Method and device for assessing the quality of bird habitats in the great lakes basin

Abstract

The application provides a kind of method and device for evaluating bird habitat quality in Great Lakes Basin, applied to the technical field of bird habitat quality evaluation, the above-mentioned method comprises: obtaining land use data and bird biodiversity data of target area, wherein the target area contains multiple different climate zones;Based on the bird biodiversity data and land use data, the habitat suitability parameters and threat factor sensitivity parameters of the Great Lakes Basin are differentiated and optimized for different climate zones, respectively, to obtain a plurality of different climate zones corresponding to the optimized parameter set of the Great Lakes Basin bird habitat quality;The land use data and threat factor data are rasterized to obtain rasterized data;The rasterized data and the optimized parameter set are input into the preset InVEST model for habitat quality evaluation to obtain the optimized habitat quality evaluation result.The application can improve the applicability of the model in the evaluation of bird habitat quality in the Great Lakes Basin by combining the characteristics of bird ecology.

Description

Technical Field

[0001] This invention relates to the field of bird habitat quality assessment technology, and in particular to a method and apparatus for assessing bird habitat quality in a large lake basin. Background Technology

[0002] Bird habitat quality assessment is a core technical means for regional biodiversity conservation. It aims to identify key degraded areas and guide ecological restoration by scientifically quantifying the match between habitat conditions and bird survival needs.

[0003] While the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model performs well in ecosystem service assessment, its application to bird habitat quality remains insufficient. Optimizing the parameters of bird habitat quality models faces numerous challenges, such as the difficulty in obtaining species distribution data, the uncertainty of habitat preference parameters, and the model's insufficient adaptability to different ecosystem types. These issues limit the scope and accuracy of InVEST models in bird habitat quality assessment.

[0004] This shows that the bird habitat quality assessment methods in related technologies have poor adaptability. Summary of the Invention

[0005] This invention provides a method and apparatus for assessing bird habitat quality in large lake basins, which addresses the shortcomings of existing bird habitat quality assessment methods in terms of poor adaptability, and improves the applicability of the model in bird habitat quality assessment by combining bird ecological characteristics.

[0006] This invention provides a method for assessing bird habitat quality in a large lake basin, comprising the following steps: Acquiring land use data and bird biodiversity data for a target area, wherein the target area includes multiple different climate zones; Based on the bird biodiversity data and the land use data, differentially adjusting and optimizing habitat suitability parameters and threat factor sensitivity parameters for each of the different climate zones to obtain optimized parameter sets corresponding to each of the multiple different climate zones; Rasterizing the land use data and threat factor data to obtain rasterized data; Inputting the rasterized data and the optimized parameter sets into a preset InVEST model for habitat quality assessment to obtain optimized habitat quality assessment results.

[0007] According to a method for assessing bird habitat quality in a large lake basin provided by the present invention, after inputting the rasterized data and the optimized parameter set into a preset InVEST model for habitat quality assessment to obtain an optimized habitat quality assessment result, the method further includes: inputting the rasterized data and the original parameter set into a preset InVEST model for habitat quality assessment to obtain an unoptimized habitat quality assessment result; performing Spearman correlation analysis on the unoptimized habitat quality assessment result and the bird biodiversity data to obtain an unoptimized correlation coefficient; performing Spearman correlation analysis on the optimized habitat quality assessment result and the bird biodiversity data to obtain an optimized correlation coefficient; and determining the assessment result of the optimized parameter set based on the difference between the optimized correlation coefficient and the unoptimized correlation coefficient.

[0008] According to a method for assessing bird habitat quality in a large lake basin provided by the present invention, the step of performing Spearman correlation analysis on the optimized habitat quality assessment results and the bird biodiversity data to obtain an optimized correlation coefficient includes: performing a rank conversion between the target habitat quality assessment results and the bird biodiversity data to determine the rank difference between each pair of sample data; and determining the optimized correlation coefficient based on the rank difference between each pair of sample data and the total number of sample data.

[0009]

[0010] in, This represents the optimized correlation coefficient. This represents the rank difference between each pair of sample data. This indicates the total number of sample data.

[0011] According to the present invention, a method for assessing bird habitat quality in a large lake basin is provided, wherein the habitat quality assessment result is determined by the following formula:

[0012]

[0013]

[0014] in, Indicates habitat quality, Indicates habitat suitability. Indicates the degree of habitat degradation. Indicates the default parameters of the model. Represents the half-saturation constant. This represents the total number of threat factors. This represents the total number of threat factor grid cells. Indicates threat factors Influence weight value, Represents grid cells Threat factor values ​​on Indicates threat factors The scope of the impact study area Represents grid cells Accessibility level Indicates land use type Threat factors The degree of sensitivity.

[0015] According to the present invention, a method for assessing bird habitat quality in a large lake basin includes: acquiring predicted land use data for future periods; inputting the predicted land use data and the optimized parameter set into a preset InVEST model to assess habitat quality, and obtaining the habitat quality assessment results for the future periods; and determining the habitat quality difference between the future periods and the preset base year based on the difference between the habitat quality assessment results for the future periods and the habitat quality assessment results for a preset base year.

[0016] According to the present invention, a method for assessing the habitat quality of birds in a large lake basin is provided, wherein the multiple different climate zones include: a large lake basin in a temperate monsoon region, a large lake basin in a subtropical monsoon region, a large lake basin in a temperate continental arid climate zone, and a large lake basin in a plateau mountain region.

[0017] This invention also provides a bird habitat quality assessment device for a large lake basin, comprising the following modules: an acquisition module for acquiring land use data and bird biodiversity data of a target area, wherein the target area includes multiple different climate zones; an adjustment module for differentially adjusting and optimizing habitat suitability parameters and threat factor sensitivity parameters for different climate zones based on the bird biodiversity data and the land use data, thereby obtaining optimized parameter sets corresponding to the multiple different climate zones; a rasterization module for rasterizing the land use data and threat factor data to obtain rasterized data; and an assessment module for inputting the rasterized data and the optimized parameter sets into a preset InVEST model for habitat quality assessment, thereby obtaining optimized habitat quality assessment results.

[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement any of the above-described methods for assessing bird habitat quality in the Great Lakes Basin.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the bird habitat quality assessment method for the Great Lakes Basin as described above.

[0020] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a bird habitat quality assessment method for the Great Lakes Basin as described above.

[0021] The present invention provides a method and apparatus for assessing bird habitat quality in the Great Lakes Basin. First, by acquiring land use and bird diversity data of the target area including multiple climate zones, the comprehensiveness and representativeness of the basic data are ensured. Second, based on these data, parameter differentiation optimization is performed for different climate zones, effectively solving the problems of single parameters and lack of regional adaptability in traditional methods, and significantly improving the accuracy of habitat suitability and threat factor sensitivity parameters. Third, by rasterizing the land use and threat factor data, the uniformity of data format and the standardization of model input are ensured. Finally, the optimized parameters and raster data are input into the InVEST model for evaluation, ultimately obtaining accurate and reliable habitat quality assessment results that highly match the characteristics of each climate zone. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced one by one below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the bird habitat quality assessment method for the Great Lakes Basin provided by this invention.

