced-impact logged subtropical Bolivian forest. For. Ecol. Manage. 235, 186–193. https://doi.org/10.1016/j.foreco.2006.08.011 (2006).Article 

Google Scholar 
Man, R., Kayahara, G. J., Rice, J. A. & MacDonald, G. B. Eleven-year responses of a boreal mixedwood stand to partial harvesting: Light, vegetation, and regeneration dynamics. For. Ecol. Manage. 255, 697–706. https://doi.org/10.1016/j.foreco.2007.09.043 (2008).Article 

Google Scholar 
Xiang, W., Lei, X. & Zhang, X. Modelling tree recruitment in relation to climate and competition in semi-natural Larix-Picea-Abies forests in northeast China. For. Ecol. Manage. 382, 100–109. https://doi.org/10.1016/j.foreco.2016.09.050 (2016).Article 

Google Scholar 
Zhang, M., Liu, Y., Guo, W., Kang, X. & Zhao, H. Spatial associations and species collocation of dominant tree spscies in a natural spruce-fir mixed forest of Changbai Mountains in Northeastern China. Appl. Ecol. Env. Res. 17, 6213–6225. https://doi.org/10.15666/aeer/1703_62136225 (2019).Garbarino, M., Weisberg, P. J. & Motta, R. Interacting effects of physical environment and anthropogenic disturbances on the structure of European larch (Larix decidua Mill.) forests. For. Ecol. Manag. 257, 1794–1802. https://doi.org/10.1016/j.foreco.2008.12.031 (2009).Gourlet-Fleury, S. et al. Silvicultural disturbance has little impact on tree species diversity in a Central African moist forest. For. Ecol. Manage. 304, 322–332. https://doi.org/10.1016/j.foreco.2013.05.021 (2013).Article 

Google Scholar 
Yu, D. & Han, S. Ecosystem service status and changes of degraded natural reserves—A study from the Changbai Mountain Natural Reserve China. Ecosyst. Serv. 20, 56–65. https://doi.org/10.1016/j.ecoser.2016.06.009 (2016).Article 

Google Scholar 
Moreau, G. et al. Long-term tree and stand growth dynamics after thinning of various intensities in a temperate mixed forest. For. Ecol. Manage. 473, 118311. https://doi.org/10.1016/j.foreco.2020.118311 (2020).Article 

Google Scholar 
Yan, Y., Zhang, C., Wang, Y., Zhao, X. & Gadow, K. Drivers of seedling survival in a temperate forest and their relative importance at three stages of succession. Ecol. Evol. 5, 4287–4299. https://doi.org/10.1002/ece3.1688 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Bai, F. et al. Long-term protection effects of national reserve to forest vegetation in 4 decades: Biodiversity change analysis of major forest types in Changbai Mountain Nature Reserve China. Sci. China Ser. C 51, 948–958. https://doi.org/10.1007/s11427-008-0122-9 (2008).Article 

Google Scholar 
Liu, Q., Li, X., Ma, Z. & Takeuchi, N. Monitoring forest dynamics using satellite imagery—a case study in the natural reserve of Changbai Mountain in China. For. Ecol. Manage. 210, 25–37. https://doi.org/10.1016/j.foreco.2005.02.025 (2005).Article 

Google Scholar 
Hao, H. et al. Patches structure succession based on spatial point pattern features in semi-arid ecosystems of the water-wind erosion crisscross region. Glob. Ecol. Conserv. 12, 158–165. https://doi.org/10.1016/j.gecco.2017.11.001 (2017).Article 

Google Scholar 
Das Gupta, S. & Pinno, B. D. Spatial patterns and competition in trees in early successional reclaimed and natural boreal forests. Acta Oecol. 92, 138–147. https://doi.org/10.1016/j.actao.2018.05.003 (2018).Hao, Z., Zhang, J., Song, B., Ye, J. & Li, B. Vertical structure and spatial associations of dominant tree species in an old-growth temperate forest. For. Ecol. Manage. 252, 1–11. https://doi.org/10.1016/j.foreco.2007.06.026 (2007).Article 

Google Scholar 
Zhao, H., Kang, X., Guo, Z., Yang, H. & Xu, M. Species interactions in spruce-fir mixed stands and implications for enrichment planting in the Changbai Mountains China. Mount. Res. Dev. 32, 187–196. https://doi.org/10.1659/MRD-JOURNAL-D-11-00125.1 (2012).Article 

Google Scholar 
Li, Y., Hui, G., Wang, H., Zhang, G. & Ye, S. Selection priority for harvested trees according to stand structural indices. iForest 10, 561–566, DOI: https://doi.org/10.3832/ifor2115-010 (2017).Zhang, Y., Drobyshev, I., Gao, L., Zhao, X. & Bergeron, Y. Disturbance and regeneration dynamics of a mixed Korean pine dominated forest on Changbai Mountain North-Eastern China. Dendrochronologia 32, 21–31. https://doi.org/10.1016/j.dendro.2013.06.003 (2014).Article 

Google Scholar 
Zhang, M. et al. Community stability for spruce-fir forest at different succession stages in Changbai Mountains, Northeast China. Chin. J. Appl. Ecol. 26, 1609–1616. https://doi.org/10.13287/j.1001-9332.20150331.024 (2015).Gong, Z., Kang, X. & Gu, L. Quantitative division of succession and spatial patterns among different stand developmental stages in Changbai Mountains. J. Mt. Sci. 16, 2063–2078. https://doi.org/10.1007/s11629-018-5142-8 (2019).Article 

Google Scholar 
Hu, Y., Min, Z., Gao, Y. & Feng, Q. Effects of selective cutting on stand growth and structure for natural mixed spruce (Picea koraiensis )-Fir (Abies nephrolepis) forests. Scientia Silvae Sinicae 47, 15–24. https://doi.org/10.11707/j.1001-7488.20110203 (2011).Article 

Google Scholar 
Hubbell, S. P. Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. Science 283, 554–557. https://doi.org/10.1126/science.283.5401.554 (1999).Seidler, T. G. & Plotkin, J. B. Seed dispersal and spatial pattern in tropical trees. PLoS Biol. 4, e344. https://doi.org/10.1371/journal.pbio.0040344 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ghalandarayeshi, S., Nord-Larsen, T., Johannsen, V. K. & Larsen, J. B. Spatial patterns of tree species in Suserup Skov—a semi-natural forest in Denmark. For. Ecol. Manage. 406, 391–401. https://doi.org/10.1016/j.foreco.2017.10.020 (2017).Article 

Google Scholar 
Harms, K. E., Wright, S. J., Calderón, O., Hernández, A. & Herre, E. A. Pervasive density-dependent recruitment enhances seedling diversity in a tropical forest. Nature 404, 493–495. https://doi.org/10.1038/35006630 (2000).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wiegand, T., Gunatilleke, C. V. S., Gunatilleke, I. A. U. N. & Huth, A. How individual species structure diversity in tropical forests. Proc. Natl. Acad. Sci. 104, 19029–19033. https://doi.org/10.1073/pnas.0705621104 (2007).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, T., Yan, Q., Wang, J. & Zhu, J. Restoring temperate secondary forests by promoting sprout regeneration: Effects of gap size and within-gap position on the photosynthesis and growth of stump sprouts with contrasting shade tolerance. For. Ecol. Manage. 429, 267–277. https://doi.org/10.1016/j.foreco.2018.07.025 (2018).Article 

Google Scholar 
Zhang, M., Kang, X., Meng, J. & Zhang, L. Distribution patterns and associations of dominant tree species in a mixed coniferous-broadleaf forest in the Changbai Mountains. J. Mt. Sci. 12, 659–670. https://doi.org/10.1007/s11629-013-2795-1 (2015).Article 

Google Scholar 
Navarro-Cerrillo, R. M. et al. Structure and spatio-temporal dynamics of cedar forests along a management gradient in the Middle Atlas Morocco. For. Ecol. Manag. 289, 341–353. https://doi.org/10.1016/j.foreco.2012.10.011 (2013).Article 

Google Scholar 
Condit, R. Spatial patterns in the distribution of tropical tree species. Science 288, 1414–1418. https://doi.org/10.1126/science.288.5470.1414 (2000).del Río, M. et al. Characterization of the structure, dynamics, and productivity of mixed-species stands: Review and perspectives. Eur. J. For. Res. 135, 23–49. https://doi.org/10.1007/s10342-015-0927-6 (2016).Article 

Google Scholar 
Wiegand, K., Jeltsch, F. & Ward, D. Do spatial effects play a role in the spatial distribution of desert-dwelling Acacia raddiana ?. J. Veg. Sci. 11, 473–484. https://doi.org/10.2307/3246577 (2000).Article 

Google Scholar 
Hui, G. & Pommerening, A. Analysing tree species and size diversity patterns in multi-species uneven-aged forests of Northern China. For. Ecol. Manage. 316, 125–138. https://doi.org/10.1016/j.foreco.2013.07.029 (2014).Article 

Google Scholar 
Graz, F. P. The behaviour of the species mingling index M sp in relation to species dominance and dispersion. Eur. J. For. Res. 123, 87–92. https://doi.org/10.1007/s10342-004-0016-8 (2004).Article 

Google Scholar 
Zhang, M. Spatial association and optimum adjacent distribution of trees in a mixed coniferous-broadleaf forest in northeastern China. Appl. Ecol. Environ. Res. 15, 1551–1564. https://doi.org/10.15666/aeer/1503_15511564 (2017).Hou, J. H., Mi, X. C., Liu, C. R. & Ma, K. P. Spatial patterns and associations in a Quercus-Betula forest in northern China. J. Veg. Sci. 15, 407–414. https://doi.org/10.1111/j.1654-1103.2004.tb02278.x (2004).Article 

