Multi-scenario Simulation Analysis of the Impact of Land Use Change on Habitat Quality in Zhongwei Based on the PLUS Model Coupled with the InVEST Model

Multi-scenario Simulation Analysis of the Impact of Land Use Change on Habitat Quality in Zhongwei Based on the PLUS Model Coupled with the InVEST Model | 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 Article Multi-scenario Simulation Analysis of the Impact of Land Use Change on Habitat Quality in Zhongwei Based on the PLUS Model Coupled with the InVEST Model Xiao Wang, Bing Liu, Jingzhong Chen, Malekian Arash, Bo Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5002484/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Land use change is a key factor affecting habitat quality. In order to reveal the impacts of urban land use changes on habitat quality, this paper uses the city of Zhongwei, China, as a case study. Based on the land use data from 1980, 2000 and 2020, the PLUS-InVEST coupled model was used to predict and assess the land use and habitat quality of Zhongwei. The results showed that from 1980 to 2020, the areas of construction land and woodland increased significantly, while the areas of water and unused land decreased slightly, and the other lands remained essentially unchanged. The main factors such as precipitation, temperature, population and distance from government distance influenced the land expansion. Moreover, the habitat quality in Zhongwei showed a decreasing trend. The overall area of low habitat quality increased, while the overall area of relatively low and medium habitat quality decreased, and the other remained essentially unchanged between 2000 and 2020. The predicted habitat quality of the study area in 2040 was compared under different development scenarios. The comparison of results showed that highest habitat quality and the lowest habitat degradation under the Ecological protection scenario. Although the afforestation and desertification control projects in Zhongwei have proved successful in increasing woodland and improving habitat quality, its ecological restoration measures have not yet completely counteracted the adverse effects of ongoing urbanization and industrialization on habitat quality, resulting in a persistent decline in overall habitat quality. Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Land use change PLUS model InVEST model habitat quality multi-scenario simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Land, a core natural resource, profoundly impacts human economic activities and the health and stability of ecosystems 1 . Habitat quality is crucial, indicating the survival conditions and suitability for biological species in biomes 2 . Changes in land use and patterns driven by population growth and urbanization significantly affect the structure and function of natural environments and biological habitats 3 , 4 , 5 , 6 . These highlight the deep connection between socio-economic activities and the natural environment, which plays a pivotal role in influencing habitat quality 7 , 8 , 9 . In northwestern China, Zhongwei is essential for maintaining biodiversity, conserving soil and water, purifying the air, and controlling wind and sand. Located in a transition zone between arid and semi-arid climates, with complex geographic conditions and a fragile ecological environment, Zhongwei faces multiple ecological challenges, including vegetation degradation, land desertification, biodiversity decline, and soil erosion. Therefore, analyzing the characteristics of land use changes and the drivers of land expansion in Zhongwei is critically important for protecting ecosystems and managing ecological risks. This analysis helps predict the trends and causes affecting habitat quality 10 , 11 , 12 . Land use prediction refers to the analysis of historical data and the prediction of the future land use trends, which is vital for stipulating scientific land management policies 13 , 14 . With the technological development in GIS, remote sensing, machine learning, and artificial intelligence, great breakthroughs have been made in the theory, method and application of land use prediction 10 , 11 , 12 , 13 , 14 . In current research, the most commonly used models for land use prediction are the coupled models such as Markov-FLUS and CA-Markov 3 . These excel in processing large-scale spatial data, simulating time series changes in land use, integrating multiple influencing factors, and providing dynamic simulations 3 , 4 , 12 . However, these models still exhibit significant limitations in their strategy for mining transformation rules and in the simulation of landscape dynamics 3 , 4 . The PLUS model, an emerging tool in land use change simulation, integrates deep learning and multi-scale spatial analysis to enhance adaptability and accuracy in managing complex landscape dynamics, while preserving detailed spatial data. This model addresses the deficiencies of the traditional coupled models in transformational rule mining strategies and the simulation of landscape dynamics, thus providing a scientific basis for land resource management and planning 3 , 4 , 10 , 12 , 15 . Habitat quality assessment involves the systematic analysis and evaluation of the conditions of biological habitats within a specific area, assessing factors such as ecosystem structure and function, species diversity, and overall habitat health. Typically, this assessment is conducted by constructing ecological indicators for habitats using various ecological models, supported by field surveys and remote sensing techniques 16 , 17 . Currently, Recently, the InVEST model, which accounts for threatening factors, has been widely used in habitat quality assessment due to its advantages in spatial visualization and accuracy 18 , 19 , 20 . However, studies that combine the PLUS model and the InVEST model to predict land use and habitat quality in the arid zone of northwestern China have not yet been reported. Zhongwei, an important ecological security barrier in China, has a fragile ecological environment and serious land sandification problems 21 . The unique geographic and climatic conditions of the region make it an ideal subject for studying land use changes and their impacts on the ecosystem. Additionally, the distinctive natural conditions provide a representative and valuable basis for research in land use, ecological protection, and agricultural development. In recent years, as the economy and society in this region have rapidly developed, particularly in the fields of tourism and agriculture, land use has undergone significant changes. These changes have placed considerable pressure on land resources 22 . Consequently, a critical challenge now facing this region is how to utilize land resources scientifically while protecting and enhancing habitat quality. Simulating and predicting land use changes and their ecological impacts under different development scenarios is crucial for assessing and optimizing conservation measures. This promotes ecological security and sustainable development in the region. Therefore, we utilized land use data from 1980, 2000, and 2020 to couple the PLUS and InVEST models. This allowed us to predict and analyze the spatial and temporal evolution, characteristics, and expansion drivers of land use patterns and habitat quality in Zhongwei. We considered three scenarios: natural development, cultivated land protection, and ecological protection. This study aims to provide a reference and share experiences relevant to sustainable land management and development in Zhongwei and the broader Northwest China. 2. Materials and Methods 2.1 Study Area The study area is Zhongwei City, Ningxia Hui Autonomous Region, in the northwestern China (104°17'-106°06'E, 36°06'-37°50'N, Fig. 1 ). Situated at the intersection of Loess Plateau and Inner Mongolia Steppe, the area covers 13,648.12 km 2 . It experiences an arid and semi-arid continental climate, characterized by cold, long winters and hot, windy summers. The mean annual temperature is approximately 10°C, with the lowest average temperature in January reaching − 10°C and the highest in July hitting 33°C. Annual precipitation is sparse, ranging from 150 to 250 mm, predominantly concentrated between June and September. Sunshine conditions are favorable, with the mean annual sunshine duration exceeding 2,800 hours, and evaporation rates varying from 180 to 367 mm. The frost-free period extends roughly 160 days, with the growing season lasting from April to the end of September. Vegetation and soil types exhibit distinct transitional characteristics. Moving from north to south, the zonal soils transition from wind-formed sandy soils to dark loess and mildly saline soils. The main vegetation types include shrub communities, dry grasslands, and meadows, dominated by poplar, sea buckthorn, and various drought-tolerant herbs. 2.2 Data Source and Processing Building on the foundation of previous studies and considering the characteristics of the study area, along with the availability and timeliness of data, 12 driving factors influencing land use change were selected 3 , 4 . Table 1. Table of driving factors. Data Type Secondary Data Type Data Source Land use data Data for 1980, 2000 and 2020 RAESADC,(30 \(\:m\times\:30m\) ) Socio-economic data GDP RAESADC(2020) Population Population density,RAESADC Climate data Precipitation Annual average rainfall data ,RAESADC Temperature Annual average temperature data,RAESADC Soil data Soil texture RAESADC Terrain dat DEM GDC, calculated using ArcGIS10.8 Slope GDC, calculated using ArcGIS10.8 Road accessibility data Distance from water NCSFGI, calculated using ArcGIS10.8 Distance from highway NCSFGI, calculated using ArcGIS10.8 Distance from railroad NCSFGI, calculated using ArcGIS10.8 Distance from Government NCSFGI, calculated using ArcGIS10.8 RAESADC represent Resource and Environment Science and Data Center( http://www.resdc.cn ). GDC represent Geospatial Data Cloud( http://www.gscloud.cn/ ). NCSFGI represent National Catalogue Service For Geographic Information( https://www.webmap.cn/main.do?method=index ) 2.3 Methodology 2.3.1 Land Use Dynamic Degree The land use dynamic degree reflects the change rate of land resources and use and quantifies the transformation process of land use types in the studied region. It is calculated by the formula 23 : $$\:{K}_{i}=\frac{{U}_{b}-{U}_{a}}{T\times\:{U}_{a}}$$ 1 Where \(\:{K}_{i}\) is the dynamic degree of land use type \(\:i\) in Zhongwei, Ningxia; \(\:{U}_{a}\) is the area of a specific land type at the beginning of the study; \(\:{U}_{b}\) is the area of a specific land type at the end of the study; \(\:T\) is the duration of the study, in years. 2.3.2 Land Use Dynamic Degree Land use transfer matrix is used to describe the conversion between different land use types, which is a key tool to study the impact of human activities on land use changes. The formula is as follows 24 : $$\:{A}_{ij=}\left[\begin{array}{cc}\begin{array}{cc}{A}_{11}&\:{A}_{12}\\\:{A}_{21}&\:{A}_{22}\end{array}&\:\begin{array}{cc}\dots\:&\:{A}_{1n}\\\:\dots\:&\:{A}_{2n}\end{array}\\\:\begin{array}{cc}⋮&\:⋮\\\:{A}_{n1}&\:{A}_{n2}\end{array}&\:\begin{array}{cc}\dots\:&\:⋮\\\:\dots\:&\:{A}_{nm}\end{array}\end{array}\right]$$ 2 Where \(\:{A}_{ij}\) refers to the area of the land transferred from land use type \(\:i\) to j ; \(\:i\) and \(\:j(i,j=\text{1,2},3\dots\:,\:\:n)\) refer to the land use types before and after the transfer; \(\:n\) is the number of land use types before and after the transfer. 