Cave fish community exposure risk assessment method under influence of climate and human activities

Abstract

The invention provides a cave fish community exposure risk assessment method under the influence of climate and human activities, and the method comprises the following steps: collecting historical existence point data of target cave fishes, collecting annual average temperature (BIO1), annual average rainfall (BIO12), land utilization type (LC) and karst landform (KRT) data which affect the distribution of the cave fishes, and building a variable set; checking the colinearity of variables by using VIF and Pearson correlation coefficients, constructing a species distribution model of each genus in the cave fish community by using a MaxEnt algorithm, and simulating the distribution of the cave fish community based on SDMs; evaluating the performance of the SDMs model according to the AUC, checking the relative contribution degree of modeling factors, and achieving the stacking and simulation of the model; and according to the karst landform information, evaluating the cave fish exposure risk of the groundwater environment and the surface water environment. According to the method, the cave fish exposure risks of groundwater and surface water environments are respectively evaluated and integrated, and the problem that the adaptability to evaluation of the exposure risks of rare freshwater fishes such as cave fishes is insufficient in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of ecological environmental protection technology, and in particular to a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities. Background Art

[0002] Freshwater fish have irreplaceable ecological value and extremely high economic value. The protection of freshwater fish biodiversity has been widely studied and measures have been taken. Cave fish are rare species among freshwater fish. They refer to freshwater fish that must complete all or part of their life history in caves or groundwater bodies in their natural state. The unique evolutionary characteristics of cave fish have important implications for fields including evolution, development, neuroscience and disease treatment.

[0003] The assessment methods in the prior art are mainly aimed at common freshwater fish living in surface rivers. They are not applicable to rare freshwater fish communities that live both underground or in caves and in surface rivers, which may cause a waste of human and material resources when formulating targeted protection measures. Based on the species distribution model, conducting exposure risk assessments on surface rivers and groundwater separately and combining them is an effective solution to the risk assessment and protection problems of rare fish such as cave fish, and effective and targeted protection measures can be proposed. However, conventional freshwater fish risk assessment methods are not suitable for rare freshwater fish such as cave fish, and it is impossible to effectively assess and quantify the exposure risk of cave fish communities, and it is difficult to implement protection, resulting in a waste of human and material resources. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities, so as to solve the problems raised in the above-mentioned background technology. The present invention can comprehensively assess the exposure risk of cave fish from two perspectives: surface water environment and groundwater environment, thereby solving various biodiversity conservation problems facing multiple stressors.

[0005] In order to achieve the above object, the present invention is implemented by the following technical scheme: a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities, comprising the following steps:

[0006] Step 1: Collect historical data on the presence and absence of cave fish communities, and collect data that affect cave fish distribution to establish a set of modeling variables;

[0007] Step 2: The variance inflation factor (VIF) and Pearson correlation coefficient were used to test the collinearity of the modeling factors, and the maximum entropy machine learning algorithm MaxEnt was selected to construct the species distribution models (SDMs) of each organism in the cave fish community, and all the species distribution models (SDMs) were stacked to simulate the distribution of cave fish communities;

[0008] Step 3: Test the performance of the model based on the area under the receiver operating characteristic curve (AUC);

[0009] Step 4: Collect the surface exposure sources (ES) and underground exposure sources (EG) that threaten the distribution of cavefish, and calculate the exposure risk (ER) of cavefish communities based on karst topography (KRT) information and species distribution model output results.

[0010] Furthermore, the data affecting the distribution of cave fish in step 1 include the average annual temperature (BIO1), the average annual precipitation (BIO12), the land use type (LC) and the karst landform (KRT).

[0011] Furthermore, the specific process of step 2 includes:

[0012] Step 2.1: Calculate the variance inflation factor (VIF) and Pearson correlation coefficient values ​​for the annual mean odor (BIO1), annual mean precipitation (BIO12), land use type (LC), and karst topography (KRT) data;

[0013] Step 2.2: Generate 10,000 background points randomly, of which 50% are randomly selected from the entire study area and the remaining 50% are selected from the buffer zone around the real historical existence points;

[0014] Step 2.3: Use the Bootstrap method to randomly divide the filtered historical presence points and background points into a training set (70%) and a test set (30%);

[0015] Step 2.4: Repeat steps 2.2, 2.3, and 2.4 ten times to eliminate the bias of data set splitting caused by the uncertainty of a single model calculation.

[0016] Furthermore, the VIF values ​​in step 2.1 are all less than 10, and the Pearson correlation coefficients are all less than 0.8.

