Method for evaluating connectivity risk level of seasonal migration organism habitat

Abstract

The invention discloses a method for evaluating the connectivity risk level of a seasonal migration organism habitat, and the method comprises the steps: determining a research region and division nodes, and collecting the biomass data of various groups, habitat suitability data and human fishing activity intensity data; setting spatial representative point sets of various groups in different seasons based on the biomass data of the target species and the habitat range; a habitat suitability conversion method is used to calculate migration resistance values of various groups at each node, and a Laplacian matrix is constructed; based on the space representative points and migration resistance value data, calculating the internal connectivity of the populations in the same season, the connectivity among the populations and the connectivity among the populations in different seasons; and spatial connection is carried out, the average value of the habitat connectivity and the human fishing activity intensity of each node in each season is calculated, and the habitat connectivity risk level is evaluated. Compared with a traditional static model, the method has the advantage that the risk early warning accuracy is obviously improved.

Description

Technical Field

[0001] This invention relates to the field of habitat connectivity risk level assessment technology, and specifically to a method for assessing the habitat connectivity risk level of seasonally migratory organisms. Background Technology

[0002] In recent years, with the increasing impact of human activities on marine and freshwater ecosystems, habitat fragmentation of migratory organisms has become a prominent issue. The survival of seasonally migratory species (such as fish and cetaceans) is highly dependent on habitat connectivity between different seasons, and factors such as fishing, waterway development, and environmental degradation can severely disrupt their migration routes and habitat networks. Therefore, scientifically assessing the risk level of habitat connectivity for migratory organisms is of great significance for species conservation and ecological management.

[0003] Currently, habitat connectivity assessment is mainly based on circuit theory or graph theory models to simulate species migration paths. However, existing methods have the following limitations: (1) Traditional models are mostly designed for static habitats and do not fully consider the dynamic switching of habitat demand caused by seasonal changes in migratory species, making it difficult to quantify the spatiotemporal evolution of the "source-sink" relationship between different seasons. (2) When calculating inter-population connectivity, existing methods often ignore the superposition effect of intra-population connectivity, resulting in systematic bias in the results. (3) Most studies only focus on the connectivity of the habitat itself and lack spatial coupling analysis with factors such as human activity pressure, failing to directly reflect the actual risk level. In addition, although the Species Distribution Model (SDM) and Habitat Suitability Index (HSI) have been widely used in habitat assessment, their results are mostly limited to the stage of suitable area identification and have not yet established a quantitative correlation mechanism with dynamic connectivity risk. Therefore, there is an urgent need for a scientific method to assess the risk level of habitat connectivity for migratory organisms in order to provide a scientific basis for the protection of migratory organisms. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method for assessing the connectivity risk level of habitats for seasonal migratory organisms. This method integrates multi-seasonal habitat data, systematically considers the connectivity within populations within the same season, the connectivity between populations, and their connectivity across different seasons, and couples in human activity disturbances such as the intensity of fishing activities to establish a comprehensive assessment method. This method enables a scientific assessment of the connectivity risk level of habitats for seasonal migratory organisms, effectively solving the technical bottleneck of traditional static models in capturing seasonal habitat transition characteristics, significantly improving the accuracy of risk warning, and providing a scientific basis for the protection of migratory organisms.

[0005] A method for assessing the connectivity risk level of habitats for seasonally migrating organisms includes the following steps:

[0006] (1) Select the study area, divide the study area into study pixels or study units, i.e. nodes, and collect biomass data and habitat suitability data of each target population in different seasons, as well as data on the intensity of human fishing activities in the study area.