2.3.3 PLUS Model The Markov model is a widely used model in land transfer study to predict the probability of a system changing from one state to another 25 . It is assumed that future states depend only on the current state, not on the events that occurred before it. It is formulated as: $$\:{S}_{(t+1)}={P}_{ij}\times\:{S}_{t}$$ 3 Where \(\:{S}_{t}\) and \(\:{S}_{(t+1)}\) represent the land use states at moment \(\:t\) and moment \(\:t+1\) , respectively; \(\:{P}_{ij}\) is the land use transfer matrix. In this study, we used the stochastic fitting of land types and their drivers based on the Markovian model prediction of the PLUS model. The PLUS model introduces the LEAS module, which combines the strengths of the Target Area Strategy (TAS) and Probability Adjustment Strategy (PAS). The LEAS converts the land use type dynamics into a simple issue of binary classification. Through ensemble learning methods, the classification results of the training samples are combined from a multitude of decision trees, to select random sub-samples from the original dataset. Finally, the model predicts the growth probability of land use types 26 . The formula is as follows: $$\:{P}_{i,k}^{d}\left(x\right)=\frac{{\sum\:}_{n=1}^{M}I\left({h}_{n}\left(x\right)=d\right)}{M}$$ 4 Where \(\:{P}_{i,k}^{d}\left(x\right)\) represents the growth probability of land use type 𝑘 in cell \(\:i\) ; \(\:d\) goes 0 or 1: when it goes 1, it indicates that other land use types are transformed into land use type \(\:k\) ; and when it goes 0, it indicates that there are other transformations. \(\:x\) represents a vector composed of driving factors; \(\:I\left(x\right)\) represents an indicator function for decision number sets; \(\:{h}_{n}\left(x\right)\) represents the \(\:n\) th predicted type of the decision tree of the vector \(\:x\) ; and \(\:M\) represents the sum of the decision trees. The PLUS model calculates the total probability based on a multi-type random patch seeding mechanism with descending thresholds, in order to simulate the patch evolution of each land use type 27 , 28 . The mechanism adopts Monte Carlo method to calculate the “seeds” of land use types that change in neighborhood effect, which is formulated as follows: $$\:If{\sum\:}_{k}^{N}\left|{G}_{c}^{t-1}\right|-{\sum\:}_{k=1}^{N}\left|{G}_{c}^{t}\right|\tau\:\:and\:T{M}_{k,c}=1\\\:no\:change\:P{d}_{i,c}^{d=1}\le\:\tau\:\:or\:T{M}_{k,c}=0\end{array}\:\:\:\tau\:={\delta\:}^{l}\times\:rl\right.$$ 6 Where 𝑆𝑡𝑒𝑝 is the step size of the land use demand fitted by the PLUS model; \(\:T{M}_{k,c}\) is the transfer matrix, which determines whether the land class 𝑘 can be transformed into the land use type 𝑐; 𝛿 is the decay coefficient of the descending threshold \(\:\tau\:\) , \(\:\delta\:ϵ[0,\:1]\) , whose exact value is set by experts; 𝑙 refers to the number of decay steps; 𝑟𝑙 is a normally distributed random value with the mean of 1, \(\:rl\in\:[0,\:\:1]\) . This descending threshold can maximize the chance of changes in grid cells with higher overall probability. The CA model based on multi-type random patch seeds shows spatiotemporal consistency and allows new land use patches to develop freely, subject to development probabilities. After the simulation, we used kappa coefficient for accuracy test, which is formulated as follows: 3 $$\:Kappa=\frac{{P}_{0}-{P}_{c}}{{P}_{a}-{P}_{c}}$$ 7 Where \(\:{P}_{0}\) is the number of simulated correct grids/total grids; \(\:{P}_{c}\) is the number of randomly simulated correct grids/total grids; \(\:{P}_{a}\) is the number of randomly simulated correct grids/total grids. The kappa coefficients between 0.6 and 0.8 represent good modeling results. The land use data of Zhongwei in 1980 and 2000 were used to predict those in 2020, and the predicted result in 2020 was then compared with the actual data in the same year according to the Validation module of the PLUS model. From the process, we obtained the kappa coefficient of 80.32%, indicating that the predicted land use results of 2040 are credible. 2.3.4 Multiple Scenarios Setup for PLUS Model Simulation of Future Land Use (1) Natural Development Scenario (NDS). The scenario sets a continuation of the land use change trend from 2000 to 2020, without the conversion probability between various land types or the development policy requirements involved. With an interval of 20 years, we used the Markov-Chain in the PLUS model to predict the land use demand in the natural development scenario in 2040, in which the parameters such as land expansion capacity, land use transfer matrix, domain factor weights, and habitat quality index were kept consistent with those in the 2000–2020 models. (2) Cultivated Land Protection Scenario (CLPS). The security of cultivated land is the foundation for food security. Cultivated land can be protected by controlling the conversion probability of agricultural lands to other land types and the expansion of construction lands. The conversion probability of cultivated land to other land types is controlled in the 60% reduction, to ensure the cultivated land protection requirements are strictly satisfied. (3) Ecological Protection Scenario (EPS). Taking into account food security and resource environmental bearing capacity to protect ecosystems and biodiversity, the Chinese government sets up in the EPS the 50% reduction of the probability of woodlands, grasslands and waters converted to unused lands and the 50% increase of the probability of unused lands converted to woodlands, grasslands and waters. The waters in this area are constrained from arbitrary conversion. 2.3.5 Habitat Quality Evaluation Based on the InVEST Model The Habitat Quality Module of the InVEST model was used to evaluate habitat quality through the Habitat Quality Index (HQI) and the Habitat Degradation Index (HDI). The Habitat Degradation Index (HDI) is commonly used to describe the degree of habitat degradation in a region, with higher values indicating higher threat levels. Its value ranges from 0 to 1 3,4 . The formula is as follows: $$\:{D}_{xj}=\sum\:_{r=1y=1}^{R}\sum\:_{y=1}^{{r}_{y}}\left(\frac{{w}_{r}}{{\sum\:}_{r=1}^{R}{w}_{r}}\right){r}_{y}{i}_{rxy}{\beta\:}_{x}{S}_{ir}$$ 8 Where \(\:{D}_{xj}\) is the degradation degree of habitat; \(\:R\) is the number of threat factors; \(\:r\) is the number of grids in the threat layer on the base map; \(\:w\) is the weight of threat factor; \(\:{r}_{y}\) is the threat intensity; \(\:{i}_{rxy}\) is the (linear or exponential) effect of \(\:r\) on each grid of the habitat; \(\:{\beta\:}_{x}\) is the effect of local conservation policies; \(\:{S}_{ir}\) indicates the relative sensitivities of the habitats to different threat sources. The Habitat Quality Index (HQI) measures the suitability of habitat conditions in a region for one or more species, which ranges from 0 to 1, with the higher value indicating the higher habitat quality 3 , 4 . The formula is as follows: $$\:{Q}_{xj}={H}_{j}(1-\frac{{D}_{xj}^{z}}{{D}_{xj}^{z}z+{k}^{z}})$$ 9 Where \(\:{Q}_{xj}\) is the habitat quality index of raster \(\:x\) in land use type \(\:j\) ; \(\:{H}_{j}\) is the habitat suitability of land type \(\:j\) ; \(\:{D}_{xjz}\) is the habitat degradation of raster \(\:x\) in land type \(\:j\) ; \(\:z\) is a default parameter; and \(\:k\) is a half-saturation constant, which is 0.2 in this paper. With reference to previous research 29 , 30 , 31 , this paper took the farmland, construction land, and unused land as threat factors and set the impact distance, weighting and sensitivity for different threat factors (Table 2 and Table 3 ) Table 2 Threat feed parameters. Threat Factor Maximum Impact Distance/km Weight Distance Decay Function Construction land 10 1 exponential Farmland 5 0.4 linear Unused 7 0.5 linear Table 3 Sensitivity of threat factors Landscape Types Habitat Suitability Construction Land Unused Farmland Farmland 0.3 0.6 0.3 0.4 Woodland 1 0.5 0.8 0.5 Grassland 0.6 0.4 0.4 0.8 water 0.7 0.3 0.3 0.2 Construction land 0 0 0.2 0 Unused land 0 0.6 0 0 3. Results 3.1 Land Use analysis 3.1.1 Land Use Change in Zhongwei from 1980 to 2020 Farmland and grassland are the dominant land use types in Zhongwei, comprising over 80% of the total area (Fig. 2 and Table 4 ). From 1980 to 2020, the total area of farmland in Zhongwei increased by 336 km². However, there was a decrease of 304 km² in farmland area from 2000 to 2020. The area of woodland experienced a continuous increase from 1980 to 2020. However, the dynamics of land use from 1980 to 2000 were less intense compared to those from 2000 to 2020. Additionally, the recovery rate of woodland has slowed since 2000. The area of grassland and water bodies decreased overall from 1980 to 2020, although there was an increase observed from 2000 to 2020. The area of land allocated for construction has continued to grow. The dynamic changes in land use between 2000 and 2020 were significantly greater than those observed from 1980 to 2020, indicating a sharp increase in construction land since 2000. Additionally, the area of unutilized land exhibited an upward trend from 1980 to 2000 and a downward trend from 2000 to 2020. The degree of land utilization has shown an upward trend since 2000(Table 4 ). Table 4 Land use area changes and dynamics in Zhongwei from 1980 to 2020. Land use types 1980(km 2 ) 1980–2020 Land use dynamics(%) 2000(km 2 ) 2000–2020 Land use dynamics(%) 2020(km 2 ) 1980–2020 Land use dynamics(%) Total change area(km 2 ) Farmland 5807.30 0.55 6448.20 -0.23 6143.31 0.14 336.00 Woodland 358.24 1.14 440.45 0.51 485.60 0.88 127.35 Grassland 11873.87 -0.29 11171.83 0.015 11207.51 -0.14 -666.36 water 432.39 -1.10 337.23 0.078 342.53 -0.51 -89.85 Construction land 217.31 0.35 232.80 6.38 530.09 3.59 312.77 Unused land 2657.77 0.11 2716.38 -0.14 2636.28 -0.020 -21.49 In terms of land transfer(Table 4 and Fig. 3 ), construction land expanded gradually from 1980 to 2020 into the eastern part of Shapotou District and the central part of Zhongning County, with a large area of farmland and grassland being converted into construction lands. During the study period, the increased area of woodland mostly came from grassland, mainly located in Shapotou District. The area of farmland decreased by 304.89km² from 2000 to 2020, primarily being converted into grassland. Between 1980 and 2000, the water area significantly decreased due to conversion to farmland. However, since 2000, there has been a resurgence in water area. Between 2000 and 2020, the area of unused land decreased by 80.1km², primarily through conversion to farmland and grassland, resulting in an optimized land use structure. 3.1.2 Land Use Expansion Factors The land use data of Zhongwei with 12 drivers were imported into the PLUS model for spatial overlay, and the expansion factors were analyzed using the LEAS module of the PLUS model to obtain the contribution values of the land type expansion drivers (Fig. 4 ). The top three were population, precipitation and government from distance in the case of farmland; precipitation, government from distance and temperature in the case of woodland; DEM, railway from distance and government from distance in the case of grassland; temperature, precipitation and water from distance in the case of water; government from distance, water from distance and highway from distance in the case of construction land; and railway from distance, population and precipitation in the case of unused land. In general, the factors affecting the land use types in Zhongwei were mainly precipitation, temperature, population and government from distance. Slope and aspect contributed moderately to land expansion, showing an insignificant role. 3.2 Habitat Quality change in Zhongwei from 1980 to 2020 The habitat degradation index (HDI) in the study area was calculated according to the habitat quality module of the InVEST model(Fig. 5 ). The results are classified into five classes: low (0, 0.089), relatively low (0.089, 0.18), medium (0.18, 0.26), relatively high (0.26, 0.35), and high (0.35, 1) (Fig. 5 ). From 1980 to 2020, the mean values of the HDI consistently increased and were 0.210, 0.214, and 0.235, respectively, with a continuous increase in the areas of the high and relatively high habitat degradations and a continuous decrease in the areas of the low and relatively low habitat degradations. The areas of the high degradation lied in the eastern part of Shapotou District, the central part of Zhongning County and the southern part of Zhongwei, where the grassland and farmland were under greater threats. In addition, the HDI of grassland in the southern part of Zhongwei rose continuously. The Habitat Quality Index (HQI) in the study area was calculated according to the habitat quality module of the InVEST model. The results are classified into five classes: low (0, 0.29), relatively low (0.29, 0.49), medium (0.49, 0.60), relatively high (0.60, 0.78), and high (0.78, 1) (Fig. 6 ). The mean values of the HQI for the years 1980, 2000 and 2020 ranked as follows: 1980(0.38) > 2000(0.35) > 2020(0.33), suggesting that the HQI decreased year to year. From 1980 to 2020, the areas of the low, relatively low and high HQI rose, while the areas of medium and relatively high HQI decreased, indicating that the land use structure in Zhongwei became more compact and the land conservation was improved. 