[0017] Furthermore, in step three, the relative contribution of the modeling factors is checked to ensure the excellence of the model performance and the rationality of the selected modeling factors.

[0018] Furthermore, the specific process of step 4 includes:

[0019] Step 4.1: Select m surface exposure factors ES and n underground exposure factors EG that threaten the distribution of cave fish;

[0020] Step 4.2: Convert karst landform data KRT into binary data;

[0021] Step 4.3: Calculate the exposure risk (ER) of cavefish communities based on karst topography (KRT) information and species distribution model outputs.

[0022] Furthermore, in the step 4.2, the binary data 1 indicates that the land is a karst landform, and 0 indicates that the land is a non-karst landform.

[0023] Furthermore, the calculation formula for the cave fish community exposure risk (ER) is as follows:

[0024] ER=SR×[KRT×max(EG i )+(1-KRT)×max(ES j )]

[0025] Where SR is the species richness of cave fish output by the species distribution model; KRT is the karst landform information; EG is the groundwater exposure factor; ES is the surface water exposure factor, where i = 1, 2, ..., n, j = 1, 2, ..., m; n is the number of underground exposure factors considered; m is the number of surface exposure factors considered.

[0026] Furthermore, when the grid is a karst landform, KRT=1, and when the grid is not a karst landform, KRT=0.

[0027] Further, the evaluation method also includes an established evaluation system, wherein the evaluation system includes a variable set module established: for collecting historical existence point data of cave fish communities, collecting annual average temperature (BIO1), annual average precipitation (BIO12), land use type (LC) and karst landform (KRT) data that affect the distribution of cave fish to establish a variable set;

[0028] Environmental factor test module: used to test the collinearity of modeling factors using variance inflation factor (VIF) and Pearson correlation coefficient, simulate cave fish community distribution based on the algorithm MaxEnt in the species distribution model, and check the relative contribution of modeling factors;

[0029] Exposure risk assessment module: used to collect surface exposure sources (ES) and underground exposure sources (EG) that threaten the distribution of cave fish, and calculate the exposure risk (ER) of cave fish communities based on karst landform (KRT) information and species distribution model output results.

[0030] Beneficial effects of the present invention:

[0031] 1. This exposure risk assessment method solves the problem of insufficient adaptability of existing technologies in assessing exposure risks of rare freshwater fish such as cave fish. It improves the simulation of the distribution of cave fish communities and the accuracy of exposure risk assessment, and also provides a scientific basis for the formulation of protection and management strategies. It can accurately simulate the species richness of cave fish communities; it uses the area under the receiver operating characteristic curve (AUC) evaluation indicator and selects the maximum entropy algorithm MaxEnt with superior performance to achieve stacking and simulation of species distribution models. This process not only ensures the efficiency and accuracy of the model, but also ensures the stability and reliability of the model results through multiple runs and evaluations. Significant technical advances have been achieved in data processing, model construction, and results analysis.

[0032] 2. This exposure risk assessment method successfully solves the problem of the lack of adaptability of existing technologies in assessing the exposure risk of rare freshwater fish such as cave fish through systematic data processing and model building methods. Its significant technical progress is reflected in improving the adaptability of the exposure risk calculation method to cave fish, providing a scientific basis and practical tools for the protection and management of freshwater ecosystems. The promotion and application of this method will have a far-reaching impact on the cause of freshwater ecological protection.

[0033] 3. This exposure risk assessment method ensures the stability and scientificity of the model, overcomes the model inaccuracy problem caused by variable collinearity in traditional methods, and significantly improves the accuracy and efficiency of variable selection. At the same time, it also overcomes the problem of insufficient ability of existing models in simulating the distribution of rare species, and significantly improves the prediction accuracy and stability of the model.

[0034] 4. The separation method used in the present invention independently analyzes the exposure risk of cave fish based on the exposure factors of the groundwater environment and the surface water environment. Based on the karst landform information data, the exposure risk of cave fish communities in the groundwater environment and the surface water environment are calculated separately. This method effectively solves the problem of inaccurate exposure risk assessment of rare species such as cave fish in traditional methods, and significantly enhances the ability to identify exposure risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of a method for assessing the risk of cave fish communities exposed to climate and human activities according to the present invention;

[0036] Figure 2 A global cave fish research area distribution map provided by an embodiment of the present invention;

[0037] Figure 3 A global cave fish species richness distribution map provided by an embodiment of the present invention;

[0038] Figure 4 A global cave fish exposure risk distribution map provided by an embodiment of the present invention;

[0039] Figure 5 A structural diagram of a cave fish community exposure risk assessment system under the influence of climate and human activities provided by an embodiment of the present invention;