Google Scholar 
Boyden, S., Binkley, D. & Shepperd, W. Spatial and temporal patterns in structure, regeneration, and mortality of an old-growth ponderosa pine forest in the Colorado Front Range. For. Ecol. Manage. 219, 43–55. https://doi.org/10.1016/j.foreco.2005.08.041 (2005).Article 

Google Scholar 
Li, J., Niu, S. & Liu, Y. Forest Ecology. Higher Education Press, (2010).Hui, G. et al. Theory and practice of structure-based forest management. Science Press, (2020).Gong, Z. et al. Interspecific association among arbor species in two succession stages of spruce-fir conifer and broadleaved mixed forest in Changbai Mountains, northeastern China. J. Beijing For. Univ. 33, 28–33 (2011).
Google Scholar 
Suzuki, S. N., Kachi, N. & Suzuki, J.-I. Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies Forest: Analyses using spatial autocorrelation and the mark correlation function. Ann. Bot. 102, 435–441. https://doi.org/10.1093/aob/mcn113 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
Shao, G. et al. Integrating stand and landscape decisions for multi-purposes of forest harvesting. For. Ecol. Manage. 207, 233–243. https://doi.org/10.1016/j.foreco.2004.10.029 (2005).Article 

Google Scholar 
Dai, L. et al. Changes in forest structure and composition on Changbai Mountain in Northeast China. Ann. For. Sci. 68, 889–897. https://doi.org/10.1007/s13595-011-0095-x (2011).Article 

Google Scholar 
Liu, Y. et al. Determining suitable selection cutting intensities based on long-term observations on aboveground forest carbon, growth, and stand structure in Changbai Mountain, Northeast China. Scand. J. For. Res. 29, 436–454. https://doi.org/10.1080/02827581.2014.919352 (2014).CAS 
Article 

Google Scholar 
K. von Gadow and & Hui, G. Y. Characterizing Forest spatial structure and diversity. Proc. of an international workshop organized at the University of Lund, Sweden, 20–30 (2001).Baddeley, A. & Turner, R. spatstat: An R Package for Analyzing Spatial Point Patterns. J. Stat. Soft. 12, 1–42. https://doi.org/10.18637/jss.v012.i06 (2005).Illian, J., Penttinen, A., Stoyan, H. & Stoyan, D. Statistical Analysis and Modelling of Spatial Point Patterns: Illian/Statistical Analysis and Modelling of Spatial Point Patterns. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470725160 (2007).Wiegand, T. & Moloney, K. A. Handbook of Spatial Point-Pattern Analysis in Ecology. Chapman and Hall/CRC. https://doi.org/10.1201/b16195 (2013).Martínez, I., Wiegand, T., González-Taboada, F. & Obeso, J. R. Spatial associations among tree species in a temperate forest community in North-western Spain. For. Ecol. Manage. 260, 456–465. https://doi.org/10.1016/j.foreco.2010.04.039 (2010).Article 

Google Scholar 
Wang, X. et al. Species associations in an old-growth temperate forest in north-eastern China. J. Ecol. 98, 674–686. https://doi.org/10.1111/j.1365-2745.2010.01644.x (2010).Article 

Google Scholar 
Getzin, S., Wiegand, T. & Hubbell, S. P. Stochastically driven adult–recruit associations of tree species on Barro Colorado Island. Proc. R. Soc. B. 281, 20140922. https://doi.org/10.1098/rspb.2014.0922 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Nakashizuka, T. Species coexistence in temperate, mixed deciduous forests. Trends Ecol. Evol. 16, 205–210 (2001).CAS 
Article 

Google Scholar 
Mugglestone, M. & Renshaw, E. Spectral tests of randomness for spatial point patterns. Environ. Ecol. Stat. 237–251. https://doi.org/10.1023/A:1011339607376 (2001).Stoyan, D. & Stoyan, H. Fractals, random shapes, and point fields: methods of geometrical statistics. Wiley, (1994).Liu, P. et al. Competition and facilitation co-regulate the spatial patterns of boreal tree species in Kanas of Xinjiang, northwest China. For. Ecol. Manage. 467, 118167. https://doi.org/10.1016/j.foreco.2020.118167 (2020).Article 

Google Scholar 
Wiegand, T., Moloney, A. & Rings, K. circles, and null-models for point pattern analysis in ecology. Oikos 104, 209–229. https://doi.org/10.1111/j.0030-1299.2004.12497.x (2004).Article 

