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Study area and speciesWe selected three sites across an elevation gradient in the western Swiss Alps (Bex, Canton de Vaud), situated at 890, 1400 and 1900 m above sea level (hereafter, the low, middle, and high sites; Supplementary Fig. 1). The three sites span a temperature gradient ranging from 2.5 to 9.6 °C (mean annual temperature from 1981 to 201544; Supplementary Table 1). With increasing elevation, soil moisture increased, and the growing season length was shortened by a longer snow-covered period, as measured from July 2019 to June 2020 (Supplementary Fig. 2). All sites were established on south-facing and shallow slopes in pasture and fenced to exclude livestock.We included 14 herbaceous focal species that frequently occur in this region, half of which originated from low elevation (hereafter lowland species) and half from high elevation (highland species, Supplementary Table 2). Lowland species had upper range limits (defined as the 90th percentile of their elevation distribution) below 1500 m (with the exception of Plantago lanceolata, with a 90th percentile of 1657 m), while highland species had lower range limits (defined as the 10th percentile of their elevation distribution) above 1500 m, based on a dataset of 550 vegetation plots from the study area45. These species consisted of 12 perennial and two biennial species, which are the dominant life histories in this region. Species were selected to include a range of functional types (7 forbs, 4 grasses, 3 legumes) and functional traits (based on plant height, specific leaf area and seed mass). Seeds were obtained from regional suppliers given the large quantities that were needed to establish the experiment (Supplementary Table 2).Field competition experimentWe designed a field experiment to study the effects of elevation on population growth rates and competitive outcomes by growing focal plants either without competition or competing with a background monoculture of the same or another species (Supplementary Fig. 1). In spring 2017, we established 18 plots (1.6 × 1 m, 0.2 m deep) at each of the three field sites, lined with wire mesh to exclude rodents (except at the high site) and with weed-suppressing fabric on the sides to prevent roots growing in from outside. To control for soil effects, the beds were then filled with a silt loam soil that originated from a nutrient-poor meadow at 1000 m a.s.l. within the study area. Four plots were maintained as bare soil plots (non-competition plots). The other 14 plots received 9 g m−2 of viable seeds of each species, which allowed the establishment of a monoculture of relatively high density (competition plots). We then periodically weeded the plots to maintain monocultures over the course of the experiment. All species except for two (Arnica montana and Daucus carota) successfully established monocultures, of which 11 species (including six lowland species and five highland species) were fully established by autumn 2017. We then resowed the other plots that failed to establish, which subsequently established either in spring 2018 (Poa trivialis and Poa alpina in the low site and Bromus erectus in the middle site) or autumn 2018 (Aster alpinus, P. trivialis and P. alpina in the middle site and Sesleria caerulea in the low and high sites). Species that failed to establish were included only as focal species for the calculation of invasion population growth rates (i.e. the density was low for A. montana and D. carota in all sites, Trifolium badium in the low site and S. caerulea in the middle site, probably due to high mortality rates caused by drought).We first raised focal seedlings of each species in a greenhouse for six weeks on standard compost and then transplanted them into the field (Supplementary Fig. 1). To test for responses to elevation in the absence of competition, focal plants were transplanted into non-competition plots at 25 cm apart in autumn 2017 (n = 9 per species and site). To test for effects of competition, we transplanted focal individuals into established plots with 14 cm spacing (n = 9 per focal species, competitor and site). Focal plants that died within two weeks of transplanting were replaced (ca. 5%), assuming mortality was caused by transplant shock. Note that we transplanted focal plants into plots only when the background monocultures were fully established. In 2018 and 2019, we replaced dead focal individuals in spring and autumn (ca. 10% each time). The full design included 56 unique interspecific pairs in each site accounting for 61% of all 14 × 13 = 91 possible pairwise combinations. These pairs were selected to evenly sample differences in functional trait space based on a pilot analysis using plant height, specific leaf area and seed mass obtained from the LEDA dataset46. Each focal species competed against four lowland and four highland species, yielding 14 lowland–lowland and highland–highland pairs and 28 lowland–highland pairs. Across all three sites, this design resulted in N = 3780 individuals in total ([56 interspecific pairs × 2 + 14 intraspecific pairs + 14 non-competition] × 9 individuals × 3 sites).Demographic dataWe followed each focal individual between 2017 and 2020 to monitor individual-based demographic performance (i.e. vital rates; Supplementary Fig. 4). Survival was monitored twice a year at the beginning and the end of the growing season. Towards the end of the growing season each year (August–September), we measured all individuals to record plant size, whether they flowered, and to estimate seed production on flowering individuals. To estimate focal plant size, we measured size-related morphological traits on all focal individuals at each census (i.e. the number and/or length of flowering stalks, leaves or ramets, depending on the species) and estimated dry aboveground biomass using regression models fitted using