[0040] Figure 6 This is a distribution map of cavefish exposure risk based on traditional methods. DETAILED DESCRIPTION

[0041] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0042] See also Figures 1 to 6 The present invention provides the following technical solution: a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities, comprising the following steps:

[0043] Step 1: Collect historical data on the presence of cave fish communities, and collect data on annual mean temperature (BIO1), annual mean precipitation (BIO12), land use type (LC), and karst topography (KRT) that affect the distribution of cave fish to establish a set of modeling variables;

[0044] Step 2: The variance inflation factor (VIF) and Pearson correlation coefficient were used to test the collinearity of the modeling factors, and the maximum entropy machine learning algorithm MaxEnt was selected to construct the species distribution models (SDMs) of each organism in the cave fish community, and all the species distribution models (SDMs) were stacked to simulate the distribution of cave fish communities;

[0045] Step 3: Test the performance of the model based on the area under the receiver operating characteristic curve (AUC) to check the relative contribution of the modeling factors to ensure the excellence of the model performance and the rationality of the selected modeling factors;

[0046] Step 4: Collect the surface exposure sources (ES) and underground exposure sources (EG) that threaten the distribution of cavefish, and calculate the exposure risk (ER) of cavefish communities based on karst topography (KRT) information and species distribution model output results.

[0047] Further, the specific steps of step 2 are as follows:

[0048] Step 2.1: Calculate the variance inflation factor (VIF) and Pearson correlation coefficient values ​​of the annual average odor (BIO1), annual average precipitation (BIO12), land use type (LC) and karst landform (KRT) data, and ensure that their VIF values ​​are less than 10 and Pearson correlation coefficients are less than 0.8;

[0049] Step 2.2: Generate 10,000 background points randomly, of which 50% are randomly selected from the entire study area and the remaining 50% are selected from the buffer zone around the real historical existence points;

[0050] Step 2.3: Use the Bootstrap method to randomly divide the filtered historical presence points and background points into a training set (70%) and a test set (30%);

[0051] Step 2.4: Repeat steps 2.2, 2.3, and 2.4 ten times to eliminate the bias of data set splitting caused by the uncertainty of a single model calculation.

[0052] Further, the specific steps of step 4 are as follows:

[0053] Step 4.1: Select two surface exposure factors ES and two underground exposure factors EG that threaten the distribution of cavefish (Table 1);

[0054] Step 4.2: Convert the karst landform data KRT into binary data, where 1 indicates that the area is karst landform and 0 indicates that the area is non-karst landform;

[0055] Step 4.3: Calculate the exposure risk (ER) of the cave fish community based on the karst topography (KRT) information and the output of the species distribution model; the calculation formula of the exposure risk (ER) of the cave fish community is as follows:

[0056] ER=SR×[KRT×max(EG i )+(1-KRT)×max(ES j )] (1)

[0057] Where, (1) SR is the species richness of cave fish output by the species distribution model; (2) KRT is the karst landform information. When the grid is karst landform, KRT = 1, otherwise it is 0; (3) EG is the groundwater exposure factor, and ES is the surface water exposure factor. Their values ​​are both 0-1 and have probabilistic meanings, where i = 1, 2, j = 1, 2.

[0058] Table 1 Surface and subsurface exposure factors faced by cavefish communities

[0059]

[0060] like Figure 5 As shown, this embodiment also provides a cave fish community exposure risk assessment system under the influence of climate and human activities, which is used to further illustrate the above method, specifically including:

[0061] The variable set module was established to collect the historical presence point data of cave fish communities, and the annual mean temperature (BIO1), annual mean precipitation (BIO12), land use type (LC) and karst landform (KRT) data that affect the distribution of cave fish were collected to establish the variable set;

[0062] The environmental factor test module is used to test the collinearity of modeling factors using the variance inflation factor (VIF) and Pearson correlation coefficient, simulate the distribution of cave fish communities based on the algorithm MaxEnt in the species distribution model, and check the relative contribution of modeling factors;

[0063] The exposure risk assessment module is used to collect surface exposure sources (ES) and underground exposure sources (EG) that threaten the distribution of cavefish, and calculate the exposure risk (ER) of cavefish communities based on karst landform (KRT) information and species distribution model output results.

