Identification and effectiveness evaluation of urban bird priority protection space based on umbrella species

Abstract

This invention provides a method for prioritizing urban bird conservation spatial identification and quantifying its effectiveness based on umbrella species. The method includes: dividing the study area into kilometer-level grids as basic units to obtain bird species distribution data and environmental variable data; screening effective units based on bird survey integrity assessment; calculating the umbrella protection index for each species, which integrates the average percentage of co-occurring species, moderate rarity, and disturbance sensitivity, while also considering specific umbrella species based on trophic level, residency type, and protection level; identifying the suitability probability distribution of umbrella species using the MaxEnt model and overlaying it with existing green spaces to determine habitats; constructing conservation cost constraints based on landscape resistance factors affecting bird dispersal; setting multiple target thresholds for protected areas based on existing habitat conditions; and finally, using the systematic conservation planning tool Marxan to identify priority conservation spatial units for different target thresholds, and employing spatial overlay analysis to verify the effectiveness of the identification results for the conservation of other bird species.

Description

Technical Field

[0001] This invention relates to the technical field of quantifying urban biodiversity conservation, and in particular to a method and its application based on quantitative screening of umbrella species, identification of priority protection spaces, and quantitative assessment of their protection effectiveness. Background Technology

[0002] Cities are not only highly concentrated areas of population and economic activity, but also provide habitats for diverse species and maintain various ecosystem services and functions, making them crucial for biodiversity conservation. As the world enters the urban age, the role of cities in biodiversity conservation has become increasingly indispensable. The effective use of urban land and the management of natural ecosystems can benefit both the city and its surrounding residents and biodiversity, making cities an increasingly important component of solutions to curb global biodiversity loss. Goal 12 of the Kunming-Montreal Global Biodiversity Framework emphasizes enhancing green space development and ensuring biodiversity inclusiveness in urban planning. How to coordinate conservation and development in highly intensively used urban spaces is a critical issue in urban biodiversity conservation.

[0003] Spatial planning can play a crucial role in urban biodiversity conservation. However, the current practice of incorporating biodiversity conservation goals into national spatial planning still faces numerous technical challenges and uncertainties. First, given limited resources, implementing targeted protection for all species is extremely difficult in practice. Traditional conservation plans often focus on a single flagship species or a few rare species, and the habitat and ecological needs covered by their conservation actions are often quite specific. This can lead to fragmented spatial layouts of conservation efforts, making it difficult to effectively benefit a wider range of biological groups, and thus, conservation efficiency and cost-effectiveness need to be improved.

[0004] Secondly, existing methods for identifying critically endangered species often fail to systematically and comprehensively consider factors such as the species' sensitivity to human disturbance, its rarity within the region, its ecological relationships with other species, its functional traits, its conservation status, and its geographical distribution. This may result in insufficient "protection leverage" for the selected target species, failing to ensure effective coverage of numerous symbiotic species indirectly through the protection of that species. Finally, the lack of an operational technical framework that couples critical species identification, potential habitat assessment, and systematic conservation planning leads to a degree of subjectivity in the delineation of conservation priority spaces, making it difficult to quantitatively assess and verify the effectiveness of conservation efforts.

[0005] Therefore, there is an urgent need for a method that can identify key species with high conservation efficacy and, based on this, efficiently and quantitatively delineate priority spaces for urban biodiversity conservation. This method is of pressing practical need and important scientific value for promoting the true "mainstreaming" of biodiversity conservation in urban spatial planning and building bio-friendly cities. Summary of the Invention

