{"paper_id":"05f6ce3a-540d-4164-82c3-d940be43b8cc","body_text":"Spatial trade-offs and synergies among ecosystem services in Guangdong Province, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial trade-offs and synergies among ecosystem services in Guangdong Province, China Qian Xu, Ying Yang, Ren Yang, Lisi Zha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3037558/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The trade-offs between ecosystem services directly affect the quality of the ecological environment and the survival and development of human society, which is of great concern to academia, governments, and non-governmental organizations. Based on ecosystem service data from the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences, the trade-offs and synergies among different ecosystem services in Guangdong Province in China were analyzed. Moreover, the differences in their impact and impact mechanisms were investigated. Our results showed three main points: (1) The ecosystem services in Guangdong Province showed clear spatial heterogeneity. Also, Northern Guangdong has high water retention, with a value of 5,804.73×10 4 m 3 /km 2 and high values for carbon sequestration and soil retention. Western Guangdong is a functional area for food production, and the Pearl River Delta is an economically developed region. (2) In the overall Guangdong Province, three pairs of ecosystem services, namely water retention–soil, carbon sequestration–water, and carbon sequestration–soil retention, showed a strong positive correlation and a good synergistic relationship. The other three pairs of relationships show strong trade-off effects. (3) The trade-offs and synergies between pairs of ecosystem services are clearly different in space, and the relationships between the same ecosystem services show completely different characteristics in different regions, resulting from the complex influence of different natural local conditions and human activities. Ecosystem services Trade-off Synergy Spatial relation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Ecosystem services are the environmental conditions and effects of ecosystem formation and maintenance on human survival and development (Daily, 1997 ). However, ecosystem services do not exist or develop independently. Complex reciprocal relationships exist among various services within an ecosystem and among several ecosystems (Niu et al., 2022 ). These interactions mainly manifest as trade-offs between wanes and waxes or synergies of mutual gains. Diverse and complex ecological environments provide various services for human well-being, and the impact of human activities is often at the expense of certain service levels (European Environment Agency, 2021 ). The trade-offs between different services and their influencing factors must be analyzed because they only affect the level of ecosystem services and the stability and development of the whole ecosystem (Belaire et al., 2022 ; Wang et al., 2022 ). Therefore, the spatial trade-offs and synergistic relationship between ecosystem services has important research significance and has become a popular topic in multi-disciplinary research, especially in that involving geography, environmental science, and ecology. Trade-offs in ecosystem services are mainly generated from human demand preferences. When people consume certain ecosystem services, they will have an impact on other ecosystem services intentionally or unintentionally, leading to trade-offs and synergies of ecosystem services (Cord et al., 2017 ; Li et al., 2019 ). A scientific understanding of the functional characteristics, manifestations, driving mechanisms, and scale effects of ecosystem service trade-offs/synergies is of great significance for improving human well-being and realizing a \"win-win\" situation between human society and the ecosystem (Wu et al., 2019 ). A comprehensive understanding of the relationships between ecosystem services includes multiple dimensions, such as trade-offs, synergies, and compatibilities (Shen et al., 2020 ). A trade-off is a negative relationship in which ecosystem services are restricted by other functions, such as ebb and flow, including supporting and regulating functions (Yang et al., 2018 ; Hu et al., 2018 ). A synergy is a positive relationship, and several ecosystem services show symbiosis, enhancing or weakening together, such as support and cultural functions, and regulatory and cultural functions (Zhang et al, 2020 ). A compatibility shows no significant relationship between ecosystem services (Peng et al., 2017 ). In reality, in order to improve a certain ecosystem service, we often inevitably affect trade-offs and synergies with other services (Allen et al., 2022 ). Scholars have conducted extensive research on the interaction between ecosystem services and concluded that trade-offs and synergies among ecosystem services are universal (Damania, 2018; Kluger et al., 2020 ). Ecosystem services are influenced by various factors such as land use and cover change, human needs, parameter selection, regional differences, and imbalances (Seddon et al., 2020 ). Different regions show significant differences (Vanham et al., 2019 ; Moore, 2020). Most developing countries in an important period of economic development often pay attention to promoting economic benefits while ignoring ecological benefits. Therefore, the ultimate goal of ecosystem service research should be to maximize the comprehensive benefits of the man-earth system, ease the trade-offs between different ecosystem services, and improve human welfare (Costanza et al., 2017 ; Huang et al., 2023 ). As an important developing country, China is currently in a crucial transition period from high-speed to high-quality economic development. Therefore, urban economic development should be coordinated with environmental protection. Guangdong, a relatively developed province in China, was selected as the research area in this study. This study clarifies the main types of ecosystem services and their spatial differentiation characteristics. The study focuses on analyzing the trade-offs and synergies among different ecosystem services and the differences in their degrees of influence, as well as comparing and analyzing their spatial patterns. Then, the influence mechanisms of trade-offs and synergies between ecosystem services were analyzed and areas for improvement were determined. The findings of this study are of great significance for the improvement of regional eco-environmental carrying capacity, protection, and management, the creation of solutions for sustainable development goals, and the coordination between economic development and ecological protection. 2 Study area and methods 2.1 Study area Guangdong Province is located in the southernmost part of mainland China, with a land area of 179,800 km 2 . It is located between 20°13ʹN–25°31ʹN and 109°39ʹE–117°19ʹE and faces the South China Sea in the south. Guangdong Province has jurisdiction over 21 prefecture-level cities (including two sub-provincial cities), which are divided into four regions: the Pearl River Delta, Eastern Guangdong, Western Guangdong, and Northern Guangdong. The Pearl River Delta includes the cities of Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, Zhuhai, Jiangmen, Zhaoqing and Huizhou; Eastern Guangdong includes Shantou, Chaozhou, Jieyang, and Shanwei; Western Guangdong includes Zhanjiang, Maoming, Yangjiang, and Yunfu; and Northern Guangdong includes Shaoguan, Qingyuan, Meizhou, and Heyuan. Guangdong Province is located in the tropical and subtropical regions, with the Tropic of Cancer running through its central part. It is located on the north coast of the South China Sea, is heat rich, has abundant rainfall, a wide variety of animals and plants, and good natural conditions. Guangdong Province has complex and diverse landforms, with hills, platforms, and basins developing between the mountains. The province belongs to the East Asian monsoon region, and has middle subtropical, southern subtropical, and tropical climates from north to south. It is also one of the provinces with the most abundant light, heat, and water in China. The average sunshine duration of the whole province is 1,745.8 hours. The average annual temperature is 22.3 ℃. The average annual precipitation ranges from 1,300 to 2,500 mm. The spatial distribution of rainfall shows a high trend in the south and low in the north. Guangdong is China's most populous province with a population of 126 million in 2020. Also, since 1989, Guangdong's gross domestic product (GDP) has continuously ranked first in China, and it has become the largest economic province in the country, accounting for 1/8 of the country's total economic aggregate. In 2020, Guangdong's GDP reached 11,076.09 billion yuan, while the Pearl River Delta core region's GDP accounted for 80.83% of that of the whole province. 2.2 Methodology 2.2.1 Measurement of ecosystem services Considering the natural background and socioeconomic conditions of Guangdong Province and referring to the \"Territorial Spatial Planning of Guangdong Province\" and other relevant planning and policies, five kinds of ecosystem services were selected for analysis: carbon sequestration, water retention, soil retention, food production, and biodiversity conservation. This study used ecosystem services data from the Chinese Academy of Sciences Ecological Environmental Research Center ( http://www.sciencedb.cn/dataSet/handle/458 ). The dataset is based on remote sensing feature classification data. The management modes of land features, community structure, and ecological process differences (Ouyang 5; Zhang et al., 2018 ) were analyzed using MODIS satellite data Q13A1. Temperature and precipitation data were provided by the China Meteorological Data Sharing Network ( http://www.nmic.cn/ ) and topographic data, from the United States’ GEOM satellite. Ecosystem services in China in 2010 were simulated based on ecological process simulation methods, such as the CASA light energy utilization rate model, universal soil loss equation, water balance equation, wind model, and by summarizing literature and ground monitoring data to determine model parameters. A spatial dataset with a resolution of 250 m was created. Carbon sequestration was measured mainly based on net primary productivity (NPP), which is represented by the product of photosynthetic active radiation absorbed by plants and actual light utilization (ε). Water retention was calculated using the water balance equation. Soil retention was simulated using the general soil loss equation. In the specific calculation, the existing measured soil erosion data was used to verify the model simulation results and modify the parameters. Food production data provided the county ecosystem with food output, such as grain, aquatic products, meat, forest fruit products, uniformly converted into energy. Also, rather than using the total number of species, the measure of biodiversity conservation used in this study represented the total number of indicator species with a recorded distribution in each county, primarily nationally protected plants and animals of special significance, or species with threatened or endangered status. The biodiversity conservation values in Guangdong Province were the average values for all districts and counties, and the values of the four regions were the average values of all districts and counties in the region. 2.2.2 Evaluation of ecosystem service trade-offs and synergies The Pearson correlation coefficient method was used to evaluate ecosystem service trade-offs and synergies. If the correlation coefficient was positive, the synergies between the two services were mutually promoted. Conversely, a negative correlation coefficient indicated a trade-off between the two services. Otherwise, the two functions were independent of each other. In terms of the spatial dimension, a bivariate local spatial autocorrelation model was used to quantitatively measure the spatial distribution pattern and correlation characteristics of ecosystem service trade-offs and synergies in Guangdong Province. Cluster diagrams between the pairs of ecosystem services in the study area were obtained through bivariate local Moran's I spatial analysis using GeoDa software. In this study, a queen spatial adjacency matrix was constructed to measure the statistics of the local indicator of spatial association between two services. Specifically, \"high-high\" (HH) indicated that the two services with a high score clustered significantly in this region, \"low-low\" (LL) indicated that the two services with a low score clustered significantly in this region, \"high-low\" (HL) indicated that the first function scores were high and that the other function scores were low, and \"low-high\" (LH) indicated the opposite of HL. \"Not significant\" indicated that the two functions were independent within the regional space. HH and LL were regarded as synergies, whereas HL were LH are regarded as trade-offs. 2.2.3 Degree of influence of ecosystem service trade-offs and synergies To classify the degree of ecosystem service trade-offs and synergies, a specific method was conducted. First, the natural breakpoint method was used to divide the five ecosystem services into three levels: low, medium, and high, numbered 1, 2, and 3, respectively (Table 1 ). The five types of service standardization and classification of raster data were superimposed using ArcGIS 10.2 data: CODE = C×10,000 + W×1,000 + B×100 + F×10 + S (1) In Eq. (1), C, W, B, F, and S represent carbon sequestration, water retention, biodiversity conservation, food production, and soil retention, respectively. CODE is a five-digit code, and each code sequence is a combination of 1, 2, and 3, representing the degree of influence of the ecosystem services. Table 1 Classification levels of ecosystem service capacity Server Type Low (1) Medium (2) High (3) carbon sequestration (t/km 2 ) [0, 65] (65, 204] > 204 water retention (10 4 m/km 2 ) [0, 32] (32, 81] > 81 biodiversity conservation (numbers) [0, 78] (78, 92] > 92 food production (10 8 kcal/km 2 ) [0, 4] (4, 9] > 9 soil retention (10 4 t/km 2 ) [0, 9] (9, 28] > 28 Subsequently, criteria for classifying trade-offs and synergies were developed (Table 1 ). Trade-offs were classified as strong or weak. A strong trade-off was a state with one high service