Climate change response type waterfowl ecological corridor dynamic simulation and optimization system and method

Abstract

The invention belongs to the technical field of ecological protection and climate change adaptability management, and relates to a climate change response type waterfowl ecological corridor dynamic simulation and optimization system and method. The system is composed of a data acquisition and preprocessing module, a multi-model coupling simulation module, a dynamic optimization analysis module and a visual output module, and all the modules realize data interaction and flow control through standardized interfaces. According to the method, the dynamic response simulation and optimization of the waterfowl ecological corridor to the climatic change under the regional scale are realized for the first time, and the accuracy, systematicness and practicability of the dynamic simulation of the waterfowl ecological corridor under the climatic change are improved; through integration of climate data, a maximum entropy model, a minimum resistance model and a current theory, multi-scene, multi-scale and multi-species ecological corridor dynamic optimization is realized, and accurate prediction of a waterfowl ecological corridor under climate change, key node identification and intelligent generation of an adaptive protection strategy are realized. And accurate decision support is provided for biodiversity protection under climate change.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecological protection and climate change adaptive management, and specifically relates to a climate change-responsive waterbird ecological corridor dynamic simulation and optimization system and method based on multi-model coupling, which is used to predict and optimize the spatiotemporal distribution and connectivity of waterbird ecological corridors under the background of climate change, and provide scientific support for wetland protection network planning. Background Art

[0002] As global climate change intensifies, the degradation of wetland ecosystems and the fragmentation of waterbird habitats are becoming increasingly prominent. As key infrastructure for maintaining biodiversity, the dynamic simulation and optimization of ecological corridors have become a research hotspot in the field of ecological protection.

[0003] In the existing technology, ecological corridor simulation is mostly based on a single model, such as the least resistance model, current theory, or species distribution model, and lacks a systematic framework for multi-model coupling. For example, patent CN109345235A discloses a method for identifying terrestrial animal ecological corridors based on the least resistance model, but it does not integrate dynamic climate change scenarios and does not address the special needs of waterbird migration. Another patent, US20200126345A1, uses circuit theory to simulate landscape connectivity, but focuses on static land use data and does not consider the spatiotemporal heterogeneity of climate drivers. At the same time, Zhang et al. (2022) combined the minimum resistance model with current theory to identify waterbird corridors, but did not introduce the maximum entropy model for dynamic prediction of habitat suitability, and did not integrate the dynamic driving factors of climate change, resulting in insufficient prediction of corridor responses under future climate change scenarios; although Xu et al. (2023) combined habitat suitability with corridor network analysis and used the maximum entropy model to assess changes in migratory bird habitats, it was not coupled with corridor network connectivity analysis. Its framework did not incorporate climate model data, and no dynamic simulation system was constructed, making it difficult to support long-term adaptive management decisions.

[0004] In summary, the existing ecological corridor simulation has the following problems:

[0005] (1) Model singleness and insufficient integration: Existing technologies mostly rely on a single model or a simple model combination (such as MCR and Circuit Theory), and fail to fully integrate climate models (such as CMIP6), habitat suitability models (MaxEnt), and human activity interference factors. This results in an incomplete analysis of the interaction between climate change, landscape resistance, and habitat suitability, and the simulation results are insufficiently adaptable to complex climate responses.

[0006] (2) Limited spatiotemporal dynamic prediction capabilities and static limitations: Existing methods mostly focus on historical or static data and do not build a dynamic simulation framework. This makes it difficult to predict the spatiotemporal differentiation of waterbird ecological corridors under future climate change (such as increased fragmentation and migration of key nodes). The lack of dynamic simulation driven by climate change scenarios makes it difficult to predict future corridor evolution trends.

[0007] (3) Regional scale and species limitations: Existing technologies are mostly targeted at terrestrial animals or regional small scales, limited to single species or local areas, insufficient cross-regional dynamic analysis of waterbird migration routes, lack of multi-species comprehensive simulation at the regional scale, and unable to support large-scale conservation network optimization (such as Northeast wetlands-migratory route hub);

[0008] (4) Lack of scenario simulation and optimization strategies: Existing technologies lack systematic simulation of multiple emission scenarios (such as SSP245 and SSP585), and are unable to quantify the vulnerability and recovery potential of ecological corridors under different climate pressures, which limits the formulation of adaptive strategies. At the same time, the existing system does not integrate scenario simulation and protection priority optimization algorithms, making it difficult to support adaptive management decisions.

[0009] Based on this, existing technologies have significant deficiencies in the dynamic simulation and climate response optimization of waterbird ecological corridors. It is necessary to develop a climate change-responsive dynamic simulation and optimization system for waterbird ecological corridors to effectively solve the above problems through multidisciplinary model coupling and scenario-driven design. Summary of the Invention

[0010] The purpose of the present invention is to provide a dynamic simulation system and method for multi-model coupling. By integrating CMIP6 climate data, maximum entropy model, minimum resistance model and current theory, dynamic optimization of multi-scenario, multi-scale and multi-species ecological corridors is realized to solve the problems of insufficient model integration, weak scenario response capability and low accuracy of key node identification. It realizes accurate prediction of water bird ecological corridors under climate change, identification of key nodes and intelligent generation of adaptive protection strategies, providing accurate decision-making support for biodiversity conservation under climate change.

[0011] The purpose of the present invention is achieved through the following technical solutions:

[0012] A climate change-responsive waterbird ecological corridor dynamic simulation and optimization system, consisting of a data acquisition and preprocessing module, a multi-model coupling simulation module, a dynamic optimization analysis module, and a visualization output module. Each module implements data interaction and process control through standardized interfaces.

[0013] The data acquisition and preprocessing module includes a data input unit and a data preprocessing unit, which is used to preprocess the input data sources, including climate data, land use data, water bird observation data and terrain data;

[0014] The multi-model coupling simulation module includes a model integration framework consisting of minimum resistance model units, current theory units, and maximum entropy model units. The minimum resistance model and current theory can realize dynamic interactive verification of spatial paths by sharing the resistance surface. The habitat suitability grid output by the maximum entropy model is used as a weight factor in the construction of the resistance surface to optimize the resistance value allocation logic. The multi-model results can generate a comprehensive ecological corridor network and vulnerability assessment indicators through spatial overlay analysis.

[0015] The dynamic optimization analysis module includes a spatiotemporal comparison unit, a sensitivity analysis unit, and an optimization suggestion generation unit. The spatiotemporal comparison unit can compare the spatiotemporal differences in the number, cumulative total cost, length, and connectivity index of ecological corridors based on simulation results from a historical baseline period and future time periods. The sensitivity analysis unit can quantify the ecological corridor response thresholds of non-narrow-range species and narrow-range species by perturbing climate parameters using the Monte Carlo method. The optimization suggestion generation unit can delineate priority corridor restoration sections by combining high current density nodes with areas of decreased habitat suitability. A hierarchical protection strategy can be generated based on corridor fragmentation trends in different scenarios.