[0006] The purpose of this invention is to provide a method for identifying umbrella species that take into account species sensitivity, rarity, co-occurrence with other species, trophic level, conservation level, and geographic distribution. Based on this, priority spatial conservation units can be identified, and their spatial guiding role and conservation effectiveness in protecting other bird species can be evaluated. This helps to improve the targeting and scientific nature of bird diversity conservation and provides an operable practical path for the mainstreaming of urban biodiversity conservation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species includes the following steps: Step S1, Data Acquisition and Preprocessing: Acquire geographical information and environmental data on the distribution of bird species in the target study area, and spatially divide the study area into 1km×1km grid units; Step S2, Survey Integrity Screening: Based on the ratio of records to species, the slope of the species accumulation curve, and survey integrity parameters, the bird observation data integrity is assessed for all kilometer grid units divided in Step S1. Grid units with a survey integrity level of "sufficient survey" or "general survey" are selected for subsequent umbrella species identification and analysis. Step S3, Bird Umbrella Species Identification: Based on the bird distribution data within the valid grid cells selected in Step S2, calculate the umbrella index for each bird species, and identify the specific umbrella species in the target area by considering the species' trophic level, residency type, protection level, and geographical distribution according to the umbrella index. Step S4, Simulation of potential habitat distribution of umbrella species: Using the maximum entropy model (MaxEnt), with the environmental variables obtained in step S1 as features, input the occurrence location data of umbrella species identified in step S3, simulate the adaptive distribution probability of the umbrella species in the study area, and select areas with a probability ≥ 0.5 to overlay with existing green spaces as candidate habitat areas that need to be protected. Step S5: Construction of Protection Cost Constraints and Setting of Scenarios: Based on the potential habitat distribution map obtained in Step S4, and the landscape resistance surface that quantifies the influence of land use type, road density, and building height factors on bird migration and dispersal, protection cost constraints are constructed. The current situation type of umbrella species habitat is determined based on the current green space ratio of the study area, the proportion of umbrella species habitat in the study area (whether it is higher than 30%), and the proportion of umbrella species habitat in green space area (whether it is higher than 50%). Then, a protection target threshold for umbrella species habitat is set, where the habitat protection target threshold is the proportion of umbrella species habitat area that needs to be protected. Step S6, Priority Protection Space Unit Identification: For each protection area target threshold set in Step S5, the Marxan system protection planning tool is used to perform iterative optimization calculations with minimizing protection cost as the objective function, and outputs the optimal set of priority protection space units that meet the protection target under the threshold.

[0008] Step S7: To verify the effectiveness of the conservation priority units identified using umbrella-protected species as target species, this embodiment also identifies spatial conservation priority units using representative species of the comparison group as target species (the specific process is the same as steps S4-S6), and compares the results with the conservation spatial distribution of priority units identified using umbrella-protected species as target species to evaluate the effectiveness of the umbrella-protected species-oriented conservation spatial strategy. Considering body size (with 100g as the threshold), resident status, and species appearing more than 30 times, bird species are divided into large resident birds, small resident birds, large summer visitors, and small summer visitors. Other species are representative species from groups other than the group containing the umbrella-protected species. Since the umbrella-protected species, the Red-bellied Hawk, is a large summer visitor in this embodiment, only three categories of species—large resident birds, small resident birds, and small summer visitors—are considered in the simulated scenario of the representative species of the comparison group.

[0009] Preferably, in step S1, the environmental variable data includes at least five of the following: elevation, land use type, normalized difference vegetation index (NDVI), temperature, precipitation, road density, and population density.

[0010] Preferably, in step S2, the survey completeness assessment uses the KnowBR package in R language to evaluate indicators including the ratio of records to species, the slope of the species accumulation curve, and survey completeness. The slope of the species accumulation curve represents the relationship between the number of records and the cumulative number of species. Survey completeness is defined as the ratio of observed species richness to estimated species richness. When the slope of the species accumulation curve is below 0.02, the survey completeness is above 90%, and the ratio of records to species is greater than 15, the survey is considered "sufficient." When the slope is above 0.3, the survey completeness is below 50%, and the ratio of records to species is less than 3, the survey is considered "poor." All other cases are considered "average survey."

[0011] Preferably, in step S3, the criteria for determining the umbrella species include determining that a bird species is a candidate umbrella species when its umbrella protection index (UI) is higher than the average value of the UI of all evaluated bird species by at least one standard deviation.

[0012] Preferably, the umbrella protection index is calculated by including the average percentage of co-occurring species (PCS), the intermediate rarity (R), and the disturbance sensitivity index (DSI); the formula for calculating the umbrella protection index is: UI = PCS + R + DSI; The formula for calculating the average percentage of co-occurring species (PCS) is as follows: ; Where n is the total number of valid grid cells recording the target bird species j within the study period, and S i S represents the number of bird species contained in the i-th valid grid cell. max The theoretical maximum number of co-occurring bird species that can occur in all valid grid cells; The formula for calculating the medium rarity R is: ; in, It is equal to the ratio of the number of basic units of species j to the total number of basic units.

[0013] The Disturbance Sensitivity Index (DSI) was used to assess the responsiveness of bird species to human disturbance. This index was calculated considering various life-historical traits susceptible to human activities. Where X represents the sensitivity value assigned to the i-th trait, and n is the total number of traits included in the analysis. This refers to the sum of the maximum possible sensitivity achievable by all traits. Considering data availability and the impact of urban human activities, this embodiment selected three functional traits based on relevant literature: nest type, nest location, and residency type.