3.3 Land Use Projections to 2040 under Different Scenarios Based on the land use data in 2000 and 2020, the coupled Markov-Plus model was used to predict the distribution of land use types in Zhongwei in 2040 in the following three scenarios(Fig. 7 and Table 4 ). In the natural development scenario(NDS), the overall trend of land use remained unchanged, with the area of farmland decreasing to 5909.13 km 2 , the area of woodlands increasing to 485.60 km 2 , the area of construction land increasing to 530.09 km 2 , the areas of grasslands and waters basically remaining unchanged, and the area of unused lands decreasing. In the cultivated land protection scenario(CLPS), the total area increased by 277.20 km 2 compared to 2000, and the areas of woodland, grassland, construction land and unused lands decreased, compared to those in the NDS. In the ecological protection scenario(EPS), the areas of woodland, grassland and water were higher than those in the NDS; the area of farmlands was less than that in the CLPS but higher than that in the NDS; and the area of construction lands is less than that in the NDS but higher than that in the CLPS. 3.4 Habitat Quality Projections for Zhongwei in 2040 under Different Scenarios The mean values of the HDI in the three different scenarios predicted for 2040 ranked as follows: Cultivated land protection scenario (0.289) > Natural development scenario (0.266) > Ecological Protection scenario (0.264). All showed a growing degradation compared to those in 2020. The proportions of different degradations in NDS and EPS were similar, and the areas of the relatively high and high degradations in CLPS were more than those in NDS and EPS, while the areas of the relatively lower and low degradations were less than those in NDS and EPS(Fig. 8 ). The mean values of the HQI in the three scenarios ranked as follows: ecological protection scenario (0.35) > cultivated land protection scenario(0.34) > natural development scenario (0.33), all three figures increased compared with those in 2020, suggesting a potential uptrend in the ecological quality of Zhongwei. The proportions of the quality levels in the NDS and EPS were similar, and the areas of the low and relatively low quality in the CLPS were significantly more than those in the NDS and EPS, while the areas of the medium quality in the CLPS were significantly less than those in the NDS and EPS(Fig. 9 ). To determine the spatial variation of habitat quality, we mapped the differences between the habitat quality in 2020 and that in the three scenarios. The results were categorized into drastic decline [0.5, 1), decline[0.1, 0.5), basically unchanged [-0.1, 0.1), rise [-0.5, -0.1), and drastic rise [-1, -0.5) (Fig. 10 and Table 5 ). In the natural development scenario, the areas of the decreased habitat quality lied in the north and south of Zhongwei, while the areas of the increased habitat quality lied in the central part of Shapotou District and the eastern part of Haiyuan County. In the cultivated land protection scenario, the overall habitat quality in the study area showed a large decline, especially in the eastern part of Shapotou District and the northern part of Zhongning County. In the ecological protection scenario, the areas of the decreased habitat quality were the least, and the areas of the increased habitat quality was concentrated in the central part of Shapotou District. Table 5 Area percentage (%) of habitat quality levels in different scenarios. Different scenarios Drastic rise Rise Basically unchanged Decline Drastic decline NDS 0.17 0.33 97.04 2.37 0.078 CLPS 0.063 2.52 89.42 7.70 0.28 EPS 0.47 1.14 96.92 1.28 0.18 4. Discussion 4.1 Causes of Land Use Change Land use change is not only a key driver of changes in ecological service value, but also a significant source of ecological risks 30 , 31 , 32 . The changes in land use types in Zhongwei were influenced by many factors 4 , 33 . Overall, precipitation, temperature, population, and distance from the government are the main factors influencing land use changes in Zhongwei(Fig. 4 ). Zhongwei is located in arid and semi-arid zones, where agricultural production, particularly the expansion of arable and forest land, heavily depends on precipitation levels. Precipitation directly determines the abundance of water resources, significantly influencing the types of crops grown and their yields 34 , 35 . Additionally, temperature not only affects the growth cycles and planting choices of plants but also influences the evaporation and replenishment of water bodies, thereby indirectly affecting the distribution of land use types 36 , 37 . Furthermore, population and government proximity also play crucial roles in land use changes. In recent years, population growth in Zhongwei has increased the demand for residential and commercial land, further driving the expansion of construction land. Government proximity, as an indicator of the convenience of government services and infrastructure development, plays a key role in promoting land development and regional planning. Especially in the development of construction lands and unused lands, government policies and infrastructure developments are often decisive factors 38 , 39 . In terms of the impact of transportation infrastructure, railways and roads have a significant influence on the expansion of grassland, construction land, and unused land. The improvement of transportation facilities not only facilitates the flow of people and logistics but also promotes the development and utilization of suburban areas, enhancing their economic value and attractiveness[40]. However, the effects of slope and aspect in the land expansion of Zhongwei are relatively minor. The terrain in Zhongwei is relatively flat, and most areas are not constrained by complex topography, thus limiting the impact of slope and aspect. 4.2 Relationship between Habitat Quality Change and Land Use Change The distribution of land use types is highly correlated with the spatial pattern of habitat quality values 3 , 4 . The changes in land use and habitat quality in Zhongwei reflect the complex relationship between its ecological protection and economic development. From 1980 to 2020, the land use changes in the region had a remarkable impact on habitat quality. The continuous rise of the HDI and the yearly decline of the HQI indicated that the region witnessed an slight overall decline in habitat quality. However, improvements in ecological quality have been observed in localized areas, such as the Shapotou District(Fig. 5 and Fig. 6 ). The simulation results indicate a trend of improvement in the habitat quality of Zhongwei(Fig. 8 and Fig. 9 ).The area of regions with low habitat quality values has continued to increase, a growth that aligns with the expansion of urban areas and construction land. Therefore, the expansion of construction land is the primary cause of the decline in habitat quality, reflecting the rapid urbanization and industrialization of the study area 3 , 41 . This was attributed to the reduction of farmland and specially the rapid expansion of construction lands, which reflected the rapid urbanization and industrialization in the city. In addition, the farmlands decreased significantly between 2000 and 2020, as the result of the policies of returning farmland to forest and grassland 42 . The policies, though committed to restoring ecosystem services such as soil and water conservation and carbon fixation, may have short-term impacts on food supply. Thus, it is recommended to ensure food security by improving agricultural productivity or developing new farmland. Zhongwei, renowned for its efforts in sand prevention and desertification control, has conducted many relevant projects in Shapotou District, eastern Zhongning County and along the Yellow River. The measures included planting trees and grasses, constructing windbreak and sand-fixing forest systems, and installing sand-proof barriers, which have effectively controlled the desertification speed. The success of these projects can be embodied by the increase of woodland and the improvement of habitat quality in these regions. However, the overall declining trend in habitat quality reflects the fact that ecological restoration measures have not yet completely offset other negative impacts. 4.3 Relationship Between Habitat Quality and Land Use Change in Multi-Scenario Modeling The habitat quality and land use structure were projected to change to varying degrees in different scenarios 3 , 4 . In the natural development scenario(NDS), further expansion of construction land led to a decline in habitat quality in the north and south of Zhongwei, as the urbanization and industrialization continued to advance. The habitat quality showed an increasing trend in the central part of Shapotou District and the eastern part of Haiyuan County, where the government should keep strengthening ecological protection and restoration. In the cultivated land protection scenario (CLPS), the area of farmland increased while the areas of woodland, grassland, construction land and unused land decreased, compared to those in the NDS. Such land use restructuring resulted in an overall decline in habitat quality, especially in the eastern part of Shapotou District and the northern part of Zhongning County. This might be attributed to the overemphasis on farmland protection at the expense of other land types, leading to an imbalance in ecosystem. In the ecological protection scenario (EPS), the areas of woodland, grassland and water increased the most, with the areas of decreased habitat quality the least, while the area of construction land was still more than that in the CLPS. This shows that the EPS can balance industrial expansion, farmland and ecological protection, and control the growth of industrial and urban land, ensuring the ecological quality being higher than that in other scenarios. Overall, the EPS is more suitable for Zhongwei to achieve sustainable development 4 , 41 . In the CLPS, the trend of farmland reduction was effectively slowed down, and it is recommended that the farmland protection policy be continued and promoted. In the EPS, the increase in the areas of woodland, grassland and water had a significant effect on the improvement of habitat quality. The protection efforts in these ecologically sensitive areas should be promoted and more ecological restoration projects should be implemented, such as afforestation and wetland restoration. With the climatic and environmental factors having an impact on land use change, the adaptive management measures are recommended, such as adjusting planting structure, to enhance the resilience of agricultural systems to climate change. Strengthening ecological construction and protection can help the area ensure economic development while seeking ecological improvement. 4.4 Suggestions and Outlook The biggest problem facing Zhongwei is environmental deterioration caused by the large-scale conversion of farmland into construction lands, which requires more efforts in farmland protection. In the cultivated land protection scenario, the decreasing trend of farmland was effectively mitigated, and it is recommended that farmland protection policies continue to be promoted and improved. In the ecological protection scenario, the expansion of woodland, grassland and water significantly improved habitat quality, and protection efforts are recommended to enhance in ecologically sensitive areas and more ecological restoration projects are suggested. With the climatic and environmental factors having an impact on land use change, the adaptive management measures are recommended, such as adjusting planting structure, to enhance the resilience of agricultural systems to climate change. 