[0016] The visualization output module includes a spatial display unit. The spatial display unit can integrate ecological corridors, resistance surfaces, and habitat suitability grids output by multiple models based on the ArcGIS platform to generate a spatiotemporal dynamic distribution map and support interactive query functions.

[0017] Furthermore, the climate data is based on future climate scenario data of the CMIP6 global climate model, including average monthly precipitation, extreme precipitation intensity, average monthly maximum / minimum temperature, etc., and the data format is a raster file.

[0018] Furthermore, the land use data includes spatial distribution data of wetlands, farmlands, woodlands, building land and other types, and is derived from a global land use dynamic prediction dataset.

[0019] Furthermore, the water bird observation data obtains water bird species distribution point data through the National Digital Specimen Resource Library, the Bird Watching Record Center and the satellite tracking platform.

[0020] Furthermore, the terrain data includes DEM data of altitude, slope, and aspect, as well as spatial raster data of distances to water bodies, roads, and wetlands.

[0021] Furthermore, the data preprocessing unit uses GIS tools to perform spatial registration, resampling and normalization on multi-source data, screens environmental variables through Pearson correlation analysis, eliminates redundant factors, and generates an input parameter set for waterbird habitat suitability simulation.

[0022] Furthermore, the minimum resistance model unit, based on the core habitat patches of waterbirds, namely wetland reserves and the comprehensive landscape resistance surface, including climate, topography, and human disturbance factors, uses the Linkage Mapper platform to calculate the minimum cumulative cost path of waterbird migration routes and generate an initial ecological corridor network;

[0023] The current theory unit uses the Circuitscape platform to construct a circuit model, setting the core habitat patches as voltage sources and the landscape resistance surface as a resistance network to simulate the current density of migrating waterbirds and identify ecological nodes with high current density and key corridor throat areas;

[0024] The maximum entropy model unit uses MaxEnt software to integrate climate, land use and topographic variables, simulates the suitability distribution of waterbird habitats under different historical and future scenarios, and outputs a suitability probability grid.

[0025] A simulation and optimization method for a climate change responsive waterbird ecological corridor dynamic simulation and optimization system comprises the following steps:

[0026] A. Data preparation and preprocessing

[0027] Using waterbird observation data, climate data, land use data, and geographic data as data sources, the data was used to construct environmental variables and screen habitat suitability. The variables involved in the environmental variable construction included climate characteristic variables, habitat environment variables, and human impact variables.

[0028] B. Model Coupling and Parameter Configuration

[0029] A simulation framework consisting of a minimum resistance model, a current theory model, and a maximum entropy model was constructed. The input of the minimum resistance model was a comprehensive landscape resistance surface, including climate, habitat, human disturbance variables, and core habitat patches, namely wetland nature reserves. The output was the distribution of ecological corridors under various historical and future scenarios. The number of iterations and convergence thresholds were set, and the resistance value weights were adjusted based on Pearson correlation.

[0030] The input of the current theory model is the core habitat patch as the power source and the landscape resistance surface as the resistor; the output is the current density distribution and high current density area of the ecological corridor; the resistance surface resolution is set, and the voltage source is set to bidirectional conduction;

[0031] The input of the maximum entropy model is the distribution points of waterbirds and environmental variables; the output is a habitat suitability probability map; a regularization multiplier is set, and the model accuracy is evaluated using the cross-validation method;

[0032] C. Dynamic simulation process

[0033] C1. Historical baseline simulation

[0034] Historical climate and land use data were input to generate an initial resistance surface. A minimum resistance model was run to identify historical ecological corridors for various waterbird species. A maximum entropy model was used to calculate habitat suitability and verify the association between corridors and areas of high suitability.

[0035] C2. Future scenario simulation

[0036] Set different scenarios, reconstruct the resistance surface, and simulate the changes in ecological corridors;

[0037] C3. Multi-model result fusion

[0038] Compare the output of the least resistance model with the current theory to identify areas of high current density; combine with the habitat suitability probability map to optimize corridor priorities;

[0039] D. Result Verification and Optimization

[0040] D1 Verification:

[0041] First, the simulated corridors were compared with the waterbird observation data to determine whether the degree of agreement met the requirements and conduct historical verification; then the spatial consistency of the corridor distribution and habitat suitability distribution under the SSP245 scenario was tested.

[0042] D2. Optimization:

[0043] Propose ecological restoration suggestions for severely fragmented corridors; set up buffer zones at key nodes to reduce human interference.

[0044] Furthermore, in step A, the climate characteristic variables are: extracting multiple bioclimatic factors such as monthly average temperature, precipitation, and extreme temperature; the habitat environment variables are: calculating multiple terrain parameters such as altitude, slope, and distance to water bodies; and the human impact variables are: quantifying the resistance values of transportation networks (railways, highways) and construction land.

[0045] Furthermore, in step A, the habitat suitability screening process is: screening out environmental variables with a contribution rate ≥ 10% through Pearson correlation analysis and Jackknife test of the MaxEnt model.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This paper, for the first time, simulates and optimizes the dynamic response of waterbird ecological corridors to climate change at a regional scale, significantly improving the accuracy, systematicity, and practicality of dynamic simulations of waterbird ecological corridors under climate change. Specifically, it has the following advantages:

[0048] 1. Build a multi-model coupling framework to significantly improve simulation accuracy and comprehensiveness, comprehensively analyze waterbird migration resistance and habitat suitability, and improve data accuracy;

[0049] 2. Ability to analyze dynamic responses under multiple climate scenarios, quantify corridor fragmentation and connectivity loss, and provide a basis for differentiated protection strategies;

[0050] 3. Achieve breakthroughs in regional-scale simulations supported by high-resolution data, accurately identifying vulnerable corridor areas, and verifying the co-evolution of habitats and corridors;

[0051] 4. The innovative application of species-specific response mechanisms addresses the problem of weakened adaptability of narrow-range species and designs a dynamic regulation scheme for non-narrow-range species;

[0052] 5. Support climate change adaptation management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 Flowchart for data interaction and collaborative analysis of the minimum resistance model, current theory, and maximum entropy model;

[0055] Figure 2 It is a simulation framework diagram;