[0014] Preferably, in step S4, the operating parameters of the maximum entropy model (MaxEnt) are set as follows: 75% of the input occurrence point data is used for model training, 25% is used for model testing, the model is run 10 times, and the importance of environmental variables is quantified using the Jackknife test.

[0015] Preferably, in step S5, the current situation type of umbrella-protected species habitat is determined based on the green space ratio of the study area, the area of ​​umbrella-protected species habitat, the proportion of umbrella-protected species habitat area to the study area area, and the proportion of umbrella-protected species habitat area to the green space area, thereby determining the simulated situation for the protection of umbrella-protected species habitat area.

[0016] Preferably, in step S6, the simulation parameters of the system protection planning tool Marxan are set as follows: the boundary length correction factor (BLM) is 0.6, and the number of iterations is 100.

[0017] Preferably, in step S7, the effectiveness of the priority spatial results based on umbrella species identification for priority conservation spatial coverage based on the identification of representative species in the comparison group is quantified.

[0018] Compared with the prior art, the present invention has the following beneficial effects: This invention identifies specific umbrella species by calculating the umbrella protection index and considering trophic level, protection level, and geographical distribution, thus achieving quantitative and qualitative screening of key species. It solves the problem of "uncertainty in how to identify key urban species" and improves the objectivity and repeatability of priority protection space identification.

[0019] This invention divides the study area into 1km×1km basic units, uses the MaxEnt model to simulate the adaptive distribution probability of target bird species in the study area, and superimposes it with the existing green space in the study area to determine habitats. Under the constraint of movement resistance, it combines factors such as land use type, road and building height to quantify landscape resistance values. It considers the green space ratio of the study area, whether the area occupied by umbrella species habitat exceeds 30%, and whether the area occupied by umbrella species habitat exceeds 50% of the green space to determine the current status of the habitat situation. Then, it sets different habitat area protection ratio scenarios and uses the Marxan model to simulate the spatial distribution of priority protection units under different protection area ratio scenarios of 20%, 40%, and 60%, which solves the problem of lack of quantitative simulation path for setting the threshold of priority protection spatial protection scenarios for biodiversity in high-density urban areas.

[0020] This invention achieves quantitative verification of conservation effectiveness by spatially overlaying analysis of "priority units identified with umbrella-protected species as target species" and "priority units identified with large resident birds, small resident birds, and small summer migrants as target species." Under a 20% protection scenario, the coverage rates for large resident birds, small resident birds, and small summer migrants reach 70.45%, 68.42%, and 74.42%, respectively; under a 40% protection scenario, they reach 79.80%, 82.52%, and 81.63%; and under a 60% protection scenario, they reach 85.79%, 87.17%, and 86.41%. This demonstrates that priority units identified with umbrella-protected species as target species can cover more than 60% of the representative species protection priority units in the control group, and the coverage effectiveness further improves with the increase of the target protection area threshold, solving the problem of lack of quantitative verification of the spatial effectiveness of priority protection.

[0021] This invention achieves dynamic simulation output of spatial patterns under different protection threshold scenarios by conducting iterative simulation calculations under different protection target ratios. This results in a significant increase in the number of priority protection units as the protection target increases, gradually forming an ecological network with significantly enhanced overall connectivity. It solves the problem of lack of structural comparative analysis of priority protection spaces under different protection intensities.

[0022] This invention constructs a complete technical process encompassing data acquisition, index calculation, habitat simulation, spatial optimization, and coverage assessment. This process enables an integrated computational path for identifying priority protection spaces for urban birds and quantifying the effectiveness of protection, thus resolving the problem of ambiguous feasible paths in the priority protection space identification process.

[0023] In summary, this invention identifies umbrella species by considering species sensitivity, rarity and co-occurrence with other species, trophic level, conservation level and geographical distribution, thereby identifying priority spaces for bird habitat conservation and assessing their spatial guiding role and conservation effectiveness in biodiversity conservation. Attached Figure Description