supply capacity, and all others, medium or low. Service capacity combinations in a strong trade-off may be 1 high 4 low, 1 high 1 medium 3 low, 1 high 2 medium 2 low, etc. A weak trade-off referred to a state with two, three, or four types of high service capacities, while all other services had medium or low capacities. Service capacity combinations in a weak trade-off may be 2 high 3 low, 2 high 1 medium 2 low, 2 high 2 medium 1 low, etc. Synergies were also classified as high or low. In high synergies, all services were high. It is the most coordinated state and the ultimate goal of ecosystem management. High synergy combinations include 5 high, 4 high 1 medium, 3 high 2 medium, etc. Low synergy meant that all five types of service capacities were at a low level, which is the least ideal state. Low synergy combinations included 1 medium 4 low, 2 medium 3 low, 3 medium 2 low, etc. 3 Results 3.1 Spatial differentiation of ecosystem services in Guangdong province To ensure that different ecosystem services were comparable (Table 2 ), ecosystem service values were processed without dimensionality, and all values ranged from 0 to 1 (Table 3 ). Among the five ecosystem services in Guangdong Province, water retention was the strongest (0.71). Soil retention and carbon sequestration had values of 0.54 and 0.51, respectively. Biodiversity conservation (0.48) and food production (0.33) were ranked fourth and fifth, respectively, among the ecosystem services. Table 2 Values of ecosystem services in Guangdong Province in China in 2010 Carbon sequestration (t/km 2 ) Water retention (10 4 m/km 2 ) Soil retention (10 4 t/km 2 ) Food production (10 8 kcal/km 2 ) Biodiversity (numbers) Pearl River Delta 47.58 51.20 7.76 3.05 77 Northern Guangdong 59.91 58.05 9.26 2.08 87 Eastern Guangdong 40.77 38.58 7.59 4.53 75 Western Guangdong 39.79 49.52 6.75 6.80 76 Guangdong Province 50.06 52.40 8.11 3.63 80 Specifically, the spatial distribution of the different ecosystem services were as follows: (1) The overall carbon sequestration level in Guangdong Province was 50.06 t/km 2 , and its spatial distribution was higher in the north than in the south. The overall carbon sequestration level in the Pearl River Delta region (47.58 t/km 2 ) was higher than that in Western Guangdong (39.79 t/km 2 ) and Eastern Guangdong (40.77 t/km 2 ). However, there was a contiguous low-value area in the Pearl River Estuary (Fig. 2 ). The contiguous low-value area on the west bank of the Pearl River Estuary was larger than that on the east bank. The cities of Dongguan, Zhongshan and Foshan had the lowest carbon sequestration levels in Pearl River Delta, with 13.89 t/km 2 , 17.61 t/km 2 and 19.06 t/km 2 , respectively. The carbon sequestration level in Zhaoqing City in the Pearl River Delta was the highest of the entire province with 66.24 t/km 2 . The carbon sequestration level of Western Guangdong was the lowest among the four regions in the province, especially in the Leizhou Peninsula, the southernmost part of mainland China, with only 13.35 t/km 2 for the 21 cities in the province. The carbon sequestration level in Shantou City in Eastern Guangdong was also noticeably low with 18.59 t/km 2 , lower than that of the surrounding cities. Northern Guangdong showed a remarkably high carbon sequestration, and the four cities in the region had values greater than 55 t/km 2 . Table 3 Values without dimensionality of ecosystem services in Guangdong Province in China in 2010 Carbon sequestration (t/km 2 ) Water retention (10 4 m 3 /km 2 ) Soil retention (10 4 t/km 2 ) Food production (10 8 kcal/km 2 ) Biodiversity (numbers) Pearl River Delta 47.58 51.20 7.76 3.05 77 Northern Guangdong 59.91 58.05 9.26 2.08 87 Eastern Guangdong 40.77 38.58 7.59 4.53 75 Western Guangdong 39.79 49.52 6.75 6.80 76 Guangdong Province 50.06 52.40 8.11 3.63 80 (2) Guangdong has a humid climate, high forest coverage, and strong water retention for ecosystem services. The average value of water retention in Guangdong was 52.40×10 4 m 3 /km 2 , and the spatial distribution among cities was similar to that of carbon sequestration. Northern Guangdong was also a high-value area for water retention, with a value of 58.05×10 4 m 3 /km 2 . Except for Meizhou, the water retention in the other three cities was higher than the average provincial level. This is followed by the Pearl River Delta and Western Guangdong regions. The high-value areas in the Pearl River Delta region were distributed in its periphery. Remarkably, among the 21 cities, Yangjiang (74.10×10 4 m 3 /km 2 ) and Zhanjiang (18.16×10 4 m 3 /km 2 ) had the highest and lowest water conservation values in Western Guangdong, respectively. Water conservation in Eastern Guangdong (38.58×10 4 m 3 /km 2 ) was significantly lower than in other regions. (3) Guangdong Province had a strong soil retention service, with an average value of 8.11×10 4 m 3 /km 2 . The soil retention level of Northern Guangdong, which has a high forest coverage, was the highest, with a value of 9.26×10 4 m 3 /km 2 . Soil retention services in the Pearl River Delta and Eastern Guangdong regions were similar, with 7.76×10 4 m 3 /km 2 and 7.59×104 m 3 /km 2 , respectively, among which Huizhou had the highest of the 21 cities with 10.96×10 4 m 3 /km 2 . Western Guangdong had the lowest soil retention service value with 6.75×10 4 m 3 /km 2 . In this region, Zhanjiang had the lowest soil retention value among the 21 cities in the Leizhou Peninsula with 0.88×10 4 m 3 /km 2 , while Yunfu and Yangjiang had strong soil retention with values of 10.69×10 4 m 3 /km 2 and 10.09×10 4 m 3 /km 2 , respectively. (4) Food production data were sourced from county data, and foods such as grain, aquatic products, meat, and fruits were uniformly converted into total food supply calories. The average value of food production in Guangdong Province was 3.63×10 8 kcal/km 2 , and the main food production area was mainly concentrated in Western Guangdong, with a value of 6.80×10 8 kcal/km 2 . In this region, the cities of Zhanjiang and Maoming had the highest food production values with 10.65×10 8 kcal/km 2 , and 7.08×10 8 kcal/km 2 , respectively. This was followed by Eastern Guangdong and the Pearl River Delta, with values of 4.53×10 8 kcal/km 2 and 3.05×10 8 kcal/km 2 , respectively. In Eastern Guangdong, Shantou City had the highest food production value with 8.34×10 8 kcal/km 2 , followed by Jieyang City with 5.09×10 8 kcal/km 2 . The cities of Chaozhou and Shanwei did not reach provincial average levels. In the Pearl River Delta region, food production in Guangzhou and Jiangmen was relatively high, while very low in Shenzhen and Dongguan with only 0.03×10 8 kcal/km 2 and 0.08×10 8 kcal/km 2 , respectively. However, the food production in Northern Guangdong was the lowest with only 2.08×10 8 kcal/km 2 , less than one third of that in Western Guangdong. Northern Guangdong is an important ecological barrier in Guangdong Province with extensive forest land and a low proportion of cultivated land; thus, food production is not its main ecosystem service. (5) Guangdong Province has a complex ecological environment and a rich biodiversity. The biodiversity conservation value in Guangdong Province was 80 and, among the four regions, the biodiversity maintained in Northern Guangdong was the highest at 86. This could be expected as important forest areas, nature reserves, and natural parks in Guangdong are mostly distributed in Northern Guangdong. In fact, Shimentai Nature Reserve, the largest contiguous forest reserve in Guangdong Province, is located in the southernmost part of the Nanling Mountains in Northern Guangdong. The main objects of protection are subtropical evergreen broad-leaved forests, rare plants, and animals. There are 2,242 species of higher plants and 301 wild vertebrate species in Shimentai Nature Reserve. Among them, one species of first-class nationally protected plant and 23 of second-class were included in the study. There were four species under first-class national protected animals and 41 under second-class. Among all the districts and counties, Ruyuan County (131) and Lechang County (117) in Shaoguan City and Fogang County (114) in Qingyuan City had the highest biodiversity. The lowest number of indicator species were conserved in Eastern Guangdong with 74. 3.2 Ecosystem service trade-offs and synergies, and their influence in Guangdong Province The correlation between paired services among the five ecosystem services was obtained based on Pearson correlation analysis (Fig. 3). Water retention–soil retention, carbon sequestration–water retention, and carbon sequestration–soil retention showed strong positive correlations (r = 0.389, r = 0.299, r = 0.258, P < 0.05), indicating a strong synergistic relationship between them. These three ecosystem services are also the main functions of forestland. The correlation coefficients between carbon sequestration–biodiversity conservation, water retention–biodiversity conservation, and soil retention–biodiversity conservation were positive and statistically significant; however, the relative coefficient values were small (r = 0.073, r = 0.137, r = 0.116), indicating a poor synergistic relationship. The negative correlation between food production and carbon sequestration was low (r = -0.110, P < 0.05), indicating a weak trade-off effect. There were strong negative correlations between food production–water retention, food production–soil retention, and food production–biodiversity conservation (r = -0.179, r = -0.182, r = -0.304, P < 0.05), indicating strong trade-off effects and reciprocal relationships between them. Ecosystem services were divided into levels 1, 2, and 3 according to Table 1 , and then the strengths and weaknesses of trade-offs and synergies between different services were evaluated. Also, the spatial distribution of ecosystem service trade-offs and synergies in Guangdong Province were assessed (Fig. 4 ). This mainly showed poor synergies and strong trade-offs, accounting for 89.34% of the total area of Guangdong Province. Among them, poor synergies between different ecosystem service pairs occupied the largest area, mainly concentrated in Eastern Guangdong and most of the Pearl River Delta region. The other counties and districts in Northern Guangdong showed poor synergies, except for Lechang County and Ruyuan Yao Autonomous County in Shaoguan City, and Yingde County and Yangshan County in Qingyuan City. There were also many regions with strong trade-offs between different ecosystem service pairs, mainly in Western and Northern Guangdong and Guangzhou City in the Pearl River Delta region. The spatial distribution of the services with weak trade-offs was limited to Xinyi County in Maoming City in Western Guangdong, and Yingde County, Yangshan County, and Ruyuan Yao Autonomous County in Qingyuan City in Northern Guangdong. Few areas with good synergies were scattered in Xinyi County in Maoming City in Western Guangdong. From the combinations of ecosystem service trade-offs and synergies (Table 4 ), the trade-offs between services accounted for 44.33% of the total province area, and the strong trade-offs accounted for 34.15%, which was mainly manifested as services with levels of “1 high, 1 medium, 3 low,” especially the 13121 type (low carbon sequestration, high water retention, low biodiversity conservation, medium food production, and low soil retention), occupying an area of 4,688.845 km 2 . Weak trade-offs accounted for 10.18% of the whole province area, mainly showing combinations of “2 high, 1 medium, 2 low,” “2 high, 2 medium, 1 low,” and “2 high, 3 low,” which covered an area of more than 1,000 km 2 . These combinations included 13113 (low carbon sequestration, high water retention, low biodiversity conservation, low food production, high soil retention; 2,367.10 km 2 ), 13312 (low carbon sequestration, high water retention, high biodiversity conservation, low food production, medium soil retention; 1,245.93 km 2 ), 13213 (low carbon sequestration, high water retention, medium biodiversity conservation, low food production, high soil retention; 1,179.34 km 2 ), 23113 (medium carbon sequestration, high water retention, low biodiversity conservation, low food production, high soil retention; 1,083.80 km 2 ), and 23312 (medium carbon sequestration, high water retention, high biodiversity conservation, low food production, medium soil retention; 1018.74 km 2 ). Poor synergistic relationships between different ecosystem service pairs accounted for 55.19% of the total area, mainly with the combinations “1 medium, 4 low” (21.24%) and “2 medium, 3 low” (12.97%). Here, the three specific combinations accounting for the largest area were 11111 (low carbon sequestration, low water retention, low biodiversity conservation, low food production, low soil retention; 16,581.04 km 2 ), 11112 (low carbon sequestration, low water retention, low biodiversity conservation, low food production, medium soil retention; 15,747.79 km 2 ), and 11121 (low carbon sequestration, low water retention, low biodiversity conservation, medium food production, low soil retention; 11,942.03 km 2 ). Areas with good synergies accounted for only 0.48% of total provincial area. Table 4 Classification criteria and statistics of trade-offs and synergies among the five ecosystem services Service relationship Area ratio Subclass Area ratio Service composition Area ratio Trade-offs 44.33% Strong trade-offs 34.15% 1 high 4 low 8.75% 1 high 1 medium 3 low 12.71% 1 high 2 medium 2 low 9.20% 1 high 3 medium 1 low 3.49% Weak trade-offs 10.18 2 high 3 low 2.38% 2 high 1 medium 2 low 4.32% 2 high 2 medium 1 low 2.46% 3 high 2 low 0.45% 3 high 1 medium 1 low 0.54% 4 high 1 low 0.03% Synergies 55.67% Good synergies 0.48% 5 high 0 4 high 1 medium 0.01% 3 high 2 medium 0.11% 2 high 3 medium 0.26% 1 high 4 medium 0.10% 5 medium 0.01% Poor synergies 55.19% 1 medium 4 low 21.24% 2 medium 3 low 12.97% 3 medium 2 low 9.10% 4 medium 1 low 2.53% 5 low 9.36% 3.3 Spatial pattern characteristics of ecosystem service trade-offs and synergies in Guangdong Province The spatial distribution characteristics and rules of the trade-offs and synergies between ecosystem services pairs were explored (Fig. 5 ). Considering carbon sequestration and water retention, the areas with LL synergies were mainly concentrated in Meizhou City in Northern Guangdong, Chaozhou and Shantou Cities in Eastern Guangdong, Zhaoqing