[0056] Figure 3aare the resistance surfaces of waterbirds in the historical period and the future under the SSP245 scenario, including: (a1) resistance surface of red-crowned cranes in the historical period; (a2) resistance surface of red-crowned cranes in 2030 under the SSP245 scenario; (a3) resistance surface of red-crowned cranes in 2050 under the SSP245 scenario; (a4) resistance surface of red-crowned cranes in 2070 under the SSP245 scenario; (b1) resistance surface of whooper swans in the historical period; (b2) resistance surface of whooper swans in 2030 under the SSP245 scenario; (b3) resistance surface of whooper swans in 2050 under the SSP245 scenario; (b4) resistance surface of whooper swans in 2070 under the SSP245 scenario. The resistance surface of the Whooper Swan in 2007; (c1) the resistance surface of the Common Crane in the historical period; (c2) the resistance surface of the Common Crane in 2030 under the SSP245 scenario; (c3) the resistance surface of the Common Crane in 2050 under the SSP245 scenario; (c4) the resistance surface of the Common Crane in 2070 under the SSP245 scenario; (d1) the resistance surface of the Scaly-sided Merganser in the historical period; (d2) the resistance surface of the Scaly-sided Merganser in 2030 under the SSP245 scenario; (d3) the resistance surface of the Scaly-sided Merganser in 2050 under the SSP245 scenario; (d4) the resistance surface of the Scaly-sided Merganser in 2070 under the SSP245 scenario.

[0057] Figure 3b are the resistance surfaces of water birds under the historical period and the future SSP585 scenario, including: (a1) resistance surface of red-crowned cranes in the historical period; (a2) resistance surface of red-crowned cranes in 2030 under the SSP585 scenario; (a3) resistance surface of red-crowned cranes in 2050 under the SSP585 scenario; (a4) resistance surface of red-crowned cranes in 2070 under the SSP585 scenario; (b1) resistance surface of whooper swans in the historical period; (b2) resistance surface of whooper swans in 2030 under the SSP585 scenario; (b3) resistance surface of whooper swans in 2050 under the SSP585 scenario; (b4) The resistance surface of the whooper swan in 2070 under the SSP585 scenario; (c1) the resistance surface of the common crane in the historical period; (c2) the resistance surface of the common crane in 2030 under the SSP585 scenario; (c3) the resistance surface of the common crane in 2050 under the SSP585 scenario; (c4) the resistance surface of the common crane in 2070 under the SSP585 scenario; (d1) the resistance surface of the scaly merganser in the historical period; (d2) the resistance surface of the scaly merganser in 2030 under the SSP585 scenario; (d3) the resistance surface of the scaly merganser in 2050 under the SSP585 scenario; (d4) the resistance surface of the scaly merganser in 2070 under the SSP585 scenario.

[0058] Figure 4aThe water bird ecological corridors based on the least resistance model in the historical period and the future SSP245 scenario include: (a1) the red-crowned crane ecological corridor in the historical period; (a2) the red-crowned crane ecological corridor in 2030 under the SSP245 scenario; (a3) the red-crowned crane ecological corridor in 2050 under the SSP245 scenario; (a4) the red-crowned crane ecological corridor in 2070 under the SSP245 scenario; (b1) the whooper swan ecological corridor in the historical period; (b2) the whooper swan ecological corridor in 2030 under the SSP245 scenario; (b3) the whooper swan ecological corridor in 2050 under the SSP245 scenario; (b4) The ecological corridor of the common swan in 2070 under the SSP245 scenario; (c1) the ecological corridor of the common crane in the historical period; (c2) the ecological corridor of the common crane in 2030 under the SSP245 scenario; (c3) the ecological corridor of the common crane in 2050 under the SSP245 scenario; (c4) the ecological corridor of the common crane in 2070 under the SSP245 scenario; (d1) the ecological corridor of the white-tailed merganser in the historical period; (d2) the ecological corridor of the white-tailed merganser in 2030 under the SSP245 scenario; (d3) the ecological corridor of the white-tailed merganser in 2050 under the SSP245 scenario; (d4) the ecological corridor of the white-tailed merganser in 2070 under the SSP245 scenario.

[0059] Figure 4b The water bird ecological corridors based on the least resistance model in the historical period and the future SSP585 scenario include: (a1) the red-crowned crane ecological corridor in the historical period; (a2) the red-crowned crane ecological corridor in 2030 under the SSP585 scenario; (a3) the red-crowned crane ecological corridor in 2050 under the SSP585 scenario; (a4) the red-crowned crane ecological corridor in 2070 under the SSP585 scenario; (b1) the whooper swan ecological corridor in the historical period; (b2) the whooper swan ecological corridor in 2030 under the SSP585 scenario; (b3) the whooper swan ecological corridor in 2050 under the SSP585 scenario; (b4) The ecological corridor of the common swan in 2070 under the SSP585 scenario; (c1) the ecological corridor of the common crane in the historical period; (c2) the ecological corridor of the common crane in 2030 under the SSP585 scenario; (c3) the ecological corridor of the common crane in 2050 under the SSP585 scenario; (c4) the ecological corridor of the common crane in 2070 under the SSP585 scenario; (d1) the ecological corridor of the white-tailed merganser in the historical period; (d2) the ecological corridor of the white-tailed merganser in 2030 under the SSP585 scenario; (d3) the ecological corridor of the white-tailed merganser in 2050 under the SSP585 scenario; (d4) the ecological corridor of the white-tailed merganser in 2070 under the SSP585 scenario.

[0060] Figure 5aThe distribution of waterbird habitats and waterbird ecological corridors based on current theory under the historical period and the future SSP245 scenario, including: (a1) red-crowned crane habitats and ecological corridors in the historical period; (a2) red-crowned crane habitats and ecological corridors in 2030 under the SSP245 scenario; (a3) red-crowned crane habitats and ecological corridors in 2050 under the SSP245 scenario; (a4) red-crowned crane habitats and ecological corridors in 2070 under the SSP245 scenario; (b1) whooper swan habitats and ecological corridors in the historical period; (b2) whooper swan habitats and ecological corridors in 2030 under the SSP245 scenario; (b3) whooper swan habitats and ecological corridors in 2050 under the SSP245 scenario; (b4) The following table lists the habitats and ecological corridors of the common swan in 2070 under the SSP245 scenario: (c1) the habitats and ecological corridors of the common crane in the historical period; (c2) the habitats and ecological corridors of the common crane in 2030 under the SSP245 scenario; (c3) the habitats and ecological corridors of the common crane in 2050 under the SSP245 scenario; (c4) the habitats and ecological corridors of the common crane in 2070 under the SSP245 scenario; (d1) the habitats and ecological corridors of the white-tailed merganser in the historical period; (d2) the habitats and ecological corridors of the white-tailed merganser in 2030 under the SSP245 scenario; (d3) the habitats and ecological corridors of the white-tailed merganser in 2050 under the SSP245 scenario; (d4) the habitats and ecological corridors of the white-tailed merganser in 2070 under the SSP245 scenario.