5. Conclusions From 1980 to 2020, the areas of construction land and woodland increased significantly, while the areas of water and unused land decreased slightly, and the other lands remained essentially unchanged. In the natural development scenario, construction land continued to expand, farmland decreased, and woodland increased; in the cultivated land protection scenario, the decreasing trend of farmland was effectively slowed down; in the ecological protection scenario, priority was given to the protection and expansion of woodland, grassland, and water. In general, Precipitation, temperature, population, and distance from government significantly impacted land use change in Zhongwei. The habitat degradation index continued to rise in Zhongwei, with grassland and farmland under growing threats. Meanwhile, the habitat quality index showed a general downward trend, with the habitat suitability of the low-quality and relatively low-quality areas decreased. In the ecological protection scenario, the enhanced ecological protection measures effectively improved habitat quality. In the cultivated land protection scenario, however, farmland protection was improved but some regions witnessed a decrease in habitat quality. The natural development scenario exhibited the natural evolution of habitat quality without additional interventions. Overall, the ecological protection scenario is more suitable for the sustainable development of Zhongwei. Declarations Conflicts of Interest: The authors declare no conflicts of interest. Funding: This study was supported by the Ecological monitoring project of desertification control in Shapotou, Ningxia (Ningsha Guanhezi [2021]08), the Major Science and Technology Projects of Gansu Province-International Cooperation Projects (22ZD6WA036) and National Natural Science Foundation of China (No. 42071048). Author Contribution Author Contributions: Conceptualization, X.W., J.C., B.L. and M.A.; methodology, X.W., J.C., B.L. and M.A.; software, X.W.; validation, X.W., J.C. and B.L.; formal analysis, X.W.; Investigation, X.W., B.Z., Q.C., J.L. and W.Y.; resources, X.W.; data curation, X.W.; writing—original draft preparation, X.W., J.C. and B.L.; writing—review and editing, X.W., J.C. and B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. 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Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers agreed at journal 09 Oct, 2024 Reviewers agreed at journal 21 Sep, 2024 Reviewers invited by journal 19 Sep, 2024 Editor assigned by journal 19 Sep, 2024 Editor invited by journal 09 Sep, 2024 Submission checks completed at journal 07 Sep, 2024 First submitted to journal 30 Aug, 2024 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. 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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-5002484","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":364609611,"identity":"5fd47e8e-75bf-497c-8060-21aa25a0d2e3","order_by":0,"name":"Xiao Wang","email":"","orcid":"","institution":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Wang","suffix":""},{"id":364609612,"identity":"3503d981-70f4-4009-ba7e-ac75d6d60f9a","order_by":1,"name":"Bing Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACPmYGNoYEBgY5PgYeIrWwQbUYsxGvBYwYGBLbiNfCzmP24OGO2vQ2/rMHGH7UMMibE3YYj7lB4pnjuW0M5xIYe44xGO5sIKzFTCKx7VhuG2OPAQNvA0OCwQEitaQDGQaMf0nQUpPAxsZjwEykLWzlBoltBwzbePgSDssckzDcQEgLP//hbQ9/ttXJ8/OfPfjwTY2NPEFboOAwmAQqliBOPRDUEa1yFIyCUTAKRiAAANcdM5dEKRi8AAAAAElFTkSuQmCC","orcid":"","institution":"Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Liu","suffix":""},{"id":364609614,"identity":"ce9ab5c9-bd90-460e-b346-86d2338f6045","order_by":2,"name":"Jingzhong Chen","email":"","orcid":"","institution":"Gansu Forestry Technological College","correspondingAuthor":false,"prefix":"","firstName":"Jingzhong","middleName":"","lastName":"Chen","suffix":""},{"id":364609615,"identity":"15dbd826-6658-472a-b06b-0e743c1d60ee","order_by":3,"name":"Malekian Arash","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Malekian","middleName":"","lastName":"Arash","suffix":""},{"id":364609616,"identity":"0fc9c627-06b8-4f96-8eb0-88465307af64","order_by":4,"name":"Bo Zhang","email":"","orcid":"","institution":"Zhongwei Shapotou National Nature Reserve Administration","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":364609617,"identity":"0b44a20d-454f-4653-8d58-70e0489fd5d0","order_by":5,"name":"Qing Chang","email":"","orcid":"","institution":"Zhongwei Shapotou National Nature Reserve Administration","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Chang","suffix":""},{"id":364609618,"identity":"8f51e52c-863c-4e43-8a2e-8856692c8321","order_by":6,"name":"Jing Liu","email":"","orcid":"","institution":"Zhongwei Shapotou National Nature Reserve Administration","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""},{"id":364609619,"identity":"6b11b76f-9d64-411a-9b9d-97c7568f4919","order_by":7,"name":"Wanxue You","email":"","orcid":"","institution":"Zhongwei Shapotou National Nature Reserve Administration","correspondingAuthor":false,"prefix":"","firstName":"Wanxue","middleName":"","lastName":"You","suffix":""}],"badges":[],"createdAt":"2024-08-30 08:50:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5002484/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5002484/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-90965-6","type":"published","date":"2025-04-10T16:05:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67185936,"identity":"7e6f6a26-e18a-4d06-813f-6b930e13ca04","added_by":"auto","created_at":"2024-10-22 07:13:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":174661,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5002484/v1/a1a224a4b6933f014de85cee.png"},{"id":67184536,"identity":"b27c58f2-27a0-49d5-be33-c9c9ef17d180","added_by":"auto","created_at":"2024-10-22 07:05:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183050,"visible":true,"origin":"","legend":"\u003cp\u003eland use change: (\u003cstrong\u003ea\u003c/strong\u003e): land use in 1980; 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(b): land use in 2040 under Cultivated land protection scenario; (c): land use in 2040 under Ecological protection scenario\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5002484/v1/acde762d3174540c9f370fad.png"},{"id":67184544,"identity":"00b00524-d358-401b-b21e-710f82223e24","added_by":"auto","created_at":"2024-10-22 07:05:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":200128,"visible":true,"origin":"","legend":"\u003cp\u003e(a): habitat degradation in 2040 under Natural development scenario; (b): habitat degradation in 2040 under Cultivated land protection scenario; (c): habitat degradation in 2040 under Ecological protection scenario.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5002484/v1/d8c516371dfcc806a136da76.png"},{"id":67184541,"identity":"14cb95d5-acdd-4bc1-91a3-2562f5a876f5","added_by":"auto","created_at":"2024-10-22 07:05:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":188426,"visible":true,"origin":"","legend":"\u003cp\u003eChange in habitat quality: (a): habitat quality in 2040 under natural development scenario; (b): habitat quality in 2040 under cultivated land protection scenario; (c): habitat quality in 2040 under ecological protection scenario.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5002484/v1/46e1ceda599f8976f658ce92.png"},{"id":67184543,"identity":"0134afc7-b154-48f8-8f2b-0cfe55df2e88","added_by":"auto","created_at":"2024-10-22 07:05:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":138912,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of habitat quality levels in different scenarios.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5002484/v1/13d9f9efc70904e1ac23aa6a.png"},{"id":80559172,"identity":"7e4173e5-26b5-45d8-9c85-9bb4e3b2fbb0","added_by":"auto","created_at":"2025-04-14 16:18:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2713672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5002484/v1/27048c1a-ca31-433c-85c8-3b8d77bfaa9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-scenario Simulation Analysis of the Impact of Land Use Change on Habitat Quality in Zhongwei Based on the PLUS Model Coupled with the InVEST Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLand, a core natural resource, profoundly impacts human economic activities and the health and stability of ecosystems\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Habitat quality is crucial, indicating the survival conditions and suitability for biological species in biomes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Changes in land use and patterns driven by population growth and urbanization significantly affect the structure and function of natural environments and biological habitats\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These highlight the deep connection between socio-economic activities and the natural environment, which plays a pivotal role in influencing habitat quality\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In northwestern China, Zhongwei is essential for maintaining biodiversity, conserving soil and water, purifying the air, and controlling wind and sand. Located in a transition zone between arid and semi-arid climates, with complex geographic conditions and a fragile ecological environment, Zhongwei faces multiple ecological challenges, including vegetation degradation, land desertification, biodiversity decline, and soil erosion. Therefore, analyzing the characteristics of land use changes and the drivers of land expansion in Zhongwei is critically important for protecting ecosystems and managing ecological risks. This analysis helps predict the trends and causes affecting habitat quality\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLand use prediction refers to the analysis of historical data and the prediction of the future land use trends, which is vital for stipulating scientific land management policies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. With the technological development in GIS, remote sensing, machine learning, and artificial intelligence, great breakthroughs have been made in the theory, method and application of land use prediction\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In current research, the most commonly used models for land use prediction are the coupled models such as Markov-FLUS and CA-Markov\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These excel in processing large-scale spatial data, simulating time series changes in land use, integrating multiple influencing factors, and providing dynamic simulations\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, these models still exhibit significant limitations in their strategy for mining transformation rules and in the simulation of landscape dynamics\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The PLUS model, an emerging tool in land use change simulation, integrates deep learning and multi-scale spatial analysis to enhance adaptability and accuracy in managing complex landscape dynamics, while preserving detailed spatial data. This model addresses the deficiencies of the traditional coupled models in transformational rule mining strategies and the simulation of landscape dynamics, thus providing a scientific basis for land resource management and planning\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Habitat quality assessment involves the systematic analysis and evaluation of the conditions of biological habitats within a specific area, assessing factors such as ecosystem structure and function, species diversity, and overall habitat health. Typically, this assessment is conducted by constructing ecological indicators for habitats using various ecological models, supported by field surveys and remote sensing techniques\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Currently, Recently, the InVEST model, which accounts for threatening factors, has been widely used in habitat quality assessment due to its advantages in spatial visualization and accuracy\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, studies that combine the PLUS model and the InVEST model to predict land use and habitat quality in the arid zone of northwestern China have not yet been reported.