Google Scholar  More

Analysis of carbon sources in Guangdong farmland ecosystems under the “dual carbon” targetAnalysis of inter-annual variation in carbon emissions from farmland ecosystems in GuangdongGuangdong’s carbon emissions from farmland ecosystems showed an increasing trend year by year during 2001–2017 (Fig. 1a), with carbon emissions gradually reaching a peak of 4.153 million t a−1 in 2016 from 3.554 million t a−1 in 2001, but decreasing year by year from 2017 onwards. Eventually it’s decreasing to 3.533 million t a−1 by 2020. Showing that Guangdong’s farmland ecosystem carbon emissions have remained relatively flat over the past 20 years, with an average annual carbon emission of 3.7624 million t a−1. The carbon emissions per unit arable land area of Guangdong’s farmland ecosystems show an increasing trend year by year (Fig. 1b), from 1.12 t ha−1 in 2001 to 2.03 t ha−1 in 2020, an increase of 81.25% over 20 years, with an average annual carbon emission per unit arable land area of 1.43 t ha−1. While the carbon emissions per unit sown area show the opposite trend to the total carbon emissions, from 2001 to 2016, showing a decreasing trend year by year. The carbon emissions per unit of sown area decreased from 1.50 t ha−1 in 2001 to 1.01 t ha−1 in 2016 and then started to increase year by year from 2017 to 1.26 t ha−1 in 2020, with an overall decrease of 16% and an average annual carbon emission per unit of sown area of 1.19 t ha−1.Figure 1(a) Inter-annual variation of carbon emissions from farmland ecosystems in Guangdong; (b) inter-annual variation in carbon emissions per unit area of farmland ecosystems in Guangdong.Full size imageAnalysis of carbon sources in Guangdong farmland ecosystemsThe carbon emissions from agricultural production power (estimated by the total power of agricultural diesel and agricultural machinery) in Guangdong’s farmland ecosystems show an increasing trend year by year (Fig. 1a), from 411,000 t a−1 in 2001 to 513,000 t a−1 in 2020, an increase of nearly 25% in 20 years. Carbon emissions from tillage and irrigation inputs are relatively flat, from 116,000 t a−1 in 2001 to 109,000 t a−1 in 2020, with an average of 107,000 t a−1 over the last 20 years. Carbon emissions from chemicals in agricultural production (estimated by fertilizer, pesticide, and agricultural film inputs) have the greatest impact on the overall emissions, with carbon emissions from agricultural chemicals reaching 2.9097 million t a−1 in 2020, accounting for 82.36% of total carbon emissions from farmland ecosystems, but a relatively flat trend. Although the share of carbon emissions from agricultural production power is increasing year by year, the contribution of carbon emissions due to inputs of agricultural chemicals is still in an absolute position. The use of agricultural chemicals directly affects the carbon emissions of Guangdong’s farmland ecosystems. Therefore, a more detailed analysis of the carbon emissions of various agricultural chemicals is necessary in order to make carbon reduction proposals.Depending on Fig. 2a, although the proportion of carbon emissions caused by agricultural films has been increasing year by year, chemical fertilizers still occupy an absolute position, with their carbon emissions accounting for 78.45% of agricultural chemicals on average in the past 20 years. Which the average proportions of carbon emissions caused by pesticides and agricultural films are 15.17% and 6.38% respectively. Among the carbon emissions from various fertilizers (Fig. 2b), the annual average share of carbon emissions in the past 20 years is distributed from the largest to the smallest: 81.63% from nitrogen fertilizers, 9.57% from compound fertilizers, 5.60% from phosphate fertilizers and 3.20% from potash fertilizers. From the trend of carbon emissions of various types of fertilizers, we can learn that the carbon emissions of nitrogen fertilizers have been decreasing year by year, from 85.63% in 2001 to 78.10% in 2020, and the emissions have slowly risen from 2.061 million t a−1 in 2001 to a peak of 2.1276 million t a−1 in 2016, then gradually decreased to 1.7797 million t a−1 in 2020. Compound fertilizers, on the other hand, rose from 6.17% in 2001 to 11.40% in 2020, an increase of nearly 85%, and their carbon emissions rose year by year from 148,600 t a−1 to a peak of 305,200 t a−1 in 2016 and then gradually fell to 259,900 t a−1 in 2020, an increase of 74.90%. The share of carbon emissions from potash is relatively stable, rising from 2.89% to 3.29%, reaching a peak of 91,800 t a−1 in 2016 and then gradually decreasing to 75,500 t a−1 in 2020. The share of carbon emissions from phosphate fertilizers is also on a year-on-year rise, from 5.31% to 7.20%, an increase of 37.47%. However, the carbon emissions from phosphate fertilizers do not produce a peak in 2016 but keep increasing in a relatively stable trend, with its carbon emissions rising from 127,800 t a−1 in 2001 to 164,100 t a−1 in 2020, an increase of 28.40%.Figure 2(a) Proportion of carbon emissions from various types of agricultural chemicals in Guangdong farmland ecosystems; (b) proportion of carbon emissions from different fertilizer types in Guangdong farmland ecosystems.Full size imageAnalysis of carbon sequestration in Guangdong farmland ecosystems under the “dual carbon” targetAnalysis of inter-annual variation in the carbon sequestration function of Guangdong farmland ecosystemsIn the inter-annual variation of carbon sequestration function of farmland ecosystems in Guangdong (Fig. 3a), although there are fluctuations in the variation of total carbon sequestration in farmland ecosystems, the overall decrease is not significant. With the total carbon sequestration decreasing from 21.3176 million t a−1 in 2001 to 19.1178 million t a−1 in 2020, a decrease of 10.32% in the last 20 years, and the average annual carbon sequestration is 19.0363 million t a−1, among which the total carbon sequestration in 2008 is the lowest, only 17.2033 million t a−1.Figure 3(a) Carbon sequestration function of farmland ecosystems in Guangdong; (b) inter-annual variation of carbon sequestration function per unit area of farmland ecosystems in Guangdong.Full size imageThe total carbon sequestered in 2008 was the lowest at 17.2033 million t a−1. The inter-annual variation of carbon sequestration by food crops (paddy, wheat, corn, legumes, yams, and other food crops) is similar to that of farmland ecosystems, decreasing from 13.9742 million t a−1 to 10.209 million t a−1, a decrease of 27%. The inter-annual variation of carbon sequestration by cash crops (sugarcane, peanuts, Canola, and tobacco) and vegetables generally shows a stable upward trend, with carbon sequestration increasing by 15.54% and 55.54% respectively over the past 20 years. Meanwhile, the amount of carbon sequestered per unit sown area in Guangdong’s farmland ecosystems was generally flat (Fig. 3b), with an average annual carbon sequestration per unit sown area of 4.31 t ha−1. While the amount of carbon sequestered per unit arable land area showed an increasing trend, especially in 2017, when it started to rise rapidly, from 6.82 t ha−1 per unit arable land area in 2001 to 10.97 t ha−1 per unit arable land area in 2020, an increase of 60.85%. The average annual carbon sequestration per arable area is 7.25 t ha−1, an increase of 56.71% in the 4 years from 2017 to 2020.Analysis of the role of crop carbon sequestrations in Guangdong’s farmland ecosystemsAs can be seen from Fig. 4a, food crops play the largest role in carbon sequestration in Guangdong’s farmland ecosystems, with an average share of 56.95% of the total carbon sequestration in the past 20 years. Its share tends to decline over time, but the amount of carbon sequestered by food crops in Guangdong still reaches 10.209 million t a−1 in 2020. The carbon sequestration role of cash crops is next, rising from 29.43% in 2001 to 37.92% in 2020, with an average share of 36.17%, an increase of 28.85%, and average annual carbon sequestration of 6.8863 million t a−1. The inter-year variation of vegetables carbon sequestration also shows an increasing trend, rising from 5.02 to 8.73%, with an increase of 73.90%, and average annual carbon sequestration of 1.13112 million t a−1.Figure 4(a) Proportion of carbon sequestered by various crops in Guangdong farmland ecosystems. (b) Proportion of carbon sequestered by various food crops in Guangdong farmland ecosystems; (c) proportion of carbon sequestered by various cash crops in Guangdong farmland ecosystems.Full size imageWhen the carbon sequestration capacity of food (Fig. 4b) and cash crops (Fig. 4c) in Guangdong’s farmland ecosystems is broken down, it is easy to see that paddy is in an absolute position in terms of carbon sequestration among food crops, with an average share of 83.81% over the past 20 years and average annual carbon sequestration of 8.8946 million t a−1. Especially since 2017, the carbon sequestration share of paddy has risen to over 87% and will remain until 2020. Also, sugarcane’s share of carbon sequestration in cash crops is absolute, with average annual share of 86.73% and an average annual carbon sequestration of 5.9712 million t a−1. while, peanut’s share of carbon sequestration in cash crops is also not small, with average annual share of 12.47% and an average annual carbon sequestration of 0.8606 million t a−1.An analysis of the inter-annual variation in carbon sequestration of various crops (Fig. 5) shows that paddy and sugar cane play the largest role in carbon sequestration in Guangdong’s farmland ecosystems. Their combined annual average carbon sequestration amounting to 14.8658 million t a−1, accounting for 78.09% of the total annual average carbon sequestration in Guangdong’s farmland ecosystems. Vegetables, peanuts, and yams also play a significant role in carbon sequestration, with the combined annual average carbon sequestration of the three species being 2.9936 million t a−1, accounting for 15.73% of the total annual average carbon sequestration.Figure 5Comparison of carbon sequestration by various crops in Guangdong farmland ecosystems.Full size imageAnalysis of the carbon footprint of Guangdong’s farmland ecosystems under the “dual carbon” targetThe carbon footprint of Guangdong’s farmland ecosystem ((CEF)) is 531,100 ha a−1 per year, showing a general decrease (Fig. 6), with a 59.65% decrease from 513,900 ha a−1 in 2001 to 321,900 ha a−1 in 2020. The carbon footprint of Guangdong’s farmland ecosystems in the past 20 years (the peak value is 611,500 in 2008 ha a−1) is smaller than the ecological carrying capacity (i.e. the arable land area, the lowest value is 1.7421 million ha a−1 in 2020), and is in a state of carbon ecological surplus. Guangdong’s farmland carbon surplus ((CS)) shows a decreasing trend year by year (Fig. 6), from 2.1611 million ha a−1 in 2001 to 1.4202 million ha a−1 in 2020, a decrease of 45.61%. Although the carbon footprint and the inter-annual variation of the carbon surplus both show a decreasing trend, the productive area required to absorb the carbon emissions from farmland (i.e. the carbon footprint) rises from 16.44 to 18.48% of the arable land area in the same period.Figure 6Inter-annual variation of carbon footprint and ecological surplus of farmland ecosystems in Guangdong.Full size imageAn overview of the interannual variability of carbon emissions, sequestrations and footprints of farmland ecosystems in GuangdongIn the above analysis of the inter-annual variation of carbon emissions, sequestration, and footprint of Guangdong’s farmland ecosystems, it was found that 2017 was a special year. After 2017, which the total carbon emissions from Guangdong’s farmland ecosystems and carbon emissions due to agricultural chemicals (Fig. 1a), carbon emissions per unit of arable land area and sown area (Fig. 1b), carbon sequestration per unit of arable land area (Fig. 3b) and carbon footprint and carbon surplus (Fig. 6) all show a large turnaround. Based on the analysis of the factors after 2017 in Table 3, it can be seen that the number of various fertilizers using is gradually decreasing after 2017, especially the number of nitrogen fertilizers decreased by 149,600 t a−1 in 2018 compared with the amount of the previous year, a decrease of 14.44% in a single year, and the carbon emission decreased by 316,600 t a−1. The arable land area in Guangdong is decreasing after 2017, from 2017 to 2019, it decreased by 697,800 ha, a decrease of 26.84%, but the total carbon sequestration still remains above 19 million t a−1 (Fig. 3a), and while the area of arable land in Guangdong is decreasing, the area sown is climbing. The ratio of sown area to arable land area is used as the number of tillage per unit of arable land area in the paper, and the number of tillage per unit of the arable land area rises from 1.63 ha ha−1 in 2017 to 2.56 ha ha−1 in 2020.Table 3 Inter-annual variation of selected factors in Guangdong agro-ecosystems, 2017–2020.Full size tableBased on the conclusions obtained, the author looked up the agriculture-related policies of Guangdong Province in 2016 and 2017. And found that on 30 December 2016, the Guangdong Provincial People’s Government, in response to the soil prevention and control plan of the Central Government, formulated and issued to the cities and counties under its jurisdiction the Implementation Plan of the Guangdong Provincial Soil Pollution Prevention and Control Action Plan (here in after referred to as the “Plan”). The Plan encourages farmers in all areas to reduce the number of chemical fertilizers and apply pesticides scientifically. The effectiveness of the implementation of the Plan in Guangdong Province is remarkable as seen through the changes in the application of various fertilizers, which in the aspect of reducing fertilizer application alone resulted in a 344,900 t ha−1 reduction in carbon emissions from fertilizer inputs in 2018 compared to 2017. At the same time, the number of farmland tillage has increased, and the area of arable land has been reduced, but the total sown area of crops has remained relatively constant. In 2019, while the area of arable land in Guangdong (actual data on arable land in 2020 is missing, and the forecast alone may cause too much error, so 2019 is used as an example) is 69.78 ha less than that in 2017, the total sown area has increased by 22.43 ha, and the total agricultural output value still increased by RMB 64 billion. Which shows that the utilization rate of arable land and the output value per unit of arable land in Guangdong have both increased. More

Watson, J. B. & Morgan, J. J. B. Emotional reactions and psychological experimentation. Am. J. Psychol. 28, 163–174 (1917).Article 

Google Scholar 
Watson, J. B. & Rayner, R. Conditioned emotional reactions. J. Exp. Psychol. 3, 1–14 (1920).Article 

Google Scholar 
LeDoux, J. Fear and the brain: where have we been, and where are we going. Biol. Psychiatry 44, 1229–1238 (1998).CAS 
PubMed 
Article 

Google Scholar 
Fendt, M. & Fanselow, M. S. The neuroanatomical and neurochemical basis of conditioned fear. Neurosci. Biobehav. Rev. 23, 743–760 (1999).CAS 
PubMed 
Article 

Google Scholar 
Maren, S. & Quirk, G. J. Neuronal signalling of fear memory. Nat. Rev. Neurosci. 5, 844–852 (2004).CAS 
PubMed 
Article 