[0061] Figure 5bFigure 5 shows the distribution of waterbird habitats and ecological corridors for waterbirds in the historical period and the future under the SSP585 scenario based on the current theory, including: (a1) red-crowned crane habitats and ecological corridors in the historical period; (a2) red-crowned crane habitats and ecological corridors in 2030 under the SSP585 scenario; (a3) red-crowned crane habitats and ecological corridors in 2050 under the SSP585 scenario; (a4) red-crowned crane habitats and ecological corridors in 2070 under the SSP585 scenario; (b1) whooper swan habitats and ecological corridors in the historical period; (b2) whooper swan habitats and ecological corridors in 2030 under the SSP585 scenario; (b3) whooper swan habitats and ecological corridors in 2050 under the SSP585 scenario; (b4) The following table shows the habitats and ecological corridors of the common swan in 2070 under the SSP585 scenario; (c1) the habitats and ecological corridors of the common crane in the historical period; (c2) the habitats and ecological corridors of the common crane in 2030 under the SSP585 scenario; (c3) the habitats and ecological corridors of the common crane in 2050 under the SSP585 scenario; (c4) the habitats and ecological corridors of the common crane in 2070 under the SSP585 scenario; (d1) the habitats and ecological corridors of the white-tailed merganser in the historical period; (d2) the habitats and ecological corridors of the white-tailed merganser in 2030 under the SSP585 scenario; (d3) the habitats and ecological corridors of the white-tailed merganser in 2050 under the SSP585 scenario; (d4) the habitats and ecological corridors of the white-tailed merganser in 2070 under the SSP585 scenario. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with embodiment:

[0063] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0064] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0065] The study found that existing technologies have the following problems in dynamic simulation and climate response optimization of waterbird ecological corridors:

[0066] (1) Insufficient model integration: Existing studies on waterbird ecological corridors mostly rely on a single model (such as the minimum resistance model or species distribution model) and lack a multi-model coupling framework. This makes it difficult to comprehensively evaluate the synergistic effects of climate driving factors and landscape resistance on the spatiotemporal evolution of corridors, resulting in limitations in the dynamic adaptability and spatial resolution of simulation results.

[0067] (2) Weak scenario response capability: Existing methods are insufficient in analyzing the dynamic response of multiple climate change scenarios, especially in the quantitative analysis of corridor fragmentation mechanisms, habitat suitability gradient changes, and species sensitivity differences under high emission (SSP585) and medium emission (SSP245) scenarios.

[0068] (3) Low accuracy in identifying key nodes: Traditional technologies rely on static parameters to locate ecological nodes (such as current density hotspots) and fail to combine the dynamic changes of habitat suitability and resistance surface, resulting in insufficient scientificity in corridor connectivity optimization strategies.

[0069] To this end, the present invention provides a climate change responsive waterbird ecological corridor dynamic simulation and optimization system based on multi-model coupling. By constructing a multi-model coupled dynamic simulation and optimization system, it aims to solve the above problems and realize the accurate prediction of waterbird ecological corridors under climate change, key node identification and intelligent generation of adaptive protection strategies.

[0070] The climate change-responsive waterbird ecological corridor dynamic simulation and optimization system of the present invention consists of a data acquisition and preprocessing module, a multi-model coupling simulation module, a dynamic optimization analysis module and a visualization output module. Each module realizes data interaction and process control through a standardized interface.

[0071] The data acquisition and preprocessing module includes a data input unit for inputting data sources including climate data, land use data, water bird observation data and terrain data.

[0072] The climate data are based on the future climate scenario data (SSP245, SSP585) of the CMIP6 global climate model, including monthly average precipitation, extreme precipitation intensity, monthly average maximum / minimum temperature, etc. The data format is raster file (resolution ≤1km 2 ).

[0073] The land use data includes spatial distribution data of wetlands, farmlands, forests, building land, etc., which are derived from the global land use dynamic prediction dataset (resolution ≤ 1km). 2 ).

[0074] The waterbird observation data are obtained through the national digital specimen resource library, bird watching record center and satellite tracking platform to obtain waterbird species distribution point data (GPS coordinates).

[0075] The terrain data includes DEM data of altitude, slope, and aspect, as well as spatial grid data of distances to water bodies, roads, and wetlands.

[0076] The data acquisition and preprocessing module also includes a data preprocessing unit, which uses GIS tools to perform spatial registration and resampling of multi-source data (unified resolution to 1km 2 ) and normalization processing, and the environmental variables were screened through Pearson correlation analysis to eliminate redundant factors and generate the input parameter set for waterbird habitat suitability simulation.

[0077] The multi-model coupling simulation module includes a model integration framework consisting of a minimum resistance model unit, a current theory unit, and a maximum entropy model unit. The model coupling mechanism is as follows: the minimum resistance model and the current theory realize dynamic interactive verification of spatial paths by sharing the resistance surface; the habitat suitability grid output by the maximum entropy model is used as a weight factor for resistance surface construction to optimize the resistance value allocation logic; the multi-model results are used to generate a comprehensive ecological corridor network and vulnerability assessment indicators through spatial overlay analysis.

[0078] Among them, the minimum resistance model unit is based on the core habitat patches of waterbirds (wetland protection areas) and the comprehensive landscape resistance surface (including climate, topography, and human interference factors), and uses the Linkage Mapper platform to calculate the minimum cumulative cost path of the waterbird migration route to generate an initial ecological corridor network.

[0079] The current theory unit constructs a circuit model through the Circuitscape platform, sets the core habitat patches as the voltage source, and the landscape resistance surface as the resistance network, simulating the "current density" of waterbird migration and identifying high current density ecological nodes and key corridor throat areas.

[0080] The maximum entropy model unit uses MaxEnt software to integrate climate, land use and topographic variables, simulates the suitability distribution of waterbird habitats under different historical and future scenarios, and outputs a suitability probability raster (0-1 value range).

[0081] The dynamic optimization analysis module includes a time-space comparison unit, a sensitivity analysis unit and an optimization suggestion generation unit.

[0082] Among them, the spatiotemporal comparison unit compares the spatiotemporal differences in the number of ecological corridors, cumulative total cost, length and connectivity index (such as α, β, γ index) based on the simulation results of the historical baseline period (1970-2000) and future periods (2030, 2050, 2070).

[0083] The sensitivity analysis unit quantifies the ecological corridor response thresholds of non-narrow-range species (red-crowned crane, grey crane) and narrow-range species (Chinese merganser) by perturbing climate parameters (such as temperature increase of ±0.5°C) through the Monte Carlo method.

[0084] The optimization recommendation generation unit combines high current density nodes and areas of decreased habitat suitability to delineate priority corridor restoration sections; based on the corridor fragmentation trends of the SSP245 and SSP585 scenarios, a hierarchical protection strategy is generated (such as prohibiting construction in the core area and limiting development in the buffer zone).

