Geospatial Drivers of China’s Nature Reserves: Implications for Sustainable Agricultural Development

Abstract

The establishment and management of nature reserves play a crucial role in protecting biodiversity and supporting sustainable agriculture. This study focuses on 2538 nature reserves in 22 provinces, 5 autonomous regions and 4 municipalities directly under the central government in mainland China. Integrating GIS spatial statistics, imbalance index, and geodetector models, we reveal critical insights: (1) Pronounced spatial inequity is observed, where a small number of eastern provinces dominate the total reserve count, highlighting significant regional disparities in ecological resource allocation. The sparse kernel density in western regions, indicating sparse reserve coverage. The Standard Deviation Ellipse highlights directional dispersion and human-ecological conflicts in high-density zones. (2) Key sustainability indicators driving reserve distribution include: total water resources, water resources per capita, forest area. (3) The spatial distribution of China’s nature reserves, along with factors such as altitude, river distribution, and transportation infrastructure, plays a crucial role in their development. This research provides theoretical support for the scientific planning and policy-making of nature reserves in China and offers practical guidance for optimizing and adjusting sustainable agricultural development. The study emphasizes the vital functions of nature reserves in maintaining ecosystem balance, enhancing regional climate resilience, and serving as biodiversity reservoirs. This research offers strategic insights for integrating nature reserve spatial planning with sustainable agricultural development policies, providing a scientific basis for optimizing the eco-agricultural interface in China.

1. Introduction

Protected areas are geographically defined areas designated or regulated and managed to achieve specific conservation objectives, and are regarded as one of the main pillars for biodiversity conservation and ecosystem function maintenance. The 2030 Agenda for Sustainable Development, adopted by the 70th United Nations General Assembly in 2015, emphasizes the integration of environmental, social, and economic goals to ensure long-term global sustainability [1]. Among its key objectives, ecological conservation has emerged as a foundational pillar, underscoring the importance of preserving biodiversity and ecosystem services. Within this context, nature reserves have become vital geographical spaces legally designated and managed to ensure the long-term conservation of natural resources, ecological functions, and cultural values [2]. It is important to clarify that nature reserves are not equivalent to natural resources. Natural resources broadly include exploitable elements such as minerals, water, forests, and arable land, and are often subjected to economic development under administrative permissions. In contrast, nature reserves are spatially defined zones established under ecological protection laws where resource extraction is either restricted or entirely prohibited. While natural resources are often viewed through the lens of economic utility, nature reserves are defined by their conservation objectives, ecological integrity, and legal protections. As essential tools for safeguarding biodiversity, nature reserves increasingly support sustainable agricultural development by maintaining ecological stability, providing ecosystem services, and acting as buffers against land degradation [3,4,5]. In many regions, especially in ecologically sensitive zones, nature reserves serve as critical infrastructures that align agricultural production with environmental protection goals [6]. For instance, in Africa, biosphere reserves in Tanzania integrate community livelihoods with biodiversity protection, showing the dual role of ecological and agricultural sustainability. Similarly, in North America, protected areas are used as reference sites to understand land-use impacts and maintain ecological corridors in agricultural zones. Furthermore, the concept of sustainable livelihoods-defined as the ability of a system to maintain or enhance resource productivity, ensure long-term viability, and withstand external stress—provides a valuable framework to understand the socioeconomic functions of nature reserves [7]. Sustainable livelihoods are activities that restore, enhance and sustain long-term viability, assets and participation when people’s lives are threatened and stressed, without depleting resources and ensuring opportunities for future generations. Sustainable livelihoods include supporting assets that maintain or enhance resource productivity from the outset, securing ownership of and access to property, resources and income activities, and storing and consuming sufficient food [8] and cash to meet basic needs [9]. Natural conservation is influenced by many factors such as economy, society, culture and nature [10]. This study elucidates the spatial distribution and influencing factors of nature reserves [11].

The application of ecological theory, particularly island biogeography theory and landscape ecology, initiated foundational debates in the 1970s and 1980s concerning the design of protected areas. These debates focused on issues such as the relative effectiveness of a single large reserve versus several small ones, the optimization of core-buffer-corridor structures, and strategies for maintaining species richness through spatial connectivity. In recent years, the number and scope of protected areas have increased significantly, but the assessment of their effectiveness remains incomplete [12]. The different branches of protected area assessment are largely isolated from each other, resulting in different sets of indicators and methods [13]. Protected areas are one of the main pillars for species conservation and ecosystem function protection. According to the Convention on Biological Diversity, a protected area is “a geographically defined area designated or regulated and managed to achieve specific conservation objectives”. The ecological security pattern policy has become one of the important national strategies for coordinating ecosystem protection and economic development in China [14].

Protected areas are one of the main pillars for species conservation and ecosystem function protection. According to the Convention on Biological Diversity, a protected area is a geographically defined area designated or regulated and managed to achieve specific conservation objectives. This approach operationalizes landscape connectivity theory through corridor-based indicators [15] to address fragmentation risks identified in earlier studies. Modern indicator selection builds on these foundations by integrating landscape ecology principles [16] and systemic resilience frameworks [17]. The latest progress in the evaluation of protected areas emphasizes a multi-dimensional ecological integrity framework. The ecological integrity of the protected area was quantified through seven stability dimensions (physical structure, diversity, function, persistence, resistance, resilience, and natural variability), and a comprehensive monitoring system was established, providing a verified indicator template for terrestrial applications [18]. By combining global biodiversity hotspot data with national land use change predictions, researchers determined the key conservation gaps of China’s protected area network [19].

Given its vast geographic span, ecological diversity, and long-standing efforts in protected area governance, China represents an ideal case study for examining the spatial patterns and influencing factors of nature reserves [20]. As one of the countries with the largest number of nature reserves globally, China has implemented a range of national policies to promote the coordination between biodiversity conservation and sustainable development objectives [21]. Furthermore, the recent emphasis on building an ecological civilization and establishing a national park-based protected area system offers a uniquely integrated spatial planning model that serves as a valuable reference for global protected area management [22]. China is comprehensively promoting the construction of a nature reserve system with national parks as the main body [23]. China’s Ministry of Ecology and Environment has issued the “China Biodiversity Conservation Strategy and Action Plan (2023-2030)”. It calls for continuous scientific delineation of protection areas and functional zoning in nature reserves, accelerating the integration and optimization of various types of nature reserves, and actively promoting the establishment of a nature reserve system with national parks as the main body, nature reserves as the foundation, and various types of nature parks as the supplement. Therefore, the 2538 nature reserves in 22 provinces, 5 autonomous regions and 4 municipalities of Chinese mainland can serve as an excellent research case. In 2024, the value added by the primary industry in China was 91.414 trillion yuan, reflecting a growth rate of 3.5% compared to the previous year. The share of the primary industry in the total GDP was 6.8%. This data was published by the National Bureau of Statistics of China on February 28, 2025. The sustainable development of China’s nature reserves is crucial to the country’s agriculture [24]. The research on nature reserves is an important proposition of concern in the strategy [25]. Nature reserves are the main way to achieve survival and development [26], and have significant impacts on the ecosystem structure, process and function [27]. For instance, unreasonable activities within nature reserves-such as illegal logging, poaching, excessive tourism, and land conversion for infrastructure development-can easily induce ecological degradation [28] and environmental problems [29], which will affect the distribution of nature reserves. In China, sustainable livelihood systems in protected areas are legally supported by frameworks such as the China Biodiversity Conservation Strategy and Action Plan (2023-2030) released by the Ministry of Ecology and Environment of China on 18 January 2024, which promotes eco-friendly income-generating activities aligned with conservation goals. These systems encompass economic, social, and material strategies that allow residents to engage in livelihood practices under regulated conditions, maintaining ecological integrity while improving local well-being.