Google Scholar 
Bouton, M. E., Mineka, S. & Barlow, D. H. A modern learning theory perspective on the etiology of panic disorder. Psychol. Rev. 108, 4–32 (2001).CAS 
PubMed 
Article 

Google Scholar 
Kim, J. J. & Jung, M. W. Neural circuits and mechanisms involved in Pavlovian fear conditioning: a critical review. Neurosci. Biobehav. Rev. 30, 188–202 (2006).PubMed 
Article 

Google Scholar 
Watson, J. B. Psychology as the behaviorist views it. Psychological Rev. 20, 158–177 (1913).Article 

Google Scholar 
Pavlov, I. P. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex (Oxford University Press, 1927).Guthrie, E. R. Conditioning as a principle of learning. Psychological Rev. 37, 412–428 (1930).Article 

Google Scholar 
Kamin, L. J. in Miami Symposium on the Prediction of Behavior (ed. Jones, M. R.) 9–33 (University of Miami Press, 1968).Rescorla, R. A. Probability of shock in the presence and absence of CS in fear conditioning. J. Comp. Physiol. Psychol. 66, 1–5 (1968).CAS 
PubMed 
Article 

Google Scholar 
Wagner, A. R., Logan, F. A., Haberlandt, K. & Price, T. Stimulus selection in animal discrimination learning. J. Exp. Psychol. 76, 171–180 (1968).CAS 
PubMed 
Article 

Google Scholar 
Rescorla, R. A. & Wagner, A. R. A Theory of Pavlovian Conditioning: Variations in the Effectiveness of Reinforcement and Nonreinforcement 64–99 (Appleton-Century-Crofts, 1972).Josselyn, S. A. & Tonegawa, S. Memory engrams: recalling the past and imagining the future. Science 367, https://doi.org/10.1126/science.aaw4325 (2020).Tovote, P., Fadok, J. P. & Luthi, A. Neuronal circuits for fear and anxiety. Nat. Rev. Neurosci. 16, 317–331 (2015).CAS 
PubMed 
Article 

Google Scholar 
Haubensak, W. et al. Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature 468, 270–276 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foa, E. B. & Rothbaum, B. O. Treating the Trauma of Rape: Cognitive Behavioral Therapy for PTSD (Guilford Press, 1998).Butler, A. C., Chapman, J. E., Forman, E. M. & Beck, A. T. The empirical status of cognitive-behavioral therapy: a review of meta-analyses. Clin. Psychol. Rev. 26, 17–31 (2006).PubMed 
Article 

Google Scholar 
Delgado, M. R., Olsson, A. & Phelps, E. A. Extending animal models of fear conditioning to humans. Biol. Psychol. 73, 39–48 (2006).CAS 
PubMed 
Article 

Google Scholar 
Mahan, A. L. & Ressler, K. J. Fear conditioning, synaptic plasticity and the amygdala: implications for posttraumatic stress disorder. Trends Neurosci. 35, 24–35 (2012).CAS 
PubMed 
Article 

Google Scholar 
Craske, M. G. et al. What is an anxiety disorder? Focus 9, 20 (2011).
Google Scholar 
LeDoux, J. E. The Emotional Brain: the Mysterious Underpinnings of Emotional Life (Simon & Schuster, 1996).Fanselow, M. S. From contextual fear to a dynamic view of memory systems. Trends Cogn. Sci. 14, 7–15 (2010).PubMed 
Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation—a review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Bednekoff, P. A. Foraging in the Face of Danger 305–329 (University of Chicago Press, 2007).Stephens, D. W. Decision ecology: foraging and the ecology of animal decision making. Cogn. Affect Behav. Neurosci. 8, 475–484 (2008).PubMed 
Article 

Google Scholar 
Beckers, T., Krypotos, A. M., Boddez, Y., Effting, M. & Kindt, M. What’s wrong with fear conditioning? Biol. Psychol. 92, 90–96 (2013).PubMed 
Article 

Google Scholar 
Mobbs, D. & Kim, J. J. Neuroethological studies of fear, anxiety, and risky decision-making in rodents and humans. Curr. Opin. Behav. Sci. 5, 8–15 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Pellman, B. A. & Kim, J. J. What can ethobehavioral studies tell us about the Brain’s fear system. Trends Neurosci. 39, 420–431 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thorndike, E. Biological Lectures from the Marine Laboratory at Woods’ Holl, USA, for 1899. Nature 62, 411 (1900).Bolles, R. C. Species-specific defense reactions and avoidance learning. Psychol. Rev. 77, 32–48 (1970).Choi, J. S. & Kim, J. J. Amygdala regulates risk of predation in rats foraging in a dynamic fear environment. Proc. Natl Acad. Sci. USA 107, 21773–21777 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zambetti, P. R., Schuessler, B. P. & Kim, J. J. Sex differences in foraging rats to naturalistic aerial predator stimuli. iScience 16, 442–452 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Yilmaz, M. & Meister, M. Rapid innate defensive responses of mice to looming visual stimuli. Curr. Biol. 23, 2011–2015 (2013).CAS 
PubMed 
Article 

Google Scholar 
Papes, F., Logan, D. W. & Stowers, L. The vomeronasal organ mediates interspecies defensive behaviors through detection of protein pheromone homologs. Cell 141, 692–703 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189–208 (1948).CAS 
PubMed 
Article 

Google Scholar 
Wilensky, A. E., Schafe, G. E. & LeDoux, J. E. The amygdala modulates memory consolidation of fear-motivated inhibitory avoidance learning but not classical fear conditioning. J. Neurosci. 20, 7059–7066 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lee, T. & Kim, J. J. Differential effects of cerebellar, amygdalar, and hippocampal lesions on classical eyeblink conditioning in rats. J. Neurosci. 24, 3242–3250 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stiedl, O. & Spiess, J. Effect of tone-dependent fear conditioning on heart rate and behavior of C57BL/6N mice. Behav. Neurosci. 111, 703–711 (1997).CAS 
PubMed 
Article 

Google Scholar 
Guimaraes, F. S., Hellewell, J., Hensman, R., Wang, M. & Deakin, J. F. Characterization of a psychophysiological model of classical fear conditioning in healthy volunteers: influence of gender, instruction, personality and placebo. Psychopharmacology 104, 231–236 (1991).CAS 
PubMed 
Article 

Google Scholar 
Mackintosh, N. J. The Psychology of Animal Learning (Academic Press, 1974).Bouton, M. E. Learning and Behavior (Sinauer Associates 2007).Sheafor, P. J. “Pseudoconditioned” jaw movements of the rabbit reflect associations conditioned to contextual background cues. J. Exp. Psychol. Anim. Behav. Process 1, 245–260 (1975).CAS 
PubMed 
Article 

Google Scholar 
Rescorla, R. A. Behavioral studies of Pavlovian conditioning. Annu. Rev. Neurosci. 11, 329–352 (1988).CAS 
PubMed 
Article 

Google Scholar 
Thompson, R. F. & Krupa, D. J. Organization of memory traces in the mammalian brain. Annu. Rev. Neurosci. 17, 519–549 (1994).CAS 
PubMed 
Article 

Google Scholar 
Fanselow, M. S. & Wassum, K. M. The origins and organization of vertebrate pavlovian conditioning. Cold Spring Harb. Perspect. Biol. 8, a021717 (2015).PubMed 
Article 

Google Scholar 
Lee, H. J., Berger, S. Y., Stiedl, O., Spiess, J. & Kim, J. J. Post-training injections of catecholaminergic drugs do not modulate fear conditioning in rats and mice. Neurosci. Lett. 303, 123–126 (2001).CAS 
PubMed 
Article 

Google Scholar 
Palgi, Y., Gelkopf, M. & Berger, R. The inoculating role of previous exposure to potentially traumatic life events on coping with prolonged exposure to rocket attacks: a lifespan perspective. Psychiatry Res. 227, 296–301 (2015).PubMed 
Article 

Google Scholar 
Somer, E. et al. Israeli civilians under heavy bombardment: prediction of the severity of post-traumatic symptoms. Prehosp. Disaster Med. 24, 389–394 (2009).PubMed 
Article 

Google Scholar 
Alexander, B. K., Beyerstein, B. L., Hadaway, P. F. & Coambs, R. B. Effect of early and later colony housing on oral ingestion of morphine in rats. Pharm. Biochem. Behav. 15, 571–576 (1981).CAS 
Article 

Google Scholar 
Gage, S. H. & Sumnall, H. R. Rat Park: how a rat paradise changed the narrative of addiction. Addiction 114, 917–922 (2019).PubMed 
Article 

Google Scholar 
Fanselow, M. S. & Lester, L. S. A Functional Behavioristic Approach to Aversively Motivated Behavior: Predatory Imminence as a Determinant of the Topography of Defensive Behavior 185–212 (Lawrence Erlbaum Associates Inc, 1988).Cain, C. & LeDoux, J. Brain mechanisms of Pavlovian and instrumental aversive conditioning. Handb. Behav. Neurosci. 17, 103–124 (2008).Article 

Google Scholar 
Choi, J. S., Cain, C. K. & LeDoux, J. E. The role of amygdala nuclei in the expression of auditory signaled two-way active avoidance in rats. Learn Mem. 17, 139–147 (2014).Article 

Google Scholar 
Steimer, T. The biology of fear- and anxiety-related behaviors. Dialogues Clin. Neurosci. 4, 231–249 (2002).PubMed 
PubMed Central 
Article 