collected plant samples (mean R2 = 0.871; Supplementary Data 1; Supplementary Methods). To estimate seed production, we counted the number and measured the size of fruits on reproductive individuals; we then estimated the number of seeds produced by each individual using regression models fitted using intact fruits of each species collected at the early fruiting stage on background plants (mean R2 = 0.806; Supplementary Data 2; Supplementary Methods). We conducted a separate experiment to estimate the germination and recruitment of each species in each site (Supplementary Methods).Population modellingTo estimate population growth rates (λ), we built integral projection models to incorporate multiple vital rates across the life cycle47 (see Supplementary Table 3 for model structure and parameters). Separate IPMs were built to estimate intrinsic growth rates using plants growing in the absence of competition (in non-competition plots) and invasion growth rates using plants growing within the background monocultures (in competition plots), under the assumption that monocultures were at equilibrium (see Supplementary Fig. 5 for a test of this assumption) and that focal individuals did not interact with each other but only with the background species. We used plant size (i.e. estimated dry aboveground biomass, log scale) as a continuous state variable and fitted linear models to estimate vital rate parameters by combining multiple-year demographic data over three censuses (i.e. 2017–2018, 2018–2019, and 2019–2020; see Supplementary Methods for consideration of more complex models). We modelled the probability of survival, flowering, and seedling establishment using generalized linear models with a binomial error distribution, modelled growth and seed production using general linear models and described the offspring size distribution using Gaussian probability density functions. We modelled seed germination, seedling establishment and the seedling size distribution as size-independent functions, assuming they are unaffected by maternal size (Supplementary Fig. 4; Supplementary Table 3). For each vital rate of each species, we selected the best-fitted vital rate model by comparing all nested models of the full models using the Akaike information criterion corrected for small samples (AICc), which allowed us to avoid overfitting models and to borrow strength across competitor species and sites in cases where full models were outperformed by reduced models (Supplementary Methods; Supplementary Data 3 and 4).We calculated population growth rates (λ) as the dominant eigenvalue of the IPMs, which represents the discrete per-capita growth rate (i.e. ({N}_{t+1}=lambda {N}_{t}))47. To evaluate the uncertainty around λ, we performed parametric bootstraps for size-dependent vital rates (i.e. survival, growth, flowering, and fecundity). Specifically, we resampled the parameters of each vital rate model using multivariate normal distributions based on their means and covariance matrices48. We then fitted all IPMs and estimated λ for each of the 500 bootstrap replicates (Supplementary Data 5).Estimation of niche differences, relative fitness differences, and coexistence outcomesWe quantified niche and relative fitness differences and predicted coexistence outcomes following the method of Carroll et al.49. This method is based on species’ sensitivity to competition defined as the proportional reduction of the population growth rate of a focal species i when invading a population of a competitor species j that is at its single-species equilibrium, and is mathematically equivalent to one minus the response ratio:$${S}_{ij}=1-frac{{{{{{{rm{ln}}}}}}}(lambda_{{ij}})}{{{{{{rm{ln}}}}}}({lambda}_{i})}$$
(1)
where λij denotes the invasion growth rate of focal species i and λi is its intrinsic growth rate. The natural logarithm of discrete population growth rates λ estimated from IPMs are equivalent to per-capita growth rate in continuous population growth models50, and this transformation makes sensitivities compatible with the coexistence analysis described below. Sensitivity is greater than 0 for antagonistic interactions, with higher values equating to stronger competition, while facilitative interactions lead to negative sensitivities.For a pair of species, modern coexistence theory predicts that niche differences (ND) promote coexistence by reducing the intensity of interspecific competition experienced by both species. Therefore, a pair of species with a large niche difference should display small mean sensitivities to competition from each other. Consequently, niche differences can be calculated as one minus the geometric mean of the two sensitivities (i.e. niche overlap). In contrast, relative fitness differences (RFD) quantify the degree of asymmetry in species’ competitive abilities. Therefore, a pair of species with a large fitness difference should display large differences in their sensitivities to competition from each other, as quantified as the geometric standard deviation of sensitivities49:$${{{{{rm{ND}}}}}}=1-sqrt{{S}_{{ij}}{S}_{{ji}}}$$
(2)
$${{{{{rm{RFD}}}}}}=sqrt{{S}_{{ji}}/{S}_{{ij}}}$$
(3)