[0024] Figure 1 This invention provides an embodiment of the distribution of priority protection units under different protection scenarios in the spatial identification and effectiveness assessment of priority protection for urban birds based on umbrella species. (a)~(c) are schematic diagrams showing the priority protection target species scenarios, with protection area percentage thresholds of 20%, 40%, and 60% respectively; Figure 2 A schematic diagram illustrating the spatial distribution of urban conservation priority units for umbrella-protected birds and representative species of the control group under different protection thresholds in spatial identification and effectiveness assessment of urban bird priority protection based on umbrella-protected species, provided as an embodiment of the present invention; Figure 3 A flowchart illustrating the steps of identifying and evaluating the effectiveness of urban bird priority protection spaces based on umbrella species, as provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0026] Example This embodiment uses birds as the main group of protected organisms for empirical analysis, with the central urban area of ​​Shanghai as the study area. The bird species distribution data involved in this embodiment mainly comes from data from the China Birdwatching Record Center during the breeding season (2015-2024) and a basic bird database formed through field surveys of 20 urban parks. Simultaneously, relevant elevation data, road data, and population density data of the study area were collected as a basic information database. Umbrella species refer to species types that, by protecting themselves, can indirectly protect many co-occurring species.

[0027] like Figures 1 to 3 As shown in this embodiment, a method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species is provided, including the following steps: Step S1, Data Acquisition and Preprocessing: The study area was divided into 978 units using ArcGIS Pro with a 1km×1km kilometer grid as the basic unit. When calculating the umbrella protection index, the functional trait data came from the dataset "Life History and Ecological Characteristics of Birds in China" published in the journal Biodiversity in 2021. When simulating the probability of species suitability distribution, seven factors are considered as environmental variables: elevation, temperature, precipitation, road density, NDVI, land use type, and population density. For details of the processing, please refer to step S4.

[0028] When simulating the distribution of priority protection spatial units, the building height, road grade and current land use status are considered as constraint costs for bird species movement, and the constraint costs are adjusted according to different bird groups, as detailed in step S5. Step S2, Survey Completeness Screening: Data from birdwatching record centers within the study area from the 2015 to 2024 breeding seasons were extracted as a basic species pool and matched with basic units. The completeness of bird observation data was assessed for all kilometer-scale grid units divided in Step S1. The KnowBR package in R was used to evaluate the survey completeness of bird observation data in the urban basic units. The slope of the species accumulation curve represents the relationship between the number of records and the cumulative number of species. Survey completeness is defined as the ratio of observed species richness to estimated species richness. This rule categorizes basic units into three levels based on three indicators: the ratio of records to species, the slope of the species accumulation curve, and the completeness of the survey. These levels are: "sufficient survey" (slope of species accumulation curve ≤ 0.02, survey completeness ≥ 90%, and the ratio of records to species ≥ 15), "moderate survey" (0.02 < slope ≤ 0.3, 50% ≤ survey completeness < 90%, and 3 ≤ the ratio of records to species < 15), and "poor survey" (slope > 0.3, survey completeness < 50%, and the ratio of records to species < 3). Grid units with a survey completeness level of "sufficient survey" or "moderate survey" are selected, and 70 basic units with survey completeness identification results of "sufficient" and "moderate" are extracted for subsequent umbrella species identification analysis.

[0029] Step S3, Identification of Umbrella Species of Birds: When selecting target species for biodiversity conservation, this embodiment introduces the concept of umbrella species. Umbrella species refer to species types that can indirectly protect many co-occurring species by protecting themselves.

[0030] Based on the bird distribution data within the valid grid cells selected in step S2, the umbrella protection index for each bird species is calculated, and the specific umbrella protection species in the target area are identified by combining the species' trophic level, residency type, protection level, and geographical distribution.

[0031] First, considering nest form, nest location, and migration status, the umbrella protection index is calculated based on the species' co-occurrence, rarity, and sensitivity to human disturbance, thereby identifying candidate umbrella protection species.

[0032] The umbrella protection index is calculated by considering the average percentage of co-occurring species (PCS), the intermediate rarity (R), and the disturbance sensitivity index (DSI). The formula for calculating the umbrella protection index is: UI = PCS + R + DSI. The umbrella protection index (UI) for each species is calculated by summing the average percentage of co-occurring species (PCS), the intermediate rarity (R), and the disturbance sensitivity index (DSI).

[0033] The formula for calculating the average percentage of co-occurring species (PCS) is as follows: ; The average percentage of co-occurring species for each species was calculated using a 1km × 1km grid as the basic unit: Where n is the number of basic units of bird species j recorded during the study period, and S i S represents the number of bird species contained in the i-th basic unit. max N represents the maximum number of bird species that can theoretically co-occur in all valid grid cells. j This represents the total number of basic units observed to appear in species j.

