Rare waterbirds play an important role in ecosystems. The survival situation of rare waterbirds to some extent reflects the health and stability of the ecosystem (Liu et al., 2024). All over the world, there are a variety of factors that influence the species composition and population distribution of rare waterbirds. These factors include climate change (Zhang et al., 2024), human activities (Mahar et al., 2023), biological resource utilization (Holopainen et al., 2022), etc. Current studies on rare waterbirds focus on species composition and population distribution (Sakhri et al., 2024), conservation and management of climate change and pollution (Qiu et al., 2024), behavioral studies (Wood et al., 2017), etc. In terms of conservation and management, studies focus on protection measures for waterbird habitats (Nikolaus et al., 2022), ecological restoration methods (Howe et al., 2018) and how to achieve sustainable use can waterbird resources through scientific management. In terms of behavioral studies, they focus on socialization, reproduction (Casazza et al., 2020) and migratory behavior (Xu et al., 2021). However, most of these studies focus on a single dimension of waterbirds and ignore the multiscale, multidimensional, and synthesized nested characteristics of waterbird habitats. The habitat networks can facilitate the flow of ecological streams and help maintain ecosystem function by connecting fragmented habitats (Xu et al., 2021). Habitat networks make more sense in the long term than simply increasing the number of isolated protected areas or increasing the size of natural habitats. The networks are also a better response to climate change. In the context of global climate change, rapid urban development is leading to fundamental changes in land use and landscape patterns. The change led to environmental and ecological problems such as loss of biodiversity and degradation of ecosystem services. Therefore, there is an urgent need to conduct rare waterbird habitat network studies at the watershed level and clarify the habitat network pattern of waterbirds in the region. Studying habitat networks can improve the structure and function of regional ecosystems.
In recent years, studies of ecological networks have received increasing attention. The studies mainly focused on spatial planning of ecological security patterns (Li et al., 2024), biodiversity protection (Treml et al., 2015), green infrastructure construction (Bai & Guo, 2021), and regional ecosystem conservation ( Wei). et al., 2022) etc. The ecological network studies have formed the paradigm of “source identification-resistance surface construction-ecological corridor and node extraction” (Yang et al., 2024), and the related study methods are diverse. When identifying sources, in most studies the large nature reserves or forest parks in the study area were directly selected. However, the standard for selecting sources in this approach is relatively uniform and subjective. The approach lacks concrete data to support the study. Some studies used Morphological Spatial Pattern Analysis (MSPA) (Carlier & Moran, 2019), ecological sensitivity assessment (Tong et al., 2024), ecosystem importance assessment (Newton et al., 2012), and landscape connectivity analysis (Liang et al., 2023), empirical species distribution models (SDMs) (An et al., 2023) and other methods of source selection. Among other things, Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity do not take into account the heterogeneity of the internal environment. When assessing the importance of ecosystems, the impact of synthetic environments on regional environments is often ignored. Ecological sensitivity assessments weaken the connectivity function of landscape areas. The Waterbird Habitat Index is an important indicator for assessing habitat quality and reflects population size, network distribution and breeding of waterbirds. Based on the geographical distribution points and corresponding environmental data to estimate the ecological requirements of species, the Empirical Species Distribution Models (SDMs) predict mathematical models of their distribution in the target area (Guan et al., 2023). As one of the most commonly used models in empirical species distribution models (SDMs), the MaxEnt model can be used to predict species habitat index and create meaningful qualitative models for many species. Therefore, the MaxEnt model is often used to predict the origin of species with relatively specific distribution dates (Phillips et al., 2006). Ecological corridors and nodes are mainly extracted by minimum cost paths (MCR) (Marulli & Mallarach, 2005), circuit theory (Gao et al., 2023), least-cost path (Adriaensen et al., 2003) and graph theory methods (Bunn et al., 2000). Among them, circuit theory and least-cost path method are the most mature methods and have been widely used in recent years (Guo et al., 2024). In the least-cost path approach, connectivity can effectively identify corridors with minimal resistance between sources, but it is difficult to identify key nodes within the corridors (Liang et al., 2023). Circuit theory exploits the properties of random electron flow in circuits to simulate the migration and spread processes of individual species or genes across a landscape. Circuit theory treats individual species or genes as electrons, landscapes as resistance surfaces, and areas with good habitats as sticking points. It helps to effectively extract the ecological corridors. In addition, circuit theory can assign appropriate resistance values to different landscapes depending on whether they are beneficial for particular ecological processes. The method then identifies key nodes within the corridors based on resistance values (McRae et al., 2008). The method provides an important scientific basis for ecological nature conservation planning.