There are three possible outcomes of competition between a given pair of species: stable coexistence, a priority effect, and competitive exclusion. These can be quantified based on either invasion criteria or the relative magnitude of niche differences versus relative fitness differences15,51. Stable coexistence is only possible when both species are able to invade each other’s equilibrium populations; this condition is met when ND  > 0 and ({{{{{rm{RFD}}}}}} , < , frac{1}{1-{{{{{rm{ND}}}}}}})49, which is equivalent to (frac{1}{{{{{{rm{RFD}}}}}}(1-{{{{{rm{ND}}}}}})} > 1), with greater values indicating more stable coexistence and providing a metric for the strength of coexistence (i.e., coexistence metric26). When neither species can invade when rare, then priority effects occur, meaning that whichever species is initially established within a community has an advantage and excludes the other. This could happen when a species pair has a small niche difference and a small relative fitness difference, that is ND  , frac{1}{1-{{{{{rm{ND}}}}}}}). We quantified competitive outcomes and coexistence metrics for each of the 500 bootstrap replicates of the dataset (Supplementary Data 6).Note that we excluded facilitative interactions that were present in 13% of all pairs because the equations for niche differences and relative fitness differences are not compatible with negative values of sensitivity (Eq. 2 and 3); we did not exclude facilitative interactions for other analyses. We quantified the coexistence determinants of species pairs in cases where either one or both of the species were predicted to be unable to persist in the absence of neighbours (i.e. ln(λintrinsic)  More

Effects of the application of different improved materials on properties of reclaimed soilSoil organic matter (SOM) and total nitrogen (TN)After the application of different improved materials, the SOM and TN contents in both 0–0.15 m and 0.15–0.30 m layers of the hollow village reclaimed soil showed an overall increasing trend (Fig. 1). In the 0–0.15 m layer, the organic matter content increased by 9.6%, 79.0%, 90.0%, 61.4%, 120.1%, and 131.7% respectively under TM, TF, TO, TMF, TMO and TFO treatments compared with CK treatment, indicating that different improved materials all played important roles in improving the organic matter content of reclaimed soil (Fig. 1a). The improvement of organic matter content in the 0–0.15 m layer of reclaimed soil by the treatments of different improved materials showed as follows: TFO  > TMO  > TO  > TF  > TMF  > TM  > CK, and TO, TMO and TFO treatments with organic fertilizer addition could significantly improve the organic matter content of the reclaimed soil (P  2 mm water-stable aggregates was increased by 88.1%, 194.5%, 203.7%, 376.2%, and 781.7% respectively under TF, TO, TMF, TMO and TFO compared with CK. The proportion of water-stable macroaggregates under different treatments showed as follows: TFO (35.8%)  > TMO (20.7%)  > TO (16.9%)  > TMF (16.3%)  > TF (12.3%)  > TM (10.1%)  > CK (9.0%), and the water-stable macroaggregates were increased by 328.2%, 130.0%, 87.8%, 81.1%, 36.7%, and 12.2% respectively compared with CK, with the maximum increase of 328.2%. In general, all six different amendment material treatments increased the proportion of water-stable macroaggregates in reclaimed soil and promoted the aggregation and cementation of water-stable microaggregates ( 0.25 mm). And the TFO showed the best effect on the increase of water-stable macroaggregates, followed by TMO, TO, and TMF, while TF and TM treatments showed little effect.Figure 2Percentage (%) of soil water-stable aggregates under the application of different improved materials at 0.15–0.30 m Depth. CK: no improved material; TM: maturing agent (ferrous sulfate); TF: fly ash; TO: organic fertilize; TMF: maturing agent + fly ash, TMO: maturing agent + organic fertilizer; TFO: fly ash + organic fertilizer. Different lowercase letters represent significant differences among different improved material treatments in the same particle-size aggregates.Full size imageFigure 3Percentage (%) of soil water-stable aggregates under the application of different improved materials at 0.15–0.30 m Layer. CK: no improved material; TM: maturing agent (ferrous sulfate); TF: fly ash; TO: organic fertilize; TMF: maturing agent + fly ash, TMO: maturing agent + organic fertilizer; TFO: fly ash + organic fertilizer. Different lowercase letters represent significant differences among different improved material treatments in the same particle-size aggregates.Full size imageIn the 0.15–0.30 m layer, the change of water-stable aggregates showed a similar trend to that in the 0–0.15 m layer compared with CK treatment. TF, TO, TMF, TMO and TFO treatments all significantly increased the proportion of  > 2 mm and 1–2 mm water-stable aggregates, and decreased the proportion of water-stable microaggregates (P  2 mm water-stable aggregates by 130.3%, 94.5%, 133.9%, 151.4%, and 309.2% respectively compared with CK, of which TFO treatment showed the most significant effect on the increase of the proportion of water-stable macroaggregates. Compared with the 0–0.15 m layer, the proportion of water-stable macroaggregates in the 0.15–0.30 m layer showed a gradual decrease with the increase of soil depth.Water-stable aggregates structure stabilityThe mean weight diameter (MWD), geometric mean diameter (GMD), unstable aggregate index (ELT), and fractal dimension (D) are important indicators reflecting the structural geometry and stability of soil aggregates, and it has been indicated in this research that the higher the MWD and GMD and the smaller the ELT and D, the better the structural stability of the aggregates and the soil structure27,28. Compared with CK treatment, the MWD and GMD showed a trend of significant increase while the D and ELT showed a trend of significant decrease (P  TF  > TMF  > TM  > CK. The combination of organic–inorganic improved materials can effectively reduce the BD of reclaimed soil, and the BD under TFO treatment was the smallest, 1.19 g cm−3. In the 0.15–0.30 m layer, through variance analysis, the effect of different improved materials on the BD showed a similar decreasing trend to that in the 0–0.15 m layer.Figure 4Effects of the application of different improved materials on BD and SMC. CK: no improved material; TM: maturing agent (ferrous sulfate); TF: fly ash; TO: organic fertilize; TMF: maturing agent + fly ash, TMO: maturing agent + organic fertilizer; TFO: fly ash + organic fertilizer; BD, soil bulk density; SMC, soil moisture content. Different lowercase letters represent significant differences among