\u003c/p\u003e \u003cp\u003eZhongwei, an important ecological security barrier in China, has a fragile ecological environment and serious land sandification problems\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The unique geographic and climatic conditions of the region make it an ideal subject for studying land use changes and their impacts on the ecosystem. Additionally, the distinctive natural conditions provide a representative and valuable basis for research in land use, ecological protection, and agricultural development. In recent years, as the economy and society in this region have rapidly developed, particularly in the fields of tourism and agriculture, land use has undergone significant changes. These changes have placed considerable pressure on land resources\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Consequently, a critical challenge now facing this region is how to utilize land resources scientifically while protecting and enhancing habitat quality. Simulating and predicting land use changes and their ecological impacts under different development scenarios is crucial for assessing and optimizing conservation measures. This promotes ecological security and sustainable development in the region. Therefore, we utilized land use data from 1980, 2000, and 2020 to couple the PLUS and InVEST models. This allowed us to predict and analyze the spatial and temporal evolution, characteristics, and expansion drivers of land use patterns and habitat quality in Zhongwei. We considered three scenarios: natural development, cultivated land protection, and ecological protection. This study aims to provide a reference and share experiences relevant to sustainable land management and development in Zhongwei and the broader Northwest China.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe study area is Zhongwei City, Ningxia Hui Autonomous Region, in the northwestern China (104\u0026deg;17'-106\u0026deg;06'E, 36\u0026deg;06'-37\u0026deg;50'N, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Situated at the intersection of Loess Plateau and Inner Mongolia Steppe, the area covers 13,648.12 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It experiences an arid and semi-arid continental climate, characterized by cold, long winters and hot, windy summers. The mean annual temperature is approximately 10\u0026deg;C, with the lowest average temperature in January reaching \u0026minus;\u0026thinsp;10\u0026deg;C and the highest in July hitting 33\u0026deg;C. Annual precipitation is sparse, ranging from 150 to 250 mm, predominantly concentrated between June and September. Sunshine conditions are favorable, with the mean annual sunshine duration exceeding 2,800 hours, and evaporation rates varying from 180 to 367 mm. The frost-free period extends roughly 160 days, with the growing season lasting from April to the end of September. Vegetation and soil types exhibit distinct transitional characteristics. Moving from north to south, the zonal soils transition from wind-formed sandy soils to dark loess and mildly saline soils. The main vegetation types include shrub communities, dry grasslands, and meadows, dominated by poplar, sea buckthorn, and various drought-tolerant herbs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Source and Processing\u003c/h2\u003e \u003cp\u003eBuilding on the foundation of previous studies and considering the characteristics of the study area, along with the availability and timeliness of data, 12 driving factors influencing land use change were selected\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eTable\u0026nbsp;1.\u003c/b\u003e Table of driving factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary Data Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData for 1980, 2000 and 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAESADC,(30\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m\\times\\:30m\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economic data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAESADC(2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation density,RAESADC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average rainfall data ,RAESADC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual average temperature data,RAESADC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAESADC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerrain dat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDC, calculated using ArcGIS10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDC, calculated using ArcGIS10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad accessibility data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCSFGI, calculated using ArcGIS10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from highway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCSFGI, calculated using ArcGIS10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from railroad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCSFGI, calculated using ArcGIS10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCSFGI, calculated using ArcGIS10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eRAESADC represent Resource and Environment Science and Data Center(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.resdc.cn\u003c/span\u003e\u003cspan address=\"http://www.resdc.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GDC represent Geospatial Data Cloud(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gscloud.cn/\u003c/span\u003e\u003cspan address=\"http://www.gscloud.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NCSFGI represent National Catalogue Service For Geographic Information(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webmap.cn/main.do?method=index\u003c/span\u003e\u003cspan address=\"https://www.webmap.cn/main.do?method=index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Methodology\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Land Use Dynamic Degree\u003c/h2\u003e \u003cp\u003eThe land use dynamic degree reflects the change rate of land resources and use and quantifies the transformation process of land use types in the studied region. It is calculated by the formula\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{K}_{i}=\\frac{{U}_{b}-{U}_{a}}{T\\times\\:{U}_{a}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the dynamic degree of land use type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e in Zhongwei, Ningxia; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{a}\\)\u003c/span\u003e\u003c/span\u003e is the area of a specific land type at the beginning of the study; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{U}_{b}\\)\u003c/span\u003e\u003c/span\u003e is the area of a specific land type at the end of the study; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T\\)\u003c/span\u003e\u003c/span\u003e is the duration of the study, in years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Land Use Dynamic Degree\u003c/h2\u003e \u003cp\u003eLand use transfer matrix is used to describe the conversion between different land use types, which is a key tool to study the impact of human activities on land use changes. The formula is as follows\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{A}_{ij=}\\left[\\begin{array}{cc}\\begin{array}{cc}{A}_{11}\u0026amp;\\:{A}_{12}\\\\\\:{A}_{21}\u0026amp;\\:{A}_{22}\\end{array}\u0026amp;\\:\\begin{array}{cc}\\dots\\:\u0026amp;\\:{A}_{1n}\\\\\\:\\dots\\:\u0026amp;\\:{A}_{2n}\\end{array}\\\\\\:\\begin{array}{cc}⋮\u0026amp;\\:⋮\\\\\\:{A}_{n1}\u0026amp;\\:{A}_{n2}\\end{array}\u0026amp;\\:\\begin{array}{cc}\\dots\\:\u0026amp;\\:⋮\\\\\\:\\dots\\:\u0026amp;\\:{A}_{nm}\\end{array}\\end{array}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{ij}\\)\u003c/span\u003e\u003c/span\u003e refers to the area of the land transferred from land use type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e to \u003cem\u003ej\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j(i,j=\\text{1,2},3\\dots\\:,\\:\\:n)\\)\u003c/span\u003e\u003c/span\u003e refer to the land use types before and after the transfer; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e is the number of land use types before and after the transfer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 PLUS Model\u003c/h2\u003e \u003cp\u003eThe Markov model is a widely used model in land transfer study to predict the probability of a system changing from one state to another\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. It is assumed that future states depend only on the current state, not on the events that occurred before it. It is formulated as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{S}_{(t+1)}={P}_{ij}\\times\\:{S}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{(t+1)}\\)\u003c/span\u003e\u003c/span\u003e represent the land use states at moment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e and moment \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t+1\\)\u003c/span\u003e\u003c/span\u003e, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the land use transfer matrix. In this study, we used the stochastic fitting of land types and their drivers based on the Markovian model prediction of the PLUS model.\u003c/p\u003e \u003cp\u003eThe PLUS model introduces the LEAS module, which combines the strengths of the Target Area Strategy (TAS) and Probability Adjustment Strategy (PAS). The LEAS converts the land use type dynamics into a simple issue of binary classification. Through ensemble learning methods, the classification results of the training samples are combined from a multitude of decision trees, to select random sub-samples from the original dataset. Finally, the model predicts the growth probability of land use types \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The formula is as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{P}_{i,k}^{d}\\left(x\\right)=\\frac{{\\sum\\:}_{n=1}^{M}I\\left({h}_{n}\\left(x\\right)=d\\right)}{M}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{i,k}^{d}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the growth probability of land use type \u0026#119896; in cell \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\)\u003c/span\u003e\u003c/span\u003e goes 0 or 1: when it goes 1, it indicates that other land use types are transformed into land use type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e; and when it goes 0, it indicates that there are other transformations. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents a vector composed of driving factors; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e represents an indicator function for decision number sets; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{n}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003eth predicted type of the decision tree of the vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:M\\)\u003c/span\u003e\u003c/span\u003e represents the sum of the decision trees.