While numerous studies have explored protected areas in specific regions or from particular perspectives, comprehensive national-scale analyses addressing the spatial distribution and influencing factors of China’s protected areas remain limited. This research gap may hinder the development of nuanced conservation strategies and the balanced integration of ecological protection with local development [30]. Substantial gaps persist in protected area assessment methodologies. Nature reserves and agricultural development are closely interlinked: the ecological services maintained by protected areas are foundational for long-term agricultural productivity. Conversely, sustainable agriculture reduces ecological pressures on surrounding reserves by minimizing land degradation and promoting landscape-level resilience. Therefore, exploring how to coordinate sustainable agricultural development with nature reserve protection has great practical significance.

To address the identified research gap, this study investigates the spatial distribution patterns of China’s nature reserves and analyzes the key influencing factors, including altitude, river systems, transportation infrastructure, and socioeconomic variables, using a geographic detector model. The study reveals the multidimensional impact on the distribution of nature reserves and proposes strategies to optimize the sustainability of nature reserves in China. Therefore, this study aims to analyze the spatial distribution pattern and influencing factors of nature reserves across China, with a focus on understanding how these spatial dynamics support sustainable agriculture through ecosystem services, biodiversity maintenance, and land use optimization, thereby providing references for ecological protection and agricultural sustainability. By innovatively combining a geographic information system and a socio-economic model, this study provides new empirical support and theoretical perspectives for understanding the complex relationship and spatial conflicts between ecological conservation objectives and socio-economic development demands, particularly between environmental protection agencies and local development actors such as governments, enterprises, and rural communities. The research results not only provide a scientific basis for China’s economic development, but also provide a practical reference for promoting nature reserves and sustainable agriculture.

Similar spatial dynamics can be observed in other countries, where ecological protection and development pressures interact in diverse geographical contexts. For example, in Brazil, protected areas in the Amazon region are heavily concentrated in ecologically sensitive zones, while southeastern urban and agricultural areas exhibit sparse coverage, reflecting a similar east-west imbalance driven by economic intensity and land-use competition. In Canada, nature reserves are primarily located in the northern boreal and arctic regions, where human activity is minimal, while southern regions with dense population and agriculture have fewer protected areas. In Africa, biosphere reserves in countries like Tanzania and Kenya integrate wildlife corridors and community-managed zones, balancing biodiversity protection with subsistence livelihoods. These international cases show that while the specific drivers vary by socio-political context and environmental conditions, the underlying spatial dynamics are broadly consistent with the Chinese experience. Therefore, the Chinese case provides valuable insights into the challenges and strategies of protected area planning that are globally applicable.

2. Methods

2.1. Research Area and Data Sources

The data on the quantity and area of nature reserves in China used in this article are derived from the Catalogue of Nature Reserves of the Ministry of Ecology and Environment of the People’s Republic of China (https://www.mee.gov.cn) (Figure 1 and Figure 2). Data on nature reserves were obtained from official public datasets, and spatial analysis was performed using point-level coordinate data representing the centroids of individual nature reserves. These coordinates were georeferenced from the national database of protected areas. These include nature reserves for forests, wetlands, marine areas, and species habitats, all managed under designated conservation objectives. The classification follows administrative-level designations rather than biogeographic delineation. These data do not include dynamic updates such as land use changes, upgrades, revocations or boundary adjustments that may occur after that date. The changes in land use over time are beyond the scope of this article. This article focuses on the spatial characteristics of the recently available protected area datasets. This study adopts this approach to align with policy-relevant spatial governance units and facilitate policy interpretation and implementation.

Figure 2 shows the number of nature reserves per province, with Guangdong, Inner Mongolia, and Heilongjiang ranking highest. Notably, the spatial distribution of nature reserves does not strictly follow the geographic size of provinces. It reflects the influence of regional ecological policies, biodiversity levels, and historical administrative designation practices.

The distribution of nature reserves in China is analyzed using two indicators, namely density (number of nature reserves per provincial area) and proportion (reserve area per provincial area), based on data from

Table 1. The density and proportion of nature reserves in China.

Serial Number	Region	The Area of Nature Reserves in China (104 km2)	Province Area (104 km2)	Density (Item·10−4 km−2)	The Proportion of Nature Reserves (%)
1	Guangdong Province	3.55	17.98	20.63	19.76
2	Nei Mongol	13.83	118.30	1.66	11.69
3	Heilongjiang Province	6.18	47.30	4.02	13.06
4	Jiangxi Province	1.10	16.69	10.43	6.60
5	Sichuan Province	8.75	48.60	3.37	18.01
6	Yunnan Province	2.84	39.41	3.86	7.21
7	Guizhou Province	0.95	17.62	7.32	5.39
8	Anhui Province	0.53	14.01	7.28	3.77
9	Liaoning Province	2.65	14.87	6.39	17.80
10	Hunan Province	1.12	21.18	4.49	5.29
11	Fujian Province	0.51	12.40	7.42	4.08
12	Guangxi Zhuang Autonomous Region	1.43	23.76	3.20	6.01
13	Shandong Province	1.10	15.81	4.74	6.94
14	Hainan Province	2.81	3.54	19.21	79.47
15	Hubei Province	0.99	18.59	3.39	5.34
16	Gansu Province	7.54	42.58	1.34	17.71
17	Chongqing	0.89	8.24	6.19	10.81
18	Shaanxi Province	1.05	20.56	2.43	5.09
19	Shanxi Province	1.14	15.67	2.94	7.27
20	Xizang Autonomous Region	41.60	120.28	0.37	34.59
21	Henan Province	0.75	16.70	2.10	4.50
22	Jilin Province	7.53	18.74	1.81	40.20
23	Hebei Province	0.57	18.88	1.80	3.00
24	Zhejiang Province	0.26	10.55	2.94	2.44
25	Jiangsu Province	0.56	10.72	2.80	5.27
26	Xinjiang Uygur Autonomous Region	21.49	166.49	0.16	12.91
27	Beijing	0.01	1.64	12.19	0.41
28	Ningxia Hui Autonomous Region	0.51	6.64	1.96	7.63
29	Qinghai Province	21.59	72.23	0.15	29.89
30	Tianjin	0.15	1.20	6.69	12.89
31	Shanghai	0.09	0.63	6.31	14.80

and Figure 2 and Figure 3. Guangdong Province has the highest reserve density, 137.5 times that of Qinghai Province, which has the lowest density. Therefore, there are significant spatial differences in the density of nature reserves in China. It is worth noting that although Hainan Province has a small area (3.54 × 10⁴ km²), the proportion of its nature reserves reaches 79.47%. The area of Guangdong Province is 17.98 × 10⁴ km², but the proportion is moderate (19.76%).