Google Scholar 
Fanselow, M. S. The role of learning in threat imminence and defensive behaviors. Curr. Opin. Behav. Sci. 24, 44–49 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Fanselow, M. S. Associative vs topographical accounts of the immediate shock freezing deficit in rats—implications for the response selection-rules governing species-specific defensive reactions. Learn. Motiv. 17, 16–39 (1986).Article 

Google Scholar 
Landeira-Fernandez, J., DeCola, J. P., Kim, J. J. & Fanselow, M. S. Immediate shock deficit in fear conditioning: effects of shock manipulations. Behav. Neurosci. 120, 873–879 (2006).CAS 
PubMed 
Article 

Google Scholar 
Hull, C. L. A functional interpretation of the conditioned reflex. Psychol. Rev. 36, 498–511 (1929).Article 

Google Scholar 
Lazarus, A. A. Behavior Therapy and Beyond (McGraw-Hill Companies, 1971).Öhman, A. & Mineka, S. Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522 (2001).PubMed 
Article 

Google Scholar 
Lee, H. & Kim, J. J. Amygdalar NMDA receptors are critical for new fear learning in previously fear-conditioned rats. J. Neurosci. 18, 8444–8454 (1998).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).CAS 
PubMed 
Article 

Google Scholar  More

This study presents a comprehensive quantification of carbon sequestration as well as CO2/CH4/N2O emissions reductions from terrestrial ecosystems based on multiple sources of data from literature, inventories, public databases and documents. The pathways considered ecosystem restoration and protection from being converted into cropland or built-up areas, reforestation, management with improved nitrogen use in cropland, restricted deforestation, grassland recovery, reducing risk from forest wildfire and others. Here we describe the cross-cutting methods that apply across all 16 NCS pathways. The definitions, detailed methods and data sources for evaluating individual pathways can be found in the Supplementary Information.Cross-cutting methodsBaseline settingWe set 2000 as the base year because the large-scale national ecological projects, such as the Grain for Green Project, were started since then. We first evaluate the historical mitigation capacity during 2000–2020, which is the first 20 years of implementing the projects. From this procedure we can determine how much mitigation capacity has been realized through the previous projects in the past two decades and to what extent additional actions can be made after 2020. Relative to the baseline 2000–2020, we then evaluate the maximum potentials of the NCS mitigation in the future 10 (2020–2030) and 40 (2020–2060) years, corresponding to the timetable of China’s NDCs: carbon peak before 2030 and carbon neutrality by 2060.The settings of baseline in this study are different from the existing assessments (2000s–2010s as a baseline and 2010–2025/2030/2050 as scenarios)1,22,23,27,28. Baseline sets the temporal and spatial reference for NCS pathway scenarios, which may have a great impact on the NCS estimates. Notably, NCS actions during 2000–2020 will have a great impact in the future periods, which we refer to as the ‘legacy effect’. The legacy effect itself, mainly reforestation, is independent of being assessed, but it is conceptually attributed to natural flux and excluded from future NCS potential estimates.Maximum potentialThe MAMP refers to the additional CO2 sequestration or avoided GHG emissions measured in CO2 equivalents (CO2e) at given flux rates in a period on the maximum extent to which the stewardship options are applied (numbers are expressed as TgCO2e yr−1 for individual pathways and PgCO2e yr−1 for national total) (Extended Data Fig. 1 and Supplementary Table 2). ‘Additional’ means mitigation outcomes due to human actions taken beyond business-as-usual land-use activities (since 2020) and excluding existing land fluxes not attributed to direct human activities1. The MAMP of CH4 and N2O are accounted by three cropland and wetland pathways (cropland nutrient management, improved rice cultivation and peatland restoration). We adopt 100 yr global warming potential to calculate the warming equivalent for CH4 (25) and N2O (298), respectively38,39 because these values are used in national GHG inventories, although some researchers have argued that using the fixed 100 yr global warming potential to calculate the warming equivalents may be problematic because they cannot differentiate the contrasting impacts of the long- and short-lived climate pollutants39. Because the flux rate of the GHG by ecosystems may vary with the time of recovery or growth, the MAMP may also change for different periods even given the same extent.The ‘maximum’ is constrained by varied factors across the NCS pathways. We constrain forest and grassland restoration by the rate of implementation, farmland red line and tree surviving rate (Extended Data Fig. 2). Surviving rate here is the ratio of the area with increased vegetation cover due to reforestation to the total reforestation area. The farmland red line refers to ‘the minimum area of cultivated land’ given by the Ministry of Land and Resources of China. It defines the lowest limit, and the current red line is ~120 Mha. It is a rigid constraint below which the total amount of cultivated land cannot be reduced. From this total amount, there is provincial farmland red line. This red line sets a constraint on the implementation of the NCS pathways associated with land-use change. We set the future scenario of farmland area that can be used for grassland or forest restoration on the basis of the provincial farmland red line. Basic farmland is closely related to national food security. By 2050, China’s population is predicted to decrease slightly, but with economic development, the per capita demand for food may increase40. We assume that the food production in the future can meet the food demand via increasing agricultural investment and technological advancement. The N fertilizer reduction scenario is set to be below the level 60%, under which crop yield is not significantly affected19, because N fertilizer is surplus in many Chinese croplands. For timber production, we assume that the demand for timber can be met if the production level is maintained at the level of 2010–2020 (83.31 million m3 yr−1). As deforestation of natural forests is 100% forbidden since 2020, the future timber will come mainly from tree plantations. For grazing optimization, we assume that livestock production is not affected by grassland fencing due to refined livestock management such as improving feed nutrient and fine-seed breeding41.The areas of historical NCS implementation during 2000–2020 were estimated using statistical data, published literature and public documents, with a supplement from remote-sensing data. The flux rates were obtained either by directly using the values from multiple literature sources or from estimates using the empirical formulae. For the estimates of future NCS potential, the flux rate and extent of the pathway were determined on the basis of the baseline (2000–2020). The extent is assumed to be achieved by using the same rate but limited by the multiple constraints stated in the preceding unless the implementation scopes have been reported in national planning documents. We estimate the legacy effect by multiplying the implementation area in the past by the flux rates in the future two periods.SaturationThe future mitigation potential that we estimate for 2030 and 2060 will not persist indefinitely because the finite potential for natural ecosystems to store additional carbon will saturate. For each NCS pathway, we estimate the expected duration of the potential for sequestration at the maximum rate (Supplementary Table 3). Forests can continue to sequester carbon for 70–100 years or more. Restored grasslands and fenced grasslands can continue to sequester carbon for >50 years. Forest-fire management and cover crops can continue to sequester carbon for 40–50 years or more. Sea grasses and peatlands can continue to sequester carbon for millennia. Avoided pathways do not saturate as long as the business-as-usual cases indicate that there are potential areas for avoided losses of ecosystems. In this case, sea grass and salt marsh would disappear entirely after 64 years, but it would be 100–300 years or more for forest, grassland and peatland.Estimation of uncertaintiesThe extent (area or biomass amount) and flux (sequestration or reduced emission per area or biomass amount in unit time) are considered to estimate uncertainty of the historical mitigation capacity or future potential for each NCS pathway. We use the IPCC approaches to combine uncertainty42. Where mean and standard deviation can be estimated from collected literature, 95% CIs are presented on the basis of multiple published estimates. Where a sample of estimates is not available but only a range of a factor, we report uncertainty as a range and use Monte Carlo simulations (with normal distribution and 100,000 iterations) to combine the uncertainties of extent and flux (IPCC Approach 2). The overall uncertainties of the 16 NCS pathways were combined using IPCC Approach 142. If the extent estimate is based on a policy determination, rather than an empirical estimate of biophysical potential, we do not consider it a source of uncertainty.MACsThe economic/cost constraints refer to the amount of NCS that can be achieved at a given social cost. The MAC curve is fitted according to the total publicly funded investment and total mitigation capacity or potential during a period. The MAC curves are drawn to estimate the historical mitigation or MAMP at the cost thresholds of US$10, US$50 and US$100 (MgCO2e)−1, respectively. The trading price in China’s current carbon market is ~US$10 USD (as the minimum cost43), and the cost-effective price point44,45 to achieve the Paris Agreement goal of limiting global warming to below 2 °C above pre-industrial levels is US$100 (as the maximum cost). A carbon price of US$50 is regarded as a medium value1,46. For the pathways of reforestation, avoided grassland conversion, grazing optimization and grassland restoration, we collected the statistical data of investments in China from 2000 to 2020 and estimated the affordable MAMP below the three mitigation costs. Due to data limitations, the points used for fitting the MAC curve are values for cost (invested funds) and benefit (mitigation capacity) in each of the provinces. We rank the ratio of benefit to cost in a descending order to obtain the maximum marginal benefit for MAC by assuming that NCS measures are first implemented in the region with the highest cost/benefit rate. We refer to the investment standard before 2020 as the benchmark and estimate the cost of each pathway for the future periods with discount rates of 3% and 5%, respectively. The social discount rate 4–6% is usually used as a benchmark discount value in carbon price studies in China compared with lower scenarios (for example, 3.6%)46,47. In a global study for estimating country-level social cost of carbon, 3% and 5% are used for scenario analysis48. Note that the mean value from the two discount rates was used in presenting the results. For the other pathways where investment data cannot be obtained, we refer to relevant references to estimate MAC. All the cost estimates are expressed in 2015 dollars, transformed on the basis of the Renminbi and US dollar exchange rate of the same year. The year 2015 represents a relatively stable condition of economic increase over the past decade (2011–2020) in China (the increase rate of gross domestic product (GDP) in 2015 is similar to the 10 yr mean). In the cases when the MAC curves exceed the estimated maximum potentials in the period, we identify the historical capacity or the MAMP as limited by the biophysical estimates.Additional mitigation required to meet Paris Agreement NDCsOn 28 October 2021, China officially submitted ‘China’s Achievements, New Goals and New Measures for Nationally Determined Contributions’ (‘New Measures 2021’ hereafter) and ‘China’s Mid-Century Long-Term Low Greenhouse Gas Emission Development Strategy’ to the Secretariat of the United Nations Framework Convention on Climate Change as an enhanced strategy to China’s updated NDCs (first submission in 2015). The goal of China’s updated NDCs is to strive to peak CO2 emissions before 2030 and achieve carbon neutralization by 2060. It specified the goals to include the following: before 2030, China’s carbon dioxide emissions per unit of GDP are expected be more than 65% lower than that in 2005, and the forest stock volume is expected to be increased by around 6.0 (previously 4.5) billion m3 over the 2005 level. In the ‘New Measures 2021’9 and ‘Master Plan of Major Projects of National Important Ecosystem Protection and Restoration (2021–2035)’5, many NCS-related opportunities are proposed to consolidate the carbon sequestration of ecosystems and increase the future NCS potential, including protecting the existing ecosystems, implementing engineering to precisely improve forest quality, continuously increasing forest area and stock volume, strengthening grassland protection and recovery and wetland protection and improving the quality of cultivated land and the agricultural carbon sinks.Industrial CO2 emissionsThe historical CO2 emissions data from 2000 to 201749,50 are used as the benchmark of industrial CO2 emissions during 2000–2020. For future projections, we use the peak value of the A1B2C2 scenario (in the range of 10,000 to 12,000 Mt) in 2030 from ref. 11. We assume that CO2 emission increases linearly from 2017 to 2030.Characterizing co-benefitsNCS activities proposed in the future measures or plans may enhance co-benefits. Four generalized types of ecosystem services are identified: improving biodiversity, water-related, soil-related and air-related ecosystem services (Fig. 1). Biodiversity benefits refer to the increase in different levels of diversity (alpha, beta and/or gamma diversity)51. Water, soil and air benefits refer to flood regulation and water purification, improved fertility and erosion prevention, and improvements in air quality, respectively, as defined in the Millennium Ecosystem Assessment52. The evidence that each pathway produces co-benefits from one or more peer-reviewed publications was collected through reviewing the literature (see the details for co-benefits of each pathway in Supplementary Information).Mapping province-level mitigationThe data for extent of implementing forest pathways are obtained from the statistical yearbook and reported at the province level. To be consistent with the forest pathways, the other pathways were also aggregated to the provincial-level estimate from the spatial data. If the flux data were available in different climate regions, the provinces are first assigned to climate regions. When a province spans multiple climate zones, the weight value is set according to the proportion of area, and finally an estimated value of rate was calculated (for fire management, some grassland and wetland pathways). For the forest pathways, we first collected the flux-rate data from reviewing literature and then averaged these flux rates to region/province. The flux rates for reforestation and natural forest management were calculated separately by province and age group. Similarly, specified flux rates are applied for different times after ecosystem restoration or conversion for other pathways.Classification of NCS typesThree types of NCS pathways were classified: protection (of intact natural ecosystems), improved management (on managed lands) and restoration (of native cover)35. In our study, four (AVFC, AVGC, AVCI, AVPI), eight (IMP, NFM, FM, BIOC, CVCR, CRNM, IMRC, GROP) and four (RF, GRR, CWR, PTR) NCS pathways were identified as protection, management and restoration types, respectively (Supplementary Table 1). These pathways can be further divided into groups of ‘single’ type or ‘mixed’ type according to their contribution to individual pathways. Specifically, in a certain area, when the mitigation capacity of a certain pathway accounts for more than 50% of the total, it is regarded as a single or dominant NCS type; if no single pathway accounts for more than 50%, it is a mixed type, named by the top pathways whose NCS sum exceeds 50% of the total mitigation capacity. More

