## 1 Introduction  
Irrational land use development is a major cause of land degradation [1, 2, 3]. This phenomenon involves a range of inappropriate land use practices, including inefficient land resource allocation, unbalanced land use, and unsustainable land management strategies. In particular, China's urbanization has been gradually speeding up since the reform and opening up [4, 5, 6]. A considerable proportion of the country's ecological resources, including forests, cropland, and grassland, have been encroached upon by construction land [7, 8, 9, 10]. Habitat quality is defined as the capacity of an ecosystem to offer desirable living environments for species [11, 12, 13]. Habitat quality is a critical indicator of the functionality and integrity of ecosystem services. Nevertheless, the intensification of land use development has resulted in a notable degradation of habitat quality [14, 15, 16, 17]. Related studies have focused on two main parts: habitat quality for a single species and overall habitat quality. While species-specific assessments provide detailed insights, they are often limited in scope and scalability [18, 19, 20]. Consequently, an increasing number of studies are using mathematical methods, including habitat suitability, the SoIVES model, and the InVEST model, to assess regional habitat quality. Among them, InVEST is the most extensively used and has reached a high level of maturity [21; 22; 23]. This model includes a wide range of tools, including habitat quality, carbon storage, and soil conservation. The habitat quality tool is capable of deriving grid-scale habitat quality results according to land use and threat factors. The methodology has been applied by numerous studies in a variety of regions [24; 25; 26]. Nevertheless, the InVEST method is insufficient to project future habitat quality conditions. It needs to be further coupled with land use simulation models. Among the various land use simulation approaches, cellular automata (CA) are the most prevalent [27; 28; 29; 30; 31]. The CA approach effectively simulates complex land use evolution by applying simple transition rules [32; 33; 34; 35]. Notably, [PERSON] et al. [36] proposed an advanced FLUS model, which builds upon the traditional CA, to account for the complex interaction among various land use classes and to improve the accuracy of land use projection. Currently, the FLUS method has been extensively utilized to simulate land use evolution [37; 38; 39]. Consequently, the FLUS and InVEST models can be integrated to project future changes in habitat quality. In particular, given the scarcity of available land resources and the equally important need to protect and develop them, it is of particular significance to combine habitat quality assessment and land use projection [40; 41; 42]. Recent studies have demonstrated that the integration of InVEST and FLUS models has significant value for predicting future changes in habitat quality. However, the majority of relevant studies have been limited to medium- and small-sized regions, including watersheds, provinces, municipalities, and ecological zones. Few studies have analyzed the spatial and temporal evolution of habitat quality at a large national scale over a long period of time. At the regional scale, [PERSON] et al. [43] employed these two models to assess the dynamic changes in habitat quality under different scenarios in Lithuania, highlighting the influence of land use on habitat quality. [PERSON] et al. [44] used these two models to simulate habitat quality trends under four land use scenarios in the Sokoto-Rima Basin, Nigeria, and found that sustainable development scenarios could significantly improve biodiversity. [PERSON] et al. [45] projected the impact of land use evolution on habitat quality in southwestern Ethiopia by combining InVEST and FLUS models. [PERSON] et al. [46] projected future habitat quality and its response to land use evolution in southwestern Iran based on InVEST and CA models.