City in the Pearl River Delta, and around the Pearl River mouth area. The areas with HL trade-offs were mainly distributed in the west of the Northern Guangdong and east of the Western Guangdong regions. The area with LH trade-offs was concentrated in Zhanjiang City, Western Guangdong.Carbon sequestration and biodiversity conservation mainly showed HL trade-offs, especially in Northern Guangdong. In Eastern Guangdong, five districts and counties in the cities of Jieyang and Shantou had LL synergy and the rest had HL trade-offs. In the Pearl River Delta region, the cities of Sihui, Foshan, and Guangzhou each have three districts and counties; Jiangmen City has six districts and counties, which were LL synergy areas, and the other cities had HL trade-off areas. In Western Guangdong, the relationship between carbon sequestration and biodiversity conservation was the most complex. Yuncheng District, Yun'an District, Luoding County in Yunfu City, Gaozhou County in Maoming City, and most areas of Lianjiang County in Zhanjiang City were LL synergy areas. Lianjiang County, some areas of Zhanjiang City, and Leizhou City were LH trade-off areas. Suixi and Xuwen Counties in Zhanjiang City were HH synergy areas. The rest were HL trade-off areas. The trade-offs and synergies between soil retention–biodiversity conservation and food production–biodiversity conservation were very similar to those between carbon sequestration– biodiversity conservation. Differences were mainly manifested in Zhanjiang City in Western Guangdong, whereas Suixi County in Zhanjiang City did not show trade-offs or synergies. Jiedong County in Jieyang City, Jinping district, Haojiang district in Shantou City, Longhu district, and most areas of the Chaoyang and Chaonan districts did not show trade-offs or synergies either. The relationships between carbon sequestration–soil retention, carbon sequestration–food production, and soil retention–food production were similar and mainly manifested as synergistic relationships. Among them, carbon sequestration–soil retention mainly showed LL synergies, except for LH trade-offs areas in the Leizhou Peninsula in Western Guangdong, and scattered distribution of HH synergies. However, the relationship between carbon sequestration–food production did not show sporadic distribution of HH synergies in the Leizhou Peninsula, whereas the relationship between soil retention–food production showed HL trade-offs in the Lianjiang and Mazhang districts of Zhanjiang City. Water retention–soil retention and water retention–food production pairs showed similar trade-offs or synergistic relationships, mainly regarding the mixed distribution of LL synergy and HL trade-off areas. In Eastern Guangdong, LL synergies were dominant, especially in Chaozhou and Shantou, and LL synergistic relationships were distributed in a continuous manner, while Shanwei and Jieyang City in the west had mixed HL trade-off relationships. In Northern Guangdong, except for Meizhou City, there mainly was an LL synergistic relationship, and the remaining areas mainly showed HL trade-offs and scattered LL synergies. The Pearl River Delta region was dominated by HL trade-offs, but Zhaoqing City and the Pearl River estuary area showed LL synergies. In Western Guangdong, the cities of Yunfu, Yangjiang, and Maoming mainly exhibited HL trade-offs. However, Zhanjiang City mainly manifested an LH trade-off between water retention–soil retention. The main regions manifested LH trade-offs between water retention–food production, except for Lianjiang County, Wuchuan County, Potou District, and Mazhang District in Zhanjiang City, which showed LL synergies. For the water retention–biodiversity conservation, HH synergies, LL synergies, LH trade-offs, and HL trade-offs were distributed in the whole province, with HH synergies being the most prominent. These were concentrated and contiguous in most areas of Qingyuan City, the whole area in the west and south of Shaoguan City, and Heyuan City connected to Huizhou City, Zhongshan City, Zhuhai City, Enping County of Jiangmen City, Shanwei City, Xinyi County, Dianbai District, Maonan District in Maoming City, Xinxing County in Yunfu City, and Yangjiang City. Meizhou City, Heyuan City, and some areas in Zhanjiang showed HL trade-offs. Jiedong County in Jieyang City, Leizhou City in Zhanjiang, and the western and southern regions of Lianjiang City showed LH trade-offs. These were mainly distributed in Guangzhou, Jiangmen, and most areas of Yunfu City. 4 Discussion 4.1 Analysis of spatial diversity mechanisms of ecosystem services Owing to the influence of different natural and socio-economic conditions, different ecosystem services in Guangdong Province showed clear spatial differences. The carbon sequestration service in the study was measured based on NPP; therefore, the carbon sequestration level was mainly affected by the surface vegetation coverage. The Nanling Mountain area in Northern Guangdong is an important ecological barrier and a core area of ecological security in Guangdong Province and in South China. The forest area in the Northern Guangdong mountains accounts for approximately 55% of the entire province woodland area, and the national key ecological area accounts for 85% of the regional land area. Therefore, the carbon sequestration value in Northern Guangdong was the highest among the four regions in Guangdong. The carbon sequestration level in Zhaoqing was the highest among the 21 cities because it is close to Northern Guangdong and has a good ecological environment and high forest cover, accounting for 70% of the city area. Zhaoqing, Huizhou, and other peripheral areas of the Pearl River Delta are important ecological barriers to the core area of the Pearl River Delta and its ecosystem services are affected by natural and social factors such as urban spatial structure, land cover, and economic development in the process of urbanization in the Pearl River Delta (Xu et al., 2021). The carbon sequestration level of Zhanjiang was the lowest among the 21 cities because it mainly consists of cultivated land and its main function is grain production, with a carbon sequestration capacity lower than that of forests. The forest area is small, with an atypical forest structure as more than 80% are commercial forests (including timber forests and economic fruit forests). In addition, as a coastal city, Zhanjiang often suffers from frequent landings of low-pressure tropical storms and typhoons, which have a great impact on forestry production. Water retention is mainly reflected on forest function. The interception and infiltration of forests can slow down surface water flow intensity, increase the amount of groundwater, control soil desertification, and reduce soil and water loss by restoring vegetation and building water conservation areas (Shah et al., 2022 ). The water retention of forests is manifested in many aspects including water storage, runoff regulation, forest flood reduction, drought resistance, and forest water purification. Through the interception, absorption, and infiltration of precipitation, its spatial and temporal redistribution is conducted to reduce ineffective water and increase effective water (Prescott and Grayston, 2023 ). High-value areas with water retention were mainly distributed in areas with high forest coverage. Therefore, Northern Guangdong, an important ecological green area in Guangdong Province, had the highest water retention value. Yangjiang City, with the highest water retention, and Zhanjiang City, with the lowest water retention, are both distributed in the west of Guangdong, but their forest coverage rates are vastly different. The forest area in Yangjiang City accounts for approximately 60% of the city area, whereas the forest area in Zhanjiang City only accounts for over 20% of the city area. Moreover, carbon sequestration and soil retention in Yangjiang City were much higher than in Zhanjiang City (soil retention was 11.5 times higher). Soil retention is an important ecosystem service that refers to the ability of the ecosystem to regulate erosions to prevent soil loss and retain sediments (Costanza et al., 1997). Therefore, soil retention is important to prevent regional land degradation and reduce flood risk (Liu et al., 2019 ). Owing to a high forest coverage rate, the soil retention services in Guangdong Province were higher than those in northern China. However, with the significant influence of human activities on rapid urbanization, the soil erosion area in Guangdong Province has been increasing since 2000. By 2019, it had increased to 1.80×10 4 km 2 . Light erosion has been observed in 10.09% of the total area of Guangdong Province, accounting for more than 80% of the total erosion area. Cities with high soil retention were in areas with high forest coverage rate, while Zhanjiang City, with the lowest soil retention value, had insufficient forest resources, atypical forest structure, and weak sediment retention ability. Moreover, the coastal area in Zhanjiang City is composed of bare coastal sand, coastal salt-marred, and coastal salt soils. Among the five ecosystem services, Food production was the weakest. Because the income of agriculture is significantly lower than that of secondary and tertiary sectors, the main rural labor force chooses to work in cities to increase family income, and the rural labor force continues to decrease (Yang et al., 2021 ). Although Guangdong Province has abundant photothermal conditions and good soil resources, which together with the poor livelihood guarantee of agricultural land and reduced rental cost of large-scale agricultural land, has led some rural returnee workers to engage in agricultural production mainly planting economic fruit forests and medicinal materials; thus, the use of non-grain agricultural land is promoted. On the other hand, the Pearl River Delta is an area with rapid urbanization and high economic level. A large amount of cultivated land is occupied by construction land and the food production function of the ecosystem is repeatedly squeezed. According to the Statistical Yearbook of Guangdong Province, the grain yield per unit area of Guangdong Province increased from 517.5 t/km 2 to 574.5 t/km 2 from 2009 to 2019 (11.01% growth). However, the total grain production decreased from 131.45×10 5 t to 124.08×10 5 t (5.61% reduction), with the most significant reduction in the mountainous areas of Northern Guangdong and the Pearl River Delta. The mountainous areas of Northern Guangdong were identified as key national ecological areas according to topographic features and location and some cultivated lands were converted to forest. The Pearl River Delta is mainly used for economic functions. The added value of land in economically developed areas is high, and cultivated land has been occupied by construction land. The food production function in Shenzhen was the lowest because its urbanization rate is 100%, there is almost no distribution of construction land and thus, no agricultural population. 4.2 Analysis of the mechanisms of influence of ecosystem service trade-offs and synergies The proportion of trade-offs and synergies between ecosystem services in Guangdong Province was basically the same. The proportion of synergies was slightly higher (55.67%) but almost all were poor synergies; that is, the five kinds of services were at low levels, which is the least ideal state. A total of 21.24% of the province area had “1 medium, 4 low” poor synergies, whereas high synergies accounted for only 0.48% of the provincial area. Most trade-offs were strong, mainly showing low carbon sequestration, high water retention, low biodiversity conservation, medium food production, and low soil retention. The trade-off regions were mainly distributed in Maoming City, Shantou City, Huilai County of Jieyang City, and parts of the Pearl River Delta. In these areas, the forest coverage rate and carbon sequestration were low, and since carbon sequestration, soil retention, and biodiversity conservation were positively correlated, soil retention and biodiversity conservation were also low. These regions are rich in water resources, and water retention services were of high value, so a high trade-off relationship was formed. In Guangdong Province, the pairwise ecosystem services involving carbon sequestration, water retention, and soil retention showed a significant synergistic relationship because these services are mainly determined by forest cover level. Forest was the land type with the highest level of carbon sequestration. Dense forestland promotes photosynthesis and increases vegetation carbon sequestration capacity. It is also conducive to enhancing water and soil retention. Dense branches, leaves, and large roots in forests can intercept precipitation and surface runoff, which helps maintain soil and prevent erosion. Therefore, the three types of ecosystem services had a higher concentration in forest areas. In bare areas, all three ecosystem services had low values. Also, biodiversity conservation showed poor synergy with carbon sequestration, water retention, and soil retention. The biodiversity function in lush forest areas may be strong and the total number of plant and animal species relatively high, but it may not have a strong relationship with nationally protected species of special significance. The trade-offs and synergies of ecosystem services in Guangdong Province showed clear spatial differences. Paired ecosystem services may show a trade-off relationship in one region and synergistic relationships in other regions. For example, for the carbon sequestration–water retention pair, Zhaoqing City and some areas around the Pearl River Estuary were LL synergy areas; however, Western Guangdong and western parts of Northern Guangdong were HL or LH trade-off areas. The relationship between the same ecosystem services may show completely different characteristics in different regions because of the combined influence of different natural environments and socio-economic characteristics (Li et al., 2022 ; Huang et al., 2023 ). The geomorphological conditions of Guangdong Province are complex as the region is known as “seven mountains, one water, and two fields.” It gradually declines from the northern mountains to the southern coastal areas, forming a geomorphic pattern dominated by the northern middle mountains, central low mountains and hills, and southern plains. Under different geomorphic conditions, the regional ecosystem service capacities and the trade-offs and synergies between the paired services also had significant differences. Guangdong Province is a large economic province in China and its economic center is mainly distributed in the Pearl River Delta region. Human interference is strong in this region, exhibited by intense land development and the destruction of various ecological environments due to industrial development (Jafarzadeh et al., 2021 ; Cueva et al., 2022 ). This decline of ecosystem service capacity and destruction of natural vegetation inhibit the positive succession of ecosystems, reducing their regulatory service capacity. In contrast, Northern Guangdong is an ecologically protected area and its overall ecological environment is better. Guangdong Province is rich in natural resources and has a high level of ecosystem services. However, poor synergies and strong trade-offs remain dominant among the ecosystem services. Sufficient attention should be paid to the protection of ecosystem services, and efforts should be made to practice ecological urban construction while steadily improving social and economic levels. 