The distribution of nature reserves in China is analyzed using two indicators, namely density (number of nature reserves per provincial area) and proportion (reserve area per provincial area), based on data from Table 1, Figure 2, Figure 3 and Figure 4. Table 1 shows the distribution of nature reserves in China. Figure 2 provides a nationwide overview of the spatial layout, while Figure 3 specifically illustrates the density of reserves, helping to reveal clustering patterns and regional disparities. Figure 4 illustrates the proportion of nature reserves in each province of China. Together, these figures offer a complementary understanding of the spatial heterogeneity and ecological implications of nature reserve distribution.

This paper uses the geographical vector model to further explore the distribution pattern of nature reserves in China. The spatial pattern of nature reserves is an objective reflection of resource elements in the region. The density of nature reserves in China shows obvious spatial heterogeneity. Analyzing the spatial distribution of nature reserves in China and its influencing factors has significant academic value. It helps identify areas that urgently need protection and provides a scientific basis for optimizing the layout of nature reserves.

2.2. Research Methods

This paper uses the ArcGIS 10.8 software tool to demonstrate the spatial distribution characteristics of China’s nature reserves using spatial statistical methods such as the imbalance index, kernel density estimation, Geographic Concentration Index, and spatial clustering analysis. Finally, the geographical detector is used to quantitatively assess drivers of the distribution of nature reserves in China, explore influencing factors of their spatial heterogeneity, and propose actionable management strategies [31].

2.2.1. Standard Deviation Ellipse

The Standard Deviation Ellipse method is a spatial statistical tool used to identify the directional trend and dispersion of geographical elements [32,33,34,35,36]. It calculates the central location, the length of the major and minor axes, and the rotation angle, offering an intuitive visualization of the spatial orientation and distribution scope of nature reserves. This method was implemented using ArcGIS 10.8 to quantify the spatial concentration and directional pattern of China’s nature reserves.

2.2.2. Imbalance Index and Lorenz Curve

The imbalance index can analyze the equilibrium of the distribution of nature reserves in China. Previous studies generally used the Lorenz curve to calculate the imbalance index to evaluate the equilibrium of the spatial distribution of nature reserves in China [37].

The calculation formula is:

$$S={\displaystyle \frac{\sum _{i=1}^n{Y}_i-50\left(n+1\right)}{100n-50\left(n+1\right)}}$$

$Y_i$ is the cumulative proportion of nature reserves in the ith region arranged in descending order, $n$ is the total number of regions. In the above formula: the value of S ranges from 0 to 1. When S = 0, it indicates that nature reserves in China are evenly distributed. When S = 1, it indicates that China’s nature reserves are concentrated in a certain area.

The greater the curvature of the Lorenz curve, the greater the spatial distribution difference of nature reserves in China. Conversely, it indicates that the spatial distribution of nature reserves in China is more balanced [38]. This can reflect the spatial consistency and development balance of nature reserves in China [39].

2.2.3. Geographic Concentration Index

Geographic Concentration Index is an index used to reflect the spatial concentration degree of nature reserves in China [40,41]. The geographical significance of this model is to define the degree of spatial concentration or dispersion of spatial elements [42], and its mathematical expression is:

$$\mathrm{G}=100\times \sqrt{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\left({\displaystyle \frac{{\mathrm{x}}_{\mathrm{i}}}{\mathrm{P}}}\right)}^2}$$

In the above formula, G represents the concentration level of the research object. ${\mathrm{x}}_{\mathrm{i}}$ denotes the number of Chinese nature reserves owned by the i-th province or city. P is the total number of Chinese nature reserves; n denotes the number of Chinese nature reserves within the i-th province or city. When the G value is larger, it means that the number of nature reserves in China is more concentrated.

2.2.4. Geographic Detector

Geographical Detector is a spatial analysis tool based on the theory of geospatial heterogeneity. Therefore, this paper uses the geodetector to study the spatial distribution of nature reserves in China and the spatial differences and interactions among influencing factors. Its function formula is:

$$q=1-{\displaystyle \frac{\sum _{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}}=1-{\displaystyle \frac{SSW}{SST}}$$

$$SSW=\sum _{h=1}^L{N}_h{\sigma }_h^2, SST=N{\sigma }^2$$

In the formula, L represents the Strata of variable Y or factor X. N_h represents the number of units in layer h, and N is the total number of units in the whole area. σ²h and σ² denote the variance of layer h and area Y values, respectively. SSW stands for the Within Sum of Squares, and SST stands for Total Sum of Squares [43]. The value of parameter q ranges from 0 to 1. The higher its value, the more significant the spatial differentiation of Y. If stratification is based on independent variable X, a larger q value indicates that X has a stronger explanatory power for attribute Y, and vice versa, indicating weaker explanatory power [44]. In extreme cases, a q value of 1 means that factor X completely determines the spatial distribution of Y, while a q value of 0 suggests no relationship between factor X and Y [45], and the q value specifically means that X explains 100 × q% of Y [46].

2.2.5. Spatial Durbin Model

The Spatial Durbin Model is an advanced spatial econometric model that accounts for spatial autocorrelation in both dependent and independent variables. It extends the traditional spatial lag and spatial error models by including spatially lagged explanatory variables, allowing the analysis of spatial spillover effects. This model is particularly suitable for analyzing the spatial interdependence of natural reserve distribution across regions, where ecological policies and investments in one province may influence neighboring regions. In this study, Spatial Durbin Model was used to cross-validate and enhance the results from the geographical detector, adding robustness to the interpretation of spatial dynamics [47].

3. Results

3.1. Spatial Distribution Patterns of Nature Reserves in China

3.1.1. Analysis of Standard Deviation Ellipses

Figure 4 shows the Standard Deviation Ellipse of the spatial distribution of China’s nature reserves, visually presenting the spatial pattern characteristics. Parameters such as the lengths of the major and minor axes, center coordinates, and the rotation angle can accurately describe the concentration trend and directionality of the distribution of nature reserves. The major axis of the ellipse extends from northeast to southwest, indicating that the distribution of nature reserves is more dispersed in this direction, while the minor axis shows its concentration in the vertical direction. Based on the Standard Deviation Ellipse method, the clustering characteristics and directionality of the spatial distribution of nature reserves in China are quantitatively revealed.