Scheiner, S. M. Genetics and evolution of phenotypic plasticity. Annu. Rev. Ecol. Syst. 24, 35–68 (1993).Article 

Google Scholar 
Via, S. et al. Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol. Evol. 10, 212–217 (1995).CAS 
PubMed 
Article 

Google Scholar 
Ghalambor, C. K. et al. Adaptive versus non‐adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Funct. Ecol. 21, 394–407 (2007).Article 

Google Scholar 
King, J. G. & Hadfield, J. D. The evolution of phenotypic plasticity when environments fluctuate in time and space. Evol. Lett. 3, 15–27 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Newman, R. A. Genetic variation for phenotypic plasticity in the larval life history of spadefoot toads (Scaphiopus couchii). Evolution 48, 1773–1785 (1994).PubMed 

Google Scholar 
Nussey, D. H. et al. Selection on heritable phenotypic plasticity in a wild bird population. Science 310, 304–306 (2005).CAS 
PubMed 
Article 

Google Scholar 
Scheiner, S. Selection experiments and the study of phenotypic plasticity 1. J. Evol. Biol. 15, 889–898 (2002).Article 

Google Scholar 
Ghalambor, C. K. et al. Non-adaptive plasticity potentiates rapid adaptive evolution of gene expression in nature. Nature 525, 372–375 (2015).CAS 
PubMed 
Article 

Google Scholar 
Reger, J. et al. Predation drives local adaptation of phenotypic plasticity. Nat. Ecol. Evol. 2, 100–107 (2018).PubMed 
Article 

Google Scholar 
Sommer, R. J. Phenotypic plasticity: from theory and genetics to current and future challenges. Genetics 215, 1–13 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Brakefield, P. M. & Reitsma, N. Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi. Ecol. Entomol. 16, 291–303 (1991).Article 

Google Scholar 
Rountree, D. & Nijhout, H. Hormonal control of a seasonal polyphenism in Precis coenia (Lepidoptera: Nymphalidae). J. Insect Physiol. 41, 987–992 (1995).CAS 
Article 

Google Scholar 
Scheiner, S. M. & Holt, R. D. The genetics of phenotypic plasticity. X. Variation versus uncertainty. Ecol. Evol. 2, 751–767 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Bonamour, S. et al. Phenotypic plasticity in response to climate change: the importance of cue variation. Philos. Trans. R. Soc. B 374, 20180178 (2019).Article 

Google Scholar 
Fox, R.J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2018.0174 (2019).Auld, J. R., Agrawal, A. A. & Relyea, R. A. Re-evaluating the costs and limits of adaptive phenotypic plasticity. Proc. R. Soc. B 277, 503–511 (2010).PubMed 
Article 

Google Scholar 
Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yampolsky, L. Y., Schaer, T. M. & Ebert, D. Adaptive phenotypic plasticity and local adaptation for temperature tolerance in freshwater zooplankton. Proc. R. Soc. B 281, 20132744 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Schmid, M. & Guillaume, F. The role of phenotypic plasticity on population differentiation. Heredity 119, 214–225 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Charlesworth, B., Lande, R. & Slatkin, M. A neo-Darwinian commentary on macroevolution. Evolution 36, 474–498 (1982).PubMed 

Google Scholar 
Lynch, M. The rate of morphological evolution in mammals from the standpoint of the neutral expectation. Am. Nat. 136, 727–741 (1990).Article 

Google Scholar 
Kingsolver, J. G. & Pfennig, D. W. Patterns and power of phenotypic selection in nature. Bioscience 57, 561–572 (2007).Article 

Google Scholar 
West-Eberhard, M. J. Developmental plasticity and the origin of species differences. Proc. Natl Acad. Sci. USA 102, 6543–6549 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turelli, M. & Barton, N. Polygenic variation maintained by balancing selection: pleiotropy, sex-dependent allelic effects and G × E interactions. Genetics 166, 1053–1079 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
Charlesworth, B. Causes of natural variation in fitness: evidence from studies of Drosophila populations. Proc. Natl Acad. Sci. USA 112, 1662–1669 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Noble, D. W., Radersma, R. & Uller, T. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proc. Natl Acad. Sci. USA 116, 13452–13461 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Draghi, J. A. & Whitlock, M. C. Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation. Evolution 66-9, 2891–2902 (2012).Article 

Google Scholar 
Houle, D. How should we explain variation in the genetic variance of traits? Genetica 102, 241–253 (1998).PubMed 
Article 

Google Scholar 
Tollrian, R. Predator‐induced morphological defenses: costs, life history shifts, and maternal effects in Daphnia pulex. Ecology 76, 1691–1705 (1995).Article 

Google Scholar 
Agrawal, A. A., Laforsch, C. & Tollrian, R. Transgenerational induction of defences in animals and plants. Nature 401, 60–63 (1999).CAS 
Article 

Google Scholar 
Tollrian, R. Neckteeth formation in Daphnia pulex as an example of continuous phenotypic plasticity: morphological effects of Chaoborus kairomone concentration and their quantification. J. Plankton Res. 15, 1309–1318 (1993).Article 

Google Scholar 
Dennis, S. et al. Phenotypic convergence along a gradient of predation risk. Proc. R. Soc. B 278, 1687–1696 (2011).CAS 
PubMed 
Article 

Google Scholar 
Hammill, E. & Beckerman, A. P. Reciprocity in predator–prey interactions: exposure to defended prey and predation risk affects intermediate predator life history and morphology. Oecologia 163, 193–202 (2010).PubMed 
Article 