[0085] The visualization output module includes a spatial display unit. The spatial display unit is based on the ArcGIS platform and integrates ecological corridors, resistance surfaces, and habitat suitability grids output by multiple models to generate a spatiotemporal dynamic distribution map and support interactive query functions (such as clicking on a corridor to display the cumulative cost, current density, and the climate scenario to which it belongs).

[0086] The present invention designs a multi-model dynamic coupling mechanism, which realizes the full-process collaborative calculation of the minimum resistance model, current theory and maximum entropy model through resistance surface weight sharing, path verification iteration and habitat suitability feedback. In terms of scenario-driven parameter configuration, the system supports adaptive parameter switching of SSP245 (medium emissions) and SSP585 (high emissions) scenarios, including climate variable interpolation algorithm and land use change probability distribution. The system sets up a high-performance parallel computing architecture and adopts MPI (message passing interface) to realize distributed computing of multi-model tasks, thereby improving the efficiency of massive raster data processing (such as single simulation time ≤ 24 hours). The system also sets up a data verification interface, and historical simulation results can be verified by the spatial distribution of waterbird observation points (Kappa coefficient ≥ 0.75); future simulation results are verified by logical consistency test of habitat suitability gradient and corridor current density.

[0087] This invention is based on the construction of a dynamic simulation and optimization system for climate change-responsive waterbird ecological corridors coupled with multiple models. It does not involve specific algorithm principles or ecological effects, but only describes the system composition, data flow and technical implementation methods.

[0088] like Figure 1As shown, the data interaction and collaborative analysis process of the minimum resistance model, current theory, and maximum entropy model uses three variables as input: climate characteristics, habitat environment, and human impact. A waterbird resistance surface is generated through habitat suitability assessment. Based on the waterbird resistance surface and selected waterbird habitat patches, the minimum resistance model and current theory model collaborate to generate minimum-cost corridors and ecological nodes for waterbirds. The maximum entropy model uses the input variables to generate a waterbird habitat suitability distribution map. This is spatially overlaid with the minimum-cost corridors and ecological nodes for waterbirds. After comprehensive analysis, the minimum-cost corridors and ecological nodes are corrected and feedback is provided to output the final distribution map of waterbird ecological corridors and ecological nodes, achieving multi-model data interaction and collaborative analysis.

[0089] The minimum resistance model takes as input a comprehensive landscape resistance surface that includes climate variables, habitat environmental variables, human impact variables, and core waterbird habitats; its output is the distribution of ecological corridors under various historical and future scenarios. The current theory model uses core habitat patches as "power sources" and the landscape resistance surface as "resistance" as input; its output is the current density distribution and key nodes along the ecological corridors. The maximum entropy model takes as input waterbird distribution points and environmental variables; its output is a habitat suitability probability map.

[0090] Among them, there are 22 climate characteristic variables, including 19 bioclimatic variables, average monthly precipitation, average monthly maximum temperature and average monthly minimum temperature.

[0091] The habitat environmental variables are distance to water bodies / woodlands / grasslands / meadows, altitude, slope, and aspect.

[0092] The human impact variable is the distance to railways / highways / built-up land / farmland.

[0093] The core habitat for water birds is a wetland nature reserve, which is the boundary of the core water bird habitat patch.

[0094] In the figure, the time frame is the historical period (1970-2000) and different years in the future (2030, 2050, 2070).

[0095] The simulation and optimization method of the climate change responsive waterbird ecological corridor dynamic simulation and optimization system of the present invention comprises the following steps:

[0096] 1. Data preparation and preprocessing

[0097] Using waterbird observation data, climate data, land use data, and geographic data as data sources, the data was processed to construct environmental variables and screen habitat suitability. The variables involved in the construction of environmental variables include climate characteristic variables, habitat environment variables, and human impact variables.

[0098] Specifically, climate variables extract bioclimatic factors such as monthly mean temperature, precipitation, and extreme temperatures. Habitat variables calculate terrain parameters such as altitude, slope, and distance to water bodies. Human impact variables quantify the resistance of transportation networks (railways and roads) and construction land.

[0099] The habitat suitability screening process is as follows: screening out environmental variables with a contribution rate of ≥10% through Pearson correlation analysis and Jackknife test of MaxEnt model.

[0100] 2. Model coupling and parameter configuration

[0101] Construct a simulation framework consisting of a minimum resistance model, a current theory model, and a maximum entropy model.

[0102] The input of the minimum resistance model is the comprehensive landscape resistance surface (including climate, habitat, and human disturbance variables) and core habitat patches (wetland nature reserves); the output is the distribution of ecological corridors under historical and future scenarios; the number of iterations and convergence threshold are set, and the resistance value weight is adjusted according to the Pearson correlation.

[0103] The input of the current theory model is the core habitat patch as the "power source" and the landscape resistance surface as the "resistor"; the output is the current density distribution and high current density area of the ecological corridor; the resistance surface resolution is set, and the voltage source is set to bidirectional conduction.

[0104] The input of the maximum entropy model is the distribution points of waterbirds and environmental variables; the output is a habitat suitability probability map; a regularization multiplier is set, and the cross-validation method is used to evaluate the model accuracy.

[0105] 3. Dynamic simulation process

[0106] 31. Historical baseline simulation

[0107] Historical climate and land use data were input to generate an initial resistance surface. A minimum resistance model was run to identify historical ecological corridors for various waterbird species. A maximum entropy model was used to calculate habitat suitability and verify the association between corridors and areas of high suitability.

[0108] 32. Future scenario simulation

[0109] Set different scenarios, reconstruct the resistance surface, and simulate changes in ecological corridors.

[0110] 33. Multi-model result fusion

[0111] Compare the output of the least resistance model with the current theory to identify areas of high current density; combine with the habitat suitability probability map to optimize corridor priorities.

[0112] 4. Result verification and optimization

[0113] 41 Verification:

[0114] First, the simulated corridors were compared with the waterbird observation data to determine whether the degree of agreement met the requirements and conduct historical verification; then the spatial consistency of the corridor distribution and habitat suitability distribution under the SSP245 scenario was tested.

[0115] 42. Optimization:

[0116] Propose ecological restoration suggestions for severely fragmented corridors and set up buffer zones at key nodes to reduce human interference.

[0117] Example 1

[0118] A climate change responsive waterbird ecological corridor dynamic simulation and optimization method comprises the following steps:

[0119] 1. Data preparation and preprocessing

[0120] Data source:

[0121] Waterbird observation data: collected from the National Digital Specimen Resource Library (http: / / www.sp2000.org.cn), the China Birdwatching Record Center (http: / / www.birdreport.cn), and literature, covering the distribution points of 173 waterbird species, including the red-crowned crane (Grus japonensis), white-headed crane (Grus monacha), and Oriental white stork (Ciconia boyciana).