4.3 Uncertainty Ecosystem services are the goods and services provided by ecosystems to society (Costanza et al., 1997; Divinsky et al., 2017 ), and include dozens of services of four kinds: providing products, regulating functions, supporting functions, and cultural services. Currently, no model can comprehensively evaluate all ecosystem services and different methods of evaluating the same ecosystem services in the same region produce different results. In this study, ecosystem services were selected for analysis according to the characteristics of the research object and the research region. This study used “a spatial dataset of ecosystem services in China,” which included six important ecosystem services, namely food production, soil retention, water retention, windbreak and sand fixation, biodiversity conservation, and carbon sequestration. The tropical and subtropical monsoon climate in the study area was significant, with abundant rainfall and abundant water resources; thus, windbreak and sand fixation were not considered in the study. In addition, it is necessary to note that these five ecosystem services were divided into three levels using a natural breakpoint method (Table 1 ). Therefore, since the level of ecosystem services was relative to that of the local region, it is possible that the low-value ranges in some ecosystem services were still higher than those in some ecologically fragile areas in northwest China. 5 Conclusions (1) The ecosystem services in Guangdong Province showed clear spatial heterogeneity. Owing to a humid climate and high forest coverage, the area showed strong water retention. Northern Guangdong had high water retention and carbon sequestration, and the highest soil retention in the province. Food production services were mainly concentrated in Western Guangdong. (2) In the overall Guangdong Province, three pairs of ecosystem services, water retention–soil retention, carbon sequestration–water retention, carbon sequestration–soil retention, showed strong positive correlations and strong synergistic relationships. There were strong negative correlations between food production–water retention, food production–soil retention, and food production–biodiversity conservation. There was a strong trade-off between food production and water retention. (3) The trade-offs and synergies between the ecosystem service pairs were spatially different, mainly between the LL synergies and HL trade-offs between carbon sequestration–water retention. The trade-offs or synergistic relationships were similar between carbon sequestration–biodiversity conservation, soil retention–biodiversity conservation, and food production–biodiversity conservation, which mainly manifested as HL trade-offs in Northern Guangdong. The trade-offs or synergistic relationships of carbon sequestration–soil retention, carbon sequestration–food production, and soil retention–food production were similar, showing mainly LL synergies, except for the Leizhou Peninsula. The synergistic HH relationship was most prominent between water retention and biodiversity conservation. The same ecosystem service pairs showed completely different characteristics in different regions, which may be explained by the influence of different natural environments and social and economic characteristics. Declarations Acknowledgements Funding: This work was supported by the National Natural Science Foundation of China (No. 42101242, 41907001); the Natural Science Foundation of Guangdong Province (No. 2023A1515012373); and the Science and Technology Program of Guangzhou, China (No. 202102080254, 202102021168). -Ethical Approval: Not applicable -Consent to Participate: Agree -Consent to Publish: Agree -Authors Contributions: Qian Xu is mainly responsible for writing the full text Ying Yang is mainly responsible for the structure of the paper Ren Yang is mainly responsible for the mechanisms of influence of ecosystem service trade-offs and synergies Lisi Zha is mainly responsible for Spatial differentiation of ecosystem services -Competing Interests: no have -Availability of data and materials: Not applicable Declaration of Interest Statement This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. 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DOI: 10.11922/csdata.180.2017.0145 Supplementary Files Highlights.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3037558\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":222595865,\"identity\":\"ea94ea6c-885e-4148-a698-f7ba54066c0c\",\"order_by\":0,\"name\":\"Qian 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2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":616687,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial distribution of ecosystem services in Guangdong Province in 2010\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3037558/v1/d1951bc5be305fa507057bdf.png\"},{\"id\":41021527,\"identity\":\"4b0bc3d1-217f-429b-bda1-9f0395694b26\",\"added_by\":\"auto\",\"created_at\":\"2023-08-03 15:13:47\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":40750,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePearson correlation coefficients between ecosystem service pairs\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3037558/v1/52b2440c3c17244ba1117133.png\"},{\"id\":41021529,\"identity\":\"79be8d40-7a31-4dd2-b7c8-3aace8a79f0e\",\"added_by\":\"auto\",\"created_at\":\"2023-08-03 15:13:47\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":589233,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial distribution of trade-offs (strong or weak) and synergies (good or poor) among ecosystem services\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3037558/v1/ad9bd66bd4ad4bedf48e3a8c.png\"},{\"id\":41021530,\"identity\":\"3614a28d-e462-48a4-9fb6-d3f72052d20a\",\"added_by\":\"auto\",\"created_at\":\"2023-08-03 15:13:47\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":618322,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpatial distribution of trade-offs and synergies between ecosystem services in Guangdong Province\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3037558/v1/b6030e2cf1d91fb537b4b27f.png\"},{\"id\":46812328,\"identity\":\"ecbde738-2b68-40a5-b5cb-a1071fc735fd\",\"added_by\":\"auto\",\"created_at\":\"2023-11-21 01:25:03\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2652440,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3037558/v1/848454f5-35fe-486e-8389-b5776b27dc62.pdf\"},{\"id\":41021531,\"identity\":\"14d2a75d-f200-446b-9319-edd9ac866dd8\",\"added_by\":\"auto\",\"created_at\":\"2023-08-03 15:13:47\",\"extension\":\"docx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":14123,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Highlights.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3037558/v1/d62b3c9e90c44c36864d66c7.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Spatial trade-offs and synergies among ecosystem services in Guangdong Province, China\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eEcosystem services are the environmental conditions and effects of ecosystem formation and maintenance on human survival and development (Daily, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e). However, ecosystem services do not exist or develop independently. Complex reciprocal relationships exist among various services within an ecosystem and among several ecosystems (Niu et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These interactions mainly manifest as trade-offs between wanes and waxes or synergies of mutual gains. Diverse and complex ecological environments provide various services for human well-being, and the impact of human activities is often at the expense of certain service levels (European Environment Agency, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The trade-offs between different services and their influencing factors must be analyzed because they only affect the level of ecosystem services and the stability and development of the whole ecosystem (Belaire et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Therefore, the spatial trade-offs and synergistic relationship between ecosystem services has important research significance and has become a popular topic in multi-disciplinary research, especially in that involving geography, environmental science, and ecology.\\u003c/p\\u003e \\u003cp\\u003eTrade-offs in ecosystem services are mainly generated from human demand preferences. When people consume certain ecosystem services, they will have an impact on other ecosystem services intentionally or unintentionally, leading to trade-offs and synergies of ecosystem services (Cord et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Li et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). A scientific understanding of the functional characteristics, manifestations, driving mechanisms, and scale effects of ecosystem service trade-offs/synergies is of great significance for improving human well-being and realizing a \\\"win-win\\\" situation between human society and the ecosystem (Wu et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). A comprehensive understanding of the relationships between ecosystem services includes multiple dimensions, such as trade-offs, synergies, and compatibilities (Shen et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). A trade-off is a negative relationship in which ecosystem services are restricted by other functions, such as ebb and flow, including supporting and regulating functions (Yang et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Hu et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). A synergy is a positive relationship, and several ecosystem services show symbiosis, enhancing or weakening together, such as support and cultural functions, and regulatory and cultural functions (Zhang et al, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). A compatibility shows no significant relationship between ecosystem services (Peng et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). In reality, in order to improve a certain ecosystem service, we often inevitably affect trade-offs and synergies with other services (Allen et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Scholars have conducted extensive research on the interaction between ecosystem services and concluded that trade-offs and synergies among ecosystem services are universal (Damania, 2018; Kluger et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Ecosystem services are influenced by various factors such as land use and cover change, human needs, parameter selection, regional differences, and imbalances (Seddon et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Different regions show significant differences (Vanham et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Moore, 2020).\\u003c/p\\u003e \\u003cp\\u003eMost developing countries in an important period of economic development often pay attention to promoting economic benefits while ignoring ecological benefits. Therefore, the ultimate goal of ecosystem service research should be to maximize the comprehensive benefits of the man-earth system, ease the trade-offs between different ecosystem services, and improve human welfare (Costanza et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Huang et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). As an important developing country, China is currently in a crucial transition period from high-speed to high-quality economic development. Therefore, urban economic development should be coordinated with environmental protection. Guangdong, a relatively developed province in China, was selected as the research area in this study. This study clarifies the main types of ecosystem services and their spatial differentiation characteristics. The study focuses on analyzing the trade-offs and synergies among different ecosystem services and the differences in their degrees of influence, as well as comparing and analyzing their spatial patterns. Then, the influence mechanisms of trade-offs and synergies between ecosystem services were analyzed and areas for improvement were determined. The findings of this study are of great significance for the improvement of regional eco-environmental carrying capacity, protection, and management, the creation of solutions for sustainable development goals, and the coordination between economic development and ecological protection.\\u003c/p\\u003e\"},{\"header\":\"2 Study area and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Study area\\u003c/h2\\u003e \\u003cp\\u003eGuangdong Province is located in the southernmost part of mainland China, with a land area of 179,800 km\\u003csup\\u003e2\\u003c/sup\\u003e. It is located between 20\\u0026deg;13ʹN\\u0026ndash;25\\u0026deg;31ʹN and 109\\u0026deg;39ʹE\\u0026ndash;117\\u0026deg;19ʹE and faces the South China Sea in the south. Guangdong Province has jurisdiction over 21 prefecture-level cities (including two sub-provincial cities), which are divided into four regions: the Pearl River Delta, Eastern Guangdong, Western Guangdong, and Northern Guangdong. The Pearl River Delta includes the cities of Guangzhou, Shenzhen, Foshan, Dongguan, Zhongshan, Zhuhai, Jiangmen, Zhaoqing and Huizhou; Eastern Guangdong includes Shantou, Chaozhou, Jieyang, and Shanwei; Western Guangdong includes Zhanjiang, Maoming, Yangjiang, and Yunfu; and Northern Guangdong includes Shaoguan, Qingyuan, Meizhou, and Heyuan.