different improved material treatments in the same soil layer.Full size imageThe soil moisture content (SMC) of the reclaimed soil in the 0–0.15 m and 0.15–0.30 m layers increased significantly after the application of different improved materials (P  TMO  > TMF  > TO  > TF≈TM  > CK (Fig. 4b). In the 0–0.15 m soil layer, the SMC under TM, TF, TO, TMF, TMO and TFO treatments was increased by13.5%, 13.8%, 21.4%, 21.9%, 32.4% and 38.3% respectively compared with CK. The TMO and TFO showed the most significant positive effect on the SMC of reclaimed soil, and the mass water content was 17.4% and 18.2% respectively. In conclusion, compared with CK, these improved materials increased the SOM content and porosity, promoted the formation and stability of aggregates, and increased the retention and transmission of water, which was helpful to maintain more water. Among them, the coupling treatment of organic and inorganic improved materials can hold more soil moisture, and the most significant increase was observed under TFO and TMO.Correlation analysis between soil organic matter and water-stable aggregates parametersTo further explore the correlation between the parameters of the reclaimed soil after the application of six different improved materials, a regression analysis was conducted in this paper on the correlation between the parameters of organic matter and water-stable aggregates with different particle sizes. From Table 2, it could be seen that the organic matter content had a highly significant positive correlation with MWD, GMD and  > 2 mm water-stable aggregates content and a highly significant negative correlation with ELT, D and water-stable microaggregates content ( 2 mm, 1–2 mm, and 0.5–1 mm) content had a significant positive correlation with MWD and GMD values and a highly significant negative correlation with ELT and D values; water-stable microaggregates ( TMO  > TO  > TMF  > TF  > TM  > CK, and different improved materials all significantly increased maize yield compared with CK (P  More

Spatiotemporal variations in N and P emissions from livestock manureManure N and P production and comparison with fertilizer usage in the Yangtze River DeltaAlthough the amounts of chemical fertilizers used in the Yangtze River Delta region fluctuated greatly before 2000, the overall trend was still increasing, and there was a continuous downward trend since 2000 (Fig. 1). Specifically, the amount of chemical fertilizer used in the Yangtze River Delta region reached a peak of nearly 40 years in 1985, with 1.87 × 109 kg of nitrogen fertilizer and 3.38 × 108 kg of phosphate fertilizer. After entering the low period in 1990, it began to recover slowly, but since the beginning of the twenty-first century, the amount of fertilizer application has shown a continuous downward trend. By 2018, the amount of fertilizer nitrogen and phosphorus applied in the Yangtze River Delta region was 7.79 × 108 kg and 1.31 × 108 kg, respectively, which was a decrease of 58.28% and 61.28% compared to 1985.Figure 1Application of N and P fertilizers and the amount of N and P emissions from organic fertilizers (manure) in the Yangtze River Delta from 1980 to 2018.Full size imageThe change trend of fertilizer application in the Yangtze River Delta may be a combined effect of the national policy and the change of cultivated land area. Since the 1970s, my country has vigorously promoted the fertilizer industry and agricultural fertilization47,48, which reached a peak in 1985. However, due to the adjustment of agricultural policies around 1990, the fertilizer market at this time is relatively chaotic49, resulting in fluctuations in the amount of fertilizer used. The amount of fertilizer application is closely related to the area of arable land. Around 2000, the area of arable land in China decreases, and the amount of fertilization is also affected. However, the use of chemical fertilizers per unit area still causes serious environmental pollution. In 2015, the Ministry of Agriculture formulated and issued the “Action Plan for Zero Growth of Chemical Fertilizer Use by 2020”, which provided new guidance for chemical fertilizer application, which led to a fundamental reduction.Over the past 40 years, emissions of N and P from manure in the Yangtze River Delta generally increased at first and then decreased. From 1980 to 2005, N and P emissions from manure in the Yangtze River Delta showed an increasing trend. Manure N increased from 2.13 × 108 kg in 1980 to 2.66 × 108 kg in 2005, increased by 24.77%. Manure P increased from 7.34 × 107 kg in 1980 to 1.03 × 108 kg in 2005, increased by 40.11%. After reaching a peak in 2005, emissions showed a continuous declining trend. In 2018, N and P emissions from manure used as organic fertilizer were 1.46 × 108 kg and 5.68 × 107 kg, respectively, back to 1980 levels.The change trend of manure nitrogen and phosphorus emissions in the Yangtze River Delta is mainly affected by economic development and national policies. From 1985 to 2005, the vigorous economic development also promoted the increasing demand for meat, eggs, and milk in people’s lives. The development of animal husbandry50, and there were almost no special regulations on the establishment of sound manure management in the livestock and poultry farming industry before 2000. Factors have contributed to the increase in the discharge of livestock and poultry manure. After 2000, the State Environmental Protection Administration successively promulgated many normative documents, which effectively curb the increase of nitrogen and phosphorus emissions from livestock manure.Spatiotemporal variation characteristics of N and P emissions from livestock manureThere have obvious temporal and spatial variabilities in manure N and P emissions in the soils among cities in the Yangtze River Delta over the past 40 years (Figs. 2, 3). N and P emissions from livestock manure in the