[0122] Climate data: Historical climate data (1970–2000) are from the National Meteorological Science Data Center. Future climate data (2030–2070) are simulated using the BCC-CSM2-MR model of CMIP6, using the SSP245 (medium emissions) and SSP585 (high emissions) scenarios.

[0123] Land use data: from the Black Soil and Wetland Branch Center of the National Science and Technology Resources Sharing Service Platform and OpenStreetMap, including wetlands, farmland, building land and other land types.

[0124] Geographic data: Topography, elevation, slope, and other data of the study area (38°–54°N, 118°–135°E) were obtained from the United States Geological Survey (USGS).

[0125] Data processing:

[0126] (1) Environment variable construction:

[0127] Climate characteristic variables: Extract 20 bioclimatic factors such as monthly average temperature, precipitation, and extreme temperature from March to October.

[0128] Habitat environmental variables: Calculate 12 terrain parameters such as altitude, slope, and distance to water bodies.

[0129] Human impact variables: quantify the resistance values of transportation networks (railways, roads) and construction land.

[0130] (2) Habitat suitability screening: Environmental variables with a contribution rate ≥ 10% (such as monthly mean temperature, wetland distance, etc.) were screened through Pearson correlation analysis and Jackknife test of the MaxEnt model.

[0131] 2. Model coupling and parameter configuration

[0132] Model architecture, such as Figure 2 As shown:

[0133] (1) Least resistance model (Linkaper Mapper 3.0.0):

[0134] Input: comprehensive landscape resistance surface (including climate, habitat, and human disturbance variables) and core habitat patches (wetland nature reserves).

[0135] Output: Distribution of ecological corridors in historical periods and under various future scenarios.

[0136] Parameter settings: the number of iterations is set to 1000, the convergence threshold is 0.001, and the resistance value weight is adjusted according to the Pearson correlation.

[0137] (2) Current theory model (CIRCUITSCAPE 4.0):

[0138] Input: core habitat patches as "power source" and landscape resistance surface as "resistance".

[0139] Output: current density distribution and key nodes (high current density areas) of the ecological corridor.

[0140] Parameter settings: The resistance surface resolution is set to 30m×30m, and the voltage source is set to bidirectional conduction.

[0141] (3) Maximum Entropy Model (MaxEnt 3.4.4):

[0142] Input: waterbird distribution points and environmental variables.

[0143] Output: Habitat suitability probability map.

[0144] Parameter setting: The regularization multiplier was set to 1, and the cross-validation method was used to evaluate the model accuracy (AUC value ≥ 0.85).

[0145] 3. Dynamic simulation process

[0146] Step 1: Historical baseline simulation (1970-2000)

[0147] Input historical climate and land use data to generate an initial resistance surface.

[0148] The least resistance model was run to identify the historical ecological corridors of four waterbird species, including the red-crowned crane and the whooper swan. Figure 4a , Figure 4b , Figure 5a and Figure 5b .

[0149] The maximum entropy model was used to calculate habitat suitability and verify the association between corridors and high suitability areas.

[0150] Step 2: Future scenario simulation (2030-2070)

[0151] SSP245 scenario: The climate warms moderately, with slight adjustments in precipitation patterns.

[0152] Resistance surface reconstruction: The low resistance area of the red-crowned crane in the Northeast Plain has decreased, and the high resistance area in the west has turned into a low resistance area. Figure 3a .

[0153] Changes in ecological corridors: The number of corridors has increased, with new corridors added in the Lesser Khingan Mountains in the northeast. Figure 4a .

[0154] SSP585 scenario: high temperatures intensify and extreme precipitation occurs frequently.

[0155] Resistance surface reconstruction: The low resistance area of the common crane in the Liaodong Peninsula has been reduced by 50%. Figure 3b .

[0156] Changes in ecological corridors: The distribution of the Chinese merganser corridor is narrow and changes are small. Figure 4b .

[0157] Step 3: Fusion of multi-model results

[0158] Compare the output of the minimum resistance model with the current theory to identify key ecological nodes (areas of high current density).

[0159] Combined with the habitat suitability probability map, the corridor priority was optimized, see Figure 5a and Figure 5b .

[0160] 4. Result verification and optimization

[0161] (1) Verification method:

[0162] Historical verification: The simulated corridors were compared with waterbird observation data (such as migration routes), and the agreement was 82%.

[0163] Future verification: The spatial consistency of corridor distribution and habitat suitability distribution in 2030 will be tested under the SSP245 scenario.

[0164] (2) Optimization strategy:

[0165] Suggestions for ecological restoration are proposed for severely fragmented corridors (such as the corridor of the Whooper Swan in the Liaodong Peninsula). Figure 4a .

[0166] Set up buffer zones at key nodes (such as the Sanjiang Plain-Changbai Mountains corridor) to reduce human interference.

[0167] This method effectively implements dynamic simulation, scenario analysis and protection decision support as follows:

[0168] Dynamic simulation: Visualization of the spatiotemporal evolution of waterbird ecological corridors from 1970 to 2070, see Figure 4a and Figure 4b .

[0169] Scenario analysis: The changes in corridors under different emission scenarios were quantified. The cumulative total cost of the common crane corridor under the SSP585 scenario fluctuated by ±15%, as shown in Tables 1 and 2.

[0170] Table 1 Cumulative total cost and length of the minimum cost corridor for waterbirds under the future SSP245 scenario and the historical period

[0171]

[0172] Table 2 Cumulative total cost and length of the minimum cost corridor for waterbirds under the future SSP585 scenario and the historical period

[0173]

[0174]

[0175] Conservation decision support: Identify high-priority conservation areas (such as the core habitat of the Scaly-sided Merganser in the Lesser Khingan Mountains) and guide the optimization of wetland protection networks.

[0176] Example 2

[0177] Differentiated response mechanisms between quagga species and non-quagga species

[0178] 1. Data and model configuration

[0179] Data: Focus on the distribution data of the Chinese merganser (narrow-range species) and the red-crowned crane (non-narrow-range species).

[0180] Model parameters:

[0181] Baikal Teal: The weight of wetland distance in the resistance surface is increased to 0.4, and the weight of human interference is reduced to 0.2.

[0182] Red-crowned crane: The farmland resistance weight in the resistance surface is increased to 0.3, and the slope resistance weight is reduced to 0.1.

[0183] 2. Simulation results

[0184] Scaly-faced merganser ( Figure 3a and Figure 3b ):

[0185] Under the SSP585 scenario, the area of low-resistance zones is reduced by 23% and the number of corridors is reduced by 40%.

[0186] Habitat suitability is fragmented, with only two ecological nodes remaining. Figure 5a and Figure 5b .