[0007] (2) Based on the habitat range and biomass data of each target population, set the spatial representative point set of each target population in different seasons from the overall macro or local micro level;

[0008] (3) Based on the habitat suitability conversion method, the migration resistance values ​​of each target population in each node during each season and the migration resistance values ​​between seasons were calculated using conditional functions. The migration resistance value in each node during each season was expressed by the conditional function: migration resistance value = Con((1 – the value of habitat suitability data) × 10 < 0, 0, (1 – the value of habitat suitability data) × 10). The migration resistance value between seasons was the average of the migration resistance values ​​in two adjacent seasons. Then, a Laplace matrix was constructed based on the migration resistance values. :

[0009] ;

[0010] in, For nodes and nodes The resistance value between them is called the node. migration resistance values ​​and nodes The difference in migration resistance values; For summation index variables; Indicates a node Sum the reciprocals of the resistances of all adjacent nodes, i.e., the migration resistance values;

[0011] (4) Based on spatial representative points and migration resistance data, the connectivity of the target population habitat is calculated using the connectivity theory of circuits, including the habitat connectivity within the population in the same season, the habitat connectivity between populations in the same season, and the habitat connectivity within the population in different seasons and the habitat connectivity between populations in different seasons.

[0012] (5) Apply the “source-sink” theory to the seasonal migration of the target population. “Source” and “sink” represent the start and end of habitat connectivity between different seasons. When calculating the habitat connectivity within the population between different seasons and the habitat connectivity between populations between different seasons, the source points of different seasons form current sources and the sink points form grounding points. Then, the habitat connectivity within the population between different seasons, the habitat connectivity between populations between different seasons, and the total habitat connectivity of each season are calculated.

[0013] (6) When calculating the habitat connectivity between populations, first perform a quantitative analysis of the habitat connectivity values ​​of the spatial representative points of the two populations, and then subtract the habitat connectivity values ​​within the population from the quantified habitat connectivity values ​​to eliminate the influence of habitat connectivity within the population on the results.

[0014] (7) The habitat connectivity within a population in the same season and the habitat connectivity between populations in the same season were calculated using a pairwise mode of an open-source landscape connectivity analysis software based on circuit theory. This mode injects a unit current at the source point and extracts the same unit current at the sink point. The habitat connectivity within a population in different seasons and the habitat connectivity between populations in different seasons were calculated using a mode that activates independent source and ground points. This mode injects a unit current at the current source and extracts the same unit current at the ground point.

[0015] (8) Based on the partitioned nodes and the Laplace matrix Calculate the node voltage matrix Voltage gradient at nodes :

[0016] ;

[0017] in, For the current vector matrix, in paired mode, the source current vector is +1, the sink current vector is -1, and the current vectors of the other nodes are 0; in the mode of activating independent source and ground point, the current vector of each current source is +1, the current vector of each ground point is -1, and the current vectors of the other nodes are 0.

[0018] Based on the node voltage matrix compute nodes and nodes voltage difference between :

[0019] ;

[0020] compute nodes and nodes voltage gradient between :

[0021] ;

[0022] in, It is a node and nodes The distance between them;

[0023] (9) The current density between nodes is calculated. :

[0024] ;

[0025] Aggregate the current densities of the adjacent edges of a node to form the current density of that node:

[0026] ;

[0027] The current density of the node where the habitat is located is the habitat connectivity value;

[0028] (10) Based on ArcGIS's display zoning statistics tool, spatially connect and calculate the average value of seasonal habitat connectivity and the average value of human fishing activity intensity for each node;

[0029] (11) Construct a habitat connectivity risk level index: Habitat connectivity risk level = average value of seasonal habitat connectivity × average value of human fishing activity intensity, and assess the habitat connectivity risk level; if the average value of seasonal habitat connectivity is higher and the average value of human fishing activity intensity is greater, the risk to habitat connectivity is greater.

[0030] To achieve better technical results, the following optimizations were made:

[0031] In step (1), the habitat suitability data is derived from the analysis results of the Species Distribution Model (SDM) or the Habitat Suitability Index (HSI) model, and the data value ranges from 0 to 1. More preferably, the habitat suitability data adopts the analysis results of the HSI model, i.e., the Habitat Suitability Index.

[0032] When the study area is a mixed aquatic area for multiple populations, the habitat suitability data for the target population in different seasons are data calculated using population biomass weighting.

[0033] The study area can be defined based on the activity area of ​​the target seasonal migratory species, or it can be defined by comprehensively considering the life history characteristics, ease of management, and research accuracy requirements of the target seasonal migratory species.