However, most existing studies are based on the current status of habitat networks. They contribute to the conservation and restoration of habitat networks to a certain extent, but the static conservation approach ignores the historical evolutionary characteristics and future development trends on the dynamic impact mechanisms of the network (Sui et al., 2024). By updating sources and resistance surfaces, future habitat networks can be simulated by creating land use data for future scenarios. Commonly used models for land use simulation include the FLUS model (Liu et al., 2017), the CFLU-S model (Verburg et al., 2002), the OS-CA model (Batty et al., 1997 ) and the PLUS model (Gao). et al., 2022), etc. Among other things, the traditional CA model has limitations in examining the factors driving land use changes and in simulation accuracy (Ma et al., 2023). Although the CLUE-S model is specifically designed for regional-scale land use analysis (Verburg et al., 2002), its spatial location simulation ability is not as good as that of the FLUS model, which has improved the traditional transformation rules and provides more accurate ones Simulation results (Luo & Fu, 2023). It is shown that the simulation results of the PLUS model are better than those of the FLUS model. The PLUS model adopts a new site expansion analysis strategy (Sui et al., 2024), which not only has higher simulation accuracy and is also more suitable for studying the effects of various factors on future land use (Guan et al., 2023) . The simulation results are of great importance for preserving the ecological environment, preserving biodiversity, promoting the sustainable development of the ecosystem and ensuring regional ecological security.
Both the middle and lower reaches of the Yangtze River are the regions with the most frequent human activities and extremely rich wetland resources in the Yangtze River basin. The wetlands make up about 7.4% of the total area of the catchment. As one of the most important ecosystems in the middle and lower reaches of the Yangtze River, lake wetlands are of great importance in maintaining the ecological balance and security of the basin (Jia et al., 2020). In recent years, relevant studies mainly focused on the assessment of the ecosystem service value of individual lakes or small areas (Jia et al., 2020), the development of waterbird habitats (Liu, 2023), the ecological preferences of the population (Zhao, 2017) and lakes hydrology and climate change (HU et al., 2022), etc. Most studies focused on general waterbird populations (Tan et al., 2023). There are few studies on waterbird habitat networks at the watershed scale, particularly on rare and endangered waterbirds. Furthermore, there are gaps in considering biodiversity conservation and ecological restoration of lake wetlands from a holistic perspective.
Therefore, this study constructed habitat networks for cranes in the middle and lower reaches of the Yangtze River using waterbird habitats and environmental data. The study used circuit theory, PLUS model and graph theory. Focuses on the following scientific questions: (1) What are the characteristics of the spatial and temporal network evolution patterns of crane habitat networks in the middle and lower reaches of the Yangtze River? (2) What are the future development trends and characteristics of their habitat networks? How are optimization and renovation measures carried out? Based on previous research on habitat selection of rare species (Zhang et al., 2024), we combined the patch-matrix-corridor theory in landscape ecology with the habitat selection behavior of wild animals. This innovative approach solves the problem of neglecting important ecosystem functions such as species habitat in existing network construction technologies. The study innovatively applied the PLUS model to simulate land use in different scenarios, compare vertically and horizontally (time history and different scenarios), and conduct a coupled study on the ecological pattern of wetlands. This compensated for the lack of consideration of future scenarios in existing habitat network studies. The study will help researchers and managers better understand the changing patterns and developmental characteristics of crane habitat networks in the middle and lower reaches of the Yangtze River. In addition, the study provides data references for the ecological protection and sustainable development of the lake wetlands in the middle and lower reaches of the Yangtze River.