[0024] Figure 2 This is a technical flowchart of the bird habitat quality assessment method for the Great Lakes Basin provided by the present invention.

[0025] Figure 3 This is a schematic diagram showing the trend of changes in bird habitat quality in the Great Lakes Basin from 1990 to 2020, provided by the present invention.

[0026] Figure 4 This is a schematic diagram of the verification analysis of bird habitat quality before and after parameter correction provided by the present invention.

[0027] Figure 5 This is a schematic diagram of the module of the bird habitat quality assessment device for the Great Lakes Basin provided by the present invention.

[0028] Figure 6This is a schematic diagram of the physical structure of the electronic device provided by the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] Current technical processes for assessing bird habitat quality typically include the following core steps: First, clearly defining the specific target species or taxa and the spatial and temporal scales of the assessment; second, systematically collecting and processing bird distribution and population data, as well as multi-source environmental data such as topography, climate, hydrology, vegetation, land use, and human disturbance, and determining the target species' requirements for key habitat factors; third, selecting and constructing a suitable assessment model and parameterizing it; fourth, running the model to quantify habitat quality and generate spatial distribution maps; fifth, identifying key areas and diagnosing the causes of degradation based on the spatial pattern of habitat quality; and finally, applying the assessment results to decision support such as conservation planning, ecological restoration, land management optimization, and future scenario prediction. The mainstream technical approaches mainly include the following categories.

[0031] Represented by the habitat quality module of the InVEST model, this approach assesses habitat quality by quantifying the degradation effects of multiple stress factors on habitat units. Its mathematical model uses habitat degradation degree as the core driving variable, and the calculation process comprehensively considers habitat suitability, sensitivity to threat factors, and spatial decay patterns. The advantage of this method lies in achieving an explicit spatial expression of habitat quality and integrating human activity indicators such as road density and construction intensity.

[0032] Species distribution models based on MaxEnt and BIOMOD2 use machine learning algorithms to correlate bird occurrence points with environmental variables such as vegetation cover and temperature gradients, generating potential suitable habitat distribution maps. This type of technology demonstrates outstanding accuracy in location prediction, and is particularly suitable for habitat identification of rare species.

[0033] Techniques such as Morphological Spatial Pattern Analysis (MSPA) interpret remote sensing images to identify the structural features of core habitat patches, ecological corridors, and marginal transition zones. This method has advantages in extracting the spatial patterns of ecological source areas and has been widely applied in wetland reserve planning.

[0034] Although bird habitat quality assessment technology has formed a relatively mature process and methodology system and plays an important role in regional biodiversity conservation, when conducting high-precision and dynamic assessments of bird diversity hotspots and key migration corridors in the Great Lakes Basin, existing technologies still have the following significant shortcomings in terms of multi-species adaptability, model parameter authenticity, dynamic process integration, and result verification, making it difficult to meet the needs of precise conservation decision-making in complex lake ecosystems.

[0035] While the InVEST model performs well in ecosystem service assessments, its application to bird habitat quality remains insufficient. Optimizing the parameters of bird habitat quality models faces numerous challenges, such as the difficulty in obtaining species distribution data, the uncertainty of habitat preference parameters, and the model's adaptability to different ecosystem types. These issues limit the scope and accuracy of the InVEST model in bird habitat quality assessment. Therefore, there is an urgent need to optimize the model parameters and incorporate bird ecological characteristics to improve the model's applicability in bird habitat quality assessment.

[0036] Current technologies are severely inadequate in capturing key dynamic processes affecting bird habitats. Species distribution models (such as MaxEnt and BIOMOD2) heavily rely on static observational data, and their predictive reliability drops sharply in data-sparse areas such as remote lake regions and migration routes. Furthermore, their architecture inherently lacks characterization of habitat functional connectivity. Landscape pattern analysis (such as MSPA) and most static models fail to effectively couple climate evolution drivers, making it impossible to predict long-term risks such as habitat patch shrinkage caused by the melting of lakes in high-altitude and cold regions.

[0037] A common and critical problem in habitat quality assessment lies in the insufficient specificity and precision of the results, the core of which is the severe lack of model validation or uncertainty analysis. Existing bird habitat quality assessment technologies are also deeply troubled by this problem. Model outputs are often considered rough estimates, while rigorous validation using field survey data is relatively rare. This leads to a significant gap between the spatial distribution of so-called high-quality habitats depicted by the models and the actual habitats effectively utilized by birds, lacking effective verification of the spatial fit of the assessment results using actual species distribution data. Even in the few studies that attempt validation, most rely only on limited sampling data, which is insufficient to fully represent the complex, spatially heterogeneous large lake basins, and large-scale, systematic validation work is generally lacking. This insufficient validation makes it difficult to reliably assess the accuracy of model outputs and their application potential in different locations, such as lakes in different climate zones. More importantly, parameter sensitivity analysis and uncertainty quantification are often missing, failing to clearly reveal how errors in key parameters propagate and ultimately affect the credibility range of habitat quality assessment results, resulting in an inadequate model accuracy assurance mechanism. These problems collectively restrict the credibility and practical application value of the assessment results.

[0038] The default parameters of the existing InVEST model are mainly based on global or specific regional experience. In the complex and ever-changing environment of the Great Lakes Basin, its accuracy and applicability for assessing bird habitat quality are insufficient.

[0039] Optionally, the bird habitat quality assessment method in the Great Lakes Basin of this application embodiment can be executed by a server, by a terminal device, or by both a server and a terminal device. Taking the execution of the bird habitat quality assessment method in the Great Lakes Basin of this embodiment by a server as an example.

[0040] Figure 1 This is a flowchart illustrating the bird habitat quality assessment method for the Great Lakes Basin provided by this invention, as shown below. Figure 1 As shown, the method includes the following steps.

[0041] Step 101: Obtain land use data and bird biodiversity data for the target area, which includes multiple different climate zones.

[0042] Large lake basins are widely distributed, and this invention selects 56 large lake basins with an area of ​​over 100 square kilometers as research subjects. Based on different climates, the large lake basins are divided into the following four categories (10 large lake basins in temperate monsoon regions; 15 large lake basins in subtropical monsoon regions; 5 large lake basins in temperate continental arid climate regions; and 28 large lake basins in plateau and mountain regions): Large lake basins in temperate monsoon regions are mainly influenced by the temperate monsoon climate, with relatively low annual precipitation; large lake basins in subtropical monsoon climate regions are located in the subtropical monsoon region and have abundant precipitation; large lake basins in plateau and mountain regions mainly rely on snowmelt for water supply; and large lake basins in temperate continental arid climate regions are located in the temperate continental climate region and suffer from severe water scarcity.