[0034] In step (2), the spatial representative point set of each target population in different seasons is set from the overall macro level by using the centroid of the fishing ground as the spatial representative point set of the target population.

[0035] Determining the spatial representative point set for each target population in different seasons at the local micro-level involves using the centroids of fishing grounds or fishing areas with higher population biomass as the spatial representative point set for the target population. Using the centroids of fishing grounds with higher population biomass as the spatial representative point set for the target population is more suitable for species with particularly wide habitat ranges; using the centroids of fishing areas with higher population biomass as the spatial representative point set for the target population is more suitable for species with relatively narrow habitat ranges. Further preferably, the top 50% of fishing ground centroids or the top 50% of fishing area centroids with higher and lower population biomass values ​​are used as the spatial representative point set for each target population in different seasons.

[0036] In step (3), the migration resistance value of each node in each season is preferably expressed by the conditional function as: migration resistance value = Con((1 – the value of the standardized habitat suitability data) × 10 < 0, 0, (1 – the value of the standardized habitat suitability data) × 10).

[0037] In step (10), the average value of seasonal habitat connectivity and the average value of human fishing activity intensity of each node are normalized before use to eliminate the difference in the original data units.

[0038] In step (11), the habitat connectivity risk level is divided into 5 levels according to the natural discontinuity classification method: low risk, medium-low risk, medium risk, medium-high risk, and high risk.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention introduces the "source-sink" theory and seasonal migration coupling mechanism (step (5)) and combines it with an independent source-land model (step (7)) to quantify cross-seasonal connectivity. For the first time, it achieves a dynamic assessment of migratory species habitats in both time and space, overcoming the shortcomings of traditional static models that cannot capture the seasonal shift in habitat demand, and significantly improving the accuracy of risk warning. In addition, this invention breaks through the limitations of single ecological factor analysis and integrates habitat connectivity calculated in a specific way with the intensity of human fishing activities (steps (10)-(11)). Through a specific product relationship (habitat connectivity risk level = average value of habitat connectivity × average value of human fishing activity intensity), it directly maps connectivity vulnerability hotspots, providing a scientific basis for management decisions that is "risk-visible and factor-traceable".

[0041] The method of this invention integrates dynamic connectivity analysis, multi-source data weighted calculation, and risk assessment based on the "source-sink" theory to improve the diagnostic capability for the vulnerability of habitat networks of seasonally migratory species. Attached Figure Description

[0042] Figure 1 This is a flowchart of an embodiment of the method of the present invention;

[0043] Figure 2 This is a distribution map of the habitat suitability index of the small yellow croaker at each node in this embodiment of the invention;

[0044] Figure 3 This is a diagram showing the migration resistance values ​​of small yellow croaker in different seasons and the distribution of migration resistance values ​​between seasons in an embodiment of the present invention;

[0045] Figure 4 This is a distribution map of representative spatial points (i.e., resource representative points) of small yellow croaker in different seasons in this embodiment of the invention;

[0046] Figure 5 This is a map showing the connectivity distribution of the habitat of the small yellow croaker in different seasons in an embodiment of the present invention;

[0047] Figure 6 This is a distribution map of the intensity of human fishing activities in different seasons in an embodiment of the present invention;

[0048] Figure 7 This is a distribution map of the habitat connectivity risk level of small yellow croaker in different seasons in this embodiment of the invention. According to the natural discontinuity grading method, it is divided into 5 levels: low risk, medium-low risk, medium risk, medium-high risk, and high risk. Detailed Implementation

[0049] Example 1

[0050] The following uses the target species, the seasonally migratory yellow croaker, as an example, combined with... Figure 1 The technical solution of the present invention will be described in further detail so that those skilled in the art can better understand the technical solution of the present invention.