[0034] The formula for calculating the medium rarity R is: ; in, It is equal to the ratio of the number of basic units of species j to the total number of basic units.

[0035] The Disturbance Sensitivity Index (DSI) was used to assess the responsiveness of bird species to human disturbance. This index was calculated considering various life-historical traits susceptible to human activities. Where X represents the sensitivity assignment for the i-th trait (1 for low sensitivity, 3 for high sensitivity), and n is the total number of traits included in the analysis. smax This refers to the sum of the maximum possible sensitivities of all traits. In this embodiment, three functional traits were selected based on relevant literature: nest form, nest location, and residency type. For each of the three parameters (Table 1), referring to existing research results, this embodiment assigned an integer value from 1 (low sensitivity) to 3 (high sensitivity) to each species, with the scoring range from 1 (least sensitive) to 3 (most sensitive).

[0036] As shown in Table 1, the Disturbance Sensitivity Index (DSI) is calculated by scoring the three life history traits of a species: nest form, nest location, and residence type. The scoring range is 1 to 3 points.

[0037] Table 1 Life history classification criteria for assessing bird sensitivity indices in the study area

[0038] The Umbrella Protection Index (UI) for each species was calculated by summing the average percentage of co-occurring species (PCS), intermediate rarity (R), and disturbance sensitivity index (DSI). A species was identified as an umbrella protection species when its UI was higher than the overall UI by more than one standard deviation. All analyses were performed in R (v4.2.2). To ensure the validity of the analysis, the base species pool consisted of focal bird species that appeared more than 30 times within the study area between 2015 and 2024. In this example, the Umbrella Protection Index (UI) ranged from 1.95 to 4.35, with an average of 3.18, and the results showed that a total of 7 bird species could be considered umbrella protection species (Table 2).

[0039] Table 2. Species Identification Results

[0040] In practice, the scoring method was adopted, considering the trophic level, resident status, conservation status, and geographic distribution of birds. Experts (researchers and experienced birdwatchers) scored the scores. Specifically, trophic level evaluates a species' position in the food chain: large raptors score 5 points, small raptors 3 points, omnivorous species 2 points, and cereal-eating species 1 point. Conservation status reflects the species' priority for protection: national first-class protected species score 5 points, national second-class protected species 4 points, and no conservation status 2 points. Resident status reflects the species' dependence on the environment to some extent: migratory birds are more sensitive to environmental changes (5 points), and resident birds are more adapted to the local environment (2 points). Geographic distribution reflects the species' coverage of different habitats: widely distributed (5 points), moderately distributed (3 points), and endemic (1 point). Each dimension was scored on a scale of 1 to 5, with higher scores indicating higher conservation value or priority for that dimension (Table 3). Finally, the large carnivorous summer migratory bird, the Red-bellied Hawk (also a national second-class protected wild animal), was selected as the target umbrella species for subsequent analysis (Table 4).

[0041] Table 3 Expert Evaluation Index System

[0042] Table 4. Scores of candidate umbrella species based on expert scoring method

[0043] Step S4, Simulation of Potential Habitat Distribution of Umbrella Species: Using the maximum entropy model MaxEnt, with the environmental variables obtained in step S1 as features, input the occurrence location data of the target umbrella species identified in step S3, simulate the suitability distribution probability of the target umbrella species in the study area, extract the areas with high potential habitat distribution probability (≥0.5) in each kilometer grid and overlay them with the green space in each kilometer grid, as the theoretically protected habitat range of the umbrella species in each kilometer grid.

[0044] First, based on bird observation data in Shanghai from 2015 to 2024 collected by the China Birdwatching Record Center during the breeding season (April to June) and field survey data from 20 urban parks, seven environmental variables—elevation, temperature, precipitation, road density, NDVI, land use type, and population density—were considered (as shown in Table 5) to simulate the potential habitat distribution probability of the target bird species in the study area. The data sources included publicly available data platforms and data obtained through spatial analysis methods based on existing data. Some variables (such as road density and population density) were obtained through spatial calculation methods.

[0045] Table 5. Main data sources and their resolutions

[0046] Secondly, the MaxEnt model was used to simulate potential habitat suitability zones. 75% of the data was used for training, and 25% for testing. The response curves of environmental variables in the model were calculated using the knife-cut method. The simulation parameters were set to 10 iterations, with the remaining parameters set to default values. Evaluation results with an AUC value above 0.8 from the simulated ROC curve were used for subsequent analysis. Finally, areas with a suitability probability distribution ≥0.5 for umbrella species were extracted in ArcGIS Pro and spatially overlaid with existing green spaces. The overlaid results were used as the habitats for which umbrella species should be protected. All analyses were performed using a unified projected coordinate system and resampled to a 10m resolution.