Google Scholar 
Hammill, E., Rogers, A. & Beckerman, A. P. Costs, benefits and the evolution of inducible defences: a case study with Daphnia pulex. J. Evol. Biol. 21, 705–715 (2008).CAS 
PubMed 
Article 

Google Scholar 
Barnard-Kubow, K. et al. Polygenic variation in sexual investment across an ephemerality gradient in Daphnia pulex. Mol. Bio. Evol. 39, msac121 (2022).Article 

Google Scholar 
Deng, H.-W. & Lynch, M. Inbreeding depression and inferred deleterious-mutation parameters in Daphnia. Genetics 147, 147–155 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Seyfert, A. L. et al. The rate and spectrum of microsatellite mutation in Caenorhabditis elegans and Daphnia pulex. Genetics 178, 2113–2121 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, S. et al. High mutation rates in the mitochondrial genomes of Daphnia pulex. Mol. Biol. Evol. 29, 763–769 (2012).CAS 
PubMed 
Article 

Google Scholar 
Collyer, M. L. & Adams, D. C. Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. Hystrix 24, 75 (2013).
Google Scholar 
Adams, D.C., Collyer, M., Kaliontzopoulou, A. & Sherratt, E. et al. Geomorph: software for geometric morphometric analyses (University of New England, 2016); https://hdl.handle.net/1959.11/21330Adams, D. C. & Collyer, M. L. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution 73, 2352–2367 (2019).PubMed 
Article 

Google Scholar 
Richards, C. L., Bossdorf, O. & Pigliucci, M. What role does heritable epigenetic variation play in phenotypic evolution? BioScience 60, 232–237 (2010).Article 

Google Scholar 
Latta, L. C. IV et al. The phenotypic effects of spontaneous mutations in different environments. Am. Nat. 185, 243–252 (2015).PubMed 
Article 

Google Scholar 
Lind, M. I. et al. The alignment between phenotypic plasticity, the major axis of genetic variation and the response to selection. Proc. R. Soc. B 282, 20151651 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Laforsch, C. & Tollrian, R. Inducible defenses in multipredator environments: cyclomorphosis in Daphnia cucullata. Ecology 85, 2302–2311 (2004).Article 

Google Scholar 
Weiss, L. C., Leimann, J. & Tollrian, R. Predator-induced defences in Daphnia longicephala: location of kairomone receptors and timeline of sensitive phases to trait formation. J. Exp. Biol. 218, 2918–2926 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Tollrian, R. & Harvell, C.D. The Ecology and Evolution of Inducible Defenses (Princeton Univ. Press, 1999).Lande, R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol. Biol. 22, 1435–1446 (2009).PubMed 
Article 
CAS 

Google Scholar 
Via, S. & Lande, R. Genotype–environment interaction and the evolution of phenotypic plasticity. Evolution 39, 505–522 (1985).PubMed 
Article 

Google Scholar 
Kvist, J. et al. Temperature treatments during larval development reveal extensive heritable and plastic variation in gene expression and life history traits. Mol. Ecol. 22, 602–619 (2013).CAS 
PubMed 
Article 

Google Scholar 
Siepielski, A. M. et al. Differences in the temporal dynamics of phenotypic selection among fitness components in the wild. Proc. R. Soc. B 278, 1572–1580 (2011).PubMed 
Article 

Google Scholar 
Muschick, M. et al. Adaptive phenotypic plasticity in the Midas cichlid fish pharyngeal jaw and its relevance in adaptive radiation. BMC Evol. Biol. 11, 116 (2011).Salzburger, W. Understanding explosive diversification through cichlid fish genomics. Nat. Rev. Genet. 19, 705–717 (2018).CAS 
PubMed 
Article 

Google Scholar 
Halligan, D. L. & Keightley, P. D. Spontaneous mutation accumulation studies in evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 40, 151–172 (2009).Article 

Google Scholar 
Houle, D., Morikawa, B. & Lynch, M. Comparing mutational variabilities. Genetics 143, 1467–1483 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eberle, S. et al. Hierarchical assessment of mutation properties in Daphnia magna. G3 Genes Genomes Genetics 8, 3481–3487 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).CAS 
PubMed 
Article 

Google Scholar 
Burgmer, T., Hillebrand, H. & Pfenninger, M. Effects of climate-driven temperature changes on the diversity of freshwater macroinvertebrates. Oecologia 151, 93–103 (2007).CAS 
PubMed 
Article 

Google Scholar 
Yan, N. D. et al. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interactive effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65, 862–877 (2008).CAS 
Article 

Google Scholar 
Reed, T. E., Schindler, D. E. & Waples, R. S. Interacting effects of phenotypic plasticity and evolution on population persistence in a changing climate. Conserv. Biol. 25, 56–63 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
ASTM, Standard Guide for Conducting Acute Toxicity Tests with Fishes, Macroinvertebrates, and Amphibians (American Society for Testing and Materials, 1988).Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, J. et al. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
PubMed 
Article 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).MarkDuplicates v.2.20 (Broad Institute, 2019); http://broadinstitute.github.io/picardMcKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Beckerman, A. P., Rodgers, G. M. & Dennis, S. R. The reaction norm of size and age at maturity under multiple predator risk. J. Anim. Ecol. 79, 1069–1076 (2010).PubMed 
Article 

Google Scholar 
Naraki, Y., Hiruta, C. & Tochinai, S. Identification of the precise kairomone-sensitive period and histological characterization of necktooth formation in predator-induced polyphenism in Daphnia pulex. Zool. Sci. 30, 619–625 (2013).Article 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
Scrucca, L. et al. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2018).Ben-Shachar, M. S., Lüdecke, D. & Makowski, D. effectsize: estimation of effect size indices and standardized parameters. J. Open Source Softw. 5, 2815 (2020).Article 

Google Scholar 
Collyer, M. L. & Adams, D. C. RRPP: an r package for fitting linear models to high‐dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).Article 

Google Scholar 
Collyer, M., Adams, D. & and Collyer, M.M. RRPP: linear model evaluation with randomized residuals in a permutation procedure. R package version 1.3 https://CRAN.R-project.org/package=RRPP (2021).Smirnov, P. robcor: Robust correlations. R package version 0.1-6.1 https://CRAN.R-project.org/package=ropcor (2014).Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).Article 

Google Scholar 
Yang, J. et al. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).Villanueva, R., Chen, Z. & Wickham, H. ggplot2: Elegant Graphics for Data Analysis Using the Grammar of Graphics (Springer-Verlag, 2016).Wilke, C. cowplot: Streamlined plot theme and plot annotations for ‘ggplot2’. R package version 0.9. 2 https://CRAN.R-project.org/package=cowplot (2020).Dowle, M. et al. data.table: Extension of ‘data.frame‘. R package version 1.14.0 https://CRAN.R-project.org/package=data.table (2021).Daniel, M. foreach: Provides foreach looping construct. R package version 1.5.1 https://CRAN.R-project.org/package=foreach (2020).Weston, S. doMC: Foreach parallel adaptor for ‘parallel’. R package version 1.3.7 https://CRAN.R-project.org/package=doMC (2020).Clarke, E. & Sherrill-Mix, S. Ggbeeswarm: Categorical scatter (violin point) plots. R package version 0.6. 0 https://CRAN.R-project.org (2017).Garnier, S. et al. viridis: Default color maps from ‘matplotlib’. R package version 0.5.1 (2018). More

Uchida, H. & Baba, O. Fishery management and the pooling arrangement in the Sakura ebi fishery in Japan, 175–189. https://www.fao.org/3/a1497e/a1497e16.pdf (2008).Omori, M. The biology of a sergestid shrimp Sergestes lucens Hansen. Bull. Ocean Res. Inst. Univ. Tokyo 4, 1–83 (1969).
Google Scholar 
Gurney, R. & Lebour, M. V. Larvae of decapod crustacea. Part VI. The genus Sergestes. Discov. Rep. 20, 1–68 (1940).
Google Scholar 
Holthuis, L. B. FAO species catalogue. Vol. 1. Shrimps and prawns of the world. An annotated catalogue of species of interest to fisheries. FAO Fish. Synop. Vol. 125, 1–271 (1980).Omori, M., Ukishima, Y. & Muranaka, F. New record of occurrence of Sergia lucens (Hansen) (Crustacea, Sergestidae) off Tung-kang, Taiwan, with special reference to phylogeny and distribution of the species. J. Oceanogr. Soc. Jpn. 44, 261–267 (1988) (in Japanese with English abstract).Article 

Google Scholar 
Isshiki, T. & Tajima, Y. The research of a sergestid shrimp, Sergia lucens (Hansen) in the mouth of Tokyo Bay I. The seasonal distribution of adult and the distribution of eggs. Bull. Kanagawa Pref. Fish. Exp. Stn. 13, 73–78 (1992) (in Japanese with English abstract).
Google Scholar 
Lee, D. A., Wu, S. H., Liao, I. C. & Yu, H. P. On three species of commercially important sergestid shrimps (Decapoda: Sergestidae) in the coastal waters of Taiwan. J. Taiwan Fish. Res. Inst. 4, 1–19 (1996) (in Chinese with English abstract).CAS 

Google Scholar 
Yinji, L. & Ratana, C. Governing in an uncertain time: The case of Sakura shrimp fishery, Japan. Marit. Stud. 20, 115–126 (2021).Article 

Google Scholar 
Isono, R. S., Kita, J. & Setoguma, T. Acute effects of kaolinite suspension on eggs and larvae of some marine teleosts. Comp. Biochem. Physiol. Part C 120, 449–455 (1998).CAS 
Article 