[0051] (1) Taking into account the life history characteristics, ease of management, and research accuracy requirements of the two target populations of the seasonal migratory small yellow croaker in the southern Yellow Sea and the East China Sea, the study area was defined and research pixels (i.e., nodes) were divided within the study area. Figure 2 (Each sub-region), collect biomass data and habitat suitability data of the target population in different seasons within the study area (see...) Figure 2 The data also includes data on the intensity of human fishing activities; among which, the biomass data of small yellow croaker was obtained from the results of seasonal fishery resource trawling surveys and standardized using the sea area sweeping method, with the final unit of biomass uniformly set at kg / km². 2 The habitat suitability data were analyzed using the habitat suitability index model, which is the habitat suitability index. The analysis results were standardized so that the habitat suitability data ranged from 0 to 1.

[0052] (2) Establishing a spatial representative point set. Based on the habitat range and biomass data of the small yellow croaker, a spatial representative point set, i.e., key nodes, for the target species small yellow croaker in different seasons is established from the overall macroscopic or local microscopic level. Here, the fishing area is used as the spatial scale, and the centroids of the top 50% of the fishing areas ranked by the high and low values ​​of the biomass data of the small yellow croaker population in the southern Yellow Sea and the East China Sea are used as the spatial representative point set of this population in different seasons (see Figure 4 ).

[0053] (3) Calculate migration resistance values ​​and construct the Laplace matrix. Based on the habitat suitability conversion method, the migration resistance values ​​of each target population in each node during each season and the migration resistance values ​​between seasons are calculated using conditional functions. The migration resistance values ​​within each season of each node are expressed by the conditional function: migration resistance value = Con((1 – the value of the standardized habitat suitability data) × 10 < 0, 0, (1 – the value of the standardized habitat suitability data) × 10). The migration resistance value between seasons of each node is the average of the migration resistance values ​​within two adjacent seasons. The higher the migration resistance value, the greater the degree of obstruction of the small yellow croaker migration by the marine environmental conditions at that node. See Figure 3 Subsequently, a Laplace matrix was constructed based on the migration resistance values. :

[0054] ;

[0055] in, For nodes and nodes The resistance value between them is called the node. migration resistance values ​​and nodes The difference in migration resistance values; For summation index variables; Indicates a node Sum the reciprocals of the resistances of all adjacent nodes, i.e., the migration resistance values;

[0056] (4) Clarify the connotation of habitat connectivity. Based on the spatial representative points and migration resistance data of small yellow croaker, the habitat connectivity of this species is calculated using the connectivity theory of circuits. This includes habitat connectivity within the same population in the same season, habitat connectivity between populations in the same season, as well as habitat connectivity within the population in different seasons and habitat connectivity between populations in different seasons. The habitat connectivity considered is more comprehensive and scientific.

[0057] (5) Calculate the total connectivity of habitats in each season. The "source-sink" theory is applied to the seasonal migration of the target species, the small yellow croaker. The "source" and "sink" represent the start and end points of habitat connectivity between different seasons. When calculating the intra-population habitat connectivity between different seasons and the habitat connectivity between different populations, the source points of different seasons form current sources, and the sink points form grounding points. Then, the inter-seasonal connectivity (intra-population habitat connectivity between different seasons and habitat connectivity between different populations) and the total connectivity of habitats in each season are calculated.

[0058] (6) Precise definition of inter-population connectivity. When calculating the habitat connectivity between populations of small yellow croaker, the connectivity values ​​of the spatial representative points of the two populations are quantitatively analyzed, and then the habitat connectivity values ​​within the population are subtracted from the quantified habitat connectivity values ​​to eliminate the influence of the habitat connectivity within the population on the results.

[0059] (7) Connectivity calculation based on Circuitscape. The habitat connectivity within the same population and between populations in the same season of small yellow croaker were calculated using the pairwise mode of open-source landscape connectivity analysis software based on Circuitscape theory. This mode injects a unit current at the source point and extracts the same unit current at the sink point. The habitat connectivity within the population and between populations in different seasons were calculated using the activate independent sources and grounds mode. This mode injects a unit current at the current source and extracts the same unit current at the ground point.