[0047] Step S5: Construction of Conservation Cost Constraints and Setting of Habitat Conservation Scenario: In this case study, the landscape resistance surface affecting bird species movement in cities is quantified based on land use type, road density, and building height factors to construct conservation cost constraints (Table 6). Urban buildings are a key factor hindering birds from entering their habitats; resistance values ​​are set based on height, referencing relevant domestic and international bird strike and flight monitoring data. Adjustments are made to the basic resistance table for different bird species groups: Resident birds generally have weaker flight capabilities than migratory birds. This is because resident birds typically only operate in localized areas and do not require long-distance migration, thus their flight altitude is relatively low. Therefore, the landscape resistance value for summer migratory birds is adjusted by multiplying the resistance value in the basic table by a coefficient of 1.5. Furthermore, larger birds generally have higher flight capabilities than smaller birds; therefore, the resistance values ​​in the basic table are halved when constructing resistance values. The resistance surface for small resident birds is constructed based on the basic resistance table, while the resistance surfaces for other bird groups are adjusted according to the above principles. All resistance values ​​are relative values, not true values, set to differentiate between different indicators for different analyzed bird species.

[0048] Table 6. Landscape resistance constraint costs

[0049] In the case study, the habitat protection scenario is determined based on the green space ratio of the study area, the habitat of umbrella species, the proportion of habitat to the study area area, and the proportion of habitat to green area. The current habitat area of ​​umbrella species is then used to determine the current situation type of the habitat area of ​​umbrella species, and then to simulate the distribution of planning units that should be given priority protection for umbrella species under different habitat area protection thresholds.

[0050] The study area covers the central urban area of ​​Shanghai, encompassing multiple administrative districts. According to publicly available data from the district greening and urban appearance management bureaus, the green coverage rate in each district ranged from 19.74% to 34.33% between 2022 and 2025. Singapore's Urban Biodiversity Index assigns a high score when "natural areas account for more than 20% of the urban area." Furthermore, existing research indicates that tree cover exceeding 30% is considered a crucial threshold for maintaining urban ecosystem services and residents' daily contact with nature. Global studies suggest that protecting 44% of the land area is essential for biodiversity conservation. Based on these findings, and considering the spatial constraints of high-density built-up areas, this study sets 30% of the study area's area as the ideal level for supporting urban biodiversity conservation in high-density urban areas, and considers 40% as the upper limit of this ideal scenario. Furthermore, considering that green spaces serve other functions besides bird habitats, when the proportion of habitat area to green space area exceeds 50%, the function of green spaces will shift from multi-functional to ecologically dominant. Therefore, it is recommended that 50% of the green space area be considered a reasonable level for species habitat area protection, and 60% as a constraint limit, to avoid excessive encroachment of recreational and landscape service functions on habitat space. The research hypotheses are as follows: Based on the above threshold settings, the research scenarios are divided into a complete protection scenario, a green space constraint scenario, a total quantity restriction scenario, and a comprehensive constraint scenario. Specifically, when the ratio of habitat area to study area is less than 30%: when the ratio of habitat area to green space area does not exceed 50%, habitat is scarce and does not exceed the available green space area, and the habitat area protection threshold can be set between 10% and 100%. When the ratio exceeds 50%, habitat is highly concentrated in green space. Considering other functions such as urban green space recreation, the habitat area to be prioritized for protection should not exceed 60% of the total green space area. When the ratio of habitat area to study area is higher than 30%, when the ratio of habitat area to green space area does not exceed 50%, it is recommended that the habitat area protection threshold be set between 40% and 80%. When the ratio exceeds 50%, the habitat area is high and highly concentrated, and the comprehensive constraints of green space and urban land use need to be considered. It is recommended that the habitat area to be prioritized for protection should not exceed 60% of the total green space area (see Table 7 for details).

[0051] Table 7. Threshold Setting Table for Species Habitat Area Protection

[0052] In this study, the habitat area of ​​umbrella species and other representative species in the control group accounted for no more than 16.00% of the green space area, and the highest proportion of the total area did not exceed 4.00%, which is consistent with the complete protection scenario in the study hypothesis. Therefore, in the subsequent analysis, habitat area protection target thresholds of 20%, 40%, and 60% were set to simulate the distribution of priority protection units for green spaces under different protection intensities.