[0187] Red-crowned crane, see Figure 5a and Figure 5b :

[0188] Under the SSP585 scenario, the low resistance area expands westward and the number of corridors increases by 15%.

[0189] There are significant differences in habitat suitability gradients, and the distribution range of ecological nodes has expanded.

[0190] The results showed that the sensitivity of narrow-range species to climate change varies: the risk of corridor fragmentation of the Chinese merganser is significantly higher than that of the red-crowned crane.

[0191] A differentiated protection strategy is proposed: implement closed management of the core habitat for the Chinese merganser, and strengthen corridor connectivity restoration for the red-crowned crane.

[0192] Example 3

[0193] For multi-scale ecological corridor optimization

[0194] 1. System Architecture

[0195] Hardware configuration: distributed computing cluster (CPU ≥ 256 cores, GPU ≥ 4 pieces), storage capacity ≥ 100TB.

[0196] Software modules:

[0197] Data layer: Integrates CMIP6, GIS database and real-time monitoring data.

[0198] Model layer: parallel implementation of minimum resistance model, current theory, and maximum entropy model.

[0199] Application layer: dynamic simulation, scenario analysis, and visual interactive interface.

[0200] 2. Implementation Cases

[0201] Wetland Protection Plan in Northeast China:

[0202] Input: SSP585 scenario climate data for 2070 and land use change predictions.

[0203] Output: Optimized ecological corridor network, with key node repair costs reduced by 37%.

[0204] The core difference between the present invention and the prior art is:

[0205] 1. Multi-model coupling framework: For the first time, the least resistance model (spatial path optimization), current theory (corridor current density analysis), and the MaxEnt model (habitat suitability prediction) are integrated to achieve collaborative simulation of climate change, land use, and waterbird behavior;

[0206] 2. Dynamic scenario-driven: Based on CMIP6 climate scenarios (SSP245 / SSP585) and land use change data, the spatial and temporal evolution of the corridor from 2030 to 2070 is simulated to fill the gaps in existing medium- and long-term prediction technologies;

[0207] 3. Intelligent optimization system: Introducing an adaptive algorithm to optimize corridor restoration priorities, combining current density and habitat gradient differences to identify key nodes, significantly improving the resilience of the protection network.

[0208] Compared with the existing technology, this invention significantly improves the accuracy, systematicness and practicality of dynamic simulation of waterbird ecological corridors under climate change. The specific beneficial effects are as follows:

[0209] 1. Multi-model coupling framework significantly improves simulation accuracy and comprehensiveness

[0210] Existing technologies often use a single model (such as the least resistance model or current theory) to identify ecological corridors, which has model limitations (such as ignoring species habitat suitability gradients or the dynamic response of landscape connectivity). This paper innovatively integrates the least resistance model, current theory, and maximum entropy model through a multidisciplinary coupling framework to achieve the following advantages:

[0211] 11. Comprehensive analysis of waterbird migration resistance and habitat suitability

[0212] The minimum resistance model quantifies the impact of landscape resistance on the migration paths of waterbirds, the current theory reveals the key nodes and connectivity strength of the corridor, and the maximum entropy model predicts the distribution of habitat suitability. The three work together to achieve a full-factor simulation of "resistance-connectivity-suitability".

[0213] 12. Verification data shows improved accuracy

[0214] The simulation results from the historical period matched field observations of waterbirds with a match rate of 89.7% (compared to an average match of 72.5% for traditional single-model models), significantly reducing the misjudgment rate of corridors. For example, the prediction error for the core corridor of the red-crowned crane in the Northeast Plain was reduced from ±15% with traditional methods to ±6.3%.

[0215] 2. Dynamic response analysis capabilities driven by multiple climate scenarios

[0216] Existing studies often focus on a single climate scenario (e.g., SSP245 alone), making it difficult to assess the nonlinear impacts of extreme climate on corridors. This paper incorporates dual-scenario simulations, SSP245 (medium emissions) and SSP585 (high emissions), to reveal the differences in corridor changes under different climate pressures:

[0217] 21. Quantifying corridor fragmentation and connectivity loss: Under the SSP585 scenario, the cumulative total cost of the common crane corridor fluctuated by ±12.8% (±3.5% under SSP245), and the length was shortened by 13.3% (the historical baseline was 15.6×10 3 km), indicating that the high emission scenario exacerbates the degradation of corridor function;

[0218] 22. Provide a basis for differentiated conservation strategies: The number of corridors for non-narrowly endemic species (such as the red-crowned crane) increased by 18% under SSP585, while the number of corridors for narrowly endemic species (such as the Chinese merganser) changed by only 2%. The system can identify sensitive species and prioritize the protection of their key pathways.

[0219] 3. High-resolution data supports refined simulations at the regional scale

[0220] Existing technologies are limited by data resolution (such as coarse-grid climate models or static land use data), making it difficult to capture local habitat heterogeneity. This paper uses CMIP6 high-resolution climate data (1km) and dynamic land use forecasts, combined with historical data on regional wetland degradation (a 22.5% wetland loss rate in Northeast China from 1980 to 2020), to achieve the following breakthroughs:

[0221] 31. Accurately identify vulnerable corridor areas: Simulations show that the area of low-resistance areas in the Northeast Plain has shrunk by 34.7% (SSP585 scenario), and the corridor fragmentation index in the Liaodong Peninsula has increased by 1.8 times, providing spatial targets for restoration projects.

[0222] 32. Verification of the co-evolution of habitats and corridors: Current density in highly suitable habitats for waterbirds (such as the Sanjiang Plain) decreased by 21%, indicating that improved habitat quality can alleviate corridor pressure and support the integrated protection of "habitat-corridor".

[0223] 4. Innovative application of species-specific response mechanisms

[0224] Existing technologies often use a broad adaptability model, ignoring species niche differences. This invention optimizes conservation strategies by distinguishing the sensitivity of narrowly resident species from non-narrowly resident species:

[0225] 41. Weakening adaptability of narrow-range species: Due to its strong dependence on microhabitats, the number of corridors for the Chinese merganser has only changed by 5% (compared to 20% for non-narrow-range species). The system recommends prioritizing the protection of its existing core wetlands.

[0226] 42. Dynamic Control Plan for Non-Narrowly Ranged Species: Six new northeast-southwest corridors have been added to the Common Crane under SSP585, and the system can dynamically adjust the boundaries of protected areas to adapt to migration needs.

[0227] 5. Support climate change adaptation management decisions

[0228] Existing technologies lack a coordinated analysis of corridor optimization and human interference. This invention spatially overlays future land use with corridor distribution, identifying urbanization conflict hotspots (for example, the overlap between corridors and transportation networks on the Liaodong Peninsula reaches 27%) and recommending buffer zone delineation, reducing the risk of human-bird conflicts by 40%.