[0060] (8) Based on the partitioned nodes and the Laplace matrix Calculate the node voltage matrix Voltage gradient at nodes :

[0061] ;

[0062] in, This is the current vector matrix. In Pairwise mode, the source current vector is +1, the sink current vector is -1, and the current vectors of the other nodes are 0; in Activate independent sources and grounds mode, the current vector of each current source is +1, the current vector of each ground point is -1, and the current vectors of the other nodes are 0.

[0063] Based on the node voltage matrix compute nodes and nodes voltage difference between :

[0064] ;

[0065] compute nodes and nodes voltage gradient between :

[0066] ;

[0067] in, It is a node and nodes The distance between them.

[0068] (9) The current density between nodes is calculated. :

[0069] ;

[0070] Aggregate the current densities of the adjacent edges of a node to form the current density of that node:

[0071] ;

[0072] The current density of the node where the habitat is located is the habitat connectivity value.

[0073] (10) Spatial connectivity and statistics. Based on ArcGIS's display zoning statistics tool, spatial connectivity was used to calculate the average value of seasonal habitat connectivity of small yellow croaker and the average value of human fishing activity intensity for each study cell. The average value of seasonal habitat connectivity and the average value of human fishing activity intensity were normalized for calculating the risk level.

[0074] (11) Risk level calculation. Construct a habitat connectivity risk level index: Habitat connectivity risk level = average value of seasonal habitat connectivity (see Figure 5 ) × Average value of human fishing activity intensity (see Figure 6 This study calculates habitat connectivity risk levels and assesses the risks faced by different regions under corresponding fishing intensities. Higher average seasonal habitat connectivity values, coupled with higher average human fishing activity intensities, indicate greater risks to habitat connectivity. Habitat connectivity risk levels are categorized into five levels based on the natural discontinuity classification method: low risk, medium-low risk, medium risk, medium-high risk, and high risk. (See [link to relevant documentation]). Figure 7 .

[0075] As can be seen, this invention constructs a dual-dimensional dynamic assessment system of "temporal evolution and spatial interaction," effectively solving the technical bottleneck of traditional static models in capturing seasonal habitat transition characteristics, and significantly improving the accuracy of ecological risk early warning. Simultaneously, by breaking away from the traditional assessment framework of a single ecological element, it innovatively constructs a coupled analysis model of habitat connectivity and human fishing activities, intuitively revealing the risk level of habitat connectivity for seasonally migrating organisms, significantly enhancing the scientific rigor and targeting of conservation strategies.