\u003c/p\u003e \u003cp\u003eThe PLUS model calculates the total probability based on a multi-type random patch seeding mechanism with descending thresholds, in order to simulate the patch evolution of each land use type\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The mechanism adopts Monte Carlo method to calculate the \u0026ldquo;seeds\u0026rdquo; of land use types that change in neighborhood effect, which is formulated as follows:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:If{\\sum\\:}_{k}^{N}\\left|{G}_{c}^{t-1}\\right|-{\\sum\\:}_{k=1}^{N}\\left|{G}_{c}^{t}\\right|\u0026lt;Step\\:Then,\\:l=l+1$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:\\left\\{\\begin{array}{c}Change\\:P{d}_{i,c}^{d=1}\u0026gt;\\tau\\:\\:and\\:T{M}_{k,c}=1\\\\\\:no\\:change\\:P{d}_{i,c}^{d=1}\\le\\:\\tau\\:\\:or\\:T{M}_{k,c}=0\\end{array}\\:\\:\\:\\tau\\:={\\delta\\:}^{l}\\times\\:rl\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026#119878;\u0026#119905;\u0026#119890;\u0026#119901; is the step size of the land use demand fitted by the PLUS model; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:T{M}_{k,c}\\)\u003c/span\u003e\u003c/span\u003e is the transfer matrix, which determines whether the land class \u0026#119896; can be transformed into the land use type \u0026#119888;; \u0026#120575; is the decay coefficient of the descending threshold \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\tau\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\delta\\:ϵ[0,\\:1]\\)\u003c/span\u003e\u003c/span\u003e, whose exact value is set by experts; \u0026#119897; refers to the number of decay steps; \u0026#119903;\u0026#119897; is a normally distributed random value with the mean of 1, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:rl\\in\\:[0,\\:\\:1]\\)\u003c/span\u003e\u003c/span\u003e. This descending threshold can maximize the chance of changes in grid cells with higher overall probability. The CA model based on multi-type random patch seeds shows spatiotemporal consistency and allows new land use patches to develop freely, subject to development probabilities.\u003c/p\u003e \u003cp\u003eAfter the simulation, we used kappa coefficient for accuracy test, which is formulated as follows:\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:Kappa=\\frac{{P}_{0}-{P}_{c}}{{P}_{a}-{P}_{c}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the number of simulated correct grids/total grids; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{c}\\)\u003c/span\u003e\u003c/span\u003e is the number of randomly simulated correct grids/total grids; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{a}\\)\u003c/span\u003e\u003c/span\u003e is the number of randomly simulated correct grids/total grids. The kappa coefficients between 0.6 and 0.8 represent good modeling results. The land use data of Zhongwei in 1980 and 2000 were used to predict those in 2020, and the predicted result in 2020 was then compared with the actual data in the same year according to the Validation module of the PLUS model. From the process, we obtained the kappa coefficient of 80.32%, indicating that the predicted land use results of 2040 are credible.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Multiple Scenarios Setup for PLUS Model Simulation of Future Land Use\u003c/h2\u003e \u003cp\u003e(1) Natural Development Scenario (NDS). The scenario sets a continuation of the land use change trend from 2000 to 2020, without the conversion probability between various land types or the development policy requirements involved. With an interval of 20 years, we used the Markov-Chain in the PLUS model to predict the land use demand in the natural development scenario in 2040, in which the parameters such as land expansion capacity, land use transfer matrix, domain factor weights, and habitat quality index were kept consistent with those in the 2000\u0026ndash;2020 models.\u003c/p\u003e \u003cp\u003e(2) Cultivated Land Protection Scenario (CLPS). The security of cultivated land is the foundation for food security. Cultivated land can be protected by controlling the conversion probability of agricultural lands to other land types and the expansion of construction lands. The conversion probability of cultivated land to other land types is controlled in the 60% reduction, to ensure the cultivated land protection requirements are strictly satisfied.\u003c/p\u003e \u003cp\u003e(3) Ecological Protection Scenario (EPS). Taking into account food security and resource environmental bearing capacity to protect ecosystems and biodiversity, the Chinese government sets up in the EPS the 50% reduction of the probability of woodlands, grasslands and waters converted to unused lands and the 50% increase of the probability of unused lands converted to woodlands, grasslands and waters. The waters in this area are constrained from arbitrary conversion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 Habitat Quality Evaluation Based on the InVEST Model\u003c/h2\u003e \u003cp\u003eThe Habitat Quality Module of the InVEST model was used to evaluate habitat quality through the Habitat Quality Index (HQI) and the Habitat Degradation Index (HDI). The Habitat Degradation Index (HDI) is commonly used to describe the degree of habitat degradation in a region, with higher values indicating higher threat levels. Its value ranges from 0 to 1\u003csup\u003e3,4\u003c/sup\u003e. The formula is as follows:\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:{D}_{xj}=\\sum\\:_{r=1y=1}^{R}\\sum\\:_{y=1}^{{r}_{y}}\\left(\\frac{{w}_{r}}{{\\sum\\:}_{r=1}^{R}{w}_{r}}\\right){r}_{y}{i}_{rxy}{\\beta\\:}_{x}{S}_{ir}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{xj}\\)\u003c/span\u003e\u003c/span\u003e is the degradation degree of habitat; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R\\)\u003c/span\u003e\u003c/span\u003e is the number of threat factors; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e is the number of grids in the threat layer on the base map; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:w\\)\u003c/span\u003e\u003c/span\u003e is the weight of threat factor; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{y}\\)\u003c/span\u003e\u003c/span\u003e is the threat intensity; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{i}_{rxy}\\)\u003c/span\u003e\u003c/span\u003e is the (linear or exponential) effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:r\\)\u003c/span\u003e\u003c/span\u003e on each grid of the habitat; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{x}\\)\u003c/span\u003e\u003c/span\u003e is the effect of local conservation policies; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{S}_{ir}\\)\u003c/span\u003e\u003c/span\u003e indicates the relative sensitivities of the habitats to different threat sources.\u003c/p\u003e \u003cp\u003eThe Habitat Quality Index (HQI) measures the suitability of habitat conditions in a region for one or more species, which ranges from 0 to 1, with the higher value indicating the higher habitat quality\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The formula is as follows:\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\:{Q}_{xj}={H}_{j}(1-\\frac{{D}_{xj}^{z}}{{D}_{xj}^{z}z+{k}^{z}})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Q}_{xj}\\)\u003c/span\u003e\u003c/span\u003e is the habitat quality index of raster \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e in land use type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{j}\\)\u003c/span\u003e\u003c/span\u003e is the habitat suitability of land type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{xjz}\\)\u003c/span\u003e\u003c/span\u003e is the habitat degradation of raster \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e in land type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:z\\)\u003c/span\u003e\u003c/span\u003e is a default parameter; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e is a half-saturation constant, which is 0.2 in this paper. With reference to previous research \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, this paper took the farmland, construction land, and unused land as threat factors and set the impact distance, weighting and sensitivity for different threat factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreat feed parameters.\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=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreat Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum Impact Distance/km\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance Decay Function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eexponential\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elinear\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elinear\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 \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity of threat factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandscape Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHabitat Suitability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConstruction Land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnused\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnused land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\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 \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Land Use analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Land Use Change in Zhongwei from 1980 to 2020\u003c/h2\u003e \u003cp\u003eFarmland and grassland are the dominant land use types in Zhongwei, comprising over 80% of the total area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). From 1980 to 2020, the total area of farmland in Zhongwei increased by 336 km\u0026sup2;. However, there was a decrease of 304 km\u0026sup2; in farmland area from 2000 to 2020. The area of woodland experienced a continuous increase from 1980 to 2020. However, the dynamics of land use from 1980 to 2000 were less intense compared to those from 2000 to 2020. Additionally, the recovery rate of woodland has slowed since 2000. The area of grassland and water bodies decreased overall from 1980 to 2020, although there was an increase observed from 2000 to 2020. The area of land allocated for construction has continued to grow. The dynamic changes in land use between 2000 and 2020 were significantly greater than those observed from 1980 to 2020, indicating a sharp increase in construction land since 2000. Additionally, the area of unutilized land exhibited an upward trend from 1980 to 2000 and a downward trend from 2000 to 2020. The degree of land utilization has shown an upward trend since 2000(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use area changes and dynamics in Zhongwei from 1980 to 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1980\u0026ndash;2020\u003c/p\u003e \u003cp\u003eLand use\u003c/p\u003e \u003cp\u003edynamics(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2000(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2000\u0026ndash;2020\u003c/p\u003e \u003cp\u003eLand use\u003c/p\u003e \u003cp\u003edynamics(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1980\u0026ndash;2020\u003c/p\u003e \u003cp\u003eLand use\u003c/p\u003e \u003cp\u003edynamics(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal change area(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5807.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6448.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6143.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e336.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e358.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e440.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e485.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e127.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11873.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11171.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11207.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-666.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e337.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e342.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-89.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruction land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e232.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e530.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e312.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnused land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2657.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2716.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2636.