The main axis of the ellipse has a northeast-southwest orientation, spatially covering the eastern coastal and central inland provinces such as Hebei Province, Shandong Province, and Guangdong Province, which highly overlap with China’s densely populated areas, characterized by “higher density in the east and lower density in the west”. The purple dots indicate that provinces with more than 100 protected areas (such as 371 in Guangdong Province, 196 in Inner Mongolia, and 190 in Heilongjiang Province) are located in the core area of the ellipse, while western regions like Xinjiang Uygur Autonomous Region, Qinghai Province, and Xizang Autonomous Region have less than 50 protected areas, and the lowest number is 4 in Shanghai (Figure 5).

3.1.2. Analysis of Imbalance Index and Lorenz Curve

To evaluate the spatial distribution equilibrium of nature reserves in China, the imbalance index and the Lorenz curve model are employed as key analytical tools. The Lorenz curve is a powerful and intuitive tool for effectively illustrating the spatial balance and concentration of nature reserves in China. Through this analysis method, researchers can gain a comprehensive understanding of the distribution of nature reserves in the whole region.

On the horizontal axis, there are 2538 nature reserves, arranged from the province (or municipality) with the largest number of nature reserves to the one with the smallest number of nature reserves. The vertical axis represents the cumulative proportion of these nature reserves, reflecting the total number of nature reserves in 22 provinces, 5 autonomous regions, and 4 municipalities in mainland China. The proportion is arranged from highest to lowest. By analyzing the shape of the Lorenz curve, it can be determined that the distribution of nature reserves in China is uneven. The Lorenz curve deviates significantly from the uniform distribution line, indicating that China’s nature reserves are highly concentrated in a few provinces, where the number of nature reserves is more prominent compared to other regions. In conclusion, the Lorenz curve not only highlights the spatial distribution differences of nature reserves in China, but also provides valuable insights for policymakers and stakeholders to promote the balanced development of nature reserves in China.

Table 2. Distribution of nature reserves in China Distribution Lorenz curve.

Serial Number	Region	Number of Protected Areas	Proportion	Cumulative Proportion	Theoretical Proportion	Cumulative Theoretical Proportion
1	Guangdong Province	371	14.62%	14.62%	3.23%	3.23%
2	Nei Mongol	196	7.72%	22.34%	3.23%	6.45%
3	Heilongjiang Province	190	7.49%	29.83%	3.23%	9.68%
4	Jiangxi Province	174	6.86%	36.68%	3.23%	12.90%
5	Sichuan Province	164	6.46%	43.14%	3.23%	16.13%
6	Yunnan Province	152	5.99%	49.13%	3.23%	19.35%
7	Guizhou Province	129	5.08%	54.22%	3.23%	22.58%
8	Anhui Province	102	4.02%	58.23%	3.23%	25.81%
9	Liaoning Province	95	3.74%	61.98%	3.23%	29.03%
10	Hunan Province	95	3.74%	65.72%	3.23%	32.26%
11	Fujian Province	92	3.62%	69.35%	3.23%	35.48%
12	Guangxi Zhuang Autonomous Region	76	2.99%	72.34%	3.23%	38.71%
13	Shandong Province	75	2.96%	75.30%	3.23%	41.94%
14	Hainan Province	68	2.68%	77.97%	3.23%	45.16%
15	Hubei Province	63	2.48%	80.46%	3.23%	48.39%
16	Gansu Province	57	2.25%	82.70%	3.23%	51.61%
17	Chongqing	51	2.01%	84.71%	3.23%	54.84%
18	Shaanxi Province	50	1.97%	86.68%	3.23%	58.06%
19	Shanxi Province	46	1.81%	88.49%	3.23%	61.29%
20	Xizang Autonomous Region	45	1.77%	90.27%	3.23%	64.52%
21	Henan Province	35	1.38%	91.65%	3.23%	67.74%
22	Jilin Province	34	1.34%	92.99%	3.23%	70.97%
23	Hebei Province	34	1.34%	94.33%	3.23%	74.19%
24	Zhejiang Province	31	1.22%	95.55%	3.23%	77.42%
25	Jiangsu Province	30	1.18%	96.73%	3.23%	80.65%
26	Xinjiang Uygur Autonomous Region	27	1.06%	97.79%	3.23%	83.87%
27	Beijing	20	0.79%	98.58%	3.23%	87.10%
28	Ningxia Hui Autonomous Region	13	0.51%	99.09%	3.23%	90.32%
29	Qinghai Province	11	0.43%	99.53%	3.23%	93.55%
30	Tianjin	8	0.32%	99.84%	3.23%	96.77%
31	Shanghai	4	0.16%	100.00%	3.23%	100.00%

, which duplicated data from the Lorenz curve, has been removed for brevity and to avoid redundancy.

Based on Table 2, the Lorenz curve is drawn (Figure 6). The spatial distribution characteristics of nature reserves in China are analyzed. Based on the analysis of the Lorenz curve, it is shown that the nature reserves in China are specifically manifested as:

(1)

The top five provinces (Guangdong, Inner Mongolia, Heilongjiang, Jiangxi, and Sichuan) account for 37.2% of China’s total nature reserves, and their actual cumulative proportion (49.1%) is significantly higher than theoretical uniform distribution value of 16.1%. This indicates the prioritized implementation of ecological protection policies in biodiversity hotspots.

(2)

The middle section of the curve (cumulative provincial proportion of 40–70%) remains steep, revealing clustering phenomenon in the mountainous ecological zones of the southwest (Yunnan Province, Guizhou Province) and the Northeast Forest Belt (Liaoning Province, Jilin Province). The actual contribution of the 15 provinces in the tail (cumulative proportion of 48.4%) is only 12.9%, especially in the economically developed coastal regions (Shanghai, Tianjin) and arid northwest areas (Ningxia Hui Autonomous Region, Qinghai Province) showing significant low values.

(3)

The curve shows local concavities at the points corresponding to Guangxi Zhuang Autonomous Region and Xizang Autonomous Region, which is related to the special protection mechanism of plateau ecosystems (such as large contiguous protected areas) and variations in administrative division statistics. This chart clearly illustrates the significant spatial imbalance in the distribution of nature reserves in China. The systematic deviation between the actual cumulative distribution curve (marked in blue) and theoretical uniform distribution curve (marked in orange) indicates that the spatial clustering degree of China’s nature reserves is highly unbalanced.

Figure 6. Lorenz curve of the spatial distribution of nature reserves in China.