\\u003c/p\\u003e \\u003cp\\u003eGuangdong Province is located in the tropical and subtropical regions, with the Tropic of Cancer running through its central part. It is located on the north coast of the South China Sea, is heat rich, has abundant rainfall, a wide variety of animals and plants, and good natural conditions. Guangdong Province has complex and diverse landforms, with hills, platforms, and basins developing between the mountains. The province belongs to the East Asian monsoon region, and has middle subtropical, southern subtropical, and tropical climates from north to south. It is also one of the provinces with the most abundant light, heat, and water in China. The average sunshine duration of the whole province is 1,745.8 hours. The average annual temperature is 22.3 ℃. The average annual precipitation ranges from 1,300 to 2,500 mm. The spatial distribution of rainfall shows a high trend in the south and low in the north.\\u003c/p\\u003e \\u003cp\\u003eGuangdong is China's most populous province with a population of 126\\u0026nbsp;million in 2020. Also, since 1989, Guangdong's gross domestic product (GDP) has continuously ranked first in China, and it has become the largest economic province in the country, accounting for 1/8 of the country's total economic aggregate. In 2020, Guangdong's GDP reached 11,076.09\\u0026nbsp;billion yuan, while the Pearl River Delta core region's GDP accounted for 80.83% of that of the whole province.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Methodology\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.1 Measurement of ecosystem services\\u003c/h2\\u003e \\u003cp\\u003eConsidering the natural background and socioeconomic conditions of Guangdong Province and referring to the \\\"Territorial Spatial Planning of Guangdong Province\\\" and other relevant planning and policies, five kinds of ecosystem services were selected for analysis: carbon sequestration, water retention, soil retention, food production, and biodiversity conservation.\\u003c/p\\u003e \\u003cp\\u003eThis study used ecosystem services data from the Chinese Academy of Sciences Ecological Environmental Research Center (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.sciencedb.cn/dataSet/handle/458\\u003c/span\\u003e\\u003cspan address=\\\"http://www.sciencedb.cn/dataSet/handle/458\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The dataset is based on remote sensing feature classification data. The management modes of land features, community structure, and ecological process differences (Ouyang 5; Zhang et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) were analyzed using MODIS satellite data Q13A1. Temperature and precipitation data were provided by the China Meteorological Data Sharing Network (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.nmic.cn/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.nmic.cn/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) and topographic data, from the United States\\u0026rsquo; GEOM satellite. Ecosystem services in China in 2010 were simulated based on ecological process simulation methods, such as the CASA light energy utilization rate model, universal soil loss equation, water balance equation, wind model, and by summarizing literature and ground monitoring data to determine model parameters. A spatial dataset with a resolution of 250 m was created.\\u003c/p\\u003e \\u003cp\\u003eCarbon sequestration was measured mainly based on net primary productivity (NPP), which is represented by the product of photosynthetic active radiation absorbed by plants and actual light utilization (ε). Water retention was calculated using the water balance equation. Soil retention was simulated using the general soil loss equation. In the specific calculation, the existing measured soil erosion data was used to verify the model simulation results and modify the parameters. Food production data provided the county ecosystem with food output, such as grain, aquatic products, meat, forest fruit products, uniformly converted into energy. Also, rather than using the total number of species, the measure of biodiversity conservation used in this study represented the total number of indicator species with a recorded distribution in each county, primarily nationally protected plants and animals of special significance, or species with threatened or endangered status. The biodiversity conservation values in Guangdong Province were the average values for all districts and counties, and the values of the four regions were the average values of all districts and counties in the region.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.2 Evaluation of ecosystem service trade-offs and synergies\\u003c/h2\\u003e \\u003cp\\u003eThe Pearson correlation coefficient method was used to evaluate ecosystem service trade-offs and synergies. If the correlation coefficient was positive, the synergies between the two services were mutually promoted. Conversely, a negative correlation coefficient indicated a trade-off between the two services. Otherwise, the two functions were independent of each other.\\u003c/p\\u003e \\u003cp\\u003eIn terms of the spatial dimension, a bivariate local spatial autocorrelation model was used to quantitatively measure the spatial distribution pattern and correlation characteristics of ecosystem service trade-offs and synergies in Guangdong Province. Cluster diagrams between the pairs of ecosystem services in the study area were obtained through bivariate local Moran's I spatial analysis using GeoDa software. In this study, a queen spatial adjacency matrix was constructed to measure the statistics of the local indicator of spatial association between two services. Specifically, \\\"high-high\\\" (HH) indicated that the two services with a high score clustered significantly in this region, \\\"low-low\\\" (LL) indicated that the two services with a low score clustered significantly in this region, \\\"high-low\\\" (HL) indicated that the first function scores were high and that the other function scores were low, and \\\"low-high\\\" (LH) indicated the opposite of HL. \\\"Not significant\\\" indicated that the two functions were independent within the regional space. HH and LL were regarded as synergies, whereas HL were LH are regarded as trade-offs.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.2.3 Degree of influence of ecosystem service trade-offs and synergies\\u003c/h2\\u003e \\u003cp\\u003eTo classify the degree of ecosystem service trade-offs and synergies, a specific method was conducted. First, the natural breakpoint method was used to divide the five ecosystem services into three levels: low, medium, and high, numbered 1, 2, and 3, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe five types of service standardization and classification of raster data were superimposed using ArcGIS 10.2 data:\\u003c/p\\u003e \\u003cp\\u003eCODE\\u0026thinsp;=\\u0026thinsp;C\\u0026times;10,000\\u0026thinsp;+\\u0026thinsp;W\\u0026times;1,000\\u0026thinsp;+\\u0026thinsp;B\\u0026times;100\\u0026thinsp;+\\u0026thinsp;F\\u0026times;10\\u0026thinsp;+\\u0026thinsp;S (1)\\u003c/p\\u003e \\u003cp\\u003eIn Eq.\\u0026nbsp;(1), C, W, B, F, and S represent carbon sequestration, water retention, biodiversity conservation, food production, and soil retention, respectively. CODE is a five-digit code, and each code sequence is a combination of 1, 2, and 3, representing the degree of influence of the ecosystem services.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eClassification levels of ecosystem service capacity\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eServer Type\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLow (1)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMedium (2)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHigh (3)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ecarbon sequestration (t/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e[0, 65]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(65, 204]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;204\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ewater retention (10\\u003csup\\u003e4\\u003c/sup\\u003e m/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e[0, 32]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(32, 81]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;81\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ebiodiversity conservation (numbers)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e[0, 78]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(78, 92]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;92\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003efood production (10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e[0, 4]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(4, 9]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003esoil retention (10\\u003csup\\u003e4\\u003c/sup\\u003e t/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e[0, 9]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e(9, 28]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubsequently, criteria for classifying trade-offs and synergies were developed (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Trade-offs were classified as strong or weak. A strong trade-off was a state with one high service supply capacity, and all others, medium or low. Service capacity combinations in a strong trade-off may be 1 high 4 low, 1 high 1 medium 3 low, 1 high 2 medium 2 low, etc. A weak trade-off referred to a state with two, three, or four types of high service capacities, while all other services had medium or low capacities. Service capacity combinations in a weak trade-off may be 2 high 3 low, 2 high 1 medium 2 low, 2 high 2 medium 1 low, etc. Synergies were also classified as high or low. In high synergies, all services were high. It is the most coordinated state and the ultimate goal of ecosystem management. High synergy combinations include 5 high, 4 high 1 medium, 3 high 2 medium, etc. Low synergy meant that all five types of service capacities were at a low level, which is the least ideal state. Low synergy combinations included 1 medium 4 low, 2 medium 3 low, 3 medium 2 low, etc.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Spatial differentiation of ecosystem services in Guangdong province\\u003c/h2\\u003e \\u003cp\\u003eTo ensure that different ecosystem services were comparable (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), ecosystem service values were processed without dimensionality, and all values ranged from 0 to 1 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Among the five ecosystem services in Guangdong Province, water retention was the strongest (0.71). Soil retention and carbon sequestration had values of 0.54 and 0.51, respectively. Biodiversity conservation (0.48) and food production (0.33) were ranked fourth and fifth, respectively, among the ecosystem services.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eValues of ecosystem services in Guangdong Province in China in 2010\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCarbon sequestration (t/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eWater retention (10\\u003csup\\u003e4\\u003c/sup\\u003e m/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSoil retention (10\\u003csup\\u003e4\\u003c/sup\\u003e t/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFood production (10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBiodiversity (numbers)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePearl River Delta\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e47.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e77\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorthern Guangdong\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e58.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Guangdong\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Guangdong\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e76\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGuangdong Province\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e80\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSpecifically, the spatial distribution of the different ecosystem services were as follows:\\u003c/p\\u003e \\u003cp\\u003e(1) The overall carbon sequestration level in Guangdong Province was 50.06 t/km\\u003csup\\u003e2\\u003c/sup\\u003e, and its spatial distribution was higher in the north than in the south. The overall carbon sequestration level in the Pearl River Delta region (47.58 t/km\\u003csup\\u003e2\\u003c/sup\\u003e) was higher than that in Western Guangdong (39.79 t/km\\u003csup\\u003e2\\u003c/sup\\u003e) and Eastern Guangdong (40.77 t/km\\u003csup\\u003e2\\u003c/sup\\u003e). However, there was a contiguous low-value area in the Pearl River Estuary (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The contiguous low-value area on the west bank of the Pearl River Estuary was larger than that on the east bank. The cities of Dongguan, Zhongshan and Foshan had the lowest carbon sequestration levels in Pearl River Delta, with 13.89 t/km\\u003csup\\u003e2\\u003c/sup\\u003e, 17.61 t/km\\u003csup\\u003e2\\u003c/sup\\u003e and 19.06 t/km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively. The carbon sequestration level in Zhaoqing City in the Pearl River Delta was the highest of the entire province with 66.24 t/km\\u003csup\\u003e2\\u003c/sup\\u003e. The carbon sequestration level of Western Guangdong was the lowest among the four regions in the province, especially in the Leizhou Peninsula, the southernmost part of mainland China, with only 13.35 t/km\\u003csup\\u003e2\\u003c/sup\\u003e for the 21 cities in the province. The carbon sequestration level in Shantou City in Eastern Guangdong was also noticeably low with 18.59 t/km\\u003csup\\u003e2\\u003c/sup\\u003e, lower than that of the surrounding cities. Northern Guangdong showed a remarkably high carbon sequestration, and the four cities in the region had values greater than 55 t/km\\u003csup\\u003e2\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eValues without dimensionality of ecosystem services in Guangdong Province in China in 2010\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCarbon sequestration (t/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eWater retention (10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSoil retention (10\\u003csup\\u003e4\\u003c/sup\\u003e t/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFood production (10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBiodiversity (numbers)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePearl River Delta\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e47.