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-21.49\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\u003eIn terms of land transfer(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), construction land expanded gradually from 1980 to 2020 into the eastern part of Shapotou District and the central part of Zhongning County, with a large area of farmland and grassland being converted into construction lands. During the study period, the increased area of woodland mostly came from grassland, mainly located in Shapotou District. The area of farmland decreased by 304.89km\u0026sup2; from 2000 to 2020, primarily being converted into grassland. Between 1980 and 2000, the water area significantly decreased due to conversion to farmland. However, since 2000, there has been a resurgence in water area. Between 2000 and 2020, the area of unused land decreased by 80.1km\u0026sup2;, primarily through conversion to farmland and grassland, resulting in an optimized land use structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Land Use Expansion Factors\u003c/h2\u003e \u003cp\u003eThe land use data of Zhongwei with 12 drivers were imported into the PLUS model for spatial overlay, and the expansion factors were analyzed using the LEAS module of the PLUS model to obtain the contribution values of the land type expansion drivers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The top three were population, precipitation and government from distance in the case of farmland; precipitation, government from distance and temperature in the case of woodland; DEM, railway from distance and government from distance in the case of grassland; temperature, precipitation and water from distance in the case of water; government from distance, water from distance and highway from distance in the case of construction land; and railway from distance, population and precipitation in the case of unused land. In general, the factors affecting the land use types in Zhongwei were mainly precipitation, temperature, population and government from distance. Slope and aspect contributed moderately to land expansion, showing an insignificant role.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Habitat Quality change in Zhongwei from 1980 to 2020\u003c/h2\u003e \u003cp\u003eThe habitat degradation index (HDI) in the study area was calculated according to the habitat quality module of the InVEST model(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results are classified into five classes: low (0, 0.089), relatively low (0.089, 0.18), medium (0.18, 0.26), relatively high (0.26, 0.35), and high (0.35, 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). From 1980 to 2020, the mean values of the HDI consistently increased and were 0.210, 0.214, and 0.235, respectively, with a continuous increase in the areas of the high and relatively high habitat degradations and a continuous decrease in the areas of the low and relatively low habitat degradations. The areas of the high degradation lied in the eastern part of Shapotou District, the central part of Zhongning County and the southern part of Zhongwei, where the grassland and farmland were under greater threats. In addition, the HDI of grassland in the southern part of Zhongwei rose continuously.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Habitat Quality Index (HQI) in the study area was calculated according to the habitat quality module of the InVEST model. The results are classified into five classes: low (0, 0.29), relatively low (0.29, 0.49), medium (0.49, 0.60), relatively high (0.60, 0.78), and high (0.78, 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The mean values of the HQI for the years 1980, 2000 and 2020 ranked as follows: 1980(0.38)\u0026thinsp;\u0026gt;\u0026thinsp;2000(0.35)\u0026thinsp;\u0026gt;\u0026thinsp;2020(0.33), suggesting that the HQI decreased year to year. From 1980 to 2020, the areas of the low, relatively low and high HQI rose, while the areas of medium and relatively high HQI decreased, indicating that the land use structure in Zhongwei became more compact and the land conservation was improved.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Land Use Projections to 2040 under Different Scenarios\u003c/h2\u003e \u003cp\u003eBased on the land use data in 2000 and 2020, the coupled Markov-Plus model was used to predict the distribution of land use types in Zhongwei in 2040 in the following three scenarios(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the natural development scenario(NDS), the overall trend of land use remained unchanged, with the area of farmland decreasing to 5909.13 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the area of woodlands increasing to 485.60 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the area of construction land increasing to 530.09 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the areas of grasslands and waters basically remaining unchanged, and the area of unused lands decreasing. In the cultivated land protection scenario(CLPS), the total area increased by 277.20 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e compared to 2000, and the areas of woodland, grassland, construction land and unused lands decreased, compared to those in the NDS. In the ecological protection scenario(EPS), the areas of woodland, grassland and water were higher than those in the NDS; the area of farmlands was less than that in the CLPS but higher than that in the NDS; and the area of construction lands is less than that in the NDS but higher than that in the CLPS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Habitat Quality Projections for Zhongwei in 2040 under Different Scenarios\u003c/h2\u003e \u003cp\u003eThe mean values of the HDI in the three different scenarios predicted for 2040 ranked as follows: Cultivated land protection scenario (0.289)\u0026thinsp;\u0026gt;\u0026thinsp;Natural development scenario (0.266)\u0026thinsp;\u0026gt;\u0026thinsp;Ecological Protection scenario (0.264). All showed a growing degradation compared to those in 2020. The proportions of different degradations in NDS and EPS were similar, and the areas of the relatively high and high degradations in CLPS were more than those in NDS and EPS, while the areas of the relatively lower and low degradations were less than those in NDS and EPS(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean values of the HQI in the three scenarios ranked as follows: ecological protection scenario (0.35)\u0026thinsp;\u0026gt;\u0026thinsp;cultivated land protection scenario(0.34)\u0026thinsp;\u0026gt;\u0026thinsp;natural development scenario (0.33), all three figures increased compared with those in 2020, suggesting a potential uptrend in the ecological quality of Zhongwei. The proportions of the quality levels in the NDS and EPS were similar, and the areas of the low and relatively low quality in the CLPS were significantly more than those in the NDS and EPS, while the areas of the medium quality in the CLPS were significantly less than those in the NDS and EPS(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo determine the spatial variation of habitat quality, we mapped the differences between the habitat quality in 2020 and that in the three scenarios. The results were categorized into drastic decline [0.5, 1), decline[0.1, 0.5), basically unchanged [-0.1, 0.1), rise [-0.5, -0.1), and drastic rise [-1, -0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the natural development scenario, the areas of the decreased habitat quality lied in the north and south of Zhongwei, while the areas of the increased habitat quality lied in the central part of Shapotou District and the eastern part of Haiyuan County. In the cultivated land protection scenario, the overall habitat quality in the study area showed a large decline, especially in the eastern part of Shapotou District and the northern part of Zhongning County. In the ecological protection scenario, the areas of the decreased habitat quality were the least, and the areas of the increased habitat quality was concentrated in the central part of Shapotou District.\u003c/p\u003e \u003cp\u003e \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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea percentage (%) of habitat quality levels in different scenarios.\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 \u003cp\u003eDifferent scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrastic rise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasically unchanged\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDrastic decline\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCLPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\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"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Causes of Land Use Change\u003c/h2\u003e \u003cp\u003eLand use change is not only a key driver of changes in ecological service value, but also a significant source of ecological risks\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The changes in land use types in Zhongwei were influenced by many factors\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Overall, precipitation, temperature, population, and distance from the government are the main factors influencing land use changes in Zhongwei(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Zhongwei is located in arid and semi-arid zones, where agricultural production, particularly the expansion of arable and forest land, heavily depends on precipitation levels. Precipitation directly determines the abundance of water resources, significantly influencing the types of crops grown and their yields\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additionally, temperature not only affects the growth cycles and planting choices of plants but also influences the evaporation and replenishment of water bodies, thereby indirectly affecting the distribution of land use types\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Furthermore, population and government proximity also play crucial roles in land use changes. In recent years, population growth in Zhongwei has increased the demand for residential and commercial land, further driving the expansion of construction land. Government proximity, as an indicator of the convenience of government services and infrastructure development, plays a key role in promoting land development and regional planning. Especially in the development of construction lands and unused lands, government policies and infrastructure developments are often decisive factors\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In terms of the impact of transportation infrastructure, railways and roads have a significant influence on the expansion of grassland, construction land, and unused land. The improvement of transportation facilities not only facilitates the flow of people and logistics but also promotes the development and utilization of suburban areas, enhancing their economic value and attractiveness[40]. However, the effects of slope and aspect in the land expansion of Zhongwei are relatively minor. The terrain in Zhongwei is relatively flat, and most areas are not constrained by complex topography, thus limiting the impact of slope and aspect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Relationship between Habitat Quality Change and Land Use Change\u003c/h2\u003e \u003cp\u003eThe distribution of land use types is highly correlated with the spatial pattern of habitat quality values\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The changes in land use and habitat quality in Zhongwei reflect the complex relationship between its ecological protection and economic development. From 1980 to 2020, the land use changes in the region had a remarkable impact on habitat quality. The continuous rise of the HDI and the yearly decline of the HQI indicated that the region witnessed an slight overall decline in habitat quality. However, improvements in ecological quality have been observed in localized areas, such as the Shapotou District(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The simulation results indicate a trend of improvement in the habitat quality of Zhongwei(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).The area of regions with low habitat quality values has continued to increase, a growth that aligns with the expansion of urban areas and construction land. Therefore, the expansion