Figure 6. Lorenz curve of the spatial distribution of nature reserves in China.

In the distribution of nature reserves in China, the value of G is 24.4596, indicating a moderate level of spatial concentration rather than extreme aggregation (

Table 3. Geographic Concentration Index of nature reserves in China.

Serial Number	Region	Count	$\frac{{\mathbf{x}}_{\mathbf{i}}}{\mathbf{P}}$	${\left(\frac{{\mathbf{x}}_{\mathbf{i}}}{\mathbf{P}}\right)}^2$
1	Guangdong Province	371	0.1461780930	0.0213680349
2	Nei Mongol	196	0.0772261623	0.0059638801
3	Heilongjiang Province	190	0.0748620961	0.0056043334
4	Jiangxi Province	174	0.0685579196	0.0047001883
5	Sichuan Province	164	0.0646178093	0.0041754613
6	Yunnan Province	152	0.0598896769	0.0035867734
7	Guizhou Province	129	0.0508274232	0.0025834269
8	Anhui Province	102	0.0401891253	0.0016151658
9	Liaoning Province	95	0.0374310481	0.0014010834
10	Hunan Province	95	0.0374310481	0.0014010834
11	Fujian Province	92	0.0362490150	0.0013139911
12	Guangxi Zhuang Autonomous Region	76	0.0299448385	0.0008966934
13	Shandong Province	75	0.0295508274	0.0008732514
14	Hainan Province	68	0.0267927502	0.0007178515
15	Hubei Province	63	0.0248226950	0.0006161662
16	Gansu Province	57	0.0224586288	0.0005043900
17	Chongqing	51	0.0200945626	0.0004037914
18	Shaanxi Province	50	0.0197005516	0.0003881117
19	Shanxi Province	46	0.0181245075	0.0003284978
20	Xizang Autonomous Region	45	0.0177304965	0.0003143705
21	Henan Province	35	0.0137903861	0.0001901747
22	Jilin Province	34	0.0133963751	0.0001794629
23	Hebei Province	34	0.0133963751	0.0001794629
24	Zhejiang Province	31	0.0122143420	0.0001491902
25	Jiangsu Province	30	0.0118203310	0.0001397202
26	Xinjiang Uygur Autonomous Region	27	0.0106382979	0.0001131734
27	Beijing	20	0.0078802206	0.0000620979
28	Ningxia Hui Autonomous Region	13	0.0051221434	0.0000262364
29	Qinghai Province	11	0.0043341214	0.0000187846
30	Tianjin	8	0.0031520883	0.0000099357
31	Shanghai	4	0.0015760441	0.0000024839

).

3.2. Spatial Environmental Factors Affecting the Distribution of Nature Reserves in China

By analyzing natural resources (such as altitude and river system) and social resources (such as transportation networks), this paper explores the influence of natural resources on the distribution of nature reserves in China.

3.2.1. Altitude

Altitude is one of the significant factors affecting the distribution of nature reserves in China. This study focuses on the elevation distribution of nature reserves in China, using kernel density estimation and spatial differentiation index methods. The ArcGIS 10.8 software is employed to create a coupling map of the spatial distribution and elevation relationship of nature reserves in China. It quantitatively reveals the regulatory mechanism of altitude on the distribution of protected areas.

The spatial distribution of nature reserves in China shows a significantly coupling coupling with the altitude gradient, presenting a pattern of “high density in high altitudes and low density in low altitudes”. Due to the topographic complexity and habitat diversity, high-altitude areas such as the Tibetan Plateau and the Hengduan Mountains have become biodiversity-rich areas and core habitats for endemic species, with a much higher density of protected areas than the low-altitude plains in the east.

The transition zone between medium and low altitudes (such as the Qinling-Huaihe River line) exhibits functional convergence characteristics. Although the density of protected areas is lower than that of the high-altitude core area, their unit ecosystem service value is more higher, forming an “efficiency funnel” for ecological protection. This phenomenon reveals the functional differentiation of China’s protected area system in terms of altitude. High-altitude areas focus on biodiversity conservation, while middle and low altitude areas emphasize the provision of ecosystem services. Future planning needs to build a collaborative network across altitude gradients, focusing on improving the coordination between conservation and sustainable use in ecological transition zones. This approach can help address spatial fragmentation between biodiversity conservation and the optimization of ecological services, while promoting the integration of ecological security patterns at the regional scale (Figure 7).

3.2.2. Rivers

Based on the spatial point data of protected areas and the vector data of the Chinese water system, this study constructs a multi-scale buffer analysis model through the ArcGIS 10.8 platform to quantitatively analyze the driving mechanism of river system on the distribution of protected areas. The spatial distribution pattern of China’s protected areas and the coupling characteristics with river systems show significant correlation in the superimposed mapping analysis of this study. Protected areas exhibit a macroscopic pattern of high density in the east and low density in the west (Figure 8).

In the Yangtze River Basin, the agglomeration effect of nature reserves is particularly prominent. Within the radiation range of 500 km along the main stream (covering Hubei, Hunan, Jiangxi and other provinces), 43.2% of the country’s protected areas are concentrated. Among these, the middle reaches of the Yangtze River in Hubei Province and Hunan Province form continuous ecological corridors.

The Pearl River Basin shows a unique network correlation pattern. The protected areas in Guangdong Province and Guangxi Zhuang Autonomous Region are radially distributed. The number of protected areas within the 10 km buffer zone of rivers accounts for 62.4%, which is significantly higher than the national average (38.1%).

The Yellow River Basin exhibits significant spatial differentiation. The annual runoff in the upper reaches of the river in Qinghai and Gansu provinces is less than 18% of that at the same latitude along the Yangtze River. This has resulted in a fragmented distribution of protected areas, with only 7.3% of them within the 50 km buffer zone of the river.

To clarify the spatial coupling approach with river systems, this study used ArcGIS 10.8 to overlay vector hydrological data with point data of 2538 nature reserves. Buffer zones of 10 km, 20 km, and 50 km were set around the main river systems to analyze proximity-based effects. Within these buffers, we calculated the proportion of protected areas falling into each range. These zones allow for the examination of hydrological connectivity and biodiversity support functions. The limitations of this method include the uniform application of buffer radii across varied river types and insufficient differentiation between primary and secondary water bodies. Future studies may incorporate hydrological modeling and catchment-level watershed delineation to improve accuracy.

3.2.3. Transportation Maps

Under the framework of spatial planning, there is an intrinsic conceptual connection between protected areas and transportation networks. This study explores the spatial synergy and contradictions between China’s nature reserves and transportation networks by integrating spatial ecology theory with infrastructure planning. The protected area aims to minimize human disturbance and maintain biodiversity. As a typical man-made infrastructure, the transportation network may exacerbate habitat fragmentation and introduce disturbances from human activities. Empirical analysis shows that protected areas are often significantly decoupled from high-density traffic areas in terms of spatial distribution. This reflects a spatial strategy that prioritizes ecological protection by proactively avoiding interference.