[0043] The topography of the Great Lakes basins varies considerably. In the temperate and subtropical monsoon regions, the basins are mostly plains with widespread arable land; in the high-altitude mountain regions, alpine meadows are abundant; and in the temperate continental arid climate zones, deserts and grasslands dominate. With the intensification of human land use patterns, lake basin ecosystems face serious problems such as habitat fragmentation, wetland degradation, and water pollution. Environmental pressures from excessive urban development and agricultural expansion have severely threatened the stability of bird habitats, necessitating the identification of key land use change characteristics affecting bird habitat quality in different types of Great Lakes basins.

[0044] The data obtained by this invention includes: land use type data, watershed boundary data, watershed boundary data, bird biodiversity data, biodiversity hotspot data, and global land use scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6).

[0045] Step 102: Based on bird biodiversity data and land use data, the habitat suitability parameters and threat factor sensitivity parameters are adjusted and optimized differently for different climate zones to obtain multiple sets of optimized parameters for different climate zones.

[0046] This invention systematically collects, organizes, and analyzes a large number of ecological studies on the response relationship between bird biodiversity and land use / cover type in the Great Lakes Basin, focusing on the core parameters that determine the accuracy of habitat quality assessment in the InVEST model—habitat suitability and habitat sensitivity to threat factors. Based on this, it quantifies the suitability of different land use types (such as cultivated land, forest land, grassland, water area, construction land, and unused land) for various bird species as habitats, as well as the stress intensity on bird habitats when they act as threat sources.

[0047] This optimization mechanism directly addresses the core issue of poor regional applicability of model parameters. By incorporating localized and bird-specific ecological knowledge, it significantly improves the scientific rigor and relevance of model parameter settings, thereby fundamentally enhancing the accuracy of the model in assessing bird habitats in the Great Lakes Basin.

[0048] In some embodiments, the target area is divided into several climate zones. For each climate zone, its unique bird community composition and habitat preferences are analyzed, while the mechanisms and impacts of major threats (such as urbanization, agricultural expansion, and road networks) on bird habitats are investigated in depth. Based on this, targeted preliminary values ​​are assigned and adjusted to the two core parameter sets in the InVEST model—the suitability levels of various land use types as habitats, and the sensitivity of various habitats to different threats.

[0049] Refined calibration is performed through iterative simulation and result comparison. The initially adjusted parameter set is input into the model for trial calculations, and the obtained habitat quality assessment results are spatially correlated with the spatial distribution data of bird diversity observed in the field within the climate zone. By iteratively adjusting the parameters and running the model, the changes in the spatial correlation between the assessment results and the observed bird diversity values ​​are monitored, and finally, the zone-specific parameter combination that achieves the highest consistency between the two is determined, thus forming the zone-specific optimized parameter set.

[0050] According to the present invention, a method for assessing the habitat quality of birds in large lake basins is provided, covering multiple different climate zones, including: large lake basins in temperate monsoon regions, large lake basins in subtropical monsoon regions, large lake basins in temperate continental arid climate zones, and large lake basins in plateau and mountainous regions.

[0051] This invention identifies that the Great Lakes Basin is distributed across four typical climate zones: temperate monsoon, subtropical monsoon, temperate continental arid, and plateau mountain regions. The dominant ecosystems, bird community composition, and major threats faced by each region differ significantly.

[0052] Therefore, this invention does not employ a single global parameter set, but instead establishes independent parameter optimization matrices for the four climate zones and Great Lakes basins mentioned above. Each matrix contains optimized habitat suitability and habitat sensitivity parameters corresponding to typical land use types in that climate zone. This improvement addresses the drawbacks of a "one-size-fits-all" approach to model parameters, fully considering the key impacts of geographical environmental heterogeneity on bird habitat requirements, enabling model parameters to more accurately reflect the actual conditions of ecosystems in different regions, and significantly improving the regional resolution and reliability of the model evaluation results.

[0053] Step 103: Rasterize the land use data and threat factor data to obtain rasterized data.

[0054] In this embodiment of the invention, rasterizing all land use / cover data and threat factor data is a crucial data preprocessing step before model execution. The purpose of this process is to uniformly convert various spatial data from different sources, formats, and coordinate systems into raster data with the same geographic coordinate reference and spatial resolution, providing a standardized and regulated data foundation for subsequent quantitative calculations and analysis in the model.

[0055] First, unified rasterization parameters need to be determined, mainly including the spatial reference system, cell size, and raster data range. The spatial reference system should be a projected coordinate system suitable for the target Great Lakes watershed study area to ensure the accuracy of spatial distance calculations. The cell size setting needs to comprehensively consider the accuracy of the original data, the size of the study area, and computational efficiency; typically, 30m × 30m or a resolution consistent with the main data source can be chosen. The processing range should completely cover all sub-regions of the Great Lakes watershed, forming a unified rectangular analysis area.

[0056] Subsequently, using a geographic information system platform (such as ArcGIS, QGIS, etc.) or professional remote sensing image processing software, the vector format land use / cover data is converted to raster using a feature-to-raster tool. In this process, the land use type attribute value for each polygon is assigned to each cell containing it, thus forming a complete land use type raster map.

[0057] For threat factor data, the processing method is adopted according to its original format: for point or line threat sources (such as residential areas and roads), the distance between them and surrounding pixels needs to be calculated first to generate a distance gradient raster map; while for area distribution or threat factors with continuous values ​​(such as population density and pollution intensity index), the corresponding raster surface can be generated directly through interpolation or resampling methods.

[0058] Ultimately, all generated raster data will be checked to ensure that their geographic coordinates, cell size, and number of rows and columns are perfectly aligned, forming a raster dataset that is spatially perfectly matched and can be directly used for model calculations.

[0059] Step 104: Input the rasterized data and the optimized parameter set into the preset InVEST model to conduct habitat quality assessment and obtain the optimized habitat quality assessment results.

[0060] In this embodiment of the invention, based on land use data, the quality of species habitats is calculated by comprehensively considering the habitat suitability of various species and assessing the sensitivity of threat factors, taking into account the distance and degree of the impact of threat factors on habitats.

[0061] The land use types considered include cultivated land (dry land, wetland), forest land (forested land, shrubland, sparse forest land, other forest land), grassland (high-coverage grassland, medium-coverage grassland, low-coverage grassland), water area (rivers, lakes, reservoirs, ponds, permanent glaciers and snowfields, tidal flats, beaches), urban and rural land, residential land, industrial and mining land, and unused land (sand land, Gobi desert, saline-alkali land, swamp land, bare land, bare rocky land, other unused land).

[0062] In this embodiment of the invention, standardized rasterized data and parameter sets optimized for each climate zone are input into the InVEST model for habitat quality assessment.

[0063] The input data consists of uniform rasterized data obtained from preprocessing, primarily including land use / cover raster maps and distance or intensity raster maps corresponding to each threat factor (such as urban land, major roads, farmland, etc.). Simultaneously, the optimized parameter sets for each climate zone are imported in a model-readable format (typically CSV or TXT table files). These table files explicitly define two key query relationships in the model: firstly, the habitat suitability index corresponding to each land use / cover type; and secondly, the sensitivity score of each habitat type to each threat factor.