[0053] Step S6, Priority Protection Spatial Unit Identification: For each protection area target threshold set in Step S5, the Marxan system protection planning tool is used to perform iterative optimization calculations and output the optimal set of priority protection spatial units that meet the habitat protection target of umbrella species under each area protection threshold.

[0054] This case meets the scenario of complete protection (Table 5). In subsequent analyses, protection targets of 20%, 40%, and 60% for umbrella species habitats were set, and the distribution of priority protection units was determined under different protection targets. The entire simulation process was implemented using Marxan. The principle is simulated annealing, which uses iterative calculations to select the optimal planning unit identification scheme. In this simulation, after multiple experiments, the BLM was set to 0.6, with 100 iterations, and other values ​​were left as default. The protection cost (movement resistance) and habitat area within each 1km basic unit were extracted using the Zonal Atatistics Table and Tabulate Area, respectively.

[0055] In this embodiment, as Figure 1 As shown, when the protection target is 20%, the priority protection units are mainly concentrated in the core water area and its surrounding areas, as well as a few large green patches; when the protection target is 40%, the number of priority protection units increases significantly, and more basic units with relatively scattered green patches are included, gradually forming an ecological network; when the protection target is 60%, the priority protection units increase significantly throughout the entire area, covering most of the green patches, and the overall connectivity is significantly enhanced.

[0056] The results show that as the protection target increases from 20% to 60%, the number of priority protection units increases significantly. At lower protection targets, priority protection areas are concentrated in areas with dense green spaces and low building density. As the target increases, the distribution tends to become more even. However, reasonably increasing the protection target can expand the protection scope, promote the connectivity of ecological units, and thus help form a more complete urban ecological network.

[0057] like Figure 2As shown, this embodiment further verifies the conservation effectiveness of the method. Step S7 is performed to verify the effectiveness of the conservation priority units identified with umbrella-protected species as the target species. This embodiment also uses representative species from the comparison group as target species to divide spatial conservation priority units, and compares the results with the priority units identified with umbrella-protected species as the target species to evaluate the effectiveness of the umbrella-protected species-oriented conservation spatial strategy. Considering body size (with 100g as the threshold), resident status, and species appearing more than 30 times, bird species are divided into large resident birds, small resident birds, large summer visitors, and small summer visitors. Since the umbrella-protected species, the Red-bellied Hawk, is a large summer visitor in this embodiment, only these three categories are considered in the simulated scenario of the representative species in the comparison group.

[0058] Under the 20% habitat coverage protection scenario, priority units identified with umbrella species as target species covered 70.45% (31 / 44) of priority units identified with large resident birds (e.g., crested goshawk), 68.42% (26 / 38) of priority units identified with small resident birds (e.g., light-vented bulbul), and 74.42% (32 / 43) of priority units identified with small summer migratory birds (e.g., barn swallow). The protection coverage effect of umbrella species gradually increased with the increase of the protection scenario threshold: under the 40% protection scenario, the above coverage proportions were 79.80%, 82.52%, and 81.63%, respectively; under the 60% protection scenario, they further increased to 85.79%, 87.17%, and 86.41%.