soils showed a trend of increasing and then decreasing, with manure nutrient emissions from livestock in some areas showed a fluctuating trend.Figure 2Manure N emissions in cities in the Yangtze River Delta.Full size imageFigure 3Manure P emissions in cities in the Yangtze River Delta.Full size imageNantong and Shanghai have always been the geographic focus of livestock manure nutrient emissions in terms of spatial change. Average N and P emissions from livestock manure in Nantong over the past 40 years was 3.47 × 107 and 1.39 × 107 kg, respectively; average N and P emissions from livestock manure in Shanghai over the past 40 years was 2.67 × 107 and 9.70 × 106 kg, respectively. During the past 40 years, the total N and P emissions from livestock manure were lowest in Zhoushan. From 1980 to 2018, the average yields of N and P in livestock manure were only 1.18 × 106 and 4.34 × 105 kg, respectively, in Zhoushan. The N and P emissions from livestock manure in other cities fluctuated 3.54 × 105 to 3.01 × 107 kg and 1.35 × 105 to 1.14 × 107 kg, respectively.Nantong and Shanghai have become the geographic focus of livestock and poultry manure emissions due to the following reasons: on the one hand, rapid economic development, abundant population resources, urban density and high degree of agricultural intensification are all factors that have caused the rapid development of livestock and poultry breeding. On the other hand, arable land resources are extremely scarce, resulting in a unit arable land carrying capacity much higher than other cities, and the dense water network has also accelerated the loss of nitrogen and phosphorus in livestock manure. As Zhoushan is located at the intersection of the golden coastline of eastern China and the golden waterway of the Yangtze River, it is China’s largest seafood production base, and its development focus is not on the livestock and poultry breeding industry.Changes in manure loads of livestock in the Yangtze River Delta from 1980 to 2018Spatiotemporal pattern of manure N and P loads in livestockFrom 1980 to 2018, the manure N load in the soils of the Yangtze River Delta showed an overall trend of increasing first and then decreasing (Fig. 4). From 1980 to 2010, most regions of the study area showed an increasing trend in manure N load. In 2010, the on average manure N load was highest. The high load area was rapidly expanding from the north and the middle east to the south of the study area, and the load center shifted from the east to the middle and southwest. From 2010 to 2018, the manure N load decreased year by year, especially in the middle of the study area, and the high-load area transferred to the edge area. In 2018, manure N load recovered roughly to the level seen in the 1990s.Figure 4Spatiotemporal distribution of manure N load in the Yangtze River Delta from 1980 to 2018.Full size imageFrom 1980 to 2010, average manure N load increased from 27.46 to 50.61 kg hm−2, representing a growth of 84.30% to the maximum manure N load of the past 40 years. This trend is basically consistent with the gradual increase in the average nitrogen pollution load per unit of arable land in China as shown by the results of earlier studies48,51,52,53. Manure N load in Zhoushan (located in the eastern coastal area), Shanghai, Huzhou, and Jiaxing (located in the central area), and Hangzhou (located in the southwest region) increased significantly: Zhoushan saw the largest increase (295.25%) from 22.04 kg hm−2 in 1980 to 87.10 kg hm−2 in 2010. On the one hand, the livestock breeding industry is affected by the price regulation and management of the agricultural material market, and on the other hand, it is affected by production price factors. Therefore, the rapid increase in demand for livestock caused by the rapid economic development, the increase in prices, and the increase in the number of breeding industries are the main reasons for the rapid increase in the manure N load during this period. From 2010 to 2018, the average manure loads dropped dramatically, decreasing from 50.61 to 30.29 kg hm−2, representing a decline of 40.15%. The average manure N load in Wuxi, Suzhou, Jiaxing (located in the central part), and Zhoushan (located in the eastern coastal area) showed significant declines, with that in Jiaxing decreasing from 100.88 to 24.60 kg hm−2, representing a decrease of 75.62%. Feng et al.54 found that policy is an important factor that makes livestock breeding industry more standardized and the environment improved. Therefore, the reduction in the manure N load during this period was largely affected by policy regulation. Over the past 40 years, the manure N load in Shanghai has been maintained at a high level, with a load of over 50 kg hm−2 throughout the period. By contrast, the manure N load in Yangzhou has been maintained at a low level for a long time, which was below 30 kg hm−2, highlighting significant regional differences. The reason is the difference caused by the difference in the degree of urban construction and economic development in different regions. In areas with relatively developed economies, the demand for livestock products is large, and the amount of livestock is large, but the area of arable land is small, and the capacity of absorbing livestock and poultry manure is limited, resulting in a relatively large manure N and P loads on cultivated land55.From 1980 to 2018, the manure N load center moved from the central and northern regions of the study area to the northwestern and eastern, and then to the southwestern and eastern regions after a peak of manure loads was reached in each city. In the 1980s, the maximum livestock manure loads were located in the middle (Jiaxing) and the northern part (Wuxi) of the study area. Subsequently, the center of gravity of manure N emissions from livestock gradually shifted to the east. In the 1990s, the livestock manure N load gravity