[0064] After all input files and parameters are set, the InVEST model automatically reads the value of each cell from all input raster data based on its built-in algorithm logic. Then, according to the provided parameter table, it performs complex spatial overlay analysis and weighted calculations. This process traverses every raster cell within the target area, comprehensively considering its land use type (which determines its basic habitat value), the presence and intensity of various threat factors within different distance ranges (to calculate the cumulative stress it experiences), and the sensitivity of the habitat type to each threat. Finally, it calculates the habitat quality index and habitat degradation degree for each cell location.

[0065] The output of the InVEST model is a raster map of habitat quality distribution with the same spatial range as the input data (i.e., habitat quality assessment results). In this result map, the value of each cell (usually between 0 and 1) represents the relative level of habitat quality at that location, with higher values ​​indicating better habitat quality.

[0066] Through the embodiments of the present invention, the habitat quality assessment results, by incorporating parameters optimized for different climate zones, exhibit a higher consistency between their spatial patterns and the actual observed bird diversity distribution patterns in the region. This significantly enhances the scientific validity and reliability of the assessment results, providing a precise data foundation for subsequent ecological analysis and management decisions.

[0067] According to the method for assessing bird habitat quality in a large lake basin provided by the present invention, after inputting rasterized data and an optimized parameter set into a preset InVEST model to assess habitat quality and obtaining the optimized habitat quality assessment results, the method further includes:

[0068] Rasterized data and the original parameter set are input into the preset InVEST model to assess habitat quality, and the habitat quality assessment results before optimization are obtained.

[0069] Spearman correlation analysis was performed on the habitat quality assessment results before optimization and the bird biodiversity data to obtain the correlation coefficient before optimization.

[0070] Spearman correlation analysis was performed on the optimized habitat quality assessment results and bird biodiversity data to obtain the optimized correlation coefficient.

[0071] The evaluation result of the optimized parameter set is determined based on the difference between the optimized correlation coefficient and the unoptimized correlation coefficient.

[0072] The Spearman correlation coefficient is a nonparametric statistical method used to assess the monotonic relationship between two variables.

[0073] In this embodiment of the invention, Spearman correlation coefficient was used to conduct correlation analysis on spatial data of bird habitat quality and bird diversity in four types of large lake basins, aiming to explore whether there is a significant monotonic relationship between the two.

[0074] In the correlation analysis, this invention selected the average bird habitat quality values ​​for 2010, 2015, and 2020. Simultaneously, this application conducted correlation analyses on the four types of large lake basins before and after parameter correction, and compared the results before and after correction.

[0075] Under identical software environment (InVEST model) and input data (rasterized land use and threat factor data), the only difference was that the input parameter set was replaced with the original default parameter set without regional adaptation adjustments. The habitat quality module was then run to obtain a set of unoptimized habitat quality assessment results. These results represent the assessment effectiveness for this study area when using general model parameters.

[0076] Subsequently, two independent correlation analyses were conducted in parallel. The bird biodiversity data relied upon for the analyses were derived from long-term field observation records at different locations within the study area. These data were independent of model computation and provided objective standards for validation. Raster maps of habitat quality assessment results before and after optimization were spatially matched and paired with bird diversity observations. The Spearman rank correlation coefficient, a nonparametric statistical method, was used to calculate the correlation between the two paired data sets.

[0077] The reason for choosing the Spearman correlation coefficient is that it does not require the data to follow a specific distribution pattern, and it can effectively capture the possible monotonic trend relationship between habitat quality and bird diversity without assuming a strict linear relationship, thus making it particularly suitable for the analysis of ecological data.

[0078] Finally, the optimization effect is determined by directly comparing the correlation coefficients obtained from the two calculations.

[0079] In this embodiment of the invention, the difference between the optimized and unoptimized correlation coefficients is calculated, and the reliability of this difference is determined using a statistical significance test. If the optimized correlation coefficient is statistically significantly higher than the unoptimized correlation coefficient, it indicates that the parameter set obtained through climate zone optimization has indeed significantly improved the evaluation performance of the InVEST model in this study area, making its output results more accurately reflect the actual ecological conditions.

[0080] According to the method for assessing bird habitat quality in a large lake basin provided by the present invention, Spearman correlation analysis is performed on the optimized habitat quality assessment results and bird biodiversity data to obtain the optimized correlation coefficient, including:

[0081] The target habitat quality assessment results and bird biodiversity data are converted into grades to determine the grade difference between each pair of sample data between the target habitat quality assessment results and bird biodiversity data;

[0082] Based on the rank difference between each pair of sample data and the total number of sample data, the optimized correlation coefficient is determined:

[0083]

[0084] in, This represents the optimized correlation coefficient. This represents the rank difference between each pair of sample data. This indicates the total number of sample data.

[0085] In this embodiment of the invention, correlation analyses were performed on four types of large lake basins before and after parameter correction, and the results before and after correction were compared to verify the correction results. The Spearman correlation coefficient formula is shown above.

[0086] In some embodiments, n is the number of samples; For each pair of data for two variables, the rank difference is calculated by first converting the observations of each variable to ranks, and then calculating the difference between the corresponding ranks.

[0087] If the correlation coefficient after optimization is significantly higher than that before optimization and is statistically significant, then the empirical evidence proves that the parameter optimization technique of the present invention effectively improves the spatial consistency between the InVEST model evaluation results and the field observation of bird diversity, and verifies the effectiveness of the optimized parameters and the superiority of the technical solution.

[0088] To scientifically verify the effectiveness of the optimized parameters, this invention utilizes an independent spatial dataset of bird species richness covering the study area as an objective benchmark. Spearman's rank correlation coefficient is used to calculate the correlation strength between the spatial distribution map of bird habitat quality output by the model and the measured spatial distribution map of bird species richness, both before and after optimization.

[0089] This invention constructs a closed-loop chain of "parameter optimization – model evaluation – result verification – confirmation of optimization effect". By comparing the significant improvement in correlation coefficients before and after optimization (e.g., the correlation coefficient value increases and is statistically significant), the superiority of the optimized parameters and their direct contribution to improving the accuracy of model evaluation are quantitatively demonstrated, providing empirical support for the reliability of the technical solution.

[0090] According to the method for assessing bird habitat quality in a large lake basin provided by the present invention, the habitat quality assessment result is determined by the following formula:

[0091]

[0092]

[0093] in, Indicates habitat quality, Indicates habitat suitability. Indicates the degree of habitat degradation. Indicates the default parameters of the model. Represents the half-saturation constant. This represents the total number of threat factors. This represents the total number of threat factor grid cells. Indicates threat factors Influence weight value, Represents grid cells Threat factor values ​​on Indicates threat factors The scope of the impact study area Represents grid cells Accessibility level Indicates land use type Threat factors The degree of sensitivity.

[0094] The calculation of habitat quality assessment results relies on the core algorithm logic of the Habitat Quality module of the InVEST model. This calculation process is a comprehensive spatial explicit assessment, which essentially quantifies the inherent habitat potential of the land use type at each grid cell location, while simultaneously assessing the cumulative stress impact of various threatening factors in the surrounding environment, ultimately yielding a habitat quality index that reflects the overall situation.