[0059] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0060] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0061] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, characterized in that, Includes the following steps: Step S1, Data Acquisition and Preprocessing: Acquire geographical information and environmental data on the distribution of bird species in the target study area, and spatially divide the study area into 1km×1km grid units; Step S2, Survey Completeness Assessment: Based on the ratio of records to species, the slope of the species accumulation curve, and survey completeness parameters, the bird observation data completeness of all kilometer grid units divided in Step S1 is assessed. Grid units with a survey completeness level of "sufficient survey" or "general survey" are selected. If there is sufficient bird record data in the study area, only "sufficient survey" grid units can be selected for subsequent umbrella species identification and analysis. Step S3, Bird Umbrella Species Identification: Based on the bird distribution data within the effective grid cells selected in Step S2, calculate the umbrella index for each bird species, and then identify the specific umbrella species in the target area by comprehensively considering trophic level, residency type, protection level, and geospatial distribution. Step S4, Simulation of potential habitat distribution of umbrella species: Using the maximum entropy model MaxEnt, with the environmental variables obtained in step S1 as features, input the occurrence point data of umbrella species identified in step S3, simulate the suitability distribution probability of the umbrella species in the study area, extract the area with suitability distribution probability ≥0.5 in each kilometer grid and overlay it with the green area in the grid, as the potential protected habitat space range of umbrella species in each grid; Step S5, Construction of protection cost constraints and setting of habitat protection scenarios: Based on the potential habitat distribution map obtained in step S4, and the landscape resistance surface quantified based on land use type, road density and building height factors, it serves as a protection cost constraint grid; based on the current green space ratio of the study area, the proportion of habitat area to the study area area, and the proportion of habitat area to green space area, the species habitat status scenario type is determined, and then the habitat protection target threshold scenario is set, wherein the habitat protection target threshold is the proportion of habitat area that the umbrella species needs to be protected; Step S6, Priority Protection Spatial Unit Identification: For each habitat protection target threshold set in Step S5, the Marxan system protection planning tool is used to perform iterative optimization calculations with the goal of minimizing protection costs, and outputs the optimal set of priority protection spatial units that meet the protection targets under that threshold. Step S7: Analysis of the effectiveness of conservation priority units: To verify the effectiveness of conservation priority units identified with umbrella species as the target species, considering factors such as weight, resident type, protection level, and the frequency of occurrence of species more than 30 times, bird species in the study area were divided into large resident birds, small resident birds, large summer migrants, and small summer migrants. Representative species from other groups outside the umbrella species group were used as the control group to identify spatial conservation priority units, following the same steps as steps S4 to S6. The results were then compared with the conservation spatial distribution of priority units identified with umbrella species as the target species to evaluate the effectiveness of the umbrella species-oriented conservation spatial strategy.

2. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S1, the environmental variable data includes at least five of the following: elevation, land use type, normalized difference vegetation index (NDVI), temperature, precipitation, road density, and population density.

3. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S2, the survey completeness assessment uses the KnowBR package in R language to evaluate indicators including the ratio of the number of records to the number of species, the slope of the species accumulation curve, and survey completeness. The slope of the species accumulation curve represents the relationship between the number of records and the cumulative number of species. Survey completeness is defined as the ratio of observed species richness to estimated species richness. When the slope of the species accumulation curve is below 0.02, the survey completeness is above 90%, and the ratio of the number of records to the number of species is greater than 15, the survey is considered sufficient. When the slope is above 0.3, the survey completeness is below 50%, and the ratio of the number of records to the number of species is less than 3, the survey is considered poor. All other cases are considered average surveys.

4. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... The umbrella protection index is calculated by including the average percentage of co-occurring species (PCS), the intermediate rarity (R), and the disturbance sensitivity index (DSI); the formula for calculating the umbrella protection index is: UI = PCS + R + DSI. The formula for calculating the average percentage of co-occurring species (PCS) is as follows: ； Where n is the total number of valid grid cells recording the target bird species j within the study period, and S i S represents the number of bird species contained in the i-th valid grid cell. max The theoretical maximum number of co-occurring bird species that can occur in all valid grid cells; The formula for calculating the medium rarity R is: ； in, It is equal to the ratio of the number of basic units of species j to the total number of basic units; The Disturbance Sensitivity Index (DSI) was used to assess the responsiveness of bird species to human disturbance. This index was calculated considering various life-historical traits susceptible to human activities. Where X represents the sensitivity value assigned to the i-th trait, and n is the total number of traits included in the analysis. It refers to the sum of the maximum possible sensitivities of all traits.

5. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S3, the criteria for determining the umbrella species include determining that a bird species is a candidate umbrella species when its umbrella protection index (UI) is higher than the average of the UI of all evaluated bird species by at least one standard deviation.

6. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S4, the running parameters of the maximum entropy model MaxEnt are set as follows: 75% of the input occurrence point data is used for model training, 25% is used for model testing, the model is run 10 times, and the importance of each environmental variable is evaluated using the Jackknife test method.

7. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S5, based on the current green space ratio of the study area, the proportion of habitat area to the study area area, and the proportion of habitat area to green space area, a protection scenario for the habitat area of ​​umbrella species is set.

8. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S6, the simulation parameters of the system protection planning tool Marxan are set as follows: the boundary length correction factor (BLM) is 0.6, and the number of iterations is 100.

9. The method for identifying and evaluating the effectiveness of priority conservation spaces for urban birds based on umbrella species, as described in claim 1, is characterized in that... In step S7, the validity is defined as: quantifying the proportion of the number of overlapping priority units based on umbrella species identification and the number of priority units based on the number of representative species identification in the comparison group to the total number of priority units based on the number of representative species identification in the comparison group.

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