Through kernel density estimation and spatial overlay analysis, it is found that the density of nature reserves is significantly higher in sparsely trafficked regions than in densely trafficked ones. The Qinghai-Xizang Plateau (road network density < 0.1 km/km²) has 28.6% of national nature reserves, while the Yangtze River Delta urban agglomeration (road network density > 1.2 km/km²) has less than 3% of national nature reserves.

The results show that the spatial configuration of China’s nature reserves is shaped by a principle of “ecological priority and avoidance of disturbance”, whereby protected areas are strategically located in regions with low human activity and high ecological value, often buffered by limited transportation access. This spatial planning logic, aimed at maximizing biodiversity protection and minimizing disturbance, aligns with international conservation practices. Key protection is implemented in ecological barriers formed by traffic barriers. At the same time, it confirms the mechanism by which the infrastructure network indirectly shapes the spatial pattern of protected areas by driving the change in the intensity of human activities (Figure 9).

3.3. Multivariate Driving Forces and Interaction Analysis of Nature Reserve Distribution in China

3.3.1. Selection of Impact Factors

In order to promote the effective protection of global nature reserves, this study stratified all independent variables using the Jenks Natural Breaks Classification method in ArcGIS, which is based on ecological theory and the principle of regional comparison. The Jenks method was chosen because it minimizes the variance within classes and maximizes the variance between classes, making it suitable for heterogeneous geospatial data. This hierarchical method conforms to the best practices of spatial statistics to verify the robustness of the results. This approach is in line with the global conservation strategy outlined in the Convention on Biological Diversity and the Intergovernmental Platform for Scientific Policy on Biodiversity and Ecosystem Services. Globally, the planning of protected area networks is increasingly incorporating social and ecological indicators such as population pressure, ecological endowment and policy investment intensity. This is reflected in the selection of variables (X₁–X₈).

The selected variables reflect the three critical domains influencing protected area distribution: natural resource endowment, socio-economic pressure, and conservation investment intensity. This design ensures both ecological relevance and empirical robustness [44]. Based on the spatial distribution characteristics of nature reserves in China, eight detection factors such as water resources and forest area were introduced to ensure the robustness of the q value. These indicators align with the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services conceptual framework for assessing drivers of biodiversity change, ensuring relevance to global conservation priorities.

We conducted a cross-examination of the spatial driving factors of the interaction among these eight detection factors. This hierarchical method conforms to the best practices of spatial statistics to verify the robustness of the results. The key method assumptions and verification protocols are as follows: (1) The Jenks Natural Breaks Classification method is adopted to define the hierarchical criteria of the geographical detector model to ensure the best intra-layer uniformity and inter-layer heterogeneity. (2) Before using geographical detector methods, natural breakpoints are adopted to stratify the independent variables. (3) We conduct significance testing (p-values) for all q-statistics to verify statistical relevance. (4) The results of the GeoDetector are cross-validated using the Spatial Durbin Model to ensure the consistency of factor effects under different models.

The management effect of nature reserves is influenced by the multi-dimensional indicator system. Socio-economic pressure reflects the crowding effect of human activities on the protected area space through “permanent resident population (X₁)”. The characteristics of the natural resources situation are “total water resources (X₂)” and “per capita water resources (X₃)”. The ecological resource endowment selects “forest area (X₄)” and “forest stock volume (X₅)” to quantify the basic supporting capacity of the terrestrial ecosystem. The intensity of capital investment reflects the implementation and management effect of policies through “investment quota for forestry and grassland (X6)” and “management and monitoring funds (X₇)”. The value of biodiversity relies on “Biodiversity conservation investment (X₈)” to assess the distribution requirements of priority conservation targets. These eight indicators (X₁–X₈) jointly construct a dynamic correlation network among population expansion, resource carrying capacity, ecological foundation, policy regulation and species conservation, providing quantitative support for analyzing the spatial competition, resource threshold, system stability and management effectiveness faced by nature reserves (

Table 4. Detection factors for spatial distribution of nature reserves in China.

Factor	Impact Factor	Coding	q Statistic	p Value
Social and economic pressure	Resident population (ten thousand Chinese yuan)	X1	0.250000	0.204818
Natural resource conditions	Total water resources (hundred million cubic meters)	X2	0.361111	0.067044 *
	Water resources per capita (m3/per person)	X3	0.288889	0.074263 *
Ecological resource endowment	Forest area (ten thousand hectares)	X4	0.383333	0.051803 *
	Forest stock (million cubic meters)	X5	0.438889	0.025500 **
Intensity of capital investment	Forestry and grassland investment quota (ten thousand Chinese yuan)	X6	0.394444	0.045302 **
	Management and monitoring funds (ten thousand Chinese yuan)	X7	0.200159	0.358260
Biodiversity values	Biodiversity conservation investment (ten thousand Chinese yuan)	X8	0.400000	0.042306 **

Note: **, and * indicate significance at the 5% and 10% statistical levels, respectively.

).

According to the principle of the geographic detector, the q value representindicates the explanatory power of thea factor forconcerning the dependent variable, and itswith a value range is from 0 to 1. The larger the value, theA higher q value signifies stronger the explanatory power. The p value is determined by the, derived from significance test toing, determines whether the explanatory power hais statistically significancet. Therefore, usuypically, only the p value needs to beis marked with a significance asterisk, while the q value itself does not directly correspond to the significance levels and thus does not need to be marked withrequire an asterisk. The symbols ***, **, and * respectively represent significance at statistical levels of 1%, 5%, and 10%, and are used to explain significance symbolsrespectively. If the p value is unsignnot marked, it indicates that the p value is greater thanexceeds 10% and has nolacks significance. SinceGiven that the selection of the detection factors should be comprehensive and representative, it is normalacceptable for the p value to be unsigned, indicating no significance. The analysis of factors affectinfluencing the spatial distribution of nature reserves in China, based on the geographical detector model shows that there are, reveals significant differences in the explanatory power (q statistic) and statistical significance (p value) of eachamong the various driving factors (

Table 5. Types of interaction between two driving factors.

Judgment Criteria	Nonlinearity Attenuation
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)<\mathrm{m}\mathrm{i}\mathrm{n}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]$	Nonlinear attenuation
$\mathrm{m}\mathrm{i}\mathrm{n}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]<\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)<\mathrm{m}\mathrm{a}\mathrm{x}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]$	Single-factor nonlinear attenuation
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)>\mathrm{m}\mathrm{a}\mathrm{x}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]$	Dual-factor enhancement
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)=\mathrm{q}\left({\mathrm{X}}_1\right)+\mathrm{q}\left({\mathrm{X}}_2\right)$	Independence
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)>\mathrm{q}\left({\mathrm{X}}_1\right)+\mathrm{q}\left({\mathrm{X}}_2\right)$	Nonlinear enhancement

).