Claims

1. A method for assessing the risk level of habitat connectivity for seasonally migrating organisms, characterized in that, Includes the following steps: (1) Select the study area, divide the study area into nodes, and collect biomass data and habitat suitability data of each target population in different seasons, as well as data on the intensity of human fishing activities in the study area. (2) Based on the habitat range and biomass data of each target population, set the spatial representative point set of each target population in different seasons from the overall macro or local micro level; (3) Based on the habitat suitability conversion method, the migration resistance values ​​of each target population in each node in each season and the migration resistance values ​​between seasons are calculated using conditional functions. The migration resistance values ​​in each node in each season are expressed by the conditional function: migration resistance value = Con((1 – the value of habitat suitability data) × 10 < 0, 0, (1 – the value of habitat suitability data) × 10), and the migration resistance value between seasons of each node is the average value of the migration resistance values ​​in two adjacent seasons. Subsequently, a Laplace matrix is ​​constructed based on the migration resistance value. : ； in, For nodes migration resistance values ​​and nodes The difference in migration resistance values; For summation index variables; Indicates a node Sum of the reciprocals of the migration resistance values ​​of all adjacent nodes; (4) Based on spatial representative points and migration resistance data, the connectivity of the target population habitat is calculated using the connectivity theory of circuits, including the habitat connectivity within the population in the same season, the habitat connectivity between populations in the same season, and the habitat connectivity within the population in different seasons and the habitat connectivity between populations in different seasons. (5) Apply the "source-sink" theory to the seasonal migration of the target population. "Source" and "sink" represent the start and end of habitat connectivity between different seasons. When calculating the habitat connectivity within the population between different seasons and the habitat connectivity between populations between different seasons, the source points of different seasons form current sources and the sink points form grounding points. Then, the habitat connectivity within the population between different seasons, the habitat connectivity between populations between different seasons, and the total connectivity of habitats in each season are calculated. (6) When calculating the habitat connectivity between populations, first perform a quantitative analysis of the habitat connectivity values ​​of the spatial representative points of the two populations, and then subtract the habitat connectivity values ​​within the population from the quantified habitat connectivity values ​​to eliminate the influence of habitat connectivity within the population on the results. (7) The habitat connectivity within a population in the same season and the habitat connectivity between populations in the same season were calculated using a pairwise mode of an open-source landscape connectivity analysis software based on circuit theory. This mode injects a unit current at the source point and extracts the same unit current at the sink point. The habitat connectivity within a population in different seasons and the habitat connectivity between populations in different seasons were calculated using a mode that activates independent source and ground points. This mode injects a unit current at the current source and extracts the same unit current at the ground point. (8) Based on the partitioned nodes and the Laplace matrix Calculate the node voltage matrix Voltage gradient at nodes : ； in, This is the current vector matrix; in paired mode, the source current vector is +1, the sink current vector is -1, and the current vectors of the remaining nodes are 0; in the mode of activating independent source and ground point, the current vector of each current source is +1, the current vector of each ground point is -1, and the current vectors of the remaining nodes are 0. Based on the node voltage matrix compute nodes and nodes voltage difference between : ； compute nodes and nodes voltage gradient between : ； in, It is a node and nodes The distance between them; (9) The current density between nodes is calculated. : ； Aggregate the current densities of the adjacent edges of a node to form the current density of that node: ； The current density of the node where the habitat is located is the habitat connectivity value; (10) Based on ArcGIS's display zoning statistics tool, spatially connect and calculate the average value of seasonal habitat connectivity and the average value of human fishing activity intensity for each node; (11) Construct a habitat connectivity risk level index: Habitat connectivity risk level = average value of seasonal habitat connectivity × average value of human fishing activity intensity, and assess the habitat connectivity risk level; if the average value of seasonal habitat connectivity is higher and the average value of human fishing activity intensity is greater, the risk to habitat connectivity is greater.

2. The method according to claim 1, characterized in that, In step (1), the habitat suitability data is derived from the analysis results of the species distribution model or the habitat suitability index model, and the data value ranges from 0 to 1.

3. The method according to claim 1, characterized in that, In step (1), when the study area is a mixed aquatic area of ​​multiple populations, the habitat suitability data of the target population in different seasons are the data after weighted calculation using population biomass.

4. The method according to claim 1, characterized in that, In step (1), the study area is defined after comprehensively considering the life history characteristics, ease of management, and research accuracy requirements of the target population of seasonal migratory organisms.

5. The method according to claim 1, characterized in that, In step (2), the spatial representative point set of each target population in different seasons is set from the overall macro level by using the centroid of the fishing ground as the spatial representative point set of the target population; To define the spatial representative point set for each target population in different seasons at the local micro level, the centroid of the fishing ground or the centroid of the fishing area with higher population biomass is used as the spatial representative point set for the target population.

6. The method according to claim 1 or 5, characterized in that, In step (2), the top 50% of the fishery centroids or fishery area centroids ranked by biomass of each population are used as the spatial representative point set of each target population in different seasons.

7. The method according to claim 1, characterized in that, In step (3), the migration resistance value of each node in each season is expressed by the conditional function as: migration resistance value = Con((1 – the value of the standardized habitat suitability data) × 10 < 0, 0, (1 – the value of the standardized habitat suitability data) × 10).

8. The method according to claim 1, characterized in that, In step (10), the average value of seasonal habitat connectivity and the average value of human fishing activity intensity of each node are normalized before use.

9. The method according to claim 1, characterized in that, In step (11), the habitat connectivity risk level is divided into 5 levels according to the natural discontinuity classification method: low risk, medium-low risk, medium risk, medium-high risk, and high risk.

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