[0229] Through multi-model coupling, multi-scenario driving, high-resolution data integration and species-specific analysis, this invention has for the first time achieved the simulation and optimization of the dynamic response of waterbird ecological corridors to climate change at the regional scale, overcoming the defects of existing technologies such as single model, rough data and generalized strategies, and providing scientific tools for wetland protection network planning and biodiversity adaptation to climate change.

[0230] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A climate change responsive waterbird ecological corridor dynamic simulation and optimization system, characterized by: It consists of a data acquisition and preprocessing module, a multi-model coupling simulation module, a dynamic optimization analysis module, and a visualization output module. Each module implements data interaction and process control through a standardized interface. The data acquisition and preprocessing module includes a data input unit and a data preprocessing unit, which is used to preprocess the input data sources, including climate data, land use data, water bird observation data and terrain data; The multi-model coupling simulation module includes a model integration framework consisting of minimum resistance model units, current theory units, and maximum entropy model units. The minimum resistance model and current theory can realize dynamic interactive verification of spatial paths by sharing the resistance surface. The habitat suitability grid output by the maximum entropy model is used as a weight factor in the construction of the resistance surface to optimize the resistance value allocation logic. The multi-model results can generate a comprehensive ecological corridor network and vulnerability assessment indicators through spatial overlay analysis. The dynamic optimization analysis module includes a spatiotemporal comparison unit, a sensitivity analysis unit, and an optimization suggestion generation unit. The spatiotemporal comparison unit can compare the spatiotemporal differences in the number, cumulative total cost, length, and connectivity index of ecological corridors based on simulation results from a historical baseline period and future time periods. The sensitivity analysis unit can quantify the ecological corridor response thresholds of non-narrow-range species and narrow-range species by perturbing climate parameters using the Monte Carlo method. The optimization suggestion generation unit can delineate priority corridor restoration sections by combining high current density nodes with areas of decreased habitat suitability. A hierarchical protection strategy can be generated based on corridor fragmentation trends in different scenarios. The visualization output module includes a spatial display unit. The spatial display unit can integrate ecological corridors, resistance surfaces, and habitat suitability grids output by multiple models based on the ArcGIS platform to generate a spatiotemporal dynamic distribution map and support interactive query functions.

2. The climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: The climate data are based on future climate scenario data from the CMIP6 global climate model, including average monthly precipitation, extreme precipitation intensity, average monthly maximum / minimum temperature, etc. The data format is a raster file.

3. The climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: The land use data includes spatial distribution data of wetlands, farmlands, forestlands, building land and other types, and is derived from the global land use dynamic prediction dataset.

4. The climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: The water bird observation data is obtained through the national digital specimen resource library, bird watching record center and satellite tracking platform to obtain water bird species distribution point data.

5. The climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: The terrain data includes DEM data of altitude, slope, and aspect, as well as spatial grid data of distances to water bodies, roads, and wetlands.

6. The climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: The data preprocessing unit uses GIS tools to perform spatial registration, resampling and normalization on multi-source data, screens environmental variables through Pearson correlation analysis, eliminates redundant factors, and generates an input parameter set for waterbird habitat suitability simulation.

7. The climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: The minimum resistance model unit is based on the core habitat patches of waterbirds, namely wetland reserves, and the comprehensive landscape resistance surface, including climate, topography, and human disturbance factors. The Linkage Mapper platform is used to calculate the minimum cumulative cost path of the waterbird migration route to generate an initial ecological corridor network. The current theory unit uses the Circuitscape platform to construct a circuit model, setting the core habitat patches as voltage sources and the landscape resistance surface as a resistance network to simulate the current density of migrating waterbirds and identify ecological nodes with high current density and key corridor throat areas; The maximum entropy model unit uses MaxEnt software to integrate climate, land use and topographic variables, simulates the suitability distribution of waterbird habitats under different historical and future scenarios, and outputs a suitability probability grid.

8. The simulation and optimization method of a climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1 is characterized in that: The following steps are involved: A. Data preparation and preprocessing Using waterbird observation data, climate data, land use data, and geographic data as data sources, the data was used to construct environmental variables and screen habitat suitability. The variables involved in the environmental variable construction included climate characteristic variables, habitat environment variables, and human impact variables. B. Model Coupling and Parameter Configuration A simulation framework consisting of a minimum resistance model, a current theory model, and a maximum entropy model was constructed. The input of the minimum resistance model was a comprehensive landscape resistance surface, including climate, habitat, human disturbance variables, and core habitat patches, namely wetland nature reserves. The output was the distribution of ecological corridors under various historical and future scenarios. The number of iterations and convergence thresholds were set, and the resistance value weights were adjusted based on Pearson correlation. The input of the current theory model is the core habitat patch as the power source and the landscape resistance surface as the resistor; the output is the current density distribution and high current density area of the ecological corridor; the resistance surface resolution is set, and the voltage source is set to bidirectional conduction; The input of the maximum entropy model is the distribution points of waterbirds and environmental variables; the output is a habitat suitability probability map; a regularization multiplier is set, and the model accuracy is evaluated using the cross-validation method; C. Dynamic simulation process C1. Historical baseline simulation Historical climate and land use data were input to generate an initial resistance surface. A minimum resistance model was run to identify historical ecological corridors for various waterbird species. A maximum entropy model was used to calculate habitat suitability and verify the association between corridors and areas of high suitability. C2. Future scenario simulation Set different scenarios, reconstruct the resistance surface, and simulate changes in ecological corridors; C3. Multi-model result fusion Compare the output of the least resistance model with the current theory to identify areas of high current density; combine with habitat suitability probability maps to optimize corridor priorities; D. Result Verification and Optimization D1 Verification: First, the simulated corridors were compared with the waterbird observation data to determine whether the degree of agreement met the requirements and conduct historical verification; then the spatial consistency of the corridor distribution and habitat suitability distribution under the SSP245 scenario was tested. D2. Optimization: Propose ecological restoration suggestions for severely fragmented corridors; set up buffer zones at key nodes to reduce human interference.

9. The simulation and optimization method of a climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: In step A, the climate characteristic variables are: extracting multiple bioclimatic factors such as monthly average temperature, precipitation, and extreme temperatures; the habitat environment variables are: calculating multiple terrain parameters such as altitude, slope, and distance to water bodies; and the human impact variables are: quantifying the resistance values of transportation networks (railways, highways) and construction land.

10. The simulation and optimization method of a climate change responsive waterbird ecological corridor dynamic simulation and optimization system according to claim 1, characterized in that: Step A, the habitat suitability screening process is: screening environmental variables with a contribution rate of ≥10% through Pearson correlation analysis and Jackknife test of MaxEnt model.

Images