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e77\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNorthern Guangdong\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e59.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e58.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEastern Guangdong\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e75\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWestern Guangdong\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e76\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGuangdong Province\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e80\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e(2) Guangdong has a humid climate, high forest coverage, and strong water retention for ecosystem services. The average value of water retention in Guangdong was 52.40\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e, and the spatial distribution among cities was similar to that of carbon sequestration. Northern Guangdong was also a high-value area for water retention, with a value of 58.05\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e. Except for Meizhou, the water retention in the other three cities was higher than the average provincial level. This is followed by the Pearl River Delta and Western Guangdong regions. The high-value areas in the Pearl River Delta region were distributed in its periphery. Remarkably, among the 21 cities, Yangjiang (74.10\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e) and Zhanjiang (18.16\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e) had the highest and lowest water conservation values in Western Guangdong, respectively. Water conservation in Eastern Guangdong (38.58\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e) was significantly lower than in other regions.\\u003c/p\\u003e \\u003cp\\u003e(3) Guangdong Province had a strong soil retention service, with an average value of 8.11\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e. The soil retention level of Northern Guangdong, which has a high forest coverage, was the highest, with a value of 9.26\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e. Soil retention services in the Pearl River Delta and Eastern Guangdong regions were similar, with 7.76\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e and 7.59\\u0026times;104 m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively, among which Huizhou had the highest of the 21 cities with 10.96\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e. Western Guangdong had the lowest soil retention service value with 6.75\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e. In this region, Zhanjiang had the lowest soil retention value among the 21 cities in the Leizhou Peninsula with 0.88\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e, while Yunfu and Yangjiang had strong soil retention with values of 10.69\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e and 10.09\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively.\\u003c/p\\u003e \\u003cp\\u003e(4) Food production data were sourced from county data, and foods such as grain, aquatic products, meat, and fruits were uniformly converted into total food supply calories. The average value of food production in Guangdong Province was 3.63\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, and the main food production area was mainly concentrated in Western Guangdong, with a value of 6.80\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e. In this region, the cities of Zhanjiang and Maoming had the highest food production values with 10.65\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, and 7.08\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively. This was followed by Eastern Guangdong and the Pearl River Delta, with values of 4.53\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e and 3.05\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively. In Eastern Guangdong, Shantou City had the highest food production value with 8.34\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, followed by Jieyang City with 5.09\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e. The cities of Chaozhou and Shanwei did not reach provincial average levels. In the Pearl River Delta region, food production in Guangzhou and Jiangmen was relatively high, while very low in Shenzhen and Dongguan with only 0.03\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e and 0.08\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, respectively. However, the food production in Northern Guangdong was the lowest with only 2.08\\u0026times;10\\u003csup\\u003e8\\u003c/sup\\u003e kcal/km\\u003csup\\u003e2\\u003c/sup\\u003e, less than one third of that in Western Guangdong. Northern Guangdong is an important ecological barrier in Guangdong Province with extensive forest land and a low proportion of cultivated land; thus, food production is not its main ecosystem service.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e(5) Guangdong Province has a complex ecological environment and a rich biodiversity. The biodiversity conservation value in Guangdong Province was 80 and, among the four regions, the biodiversity maintained in Northern Guangdong was the highest at 86. This could be expected as important forest areas, nature reserves, and natural parks in Guangdong are mostly distributed in Northern Guangdong. In fact, Shimentai Nature Reserve, the largest contiguous forest reserve in Guangdong Province, is located in the southernmost part of the Nanling Mountains in Northern Guangdong. The main objects of protection are subtropical evergreen broad-leaved forests, rare plants, and animals. There are 2,242 species of higher plants and 301 wild vertebrate species in Shimentai Nature Reserve. Among them, one species of first-class nationally protected plant and 23 of second-class were included in the study. There were four species under first-class national protected animals and 41 under second-class. Among all the districts and counties, Ruyuan County (131) and Lechang County (117) in Shaoguan City and Fogang County (114) in Qingyuan City had the highest biodiversity. The lowest number of indicator species were conserved in Eastern Guangdong with 74.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Ecosystem service trade-offs and synergies, and their influence in Guangdong Province\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\n\\u003cp\\u003eThe correlation between paired services among the five ecosystem services was obtained based on\\u0026nbsp;Pearson correlation analysis (Fig. 3). Water\\u0026nbsp;retention\\u0026ndash;soil\\u0026nbsp;retention, carbon sequestration\\u0026ndash;water\\u0026nbsp;retention, and carbon sequestration\\u0026ndash;soil\\u0026nbsp;retention\\u0026nbsp;showed strong positive correlations (r = 0.389, r = 0.299, r = 0.258, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05), indicating a strong synergistic relationship between them.\\u0026nbsp;These three ecosystem services are also the main functions of forestland. The correlation coefficients between carbon sequestration\\u0026ndash;biodiversity conservation, water\\u0026nbsp;retention\\u0026ndash;biodiversity\\u0026nbsp;conservation, and soil\\u0026nbsp;retention\\u0026ndash;biodiversity\\u0026nbsp;conservation\\u0026nbsp;were positive and statistically significant; however, the relative coefficient values were small (r = 0.073, r = 0.137, r = 0.116), indicating a poor synergistic relationship. The negative correlation between food production and carbon sequestration was low (r = -0.110, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05), indicating\\u0026nbsp;a weak trade-off\\u0026nbsp;effect. There were strong negative correlations between food production\\u0026ndash;water\\u0026nbsp;retention, food production\\u0026ndash;soil\\u0026nbsp;retention, and food production\\u0026ndash;biodiversity\\u0026nbsp;conservation\\u0026nbsp;(r = -0.179, r = -0.182, r = -0.304, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05), indicating strong trade-off effects and reciprocal relationships between them.\\u003c/p\\u003e\\n\\u003cp\\u003eEcosystem services were divided into levels 1, 2, and 3 according to Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, and then the strengths and weaknesses of trade-offs and synergies between different services were evaluated. Also, the spatial distribution of ecosystem service trade-offs and synergies in Guangdong Province were assessed (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). This mainly showed poor synergies and strong trade-offs, accounting for 89.34% of the total area of Guangdong Province. Among them, poor synergies between different ecosystem service pairs occupied the largest area, mainly concentrated in Eastern Guangdong and most of the Pearl River Delta region. The other counties and districts in Northern Guangdong showed poor synergies, except for Lechang County and Ruyuan Yao Autonomous County in Shaoguan City, and Yingde County and Yangshan County in Qingyuan City. There were also many regions with strong trade-offs between different ecosystem service pairs, mainly in Western and Northern Guangdong and Guangzhou City in the Pearl River Delta region. The spatial distribution of the services with weak trade-offs was limited to Xinyi County in Maoming City in Western Guangdong, and Yingde County, Yangshan County, and Ruyuan Yao Autonomous County in Qingyuan City in Northern Guangdong. Few areas with good synergies were scattered in Xinyi County in Maoming City in Western Guangdong.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFrom the combinations of ecosystem service trade-offs and synergies (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e), the trade-offs between services accounted for 44.33% of the total province area, and the strong trade-offs accounted for 34.15%, which was mainly manifested as services with levels of \\u0026ldquo;1 high, 1 medium, 3 low,\\u0026rdquo; especially the 13121 type (low carbon sequestration, high water retention, low biodiversity conservation, medium food production, and low soil retention), occupying an area of 4,688.845 km\\u003csup\\u003e2\\u003c/sup\\u003e. Weak trade-offs accounted for 10.18% of the whole province area, mainly showing combinations of \\u0026ldquo;2 high, 1 medium, 2 low,\\u0026rdquo; \\u0026ldquo;2 high, 2 medium, 1 low,\\u0026rdquo; and \\u0026ldquo;2 high, 3 low,\\u0026rdquo; which covered an area of more than 1,000 km\\u003csup\\u003e2\\u003c/sup\\u003e. These combinations included 13113 (low carbon sequestration, high water retention, low biodiversity conservation, low food production, high soil retention; 2,367.10 km\\u003csup\\u003e2\\u003c/sup\\u003e), 13312 (low carbon sequestration, high water retention, high biodiversity conservation, low food production, medium soil retention; 1,245.93 km\\u003csup\\u003e2\\u003c/sup\\u003e), 13213 (low carbon sequestration, high water retention, medium biodiversity conservation, low food production, high soil retention; 1,179.34 km\\u003csup\\u003e2\\u003c/sup\\u003e), 23113 (medium carbon sequestration, high water retention, low biodiversity conservation, low food production, high soil retention; 1,083.80 km\\u003csup\\u003e2\\u003c/sup\\u003e), and 23312 (medium carbon sequestration, high water retention, high biodiversity conservation, low food production, medium soil retention; 1018.74 km\\u003csup\\u003e2\\u003c/sup\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePoor synergistic relationships between different ecosystem service pairs accounted for 55.19% of the total area, mainly with the combinations \\u0026ldquo;1 medium, 4 low\\u0026rdquo; (21.24%) and \\u0026ldquo;2 medium, 3 low\\u0026rdquo; (12.97%). Here, the three specific combinations accounting for the largest area were 11111 (low carbon sequestration, low water retention, low biodiversity conservation, low food production, low soil retention; 16,581.04 km\\u003csup\\u003e2\\u003c/sup\\u003e), 11112 (low carbon sequestration, low water retention, low biodiversity conservation, low food production, medium soil retention; 15,747.79 km\\u003csup\\u003e2\\u003c/sup\\u003e), and 11121 (low carbon sequestration, low water retention, low biodiversity conservation, medium food production, low soil retention; 11,942.03 km\\u003csup\\u003e2\\u003c/sup\\u003e). Areas with good synergies accounted for only 0.48% of total provincial area.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eClassification criteria and statistics of trade-offs and synergies among the five ecosystem services\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eService relationship\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eArea ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSubclass\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eArea ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eService composition\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eArea ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"9\\\" rowspan=\\\"10\\\"\\u003e \\u003cp\\u003eTrade-offs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\" morerows=\\\"9\\\" rowspan=\\\"10\\\"\\u003e \\u003cp\\u003e44.33%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eStrong trade-offs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003e34.15%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 high 4 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.75%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 high 1 medium 3 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12.71%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 high 2 medium 2 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e9.20%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 high 3 medium 1 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.49%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003eWeak trade-offs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e10.