[0095] The InVEST model first assigns an initial habitat suitability value to each raster cell based on the input land use raster data. This value is directly derived from the parameter set optimized for different climate zones and clearly reflects the original suitability of a specific land use type (such as woodland, grassland, water, etc.) as a bird habitat. The higher the value, the better the habitat conditions provided by the land type itself.

[0096] The InVEST model comprehensively considers multiple spatial factors: first, the spatial distribution of various threat factors (such as towns, roads, and farmland) and their respective relative impact weights; second, the impact mode of each threat factor, including its maximum spatial range and the attenuation of its influence with increasing distance; third, the accessibility level of each grid cell due to its geographical location characteristics (such as whether it is located within a protected area), which determines its susceptibility to human disturbance; and finally, the key land use type sensitivity parameter, which defines the resilience of a specific habitat type (such as woodland) to specific threats (such as road noise). Through overlay analysis and distance-weighted calculations, the InVEST model accumulates the potential negative impacts of all threat sources on the target habitat grid, ultimately obtaining a comprehensive habitat degradation index. The higher the index, the greater the external stress pressure on that location.

[0097] The InVEST model synthesizes habitat suitability, representing intrinsic potential, and habitat degradation, representing external stress, through a nonlinear function. The form of this function ensures that when external stress is low, habitat quality primarily depends on its intrinsic suitability; however, as external stress approaches or exceeds the threshold of intrinsic resilience, habitat quality exhibits an accelerated decline. Ultimately, each grid cell is assigned a final habitat quality score between 0 and 1.

[0098] Through the embodiments of the present invention, a specific formula is used to comprehensively and accurately assess the habitat quality of birds in the Great Lakes Basin by taking into account factors such as habitat suitability, habitat degradation (which is calculated by incorporating factors such as the number of threat factors, grid cell threat factor values, impact weights, impact ranges, accessibility levels, and the sensitivity of land use types to threat factors), as well as model default parameters and half-saturation constants.

[0099] According to the present invention, a method for assessing bird habitat quality in a large lake basin is provided, the method further comprising:

[0100] Obtain predictive land use data for future periods;

[0101] The predicted land use data and optimized parameter set are input into the preset InVEST model to assess habitat quality, and the results of the habitat quality assessment in the future period are obtained.

[0102] The habitat quality difference between the future period and the baseline year is determined by the difference between the habitat quality assessment results of the future period and the habitat quality assessment results of the preset baseline year.

[0103] In this embodiment of the invention, a land use dataset based on the shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) scenarios published in CMIP6 is introduced, which includes five SSP-RCP combination scenarios: SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, SSP4-RCP3.4 and SSP5-RCP8.5.

[0104] Refer to Table 1, which is a land use scenario setting table provided by the present invention.

[0105] Table 1

[0106]

[0107] In some embodiments, by combining CMIP6 scenario land use data in 2050 with a bird habitat quality assessment model, the evolution trend of bird habitat quality under different future land use scenarios in four types of large lake basins is simulated and analyzed to obtain prediction results. The difference between habitat quality under each scenario and habitat quality in 2020 is calculated to clarify the differences in habitat quality between scenarios.

[0108] The above predictions can provide more reliable scientific evidence based on optimized models for large lake basins in different climate zones when responding to global change and formulating regional ecological protection strategies (such as land use planning and the delineation of priority areas for biodiversity conservation).

[0109] refer to Figure 2 , Figure 2 This is a technical flowchart of the bird habitat quality assessment method for the Great Lakes Basin provided by the present invention.

[0110] The data preparation phase showcases the collection and preprocessing of fundamental geographic and ecological data. This includes acquiring global lake boundary data, global watershed boundary data, multi-period land use data, Chinese biodiversity hotspot data, species richness data, and CMIP6 global land use scenario data. Subsequently, all data were standardized in spatial resolution and coordinate system, and large lake basin classification and characteristic analysis were performed. The figure clearly divides global large lake basins into four climate zones: Subtropical Monsoon Climate Zone (STMZ), Temperate Monsoon Climate Zone (TMZ), Temperate Continental Arid Climate Zone (TCACZ), and Highland Mountain Climate Zone (MPA), and describes the climatic and geomorphological characteristics of each zone. Finally, land types were reclassified to prepare for model computation.

[0111] The parameter optimization phase revealed the calibration process of the model's core parameters. Based on a literature meta-analysis and field observation data, an initial parameter matrix was first established. Subsequently, parameters such as habitat suitability and sensitivity to threat factors were differentially adjusted and optimized according to the habitat characteristics of the four different climate zones of the Great Lakes basin, ultimately generating an optimized parameter matrix applicable to each climate zone.

[0112] The model evaluation phase describes the process of performing evaluation calculations using optimized parameters. The optimized parameter matrix is ​​input into the Habitat Quality module of the InVEST model for execution, ultimately outputting a high-precision spatial distribution map of habitat quality, which serves as the basis for effect verification and future simulations.

[0113] The effectiveness of the optimized parameters was verified through a dual-path comparison. The model was run with both the pre-optimization and post-optimization parameters, generating two sets of habitat quality maps, which were then spatially matched with actual species richness maps. The Spearman correlation coefficient was calculated to quantitatively compare the consistency between the two sets of results and the field observation data, thus verifying that parameter optimization significantly improved the model evaluation accuracy.

[0114] The scenario simulation phase demonstrated the predictive capabilities of the optimized model. Multiple scenarios combining shared socioeconomic pathways and representative concentration pathways (including SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, and SSP4-RCP3.4) provided by CMIP6 were used to set future land use change scenarios. Based on these scenarios, a validated bird habitat quality assessment model was run, and the assessment results were overlaid with biodiversity hotspots, ultimately providing a scientific basis for developing differentiated ecological protection strategies for different climatic zones.

[0115] The following describes an example of the practical application of the bird habitat quality assessment method for the Great Lakes Basin provided by this invention.

[0116] Overall, the bird habitat quality before parameter optimization was significantly higher than that after optimization. This was mainly reflected in the temperate monsoon region (the former was 0.30 higher than the latter), the subtropical monsoon region (0.29), and the Great Lakes basin in the temperate continental arid climate zone (0.30). However, the differences between these regions were significantly smaller before parameter optimization, which did not adequately reveal the differences in bird habitat quality.

[0117] The bird habitat quality results after parameter optimization showed significant differences compared to before optimization: the bird habitat quality in the temperate monsoon lake basin and the subtropical monsoon lake basin changed from a generally high value to a multi-level distribution (the area with a value ≥0.6 decreased from 84.2% to 19.8% in the temperate monsoon region, and from 88.4% to 32.3% in the subtropical monsoon region). In the temperate continental arid climate zone lake basin, the high-value areas of bird habitat quality before optimization became medium-value areas after optimization (changing from the 0.8-1.0 range to the 0.4-0.6 range), while the low-value areas remained unchanged.