center was located in the northwestern (Zhenjiang) and eastern (Shanghai) areas. At the beginning of the twenty-first century, the livestock manure N load was maintained at a relatively high level in most cities, and the emission center gradually shifted to the east. In 2018, the livestock manure N load was centred in the southwestern region (Hangzhou) and the eastern region (Nantong, Shanghai).Considering that there is no systematic standard limit of organic fertilizer N in China, we used the European Union’s farmland manure N limit standard of as the basis for determining the manure N load51. From 1980 to 2018, the manure N load did not exceed the European Union’s standard (170 kg hm−2), but still showed an increasing trend. It can be seen that the discharge of livestock manure in each city has had adverse impact on the environment of the Yangtze River Delta.From 1980 to 2018, the spatiotemporal evolution pattern of livestock manure P load in the soils was very similar to that of the manure N load, with an overall trend of first increasing and then decreasing (Fig. 5). From 1980 to 2010, the livestock manure P load showed an increasing trend. By 2010, the livestock manure P load had reached its maximum over the past 40 years. Areas with high livestock manure P load spread from the central area to the surrounding cities, and finally radiated to the surrounding regions with the central area as the load center; from 2010 to 2018, the livestock manure P load decreased significantly, and areas with high manure P load migrated from the central region to the southwestern marginal region and the northeastern coastal cities.Figure 5Spatiotemporal distribution of manure P load in the Yangtze River Delta from 1980 to 2018.Full size imageFrom 1980 to 2010, the average livestock manure P load in the Yangtze River Delta increased from 9.36 to 19.47 kg hm−2, representing a growth rate of 108.02%. Average manure P loads in Zhoushan, Ningbo (located in the eastern coastal area), and Jiaxing, Hangzhou, and Huzhou (located in the central and southern regions) showed significant increases of 347.66%, 323.28%, 198.13%, 181.88%, and 155.39%, respectively. From 2010 to 2018, the average livestock manure P load was reduced from 19.47 to 11.74 kg hm−2, representing a reduction of 39.71%. Zhoushan (located in the eastern coastal area) and Jiaxing and Suzhou (located in the central area) saw the most obvious declines of 73.26%, 72.49%, and 68.05%, respectively. In addition, the average manure P load content was greater than 20 kg hm−2 in the central region (Huzhou, Jiaxing, Shanghai) and the southwestern region (Hangzhou), but lower than 15 kg hm−2 in the southeastern region (Tai) and northwestern region (Yangzhou, Taizhou).From 1980 to 2018, the center of the livestock manure P load shifted from the central and northern regions of the study area to the eastern and northwestern regions, and then to the northeastern and southwestern regions after reaching peak P loading in each city. In the 1980s, the livestock manure P load was mainly concentrated in the middle (Jiaxing) and the northern region (Wuxi); in the 1990s, the center of the livestock manure P load moved to the east (Shanghai) and the northwest (Zhenjiang); at the beginning of the twenty-first century, the livestock manure P load remained at high levels in most cities. By 2018, the center of the livestock manure P load had accumulated toward the edge of the Yangtze River Delta, mainly in the northeastern (Nantong) and southwestern regions (Hangzhou).It is generally believed that annual P application from manure should not exceed 35 kg hm−256, otherwise excessive P will result in leaching of soil P and eutrophication of the water body. Livestock manure P load in Jinxing exceeded this standard in 2010, having a certain negative impact on the local environment. The rational treatment and utilization of livestock manure is imminent57.In summary, the Yangtze River Delta’s livestock manure has increased first and then decreased in the past 40 years. The reason for its significant increase before 2010 is mainly due to the urban construction and rapid economic development in various regions, and it spreads to the surrounding areas. With urban development, the area of agricultural land has been greatly reduced, and the area carrying livestock manure nutrient has been relatively reduced, resulting in an increase in the manure N and P load in the Yangtze River Delta. After 2010, the reason for the decrease in the manure N and P load in the Yangtze River Delta may be related to the successive introduction and implementation of environmental protection policies for livestock breeding in various provinces and cities. After the promulgation of the “Pollution Prevention and Control Technology Policy for Livestock and Poultry Breeding Industry” in 2010, the state has strengthened the macro-control of the livestock breeding industry, the Yangtze River Delta has been listed as a restricted development zone, the industrial structure has been further optimized and adjusted, and the amount of livestock breeding has been significantly reduced. This leads to a decrease in the manure N and P load in the Yangtze River Delta. The manure N and P load has been weakened year by year. This change pattern is also consistent with the trend of policy measures. To a certain extent, it shows that the current livestock and poultry pollution prevention and control measures have achieved remarkable results58.Changes in livestock manure N and P loads from 1980 to 2018Between 1980 and 2018, manure N and P loads showed significant spatial variability (Fig. 6). Due to the influence of government guidance, large demand for production land, and environmental protection pressure, changes to animal husbandry space have been promoted59,60,61. Livestock manure N and P loads in the northwestern and central regions have decreased significantly, while