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 high 3 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.38%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 high 1 medium 2 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.32%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 high 2 medium 1 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.46%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 high 2 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.45%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 high 1 medium 1 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.54%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 high 1 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.03%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"10\\\" rowspan=\\\"11\\\"\\u003e \\u003cp\\u003eSynergies\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\" morerows=\\\"10\\\" rowspan=\\\"11\\\"\\u003e \\u003cp\\u003e55.67%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003eGood synergies\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"5\\\" rowspan=\\\"6\\\"\\u003e \\u003cp\\u003e0.48%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 high\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 high 1 medium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 high 2 medium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.11%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 high 3 medium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.26%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 high 4 medium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.10%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 medium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.01%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003ePoor synergies\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e \\u003cp\\u003e55.19%\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 medium 4 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e21.24%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 medium 3 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12.97%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3 medium 2 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e9.10%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 medium 1 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.53%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e9.36%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Spatial pattern characteristics of ecosystem service trade-offs and synergies in Guangdong Province\\u003c/h2\\u003e \\u003cp\\u003eThe spatial distribution characteristics and rules of the trade-offs and synergies between ecosystem services pairs were explored (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eConsidering carbon sequestration and water retention, the areas with LL synergies were mainly concentrated in Meizhou City in Northern Guangdong, Chaozhou and Shantou Cities in Eastern Guangdong, Zhaoqing City in the Pearl River Delta, and around the Pearl River mouth area. The areas with HL trade-offs were mainly distributed in the west of the Northern Guangdong and east of the Western Guangdong regions. The area with LH trade-offs was concentrated in Zhanjiang City, Western Guangdong.Carbon sequestration and biodiversity conservation mainly showed HL trade-offs, especially in Northern Guangdong. In Eastern Guangdong, five districts and counties in the cities of Jieyang and Shantou had LL synergy and the rest had HL trade-offs.\\u003c/p\\u003e \\u003cp\\u003eIn the Pearl River Delta region, the cities of Sihui, Foshan, and Guangzhou each have three districts and counties; Jiangmen City has six districts and counties, which were LL synergy areas, and the other cities had HL trade-off areas. In Western Guangdong, the relationship between carbon sequestration and biodiversity conservation was the most complex. Yuncheng District, Yun'an District, Luoding County in Yunfu City, Gaozhou County in Maoming City, and most areas of Lianjiang County in Zhanjiang City were LL synergy areas. Lianjiang County, some areas of Zhanjiang City, and Leizhou City were LH trade-off areas. Suixi and Xuwen Counties in Zhanjiang City were HH synergy areas. The rest were HL trade-off areas. The trade-offs and synergies between soil retention\\u0026ndash;biodiversity conservation and food production\\u0026ndash;biodiversity conservation were very similar to those between carbon sequestration\\u0026ndash; biodiversity conservation. Differences were mainly manifested in Zhanjiang City in Western Guangdong, whereas Suixi County in Zhanjiang City did not show trade-offs or synergies. Jiedong County in Jieyang City, Jinping district, Haojiang district in Shantou City, Longhu district, and most areas of the Chaoyang and Chaonan districts did not show trade-offs or synergies either.\\u003c/p\\u003e \\u003cp\\u003eThe relationships between carbon sequestration\\u0026ndash;soil retention, carbon sequestration\\u0026ndash;food production, and soil retention\\u0026ndash;food production were similar and mainly manifested as synergistic relationships. Among them, carbon sequestration\\u0026ndash;soil retention mainly showed LL synergies, except for LH trade-offs areas in the Leizhou Peninsula in Western Guangdong, and scattered distribution of HH synergies. However, the relationship between carbon sequestration\\u0026ndash;food production did not show sporadic distribution of HH synergies in the Leizhou Peninsula, whereas the relationship between soil retention\\u0026ndash;food production showed HL trade-offs in the Lianjiang and Mazhang districts of Zhanjiang City.\\u003c/p\\u003e \\u003cp\\u003eWater retention\\u0026ndash;soil retention and water retention\\u0026ndash;food production pairs showed similar trade-offs or synergistic relationships, mainly regarding the mixed distribution of LL synergy and HL trade-off areas. In Eastern Guangdong, LL synergies were dominant, especially in Chaozhou and Shantou, and LL synergistic relationships were distributed in a continuous manner, while Shanwei and Jieyang City in the west had mixed HL trade-off relationships. In Northern Guangdong, except for Meizhou City, there mainly was an LL synergistic relationship, and the remaining areas mainly showed HL trade-offs and scattered LL synergies. The Pearl River Delta region was dominated by HL trade-offs, but Zhaoqing City and the Pearl River estuary area showed LL synergies. In Western Guangdong, the cities of Yunfu, Yangjiang, and Maoming mainly exhibited HL trade-offs. However, Zhanjiang City mainly manifested an LH trade-off between water retention\\u0026ndash;soil retention. The main regions manifested LH trade-offs between water retention\\u0026ndash;food production, except for Lianjiang County, Wuchuan County, Potou District, and Mazhang District in Zhanjiang City, which showed LL synergies.\\u003c/p\\u003e \\u003cp\\u003eFor the water retention\\u0026ndash;biodiversity conservation, HH synergies, LL synergies, LH trade-offs, and HL trade-offs were distributed in the whole province, with HH synergies being the most prominent. These were concentrated and contiguous in most areas of Qingyuan City, the whole area in the west and south of Shaoguan City, and Heyuan City connected to Huizhou City, Zhongshan City, Zhuhai City, Enping County of Jiangmen City, Shanwei City, Xinyi County, Dianbai District, Maonan District in Maoming City, Xinxing County in Yunfu City, and Yangjiang City. Meizhou City, Heyuan City, and some areas in Zhanjiang showed HL trade-offs. Jiedong County in Jieyang City, Leizhou City in Zhanjiang, and the western and southern regions of Lianjiang City showed LH trade-offs. These were mainly distributed in Guangzhou, Jiangmen, and most areas of Yunfu City.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4 Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Analysis of spatial diversity mechanisms of ecosystem services\\u003c/h2\\u003e \\u003cp\\u003eOwing to the influence of different natural and socio-economic conditions, different ecosystem services in Guangdong Province showed clear spatial differences. The carbon sequestration service in the study was measured based on NPP; therefore, the carbon sequestration level was mainly affected by the surface vegetation coverage. The Nanling Mountain area in Northern Guangdong is an important ecological barrier and a core area of ecological security in Guangdong Province and in South China. The forest area in the Northern Guangdong mountains accounts for approximately 55% of the entire province woodland area, and the national key ecological area accounts for 85% of the regional land area. Therefore, the carbon sequestration value in Northern Guangdong was the highest among the four regions in Guangdong. The carbon sequestration level in Zhaoqing was the highest among the 21 cities because it is close to Northern Guangdong and has a good ecological environment and high forest cover, accounting for 70% of the city area. Zhaoqing, Huizhou, and other peripheral areas of the Pearl River Delta are important ecological barriers to the core area of the Pearl River Delta and its ecosystem services are affected by natural and social factors such as urban spatial structure, land cover, and economic development in the process of urbanization in the Pearl River Delta (Xu et al., 2021). The carbon sequestration level of Zhanjiang was the lowest among the 21 cities because it mainly consists of cultivated land and its main function is grain production, with a carbon sequestration capacity lower than that of forests. The forest area is small, with an atypical forest structure as more than 80% are commercial forests (including timber forests and economic fruit forests). In addition, as a coastal city, Zhanjiang often suffers from frequent landings of low-pressure tropical storms and typhoons, which have a great impact on forestry production.\\u003c/p\\u003e \\u003cp\\u003eWater retention is mainly reflected on forest function. The interception and infiltration of forests can slow down surface water flow intensity, increase the amount of groundwater, control soil desertification, and reduce soil and water loss by restoring vegetation and building water conservation areas (Shah et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The water retention of forests is manifested in many aspects including water storage, runoff regulation, forest flood reduction, drought resistance, and forest water purification. Through the interception, absorption, and infiltration of precipitation, its spatial and temporal redistribution is conducted to reduce ineffective water and increase effective water (Prescott and Grayston, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). High-value areas with water retention were mainly distributed in areas with high forest coverage. Therefore, Northern Guangdong, an important ecological green area in Guangdong Province, had the highest water retention value. Yangjiang City, with the highest water retention, and Zhanjiang City, with the lowest water retention, are both distributed in the west of Guangdong, but their forest coverage rates are vastly different. The forest area in Yangjiang City accounts for approximately 60% of the city area, whereas the forest area in Zhanjiang City only accounts for over 20% of the city area. Moreover, carbon sequestration and soil retention in Yangjiang City were much higher than in Zhanjiang City (soil retention was 11.5 times higher).\\u003c/p\\u003e \\u003cp\\u003eSoil retention is an important ecosystem service that refers to the ability of the ecosystem to regulate erosions to prevent soil loss and retain sediments (Costanza et al., 1997). Therefore, soil retention is important to prevent regional land degradation and reduce flood risk (Liu et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Owing to a high forest coverage rate, the soil retention services in Guangdong Province were higher than those in northern China. However, with the significant influence of human activities on rapid urbanization, the soil erosion area in Guangdong Province has been increasing since 2000. By 2019, it had increased to 1.80\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e km\\u003csup\\u003e2\\u003c/sup\\u003e. Light erosion has been observed in 10.09% of the total area of Guangdong Province, accounting for more than 80% of the total erosion area. Cities with high soil retention were in areas with high forest coverage rate, while Zhanjiang City, with the lowest soil retention value, had insufficient forest resources, atypical forest structure, and weak sediment retention ability. Moreover, the coastal area in Zhanjiang City is composed of bare coastal sand, coastal salt-marred, and coastal salt soils.\\u003c/p\\u003e \\u003cp\\u003eAmong the five ecosystem services, Food production was the weakest. Because the income of agriculture is significantly lower than that of secondary and tertiary sectors, the main rural labor force chooses to work in cities to increase family income, and the rural labor force continues to decrease (Yang et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Although Guangdong Province has abundant photothermal conditions and good soil resources, which together with the poor livelihood guarantee of agricultural land and reduced rental cost of large-scale agricultural land, has led some rural returnee workers to engage in agricultural production mainly planting economic fruit forests and medicinal materials; thus, the use of non-grain agricultural land is promoted. On the other hand, the Pearl River Delta is an area with rapid urbanization and high economic level. A large amount of cultivated land is occupied by construction land and the food production function of the ecosystem is repeatedly squeezed. According to the Statistical Yearbook of Guangdong Province, the grain yield per unit area of Guangdong Province increased from 517.5 t/km\\u003csup\\u003e2\\u003c/sup\\u003e to 574.5 t/km\\u003csup\\u003e2\\u003c/sup\\u003e from 2009 to 2019 (11.01% growth). However, the total grain production decreased from 131.45\\u0026times;10\\u003csup\\u003e5\\u003c/sup\\u003e t to 124.08\\u0026times;10\\u003csup\\u003e5\\u003c/sup\\u003e t (5.61% reduction), with the most significant reduction in the mountainous areas of Northern Guangdong and the Pearl River Delta. The mountainous areas of Northern Guangdong were identified as key national ecological areas according to topographic features and location and some cultivated lands were converted to forest. The Pearl River Delta is mainly used for economic functions. The added value of land in economically developed areas is high, and cultivated land has been occupied by construction land. The food production function in Shenzhen was the lowest because its urbanization rate is 100%, there is almost no distribution of construction land and thus, no agricultural population.