of construction land is the primary cause of the decline in habitat quality, reflecting the rapid urbanization and industrialization of the study area\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This was attributed to the reduction of farmland and specially the rapid expansion of construction lands, which reflected the rapid urbanization and industrialization in the city. In addition, the farmlands decreased significantly between 2000 and 2020, as the result of the policies of returning farmland to forest and grassland\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The policies, though committed to restoring ecosystem services such as soil and water conservation and carbon fixation, may have short-term impacts on food supply. Thus, it is recommended to ensure food security by improving agricultural productivity or developing new farmland. Zhongwei, renowned for its efforts in sand prevention and desertification control, has conducted many relevant projects in Shapotou District, eastern Zhongning County and along the Yellow River. The measures included planting trees and grasses, constructing windbreak and sand-fixing forest systems, and installing sand-proof barriers, which have effectively controlled the desertification speed. The success of these projects can be embodied by the increase of woodland and the improvement of habitat quality in these regions. However, the overall declining trend in habitat quality reflects the fact that ecological restoration measures have not yet completely offset other negative impacts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Relationship Between Habitat Quality and Land Use Change in Multi-Scenario Modeling\u003c/h2\u003e \u003cp\u003eThe habitat quality and land use structure were projected to change to varying degrees in different scenarios\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In the natural development scenario(NDS), further expansion of construction land led to a decline in habitat quality in the north and south of Zhongwei, as the urbanization and industrialization continued to advance. The habitat quality showed an increasing trend in the central part of Shapotou District and the eastern part of Haiyuan County, where the government should keep strengthening ecological protection and restoration. In the cultivated land protection scenario (CLPS), the area of farmland increased while the areas of woodland, grassland, construction land and unused land decreased, compared to those in the NDS. Such land use restructuring resulted in an overall decline in habitat quality, especially in the eastern part of Shapotou District and the northern part of Zhongning County. This might be attributed to the overemphasis on farmland protection at the expense of other land types, leading to an imbalance in ecosystem. In the ecological protection scenario (EPS), the areas of woodland, grassland and water increased the most, with the areas of decreased habitat quality the least, while the area of construction land was still more than that in the CLPS. This shows that the EPS can balance industrial expansion, farmland and ecological protection, and control the growth of industrial and urban land, ensuring the ecological quality being higher than that in other scenarios. Overall, the EPS is more suitable for Zhongwei to achieve sustainable development\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In the CLPS, the trend of farmland reduction was effectively slowed down, and it is recommended that the farmland protection policy be continued and promoted. In the EPS, the increase in the areas of woodland, grassland and water had a significant effect on the improvement of habitat quality. The protection efforts in these ecologically sensitive areas should be promoted and more ecological restoration projects should be implemented, such as afforestation and wetland restoration. With the climatic and environmental factors having an impact on land use change, the adaptive management measures are recommended, such as adjusting planting structure, to enhance the resilience of agricultural systems to climate change. Strengthening ecological construction and protection can help the area ensure economic development while seeking ecological improvement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Suggestions and Outlook\u003c/h2\u003e \u003cp\u003eThe biggest problem facing Zhongwei is environmental deterioration caused by the large-scale conversion of farmland into construction lands, which requires more efforts in farmland protection. In the cultivated land protection scenario, the decreasing trend of farmland was effectively mitigated, and it is recommended that farmland protection policies continue to be promoted and improved. In the ecological protection scenario, the expansion of woodland, grassland and water significantly improved habitat quality, and protection efforts are recommended to enhance in ecologically sensitive areas and more ecological restoration projects are suggested. With the climatic and environmental factors having an impact on land use change, the adaptive management measures are recommended, such as adjusting planting structure, to enhance the resilience of agricultural systems to climate change.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eFrom 1980 to 2020, the areas of construction land and woodland increased significantly, while the areas of water and unused land decreased slightly, and the other lands remained essentially unchanged. In the natural development scenario, construction land continued to expand, farmland decreased, and woodland increased; in the cultivated land protection scenario, the decreasing trend of farmland was effectively slowed down; in the ecological protection scenario, priority was given to the protection and expansion of woodland, grassland, and water. In general, Precipitation, temperature, population, and distance from government significantly impacted land use change in Zhongwei. The habitat degradation index continued to rise in Zhongwei, with grassland and farmland under growing threats. Meanwhile, the habitat quality index showed a general downward trend, with the habitat suitability of the low-quality and relatively low-quality areas decreased. In the ecological protection scenario, the enhanced ecological protection measures effectively improved habitat quality. In the cultivated land protection scenario, however, farmland protection was improved but some regions witnessed a decrease in habitat quality. The natural development scenario exhibited the natural evolution of habitat quality without additional interventions. Overall, the ecological protection scenario is more suitable for the sustainable development of Zhongwei.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported by the Ecological monitoring project of desertification control in Shapotou, Ningxia (Ningsha Guanhezi [2021]08), the Major Science and Technology Projects of Gansu Province-International Cooperation Projects (22ZD6WA036) and National Natural Science Foundation of China (No. 42071048).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, X.W., J.C., B.L. and M.A.; methodology, X.W., J.C., B.L. and M.A.; software, X.W.; validation, X.W., J.C. and B.L.; formal analysis, X.W.; Investigation, X.W., B.Z., Q.C., J.L. and W.Y.; resources, X.W.; data curation, X.W.; writing\u0026mdash;original draft preparation, X.W., J.C. and B.L.; writing\u0026mdash;review and editing, X.W., J.C. and B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWilson, T.S.; Van Schmidt, N.D.; Langridge, R. Land-use change and future water demand in California\u0026rsquo;s Central Coast. \u003cem\u003eLand\u003c/em\u003e. 9(9), 322(2020).\u003c/li\u003e\n\u003cli\u003eRegolin, A.L.; Oliveira-Santos, L.G.; Ribeiro, M.C.; Bailey, L.L. Habitat quality, not habitat amount, drives mammalian habitat use in the Brazilian Pantanal. \u003cem\u003eLandscape Ecology\u003c/em\u003e. \u003cem\u003e36\u003c/em\u003e(9), 2519-2533(2015).\u003c/li\u003e\n\u003cli\u003eLi, X.; Liu, Z.; Li, S.; Li, Y. 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Plan. 12, 4516-4525(2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Land use change, PLUS model, InVEST model, habitat quality, multi-scenario simulation","lastPublishedDoi":"10.21203/rs.3.rs-5002484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5002484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand use change is a key factor affecting habitat quality. In order to reveal the impacts of urban land use changes on habitat quality, this paper uses the city of Zhongwei, China, as a case study. Based on the land use data from 1980, 2000 and 2020, the PLUS-InVEST coupled model was used to predict and assess the land use and habitat quality of Zhongwei. The results showed that from 1980 to 2020, the areas of construction land and woodland increased significantly, while the areas of water and unused land decreased slightly, and the other lands remained essentially unchanged. The main factors such as precipitation, temperature, population and distance from government distance influenced the land expansion. Moreover, the habitat quality in Zhongwei showed a decreasing trend. The overall area of low habitat quality increased, while the overall area of relatively low and medium habitat quality decreased, and the other remained essentially unchanged between 2000 and 2020. The predicted habitat quality of the study area in 2040 was compared under different development scenarios. The comparison of results showed that highest habitat quality and the lowest habitat degradation under the Ecological protection scenario. Although the afforestation and desertification control projects in Zhongwei have proved successful in increasing woodland and improving habitat quality, its ecological restoration measures have not yet completely counteracted the adverse effects of ongoing urbanization and industrialization on habitat quality, resulting in a persistent decline in overall habitat quality.\u003c/p\u003e","manuscriptTitle":"Multi-scenario Simulation Analysis of the Impact of Land Use Change on Habitat Quality in Zhongwei Based on the PLUS Model Coupled with the InVEST Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-22 07:05:39","doi":"10.21203/rs.3.rs-5002484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T08:25:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-05T08:31:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332593243795900688128783523706806383923","date":"2024-10-30T09:20:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-10T14:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109605537408481965410878003717398358068","date":"2024-10-10T10:49:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62268702087537854588981292939557504038","date":"2024-10-09T09:33:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185823526951688313417267351592059552895","date":"2024-09-21T11:11:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-19T05:47:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-19T05:34:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-09T19:31:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-07T08:59:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-30T08:48:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"765c717e-4009-4579-8b54-a4822ae2c0a5","owner":[],"postedDate":"October 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38793469,"name":"Earth and environmental sciences/Ecology"},{"id":38793470,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-04-14T16:16:52+00:00","versionOfRecord":{"articleIdentity":"rs-5002484","link":"https://doi.org/10.1038/s41598-025-90965-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-10 16:05:12","publishedOnDateReadable":"April 10th, 2025"},"versionCreatedAt":"2024-10-22 07:05:39","video":"","vorDoi":"10.1038/s41598-025-90965-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-90965-6","workflowStages":[]},"version":"v1","identity":"rs-5002484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5002484","identity":"rs-5002484","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}