[0118] Overall, the changes in bird habitat quality were greatest in the temperate monsoon lake basins and smallest in the plateau mountain lake basins after parameter optimization. The order of change intensity among the four lake basins was: temperate monsoon lake basins > temperate continental arid climate lake basins > subtropical monsoon lake basins > plateau mountain lake basins.

[0119] refer to Figure 3 , Figure 3 This is a schematic diagram showing the trend of changes in bird habitat quality in the Great Lakes Basin from 1990 to 2020, provided by the present invention.

[0120] The spatial patterns of bird habitat quality in the Great Lakes Basin showed high similarity in 1990 and 2000, and also in 2010 and 2020. This indicates a significant difference in the spatial patterns of bird habitat quality in the Great Lakes Basin around 2000, primarily in the subtropical monsoon region.

[0121] Data from 1990 and 2000 show that the mean habitat quality for birds in the subtropical monsoon lake basins was the highest among the four categories of lake basins. Areas with habitat quality less than 0.5 were significantly more numerous than those with habitat quality greater than 0.5 (in 1990, areas with habitat quality ≥0.5 accounted for approximately 38.7% of the total area, while in 2000, the proportion was approximately 38.4%). In the plateau mountain region, the distribution of bird habitat quality in the lake basins was polarized, with a large number of areas having habitat quality greater than 0.5 (approximately 66.8% in 1990 and approximately 68.5% in 2000), while a certain proportion of areas had a quality lower than 0.2 (in 1990). In 1990, the habitat quality of birds in the large lake basins of the temperate continental arid climate zone was approximately 17.2%, and in 2000 it was approximately 31.4%. The distribution of bird habitat quality in the large lake basins of the temperate continental arid climate zone was relatively concentrated, with most areas having a habitat quality of <0.5 (approximately 90.4% in both 1990 and 2000), and the overall average was the lowest among the four types of large lake basins (0.2355 in 1990 and 0.2347 in 2000). In the large lake basins of the temperate monsoon region, the area with bird habitat quality >0.5 was significantly more than the area with <0.5 (the area with habitat quality >0.5 accounted for approximately 75.9% of the total in 1990, and approximately 76.0% in 2000).

[0122] Data from 2010 and 2020 show that the average habitat quality for birds in the subtropical monsoon lake basins was the highest among the four major types of lake basins, but it decreased significantly compared to the data from 1990-2000 (from 0.5674 and 0.5659 to 0.5107 and 0.4985). The habitat quality for birds in the plateau mountain region lake basins was similar to the data from 1990-2000. The habitat quality for birds in the temperate continental arid climate region lake basins was similar to the data from 1990-2000. The habitat quality for birds in the temperate monsoon region lake basins was mostly 0.6 or below (more than 80%), and the areas with habitat quality above 0.5 were significantly fewer than those below 0.5 (in 2010, the area with habitat quality ≥0.5 accounted for approximately 28.5% of the total area, while in 2020, the area with habitat quality ≥0.5 accounted for approximately 27.3%).

[0123] refer to Figure 4 , Figure 4 This is a schematic diagram of the verification analysis of bird habitat quality before and after parameter correction provided by the present invention.

[0124] In the temperate monsoon region's large lake basin: using the uncorrected model parameters, the relationship between habitat quality and bird diversity is not significant, with a correlation coefficient of only 0.048 and a p-value of 0.07. However, using the corrected model parameters, habitat quality and bird diversity show a highly significant positive correlation, with a correlation coefficient of 0.353 and a p-value < 0.01.

[0125] In the subtropical monsoon region's Great Lakes basin: using the uncorrected model parameters, the relationship between habitat quality and bird diversity is not significant, with a correlation coefficient of only -0.11 and a p-value of 0.383. However, using the corrected model parameters, habitat quality and bird diversity show a highly significant positive correlation, with a correlation coefficient of 0.274 and a p-value < 0.01.

[0126] Temperate continental arid climate zone: If uncorrected model parameters are used, habitat quality and bird diversity show a highly significant positive correlation, with a correlation coefficient of only 0.126 (p < 0.01). If corrected model parameters are used, habitat quality and bird diversity show a highly significant positive correlation, with a correlation coefficient of 0.361 (p < 0.01).

[0127] In high-altitude mountainous areas: using the uncorrected model parameters, habitat quality and bird diversity showed a highly significant negative correlation, with a correlation coefficient of only -0.53 and a p-value < 0.01. Using the corrected model parameters, habitat quality and bird diversity showed a highly significant positive correlation, with a correlation coefficient of 0.156 and a p-value < 0.01.

[0128] In summary, after adjusting the model parameters, the correlation between habitat quality and bird diversity in the four types of large lake basins was significantly improved (all correlations were improved to extremely significant positive correlations). This not only demonstrates the effectiveness and rationality of the parameter adjustment in this study, but also provides more accurate data support for the assessment of bird habitat quality in China's large lake basins.

[0129] This invention uses bird biodiversity data and climate zoning to reverse-optimize the habitat quality parameter configuration in the InVEST model, thereby improving the ecological effectiveness of habitat assessment.

[0130] Specifically, based on bird spatial distribution data and climate zoning, the threat source sensitivity parameters and land use type habitat suitability parameters in the model were adjusted. This resulted in a significant spatial correlation between the optimized model output and bird diversity data (p<0.001), a substantial improvement over the original parameters (i.e., the example parameter values ​​of the InVEST model's habitat quality module). This yielded habitat quality assessment results that better match bird diversity distribution. Compared to the original parameters, the optimized parameters, tailored to the characteristics of the Great Lakes Basin, better reflect bird habitat conditions, effectively improving the accuracy and practicality of the bird habitat quality assessment model. See the table below for details.

[0131] Referring to Table 2, Table 2 shows the habitat suitability and sensitivity parameters to threat sources for different land use types for birds (temperate monsoon lake basin) provided by the present invention.

[0132] Table 2

[0133]

[0134] Referring to Table 3, Table 3 shows the habitat threats to birds and their maximum impact distance, weight, and attenuation type (temperate monsoon region, large lake basin) provided by this invention.

[0135] Table 3

[0136]

[0137] Referring to Table 4, Table 4 shows the habitat suitability and sensitivity parameters to threat sources for different land use types for birds (subtropical monsoon lake basin) provided by this invention.

[0138] Table 4

[0139]

[0140] Referring to Table 5, Table 5 shows the habitat threats to birds and their maximum impact distance, weight, and attenuation type (subtropical monsoon region, large lake basin) provided by this invention.

[0141] Table 5

[0142]

[0143] Referring to Table 6, Table 6 shows the habitat suitability and sensitivity parameters to threat sources for different land use types for birds provided by this invention (Great Lakes Basin in a temperate continental arid climate zone).

[0144] Table 6

[0145]

[0146] Referring to Table 7, Table 7 shows the habitat threats to birds and their maximum impact distance, weight, and attenuation type (Great Lakes Basin in Temperate Continental Arid Climate Zone) provided by this invention.