manure N and P loads in the surrounding areas have increased to varying degrees, showing a general shift from the central region to the surrounding cities. Specifically, in 2018, the manure N and P loads in Nanjing, Wuxi, Suzhou, and Jiaxing showed a decreasing trend. Compared with 1980, the manure N load decreased by 41.55%, 48.26%, 44.49%, and 33.46%, respectively. Compared with 1980, the manure P load decreased by 21.63%, 43.08%, 43.47%, and 17.98%, respectively. The reduction in manure N and P loads in the central region is related to policies of livestock pollution prevention, which were successively promulgated in the Tai Lake area32,33,62. The relevant departments have optimized the regional layout of animal husbandry and comprehensively made use of the livestock manure to reduce pollution, thereby reducing the manure N and P loads. All the 11 cities of Yangzhou, Zhenjiang, Changzhou, Nantong, Shanghai, Huzhou, Hangzhou, Shaoxing, Ningbo, Zhoushan, and Tai saw different degrees of increase in their manure N and P loads, and Hangzhou increased most. Compared with 1980, the manure N and P load of Hangzhou in 2018 increased by 76.72% and 112.58%, respectively. The manure N and P loads in the remaining 10 cities increase less than 100%. Increases in manure N and P loads may be related to the small scale63,64 and scattered distribution of local farms, and lack of environmental protection awareness among residents65,66.Figure 6(a) Percentage change of manure N loads from 1980 to 2018; (b) Percentage change of manure P loads from 1980 to 2018.Full size imageIdentification of high-risk areas of soil pollution caused by livestock manureThe high-risk areas for manure N and P emissions in 2018 were mainly located in the northwestern and southern regions of the Yangtze River Delta (Fig. 7), while the manure N and P emissions in some northern cities could not meet the nutrient requirements of the local land. Manure N and P emissions in Changzhou were 215.60% and 334.54% of the land’s absorption capacity, while those in Nanjing were 102.18% and 71.02%, respectively. It shows that the imbalance between the supply and demand of planting and breeding may cause a greater risk of environmental pollution of livestock and poultry, and it is necessary to reduce the scale of breeding or expand the scale of planting67,68. Manure P emissions in Wuxi, Huzhou, Jiaxing, Hangzhou, and Zhoushan were close to the maximum land absorption capacity for livestock manure nutrients, indicating that the supply and demand for planting and breeding were balanced. Therefore, the use of local organic fertilizers can be appropriately increased to reduce the amount of chemical fertilizers used and reduce the potential pollution threat caused by the enrichment of manure nutrients69,70. In the northern region, the discharge of livestock manure in Yangzhou, Taizhou, Nantong, and Zhenjiang was only 0–20% of the land absorption capacity, indicating that livestock manure nutrient in these areas cannot meet the nutrient needs of local crops. Therefore, additional nutrient supply is needed to meet the normal growth of local crops71.Figure 7(a) Manure N emission relative to land absorption capacity in 2018; (b) manure P emission relative to land absorption capacity in 2018.Full size imageSelection of typical models and main control factors based on long-term manure N and P emissionsSystematic clustering analysis of manure N and P emissionsAccording to the similarity of manure N and P emissions in the cities, we carried out variable analysis based on the Ward minimum variance method72. Cities were divided into four categories based on the change trend of manure N emissions in the Yangtze River Delta73 (Fig. 8a). Class I: Yangzhou, Tai, Wuxi, Suzhou, Shaoxing, Ningbo, Zhoushan, Hangzhou; class II: Nantong, Taizhou, Changzhou; class III: Huzhou, Jiaxing; class IV: Nanjing, Shanghai, Zhenjiang.Figure 8(a) Systematic clustering of manure N emissions from 1980 to 2018; (b) systematic clustering of manure P emissions from 1980 to 2018.Full size imageSimilar to the manure N classification method, cities were divided into four categories based on the change trend of manure P emissions in the Yangtze River Delta (Fig. 8b). Class I: Nantong, Taizhou, Changzhou, Yangzhou, Tai, Shaoxing, Ningbo, Zhoushan, Hangzhou; class II: Huzhou, Jiaxing; class III: Wuxi, Suzhou; class IV: Nanjing, Shanghai, Zhenjiang.Principal component analysis of manure N and P emissionsBased on the results of systematic clustering, typical cities were extracted to establish a typical model of manure N and P emissions and the main control factors were selected74. For manure N emissions, Yangzhou, Nantong, Huzhou, and Shanghai were selected from Class I, Class II, Class III, and Class IV, respectively. Combining these with the rising and falling trend characteristics of manure N emissions over the long study period, we established four typical models of manure N emissions as “up-down-down” model, “down-up-up” model, “down-up-down” model, and “up-up-down” model. According to the clustering results of manure P emissions, Hangzhou, Jiaxing, Suzhou, and Shanghai were selected from Class I, Class II, Class III, and Class IV, respectively. Four typical models of manure P emissions were established based on rising and falling trend characteristics of manure N emissions over the long study period as “up-up-down” model, “down-up-down” model, “down-level-down” model, and “up-down-down” model.Analysis on the main control factors for manure N emissionsFor the “up-down-down” model in Yangzhou, the total variance of the two principal components accounted for 85% (Fig. 9a); Nantong was characterized as a typical “down-up-up” model city, where the variance of the two principal components ac-counted for 82% (Fig. 9b); Huzhou represented a typical “down-up-down” model city, where the sum of the variances of the two principal