Google Scholar 
Aoki, S. & Oinuma, K. Distribution of clay minerals in surface sediments of Suruga Bay, central Japan. J. Geol. Soc. Jpn. 87(7), 429–438 (1981) (in Japanese with English abstract).Article 

Google Scholar 
Nasnodkar, M. R. & Ganapati, N. N. Clay mineralogy and chemistry of mudflat core sediments from Sharavathi and Gurupur estuaries: Source and processes. Indian J. Geo-Mar. Sci. 48(3), 379–388 (2019).
Google Scholar 
Capper, N. The effects of suspended sediment on the aquatic organisms Daphnia magna and Pimephales promelas. All Theses. 2. https://tigerprints.clemson.edu/all_theses/2 (2006).Boyd, M. B. et al. Disposal of dredge spoil, problem identification and assessment and research program development. Technical report H-72–8, U.S. army engineer waterways experiment station, CE, Vicksburg, Miss. (1972).McFarland, V. A. & Peddicord, R. K. Lethality of a suspended clay to a diverse selection of marine and estuarine macrofauna. Arch. Environ. Contam. Toxicol. 9, 733–741 (1980).CAS 
Article 

Google Scholar 
Arakawa, H. et al. The influence of suspended particles on larval development in the Manila clam Ruditapes philippinarum. Sci. Postp. 1, e00028. https://doi.org/10.14340/spp.2014.08A0002 (2014).Article 

Google Scholar 
Davis, H. C. Effects of turbidity-producing materials in sea water on eggs and larvae of the clam (Venus (Mercenaria) mercenaria). Biol. Bull. 118, 48–54 (1960).Article 

Google Scholar 
Tabata, A., Morinaga, T. & Arakawa, H. Influences of concentration, particle-size and kind of inorganic suspended matter on feed caught by Manila clam, Ruditapes philippinarum. La Mer 37, 163–171 (2000).CAS 

Google Scholar 
Annisa, Dwiatmoko, M. U., Saismana, U. & Maulanai, R. Characteristics of kaolin clay on Alluvial formation subdistrict mataraman based on physical properties and chemical properties. In MATEC Web of Conferences Vol. 280, 03009. https://doi.org/10.1051/matecconf/201928003009 (2019).Murray, H. H. Structure and composition of clay minerals and their physical and chemical properties. Dev. Clay Sci. 2, 7–31. https://doi.org/10.1016/S1572-4352(06)02002-2 (2006).Article 

Google Scholar 
Kumari, N. & Mohan, C. Basics of clay minerals and their characteristic properties. Clay Clay Miner. 1–29 (2021).Lively, J. S., Kaufman, Z. & Carpenter, E. J. Phytoplankton ecology of a barrier island estuary: Great South Bay, New York. Estuar. Coast. Shelf Sci. 16(1), 51–68 (1983).ADS 
Article 

Google Scholar 
Lloyd, D. S. Turbidity as a water quality standard for salmonid habitats in Alaska. N. Am. J. Fish. Manag. 7, 34–45 (1987).Article 

Google Scholar 
Kirk, K. L. Effects of suspended clay on Daphnia body growth and fitness. Freshw. Biol. 28, 103–109 (1992).Article 

Google Scholar 
McCabe, G. D. & O’Brien, W. J. The effects of suspended silt on feeding and reproduction of Daphnia pulex. Am. Midl. Nat. 110, 324–337 (1983).Article 

Google Scholar 
Kirk, K. L. & Gilbert, J. J. Suspended clay and the population dynamics of planktonic Rotifers and Cladocerans. Ecology 71, 1741–1755 (1990).Article 

Google Scholar 
Loosanoff, V. L. Effects of turbidity on some larval and adult bivalves. Proc. Gulf. Carib. Fish. Inst. 14, 80–95 (1961).
Google Scholar 
Arruda, J. A., Marzolf, G. R. & Faulk, R. T. The role of suspended sediments in the nutrition of zooplankton in turbid reservoirs. Ecology 64, 1225–1235 (1983).Article 

Google Scholar 
Kathyayani, S. A., Muralidhar, M., Kumar, T. S. & Alavandi, S. V. Stress quantification in Penaeus vannamei exposed to varying levels of turbidity. J. Coast. Res. 86, 177–183 (2019).CAS 
Article 

Google Scholar 
Wilber, D. H. & Clarke, D. G. Biological effects of suspended sediments: A review of suspended sediment impacts on fish and shellfish with relation to dredging activities in estuaries. N. Am. J. Fish. Manag. 21, 855–875 (2001).Article 

Google Scholar 
Lin, H., Charmantier, G., Thuet, P. & Trilles, J. Effects of turbidity on survival, osmoregulation, and gill Na+-K+ ATPase in juvenile shrimp Penaeus japonicus. Mar. Ecol. Prog. Ser. 90, 31–37 (1992).ADS 
CAS 
Article 

Google Scholar 
Davis, H. C. & Hidu, H. Effects of turbidity-producing substances in sea water on eggs and larvae of three genera of bivalve mollusks. Veliger 11, 316–323 (1969).
Google Scholar 
Nimmo, D. R., Hamaker, T. L., Matthews, E. & Young, W. T. The long-term effects of suspended particulates on survival and reproduction of the mysid shrimp, Mysidopsis bahia, in the laboratory. In Proceedings of a Symposium on the Ecological Effects of Environmental Stress, New York, 413–422 (1979).Peddicord, R. & McFarland, V. Effects of suspended dredged material on the commercial crab, Cancer magister. In Proceedings of the Specialty Conference on Dredging and Its Environmental Effects, Mobile, Alabama, 633–644 (1976).Peddicord, R. K. Direct Effects of Suspended Sediments on Aquatic Organisms. Contaminants and Sediments. Volume 1. Fate and Transport, Case Studies, Modeling, Toxicity 501–536 (Ann Arbor Science Publishers, 1980).
Google Scholar 
Wakeman, T., Peddicord, R. & Sustar, J. Effects of suspended solids associated with dredging operations on estuarine organisms. In Ocean 75 conference, 431–436 (1975).Gebauer, P., Walter, I. & Anger, K. Effects of substratum and conspecific adults on the metamorphosis of Chasmagnathus granulata (Dana) (Decapoda: Grapsidae) megalopae. J. Exp. Mar. Biol. Ecol. 223, 185–198 (1998).Article 

Google Scholar 
Carvalho, L. & Calado, R. Trade-offs between timing of metamorphosis and grow out performance of a marine caridean shrimp juveniles and its relevance for aquaculture. Aquaculture 492, 97–102 (2018).Article 

Google Scholar 
Calado, R. et al. The physiological consequences of delaying metamorphosis in the marine ornamental shrimp Lysmata seticaudata and its implications for aquaculture. Aquaculture 546, 737391. https://doi.org/10.1016/j.aquaculture.2021.737391 (2022).Article 

Google Scholar 
Murphy, R. C. Factors affecting the distribution of the introduced bivalve, Mercenaria mercenaria, in a California lagoon—The importance of bioturbation. J. Mar. Res. 43, 673–692 (1985).Article 

Google Scholar 
Bricelj, V. M. & Malouf, R. E. Influence of algal and suspended sediment concentration on the feeding physiology of the hard clam Mercenaria mercenaria. Mar. Biol. 84, 155–165 (1984).Article 

Google Scholar 
Wenger, A. S., Jacob, J. L. & Jones, G. P. Increasing suspended sediment reduces foraging, growth, and condition of a planktivorous damselfish. J. Exp. Mar. Biol. Ecol. 428, 43–48 (2012).Article 

Google Scholar 
Robinson, W. E., Wehling, W. E. & Morse, M. P. The effect of suspended clay on feeding and digestive efficiency of the surf clam Spisula solidissima (Dillwyn). J. Exp. Mar. Biol. Ecol. 74, 1–12 (1984).CAS 
Article 

Google Scholar 
Turner, E. J. & Miller, D. C. Behavior and growth of Mercenaria mercenaria during simulated storm events. Mar. Biol. 111, 55–64 (1991).Article 

Google Scholar 
Grant, J. & Thorpe, B. Effects of suspended sediment on growth, respiration, and excretion of the soft-shelled clam (Mya arenaria). Can. J. Fish. Aquat. Sci. 48, 1285–1292 (1991).Article 

Google Scholar 
Gleason, R. A., Euliss, N. H., Hubbard, D. E. & Duffy, W. G. Effects of sediment load on emergence of aquatic invertebrates and plants from wetland soil egg and seed banks. Wetlands 23, 26–34 (2003).Article 

Google Scholar 
Jacek, R., Anna, S. & Miroslaw, S. The effect of lake sediment on the hatching success of Daphnia ephippial eggs. J. Limnol. 75, 597–605 (2016).
Google Scholar 
Newcombe, C. P. & McDonald, D. D. Effects of suspended sediment on aquatic ecosystems. N. Am. J. Fish. Manag. 11, 77–82 (1991).Article 

Google Scholar 
Chutter, F. M. The effects of silt and sand on the invertebrate fauna of streams and rivers. Hydrobiologia 34, 57–76 (1968).Article 

Google Scholar 
Hellawell, J. M. Biological indicators of freshwater pollution and environmental management. In Pollution Monitoring Series (ed. Melanby, K.) https://doi.org/10.1007/978-94-009-4315-5 (1986).Makita, M. & Kondo, M. Rearing of the larvae of Seigia Lucens (Hansen). Bull. Shizuoka Pref. Fish. Exp. Stn. 16, 97–105 (1982) (in Japanese).
Google Scholar  More