Forest stock (X₅, q = 0.43889, p = 0.025500), Fforest area (X₆, q = 0.3944, p = 0.045302), and Bbiodiversity conservation investment (X₈, q = 0.400000, p = 0.042306) are identified as the dominant influencing factors. These data, all of which pass athe significance test. (p < 0.05). Total water resources (X₂, q=0.361111, p = 0.067044), Wwater resources per capita (X₃, q = 0.288889, p = 0.074263), and Fforest area (X₄, q = 0.383333, p = 0.051803) showexhibit moderate explanatory power, suggesting spatial heterogeneity.

3.3.2. Analysis of Driving Forces

Geodetectors can effectively study spatial differentiation and detect driving factors based on the spatial distribution similarity between independent and dependent variables (Table 5). The results of the geographical detector show that all eight factors influence the spatial distribution pattern of China’s nature reserves. According to the thermal map data of interactive exploration, there is a significant multi-level correlation structure among the factors influencing the distribution characteristics of China’s nature reserves. The internal mechanism can be analyzed in three key aspects: the ecological function coordination network, the marginal effects of human-environment interactions, and the spatial differentiation of resource allocation.

Firstly, the interaction intensity of Forest area (X₄) and Forest stock (X₅) is high, indicating that regions with abundant water resources typically have higher forest coverage. These areas are more suitable for establishing nature reserves. Secondly, the interaction intensity between Water resources per capita (X₃) and Forest area (X₄) is also high, indicating that areas with higher water resources per capita are typically abundant in forest resources. These areas are more suitable for establishing protected areas to protect water resources and forest ecosystems. Thirdly, the interaction intensity between Forest area (X₄) and Forestry and grassland investment quota (X₆) is high, indicating that areas with larger forest area tend to attract more investment, making it easier to establish nature reserves. Finally, the interaction intensity of Forestry and grassland investment quota (X₆) and Biodiversity conservation investment (X₈) is also high, indicating that biodiversity conservation is closely related to economic investment. Areas with more investment pay more attention to biodiversity conservation, thus promoting the construction of nature reserve.

The results show that the distribution of protected areas in China is a composite product of natural rigidity constraints and humanistic elasticity regulation, and it is necessary to construct a three-dimensional governance framework of “protection of core natural factors, optimization of ecological network and human-land marginal regulation”, which has an important decision-making value for coordinating the contradiction between ecological security and regional development (Figure 10).

In summary, the distribution of nature reserves in China is affected by multiple factors such as Resident population (X₁), Total water resources (X₂), Water resources per capita (X₃), Forest area (X₄), Forest stock (X₅), Forestry and grassland investment quota (X₆), Management and monitoring funds (X₇), Biodiversity conservation investment (X₈).

3.3.3. Three-Dimensional Kernel Density

This study reveals the spatial distribution characteristics and regional differences of nature reserves in China through Three-dimensional Kernel Density analysis. In Figure 10, the X-axis represents the number of nature reserves, the Y-axis represents the geographical region (Northeast, North, East, South, Central, Northwest, Southwest), and the Z-axis represents the nuclear density value. The Three-dimensional Kernel Density reflects the concentration of reserves in space and a higher nuclear density value indicate a denser distribution of reserves in that area.

From the overall distribution characteristics, the nuclear density of nature reserves shows significant regional differences. The nuclear density in northeast China is the highest, with values close to 0.025, indicating that there are a large number of protected areas and their distribution is concentrated. In contrast, the nuclear density values in southwestern regions are generally below 0.01, indicating a relatively scattered distribution of protected areas. The nuclear density values in eastern and southern regions are around 0.015 and 0.01, respectively, showing a balance between economic development and ecological conservation. The nuclear density value in northwestern regions is the lowest, approaching 0.005. The nuclear density values in central and northern regions range from 0.01 to 0.02, indicating a certain degree of concentration, but lower than those in northeastern regions.

In addition, the distribution pattern of nuclear density further reveals interaction between the number of protected areas and geographical conditions. The peak nuclear density in the Northeast region occurs in areas with a higher number of protected areas, while the peak of nuclear density in Southwest China is found in the region with a low number of protected areas. This difference indicates that the distribution of protected areas is influenced not only by the number, but also by factors closely related to the geographical environment and ecological protection needs of the region (Figure 11).

In summary, this article analyzes the influencing factors of the distribution characteristics of nature reserves in China and conducts a detailed analysis of environmental factors such as altitude, rivers, and transportation among environmental factors. Based on ecological theory and other research, this paper uses the environmental factor analysis of geographic detectors to prove the driving force analysis between nature reserves and environmental factors more scientifically and specifically, and demonstrates the correlation between nature reserves and environmental conditions. The paper demonstrates, through quantitative and qualitative methods and the use of geographic detectors, that the establishment of nature reserves is due to their environmental characteristics.

The research results show that the spatial correlations between reserves and environmental factors (e.g., altitude, rivers) must be interpreted within the context of conservation policy objectives. Reserves are established in regions with high ecological value, such as biodiversity hotspots, critical watersheds, or intact habitats, which inherently possess distinct environmental characteristics. Our analysis identifies geospatial patterns resulting from intentional conservation choices, rather than implying unidirectional causation. For instance, the clustering of reserves in high-altitude areas reflects their role as biodiversity refuges, while their scarcity in arid northwestern China aligns with lower ecological carrying capacity and historical land-use priorities. This bidirectional relationship—where environmental traits inform reserve placement, and reserve management subsequently shapes regional ecology—highlights the need for adaptive governance frameworks that integrate spatial analysis with policy-driven conservation goals.

4. Discussion

A factor indicator system centered on social and economic foundations and natural endowment is established to examine the influencing factors of spatial distribution of nature reserves in China in this study. This multidimensional perspective not only affirms the heterogeneity observed through spatial analytical tools (such as Standard Deviation Ellipse, Geographic Detector), but also unpacks the synergistic roles of natural attributes (such as altitude, forest area) and anthropogenic pressures (such as population density, infrastructure proximity) in shaping reserve distribution. The shift from isolated factor analysis to interaction-based models reflects a paradigm advancement, enabling a more precise understanding of how ecological integrity is maintained or fragmented across scales. This transition mirrors broader trends in landscape sustainability science that emphasize resilience through spatial design coherence and policy embeddedness. Spatial distribution is a crucial research content for the development of protected areas. Recent studies on the factors influencing spatial distribution have transitioned from examining isolated effects to analyzing their complex interactions, emphasizing the multifactorial nature of reserve placement [48]. The paper further studies the influencing factors of the spatial distribution pattern of nature reserves in China [49].