[0147] Table 7

[0148]

[0149] Referring to Table 8, Table 8 shows the habitat suitability and sensitivity parameters to threat sources for different land use types for birds (large lake basin in plateau mountain areas) provided by the present invention.

[0150] Table 8

[0151]

[0152] Referring to Table 9, Table 9 shows the habitat threats to birds and their maximum impact distance, weight, and attenuation type (large lake basin in plateau mountain areas) provided by this invention.

[0153] Table 9

[0154]

[0155] A significant problem in past ecosystem service assessments has been the lack of specificity and precision in the results. Model validation or uncertainty analysis has been lacking in the application of many ecosystem service models. While it is often acknowledged that ecosystem service models provide rough estimates, validation through field surveys is relatively infrequent. In studies that have employed validation, many rely on limited sampling points to examine the model's accuracy within their study area. This lack of large-scale validation means that for many models, relatively little information is available regarding either the accuracy of their outputs or their potential applicability across different locations.

[0156] This invention optimizes the InVEST model parameters to assess habitat quality services for birds, and performs Spearman correlation verification on bird diversity and corresponding habitat quality results in the Great Lakes Basin of China, demonstrating the accuracy and reliability of the model and providing new data support for rapid large-scale assessment of ecosystem services and biodiversity.

[0157] This invention addresses the varying impacts of different socioeconomic assumptions and climate goals on habitat quality, innovatively simulating future bird habitat quality in Great Lakes basins based on the CMIP6 scenario. This significantly improves the reliability and practicality of future simulations of bird habitat quality in Great Lakes basins. Specifically, based on land use data from the CMIP6 scenario in 2050 (SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0, SSP4-RCP3.4, and SSP5-RCP8.5), combined with a bird habitat quality assessment model, the invention simulates bird habitats in major lake basins in China in 2050 and calculates the difference between habitat quality under each future scenario and that in 2020, clarifying the habitat quality differences between the various future scenarios. Furthermore, this application incorporates data from biodiversity hotspots in China to explore the differentiated risks faced by bird habitat quality in biodiversity hotspots under different scenarios, thus providing important guidance for assessing bird habitat risk in biodiversity hotspots.

[0158] To further enhance the scientific rigor, adaptability, and practical application value of the InVEST model evaluation results, this solution can be expanded or replaced by the following technical approaches:

[0159] Besides optimizing parameters based on bird biodiversity data and climate zoning, machine learning algorithms can also be used for parameter optimization. For example, using algorithms such as random forests and neural networks, with bird diversity data as the dependent variable and the parameters of the InVEST model as independent variables, extensive iterative training can be conducted to find the parameter combination that best matches the model output with the bird diversity data. This method does not rely on climate zoning but instead leverages the algorithm's autonomous learning capabilities to uncover the potential correlation between parameters and bird habitats.

[0160] To verify the correlation between model results and bird diversity, in addition to Spearman correlation validation, cross-validation can be used. Bird diversity data and corresponding habitat quality assessment results in the Great Lakes Basin are divided into multiple subsets, and different subsets are used alternately as training and validation sets to repeatedly validate the model's accuracy, reducing validation bias caused by uneven data distribution. Another method is species distribution model validation. The optimized InVEST model assessment results are input as environmental variables into species distribution models such as MaxEnt. By comparing the degree of agreement between the model's predicted bird distribution and the actual distribution, the reliability of the habitat quality assessment results is indirectly verified. Furthermore, error analysis can be used to calculate the error range and average error between the model-assessed habitat quality values ​​and bird habitat quality scores based on field surveys, thereby determining the accuracy of the model results.

[0161] When simulating the future bird habitat quality in the Great Lakes Basin, in addition to incorporating the CMIP6 scenario, a system dynamics model can be employed. This model constructs multiple subsystems, including socio-economic, climate, land use, and bird habitat, and analyzes the feedback relationships and dynamic changes between these subsystems to simulate the future state of bird habitat quality under different socio-economic development models. Alternatively, the Markov chain prediction method can be used to establish a land use type transition probability matrix based on historical land use change data, predicting land use changes under different future scenarios. This prediction is then input into the optimized InVEST model to obtain the future simulation results of bird habitat quality.

[0162] The following describes the bird habitat quality assessment device for the Great Lakes Basin provided by the present invention. The bird habitat quality assessment device for the Great Lakes Basin described below can be referred to in correspondence with the bird habitat quality assessment method for the Great Lakes Basin described above.

[0163] refer to Figure 5 , Figure 5 This is a schematic diagram of the module of the bird habitat quality assessment device for the Great Lakes Basin provided by the present invention.

[0164] The acquisition module 701 is used to acquire land use data and bird biodiversity data of the target area, wherein the target area includes multiple different climate zones;

[0165] The adjustment module 702 is used to perform differentiated adjustment and optimization of habitat suitability parameters and threat factor sensitivity parameters for different climate zones based on the bird biodiversity data and the land use data, so as to obtain the optimized parameter sets corresponding to the multiple different climate zones respectively;

[0166] The rasterization module 703 is used to perform rasterization processing on the land use data and threat factor data to obtain rasterized data;

[0167] The evaluation module 704 is used to input the rasterized data and the optimized parameter set into a preset InVEST model to evaluate habitat quality and obtain the optimized habitat quality evaluation result.

[0168] Specifically, the bird habitat quality assessment device for the above-mentioned large lake basin provided by the present invention can realize all the method steps implemented in the above-mentioned large lake basin bird habitat quality assessment method embodiment, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0169] Figure 6 This is a schematic diagram of the physical structure of the electronic device provided by the present invention, such as... Figure 6 As shown, the electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions in the memory 830 to execute a method for assessing bird habitat quality in the Great Lakes Basin. This method includes: acquiring land use data and bird biodiversity data for a target area, wherein the target area includes multiple different climate zones; based on the bird biodiversity data and land use data, differentially adjusting and optimizing habitat suitability parameters and threat factor sensitivity parameters for different climate zones to obtain optimized parameter sets corresponding to multiple different climate zones; rasterizing the land use data and threat factor data to obtain rasterized data; and inputting the rasterized data and optimized parameter sets into a preset InVEST model for habitat quality assessment to obtain optimized habitat quality assessment results.

[0170] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0171] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the bird habitat quality assessment method for the Great Lakes Basin provided by the above methods. The method includes: acquiring land use data and bird biodiversity data of a target area, wherein the target area includes multiple different climate zones; based on the bird biodiversity data and land use data, differentially adjusting and optimizing habitat suitability parameters and threat factor sensitivity parameters for different climate zones to obtain optimized parameter sets corresponding to multiple different climate zones; rasterizing the land use data and threat factor data to obtain raster data; and inputting the raster data and optimized parameter sets into a preset InVEST model for habitat quality assessment to obtain optimized habitat quality assessment results.