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Analysis of the mechanisms of influence of ecosystem service trade-offs and synergies\\u003c/h2\\u003e \\u003cp\\u003eThe proportion of trade-offs and synergies between ecosystem services in Guangdong Province was basically the same. The proportion of synergies was slightly higher (55.67%) but almost all were poor synergies; that is, the five kinds of services were at low levels, which is the least ideal state. A total of 21.24% of the province area had \\u0026ldquo;1 medium, 4 low\\u0026rdquo; poor synergies, whereas high synergies accounted for only 0.48% of the provincial area. Most trade-offs were strong, mainly showing low carbon sequestration, high water retention, low biodiversity conservation, medium food production, and low soil retention. The trade-off regions were mainly distributed in Maoming City, Shantou City, Huilai County of Jieyang City, and parts of the Pearl River Delta. In these areas, the forest coverage rate and carbon sequestration were low, and since carbon sequestration, soil retention, and biodiversity conservation were positively correlated, soil retention and biodiversity conservation were also low. These regions are rich in water resources, and water retention services were of high value, so a high trade-off relationship was formed.\\u003c/p\\u003e \\u003cp\\u003eIn Guangdong Province, the pairwise ecosystem services involving carbon sequestration, water retention, and soil retention showed a significant synergistic relationship because these services are mainly determined by forest cover level. Forest was the land type with the highest level of carbon sequestration. Dense forestland promotes photosynthesis and increases vegetation carbon sequestration capacity. It is also conducive to enhancing water and soil retention. Dense branches, leaves, and large roots in forests can intercept precipitation and surface runoff, which helps maintain soil and prevent erosion. Therefore, the three types of ecosystem services had a higher concentration in forest areas. In bare areas, all three ecosystem services had low values. Also, biodiversity conservation showed poor synergy with carbon sequestration, water retention, and soil retention. The biodiversity function in lush forest areas may be strong and the total number of plant and animal species relatively high, but it may not have a strong relationship with nationally protected species of special significance.\\u003c/p\\u003e \\u003cp\\u003eThe trade-offs and synergies of ecosystem services in Guangdong Province showed clear spatial differences. Paired ecosystem services may show a trade-off relationship in one region and synergistic relationships in other regions. For example, for the carbon sequestration\\u0026ndash;water retention pair, Zhaoqing City and some areas around the Pearl River Estuary were LL synergy areas; however, Western Guangdong and western parts of Northern Guangdong were HL or LH trade-off areas.\\u003c/p\\u003e \\u003cp\\u003eThe relationship between the same ecosystem services may show completely different characteristics in different regions because of the combined influence of different natural environments and socio-economic characteristics (Li et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Huang et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The geomorphological conditions of Guangdong Province are complex as the region is known as \\u0026ldquo;seven mountains, one water, and two fields.\\u0026rdquo; It gradually declines from the northern mountains to the southern coastal areas, forming a geomorphic pattern dominated by the northern middle mountains, central low mountains and hills, and southern plains. Under different geomorphic conditions, the regional ecosystem service capacities and the trade-offs and synergies between the paired services also had significant differences. Guangdong Province is a large economic province in China and its economic center is mainly distributed in the Pearl River Delta region. Human interference is strong in this region, exhibited by intense land development and the destruction of various ecological environments due to industrial development (Jafarzadeh et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Cueva et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This decline of ecosystem service capacity and destruction of natural vegetation inhibit the positive succession of ecosystems, reducing their regulatory service capacity. In contrast, Northern Guangdong is an ecologically protected area and its overall ecological environment is better.\\u003c/p\\u003e \\u003cp\\u003eGuangdong Province is rich in natural resources and has a high level of ecosystem services. However, poor synergies and strong trade-offs remain dominant among the ecosystem services. Sufficient attention should be paid to the protection of ecosystem services, and efforts should be made to practice ecological urban construction while steadily improving social and economic levels.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Uncertainty\\u003c/h2\\u003e \\u003cp\\u003eEcosystem services are the goods and services provided by ecosystems to society (Costanza et al., 1997; Divinsky et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), and include dozens of services of four kinds: providing products, regulating functions, supporting functions, and cultural services. Currently, no model can comprehensively evaluate all ecosystem services and different methods of evaluating the same ecosystem services in the same region produce different results. In this study, ecosystem services were selected for analysis according to the characteristics of the research object and the research region. This study used \\u0026ldquo;a spatial dataset of ecosystem services in China,\\u0026rdquo; which included six important ecosystem services, namely food production, soil retention, water retention, windbreak and sand fixation, biodiversity conservation, and carbon sequestration. The tropical and subtropical monsoon climate in the study area was significant, with abundant rainfall and abundant water resources; thus, windbreak and sand fixation were not considered in the study.\\u003c/p\\u003e \\u003cp\\u003eIn addition, it is necessary to note that these five ecosystem services were divided into three levels using a natural breakpoint method (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Therefore, since the level of ecosystem services was relative to that of the local region, it is possible that the low-value ranges in some ecosystem services were still higher than those in some ecologically fragile areas in northwest China.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5 Conclusions\",\"content\":\"\\u003cp\\u003e(1) The ecosystem services in Guangdong Province showed clear spatial heterogeneity. Owing to a humid climate and high forest coverage, the area showed strong water retention. Northern Guangdong had high water retention and carbon sequestration, and the highest soil retention in the province. Food production services were mainly concentrated in Western Guangdong.\\u003c/p\\u003e \\u003cp\\u003e(2) In the overall Guangdong Province, three pairs of ecosystem services, water retention\\u0026ndash;soil retention, carbon sequestration\\u0026ndash;water retention, carbon sequestration\\u0026ndash;soil retention, showed strong positive correlations and strong synergistic relationships. There were strong negative correlations between food production\\u0026ndash;water retention, food production\\u0026ndash;soil retention, and food production\\u0026ndash;biodiversity conservation. There was a strong trade-off between food production and water retention.\\u003c/p\\u003e \\u003cp\\u003e(3) The trade-offs and synergies between the ecosystem service pairs were spatially different, mainly between the LL synergies and HL trade-offs between carbon sequestration\\u0026ndash;water retention. The trade-offs or synergistic relationships were similar between carbon sequestration\\u0026ndash;biodiversity conservation, soil retention\\u0026ndash;biodiversity conservation, and food production\\u0026ndash;biodiversity conservation, which mainly manifested as HL trade-offs in Northern Guangdong. The trade-offs or synergistic relationships of carbon sequestration\\u0026ndash;soil retention, carbon sequestration\\u0026ndash;food production, and soil retention\\u0026ndash;food production were similar, showing mainly LL synergies, except for the Leizhou Peninsula. The synergistic HH relationship was most prominent between water retention and biodiversity conservation. The same ecosystem service pairs showed completely different characteristics in different regions, which may be explained by the influence of different natural environments and social and economic characteristics.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFunding: This work was supported by the National Natural Science Foundation of China (No. 42101242, 41907001); the Natural Science Foundation of Guangdong Province (No. 2023A1515012373); and the Science and Technology Program of Guangzhou, China (No. 202102080254, 202102021168).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e-Ethical Approval: Not applicable\\u003c/p\\u003e\\n\\u003cp\\u003e-Consent to Participate: Agree\\u003c/p\\u003e\\n\\u003cp\\u003e-Consent to Publish: Agree\\u003c/p\\u003e\\n\\u003cp\\u003e-Authors Contributions:\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eQian Xu is mainly responsible for writing the full text\\u003c/p\\u003e\\n\\u003cp\\u003eYing Yang is mainly responsible for the structure of the paper\\u003c/p\\u003e\\n\\u003cp\\u003eRen Yang is mainly responsible for the mechanisms of influence of ecosystem service trade-offs and synergies\\u003c/p\\u003e\\n\\u003cp\\u003eLisi Zha is mainly responsible for Spatial differentiation of ecosystem services\\u003c/p\\u003e\\n\\u003cp\\u003e-Competing Interests: no have\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e-Availability of data and materials: Not applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of Interest Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal\\u0026rsquo;s policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAllen, W.J., Bufford, J.L., Barnes, A.D., Barratt, B.I.P., Deslippe, J.R., Dickie I.A., Goldson, S.L., Howlett, B.G., Hulme, P.E., Lavorel, S., O\\u0026rsquo;Brien, S.A., Waller, L.P., Tylianakis, J.M., 2022. A network perspective for sustainable agroecosystems. Trends in Plant Sci. 27(8), 769\\u0026ndash;780. https://doi.org/10.1016/j.tplants.2022.04.002.\\u003c/li\\u003e\\n\\u003cli\\u003eCord, A.F., Bartkowski, B., Beckmann, M., Dittrich, A., Hermans-Neumann, K., Kaim, A., Lienhoop, N., Locher-Krause, K., Priess, J., Schr\\u0026ouml;ter-Schlaack, C., Schwarz, N., Seppelt, R., Strauch, M., V\\u0026aacute;clav\\u0026iacute;k, T., Volk, M., 2017. 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DOI: 10.11922/csdata.180.2017.0145\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Ecosystem services, Trade-off, Synergy, Spatial relation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3037558/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3037558/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe trade-offs between ecosystem services directly affect the quality of the ecological environment and the survival and development of human society, which is of great concern to academia, governments, and non-governmental organizations. Based on ecosystem service data from the Research Center for Eco-Environmental Sciences of the Chinese Academy of Sciences, the trade-offs and synergies among different ecosystem services in Guangdong Province in China were analyzed. Moreover, the differences in their impact and impact mechanisms were investigated. Our results showed three main points: (1) The ecosystem services in Guangdong Province showed clear spatial heterogeneity. Also, Northern Guangdong has high water retention, with a value of 5,804.73×10\\u003csup\\u003e4 \\u003c/sup\\u003em\\u003csup\\u003e3\\u003c/sup\\u003e/km\\u003csup\\u003e2\\u003c/sup\\u003e and high values for carbon sequestration and soil retention. Western Guangdong is a functional area for food production, and the Pearl River Delta is an economically developed region. (2) In the overall Guangdong Province, three pairs of ecosystem services, namely water retention–soil, carbon sequestration–water, and carbon sequestration–soil retention, showed a strong positive correlation and a good synergistic relationship. The other three pairs of relationships show strong trade-off effects. (3) The trade-offs and synergies between pairs of ecosystem services are clearly different in space, and the relationships between the same ecosystem services show completely different characteristics in different regions, resulting from the complex influence of different natural local conditions and human activities.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Spatial trade-offs and synergies among ecosystem services in Guangdong Province, China\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2023-08-03 15:13:42\",\"doi\":\"10.21203/rs.3.rs-3037558/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"fd1aecd2-15e6-4a49-a27d-03eff9a5400b\",\"owner\":[],\"postedDate\":\"August 3rd, 2023\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2023-11-21T01:16:56+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2023-08-03 15:13:42\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3037558\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3037558\",\"identity\":\"rs-3037558\",\"version\":[\"v1\"]},\"buildId\":\"7rjqhiLT3MXkJMwkYKINL\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}