(1)

Spatial distribution of China’s nature reserves

The Standard Deviation Ellipse of China’s nature reserves, the Geographic Concentration Index and the Lorenz Curve of nature reserves in China illustrate the imbalanced spatial distribution of China’s nature reserves [50]. This reflects that the spatial distribution of nature reserves in China exhibits significant regional imbalances [51]. The eastern and central regions form distinct agglomeration zones, while the western and northern regions are relatively sparse. In general, the synergistic effects of natural endowment, economic carrying capacity and policy regulation have shaped the spatial characteristics of China’s nature reserves, which are dense in the east and sparse in the west.

(2)

The Impact of Altitude

The spatial distribution of nature reserves in China is coupled with the altitude gradient, and its imbalance reveals the dual influence of natural geographic patterns and the intensity of human activities. Altitude determines the type and distribution of nature reserves to a certain extent. Low altitude areas, due to superior hydrothermal conditions and diverse ecosystem types, coupled with the long-term human settlement that have formed ecological protection needs, have led to dense clusters of protected areas in the eastern and central plains and hilly regions. This is particularly evident in river basins and areas rich in biodiversity. In contrast, high-altitude regions, while having intact ecological backgrounds, have relatively fewer protected areas due to fragmented terrain, low environmental carrying capacity and poor transportation accessibility. This distribution pattern essentially reflects the spatial mapping of natural environmental carrying capacity, ecosystem service value, and regional sustainable development aspirations.

(3)

Influence of River Distribution

Rivers and lakes can provide effective ecological barriers and are conducive to the development and evaluation of nature reserves [52]. The distribution of nature reserves and water resources in China has significant spatial coupling characteristics, reflecting the interdependent relationship between the two. This study reveals that water resources play a fundamental and crucial dual role in the formation and distribution of nature reserves. Rivers and lakes can provide good ecological barriers and are conducive to the development and evaluation of nature reserves. The distribution of nature reserves and water resources in China has significant spatial coupling characteristics, reflecting the interdependent relationship between the two. This study reveals that water resources play a fundamental and crucial dual role in the formation and distribution of nature reserves. Water resources are an indispensable element for the formation and development of nature reserves, playing an extremely important role in many aspects such as the ecology, topography and climate of the reserves. It is not only a natural constraint for the delineation of ecological red lines, but also a key regulator for alleviating the contradiction between protection and development. Differentiated control strategies need to be implemented in the network optimization of protected areas.

(4)

Influence of Transportation Distribution

The spatial distribution of nature reserves in China is significantly coupled with the transportation network, revealing the dynamic correlation between ecological protection and regional development [53]. Due to the high ecological accessibility and the concentration of management resources, the dense distribution areas of protected areas is formed in the eastern and central areas. This not only reflects the strong demand for ecological services in economically developed areas, but also demonstrates the supporting role of convenient transportation for monitoring and maintenance of protected areas. Transport networks in the western region are sparse. Although the ecological background is complete, protection and management are difficult, and most of the protected areas are located along the main roads relying on strategic ecological locations. This distribution is essentially about the efficiency of ecological protection, resource allocation capacity and sustainable development of the region.

(5)

The Impact of Geographical Detector Spatial Distribution

The level of regional economic development is an important factor affecting the spatial distribution of nature reserves. This correlation network confirms that the layout of protected areas is a result of both natural background advantages and human interventions. The three-dimensional Kernel Density of China’s nature reserve distribution quantitatively reveals the spatial clustering characteristics of protected areas. The peak of Three-dimensional Kernel Density occurs in specific combinations of area and number of protected areas, reflecting high-density patches due to ecological fragmentation of small and medium-sized protected areas in eastern China. In the west, a wide range of continuous habitats show low density and wide distribution. This bimodal distribution confirms the spatial strategy of combining intensive protection in key areas and large-scale conservation in ecologically fragile areas, and quantitatively shows the gradient characteristics of the balance between territorial space development and protection. These spatial planning patterns are not only essential for ecological preservation but also provide spatial reference for integrating biodiversity conservation with sustainable agricultural development in key transitional zones. Nature reserves contribute to the long-term ecological services that underpin sustainable agricultural systems.

5. Conclusions

In contrast to traditional theoretical approaches, this study leverages spatially explicit models to quantify and validate protected area placement, offering empirical insights that extend earlier conceptual debates from island biogeography and landscape ecology into actionable spatial governance frameworks. Over the past few decades, China’s ecological policies have evolved, reflecting a shift towards more integrated approaches in balancing biodiversity conservation with economic development. From the initial focus on biodiversity hotspots in the 1990s to the more recent emphasis on ecological civilization and national park systems, these policy periods have each contributed to the current landscape of nature reserves. These policy transitions have profound implications for contemporary debates on sustainability, as they underscore the need for adaptive management frameworks that can respond to the challenges of both ecological preservation and agricultural development. This study builds on those classical frameworks by employing advanced spatial analysis techniques (e.g., kernel density, standard deviation ellipse, and geodetector models) to empirically test how real-world reserve distributions align with ecological expectations and policy goals in a modern, rapidly developing context like China.

(1)

Research Findings

China’s nature reserves exhibit a pronounced east-west spatial imbalance, characterized by dense clustering in eastern regions and sparsity in the west, driven by the interplay of natural resource endowment and pressures. This uneven distribution impacts agricultural conditions by creating areas of high ecological value that are often located in regions of high human activity, leading to land-use conflicts and unsustainable farming practices. Conversely, the sparsity of reserves in the western regions may hinder effective ecosystem services, such as water regulation and soil conservation, essential for sustainable agricultural development. This distribution correlates negatively with population density, reflecting the encroachment of human activities on ecological spaces. Geographically, fragmented habitats in high-intensity human zones are interconnected through riverine corridors, while mid-to-high-altitude mountainous regions leverage topographic barriers to preserve contiguous core habitats. River systems enhance coastal clustering in the east and strategic water-source nodes in the west, while transportation accessibility introduces spatial heterogeneity. To reconcile ecological integrity with sustainable development, we propose a multidimensional optimization strategy, emphasizing “pattern optimization, efficiency enhancement, collaborative governance, and technological innovation” as key elements for addressing these regional disparities.

(2)

Future Research Direction

This study, through the analysis of geospatial driving factors, integrates multiple geospatial driving factors of China’s nature reserves. However, this study also has certain limitations. Firstly, the accuracy of spatial analysis results is limited by the resolution and update frequency of available GIS and remote sensing data. Secondly, although this study integrates multiple geospatial drivers, further research on dynamic ecological processes and climate change scenarios is still needed to enhance the understanding of the sustainability of nature reserves under constantly changing environmental conditions. The study, through comprehensive spatial analysis, systematically examined the geographical spatial driving factors of the spatial distribution of nature reserves in China and clarified the geographical logic of the spatial distribution of nature reserves in China.