components was 82% (Fig. 9c); in the “up-up-down” model for Shanghai, the sum of variance of the two principal com-ponents was 96% (Fig. 9d).Figure 9Manure N emissions from 1980 to 2018 and main control factors based on principal component analysis.Full size imageThere was a significant positive correlation between changes in manure N emissions and the proportion of the primary industry in Yangzhou, indicating that the “up-down-down” model of Class I is mainly affected by the primary industry. The “Pollution Prevention and Control Plan for Livestock Breeding Industry in Yangzhou” proposes to delimit forbidden and restricted areas, regulate livestock breeding, and reduce pollutant discharge from livestock breeding. Therefore, the scale of livestock breeding in Yangzhou has decreased, and the total manure N has shown a downward trend, which is consistent with the interannual changes in the proportion of the primary industry. The primary industry in this class is mainly agriculture and animal husbandry; thus, the main control factor for the total manure N is the proportion of primary industry.The total manure N in Nantong first decreased and then increased, and finally tended to be flat over the study period. There was a clear correlation between changes in manure N emissions in Nantong, meat production, and the total output value of animal husbandry, indicating that Class II is dominated by these two factors. The livestock breeding industry in Class II is relatively developed75,76, and meat production showed a consistent trend with total manure N. Hence, meat production and the total output value of animal husbandry are the factors having the greatest impact on Class II.Total manure N in Huzhou showed a trend of decreasing, then increasing, and finally decreasing. Manure N emissions changes and meat production showed a relatively obvious positive correlation, indicating that Class III is mainly affected by meat production and has little correlation with factors such as GDP, which is consistent with the changing trend of meat production. Huzhou’s agriculture is dominated by planting and fishery77, and the impact of animal husbandry is not significant, so meat production is the factor having the greatest impact on such cities.Total manure N in Shanghai began to increase over the study period, consistent with changes in meat production. There was a strong positive correlation between changes in manure N emissions and meat production, indicating that Class IV is greatly affected by arable land area. After that, Shanghai issued relevant measures to regulate livestock breeding, such as the “Shanghai Livestock a Breeding Management Measures”. Due to a decline in farmland area, meat production decreased, and manure N showed a downward trend78. Total manure N and meat production in Shanghai both increased at first and then decreased. Therefore, meat production is the most influential factor in such cities.Selection of main control factors for manure P emissionsIn the “up-up-down” model for Hangzhou, the total variance of the two principal components accounted for 91% (Fig. 10a); as a typical “down-up-down” model, the total variance of the two principal components in Jiaxing accounted for 88% (Fig. 10b); Su-zhou represented a typical “down-flat-down” model, and the sum of the variance of the two principal components accounted for 95% (Fig. 10c); as an example of the “up-down-down” model, the variance of the two principal components in Shanghai accounted for 96% (Fig. 10d).Figure 10Manure P emissions from 1980 to 2018 and main control factors based on principal component analysis.Full size imageManure P in Hangzhou first increased and then decreased, essentially the same as the trend for Hangzhou’s meat production. There was a strong positive correlation between changes in manure P emissions and the total meat production in Yangzhou, indicating that the “up-up-down” model of Class I is mainly affected by meat production. Due to the serious pollution from livestock and poultry in Hangzhou79, relevant policies have been introduced to reduce the amount of livestock breeding, thereby reducing meat production.Manure P in Jiaxing showed a trend of first decline, then rise, and finally decline. There was a large positive correlation between changes in manure P emissions and meat production in Jiaxing, indicating that the “down-up-down” model of Class II is mainly affected by meat production. Class II is dominated by agriculture and animal husbandry34,35, and the breeding industry is relatively developed. Meat production also changes with these industries, and its inter-annual variation is consistent with that of manure P.Manure P in Suzhou showed a downward trend, consistent with inter-annual changes in the area of arable land. Changes in manure P emissions and the area of arable land showed a significant positive correlation, indicating that the Class III cities with a “decrease-level-decrease” model were mainly affected by the area of arable land and the proportion of the primary industry. Suzhou is an industrial development base that cannot be ignored. Local economic development is relatively rapid80, so its arable land area is continuously decreasing81.The livestock manure P in Shanghai increased at first and then decreased, which is consistent with inter-annual changes in meat production. In 2002, the Shanghai Municipal People’s Government highlighted a special plan for Shanghai’s animal husbandry, stipulating prohibition of breeding areas, control of breeding areas and moderate breeding areas. The city’s total livestock and poultry production decreased, and meat production decreased82. There was a strong positive correlation between the changes in livestock manure P emissions and meat production, indicating that the “up-down-down” model of Class IV is mainly affected by meat production. More

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