[0172] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the bird habitat quality assessment method for the Great Lakes Basin provided by the methods described above. The method includes: acquiring land use data and bird biodiversity data of a target area, wherein the target area includes multiple different climate zones; based on the bird biodiversity data and land use data, differentially adjusting and optimizing habitat suitability parameters and threat factor sensitivity parameters for different climate zones to obtain optimized parameter sets corresponding to multiple different climate zones; rasterizing the land use data and threat factor data to obtain raster data; and inputting the raster data and optimized parameter sets into a preset InVEST model for habitat quality assessment to obtain optimized habitat quality assessment results.

[0173] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of assessing the quality of bird habitats in a large lake basin, characterized by, include: Acquire land use data and bird biodiversity data for the target area, which includes multiple different climate zones; Based on the bird biodiversity data and land use data, habitat suitability parameters and threat factor sensitivity parameters are differentially adjusted and optimized for different climate zones, resulting in optimized parameter sets for each climate zone. Specifically, for each climate zone, the unique bird community composition and habitat preference characteristics of each climate zone are analyzed, as well as the mechanism and degree of impact of threat factors on bird habitats in each climate zone. Various land use types are assigned as habitat suitability levels, and the sensitivity of various habitats to different threat factors is initially assigned and adjusted, resulting in a pre-adjusted parameter set. This pre-adjusted parameter set is then input into a preset InVEST model for trial calculations. The obtained habitat quality assessment results are spatially correlated with the spatial distribution data of bird diversity observed in the field within each climate zone. By iteratively adjusting the parameters and running the model, the spatial correlation changes between the assessment results and bird diversity observations are monitored to determine the zone-specific parameter combination that maximizes consistency between the two, forming an optimized parameter set specific to each climate zone. The land use data and threat factor data are rasterized to obtain rasterized data; The rasterized data and the optimized parameter set are input into the preset InVEST model to assess habitat quality, and the optimized habitat quality assessment results are obtained. After inputting the rasterized data and the optimized parameter set into a preset InVEST model for habitat quality assessment to obtain the optimized habitat quality assessment result, the method further includes: inputting the rasterized data and the original parameter set into a preset InVEST model for habitat quality assessment to obtain the unoptimized habitat quality assessment result; performing Spearman correlation analysis on the unoptimized habitat quality assessment result and the bird biodiversity data to obtain the unoptimized correlation coefficient; performing Spearman correlation analysis on the optimized habitat quality assessment result and the bird biodiversity data to obtain the optimized correlation coefficient; and determining the assessment result of the optimized parameter set based on the difference between the optimized correlation coefficient and the unoptimized correlation coefficient. The step of performing a Spearman correlation analysis on the optimized habitat quality assessment results and the bird biodiversity data to obtain the optimized correlation coefficient includes: performing a rank conversion between the optimized habitat quality assessment results and the bird biodiversity data to determine the rank difference between each pair of sample data; and determining the optimized correlation coefficient based on the rank difference between each pair of sample data and the total number of sample data. ； wherein, represents the optimized correlation coefficient, represents the rank difference of each pair of sample data, represents the total number of sample data.

2. The method of assessing bird habitat quality in the Great Lakes Basin according to claim 1, wherein, The habitat quality assessment results are determined using the following formula: ； ； in, Indicates habitat quality, Indicates habitat suitability. Indicates the degree of habitat degradation. Indicates the default parameters of the model. Represents the half-saturation constant. This represents the total number of threat factors. This represents the total number of threat factor grid cells. Indicates threat factors Influence weight value, Represents grid cells Threat factor values ​​on Indicates threat factors The scope of the impact study area Represents grid cells Accessibility level Indicates land use type Threat factors The degree of sensitivity.

3. The method for assessing bird habitat quality in the Great Lakes Basin according to claim 1, characterized in that, The method further includes: Obtain predictive land use data for future periods; The predicted land use data and the optimized parameter set are input into the preset InVEST model to conduct habitat quality assessment, and the habitat quality assessment results for the future period are obtained. The habitat quality difference between the future period and the base year is determined based on the difference between the habitat quality assessment results of the future period and the base year.

4. The method of assessing bird habitat quality in the Great Lakes Basin according to claim 1, wherein, The various climate zones include: the large lake basins of the temperate monsoon region, the large lake basins of the subtropical monsoon region, the large lake basins of the temperate continental arid climate region, and the large lake basins of the plateau and mountain regions.

5. A device for assessing the quality of bird habitats in the Great Lakes Basin, characterized in that, include: The acquisition module is used to acquire land use data and bird biodiversity data of the target area, wherein the target area includes multiple different climate zones; The adjustment module is used to differentiate and optimize habitat suitability parameters and threat factor sensitivity parameters for different climate zones based on the bird biodiversity data and land use data, thereby obtaining optimized parameter sets corresponding to the multiple different climate zones. Specifically, for each climate zone, the module analyzes the unique bird community composition and habitat preference characteristics of each climate zone, as well as the mechanism and degree of impact of threat factors on bird habitats in each climate zone. It also preliminarily assigns and adjusts the suitability levels of various land use types as habitats and the sensitivity of various habitats to different threat factors, thereby obtaining a preliminarily adjusted parameter set. The preliminarily adjusted parameter set is input into a preset InVEST model for trial operation, and the obtained habitat quality assessment results are spatially correlated with the spatial distribution data of bird diversity observed in the field within each climate zone. By iteratively adjusting the parameters and running the model, the module monitors the changes in the spatial correlation between the assessment results and the observed bird diversity values, and determines the zone-specific parameter combination that maximizes the consistency between the two, thus forming an optimized parameter set specific to each climate zone. The rasterization module is used to perform rasterization processing on the land use data and threat factor data to obtain rasterized data; The evaluation module is used to input the rasterized data and the optimized parameter set into the preset InVEST model to evaluate the habitat quality and obtain the optimized habitat quality evaluation result. After inputting the rasterized data and the optimized parameter set into a preset InVEST model for habitat quality assessment to obtain the optimized habitat quality assessment result, the device is further configured to: input the rasterized data and the original parameter set into a preset InVEST model for habitat quality assessment to obtain the unoptimized habitat quality assessment result; perform Spearman correlation analysis on the unoptimized habitat quality assessment result and the bird biodiversity data to obtain the unoptimized correlation coefficient; perform Spearman correlation analysis on the optimized habitat quality assessment result and the bird biodiversity data to obtain the optimized correlation coefficient; and determine the assessment result of the optimized parameter set based on the difference between the optimized correlation coefficient and the unoptimized correlation coefficient. The step of performing a Spearman correlation analysis on the optimized habitat quality assessment results and the bird biodiversity data to obtain the optimized correlation coefficient includes: performing a rank conversion between the optimized habitat quality assessment results and the bird biodiversity data to determine the rank difference between each pair of sample data; and determining the optimized correlation coefficient based on the rank difference between each pair of sample data and the total number of sample data. ； wherein, represents the optimized correlation coefficient, represents the rank difference of each pair of sample data, represents the total number of sample data.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the bird habitat quality assessment method for the Great Lakes Basin as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the bird habitat quality assessment method for the Great Lakes Basin as described in any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the bird habitat quality assessment method for the Great Lakes Basin as described in any one of claims 1 to 4.

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