[0064] The study area of ​​this example ( Figure 2 ) includes 7 regions in Asia, Oceania, Africa, North America and South America. Since cave fish are less valued than ordinary freshwater fish, but more sensitive to environmental changes than ordinary freshwater fish, it is very difficult to coordinate the protection of cave fish populations, and it is of great significance to assess the exposure risk of cave fish worldwide. Since cave fish (including typical cave fish and part of the physiological cycle of atypical cave fish) mainly live in underground environments, and part of the physiological cycle of atypical cave fish live in surface water environments, the present invention provides a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities. Taking 31 genera (including 263 species) worldwide as an example, a species distribution model SDMs is established based on the MaxEnt algorithm, and the SDMs are respectively obtained from karst areas (representing groundwater environments) and non-karst areas (representing surface water environments) ( Figure 3 ) area to assess the exposure risk of cave fish communities to help better formulate conservation strategies. The specific process is as follows:

[0065] The historical presence data of all cave fishes in the world during the period of 2001-2020 were collected, and these points were classified and summarized according to the genus of cave fishes, and filtered into 7 research areas ( Figure 2). Only cavefish genera with more than 5 historical presence points in a single study area were retained as modeling units, and 1000 background points were generated using a randomization method and merged into a total data set. Global-scale annual mean temperature (BIO1), annual mean precipitation (BIO12), land use type (LC) and karst landform (KRT) data were collected as modeling factor sets. Before modeling, the variance inflation factor (VIF) and Pearson correlation coefficient were used to test the collinearity of the modeling factors to ensure that VIF < 10 and the Pearson correlation coefficient was less than 0.8. The SDMs model of 31 genera was run 10 times (based on the MaxEnt algorithm) with the merged data set and the modeling factor set as input. The model performance was evaluated according to the area under the receiver operating characteristic curve (AUC) (average AUC> 0.85 was required) and the relative contribution of the modeling factors was tested. The output results of the SDMs model of 31 genera were stacked with species as weights to obtain the cavefish species richness of the seven study areas ( Figure 3 ). Using karst landform information as classification, based on the cavefish species richness results, the cavefish exposure risk in groundwater environment and surface water environment was calculated and assessed respectively ( Figure 4 ).

[0066] BIO1, BIO12, LC and KRT all passed the collinearity test, and the relative contribution of the models was 34.2%, 15.8%, 12.5% ​​and 37.3%, respectively. The mean AUC of the SDMs model of 31 cavefish genera was 0.91, and the AUC of most models was higher than 0.85, indicating excellent model performance. Figure 3 The results show that southwestern China is the region with the richest cave fish in the world, and South America (SA) is the region with the most extensive distribution of cave fish. Figure 4 It shows that southwestern China is also the area with the highest risk of cave fish exposure.

[0067] 1. Relevant evidence of the technical effects obtained by the embodiments of the present invention.

[0068] Cave fish are rare species among freshwater fish. If the exposure risk of cave fish is assessed according to the traditional method, it will seriously underestimate the survival threat faced by cave fish (compare the following Figure 6 and Figure 4 ), which is not conducive to the formulation and implementation of protection policies.

[0069] This paper innovatively proposes a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities, which more reasonably quantifies and assesses the exposure risk of cave fish ( Figure 4 ), can improve the efficiency of cave fish protection and avoid wasting manpower and material resources.

[0070] This embodiment shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0071] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for assessing the exposure risk of cave fish communities under the influence of climate and human activities, characterized in that: The following steps are involved: Step 1: Collect historical data on the presence and absence of cave fish communities, and collect data that affect cave fish distribution to establish a set of modeling variables; Step 2: Use variance inflation factor and Pearson correlation coefficient to test the collinearity of modeling factors, select the maximum entropy machine learning algorithm MaxEnt, construct the species distribution model of each organism in the cave fish community, and stack all the species distribution models to simulate the distribution of cave fish communities; Step 3: Test the performance of the model based on the area under the receiver operating characteristic curve; Step 4: Collect the surface exposure sources and underground exposure sources that threaten the distribution of cave fish respectively, and calculate the exposure risk of cave fish communities based on karst landform information and species distribution model output results.

2. The method for assessing the risk of cave fish community exposure under the influence of climate and human activities according to claim 1, characterized in that: The data affecting the distribution of cave fish in step 1 include the average annual temperature, the average annual precipitation, the land use type and the karst landform.

3. The method for assessing the risk of cave fish community exposure under the influence of climate and human activities according to claim 1, characterized in that: The specific process of step 2 includes: Step 2.1: Calculate the variance inflation factor and Pearson correlation coefficient values ​​for the annual average odor, annual average precipitation, land use type, and karst landform data; Step 2.2: Generate 10,000 background points randomly, of which 50% are randomly selected from the entire study area and the remaining 50% are selected from the buffer zone around the real historical existence points; Step 2.3: Use the Bootstrap method to randomly divide the filtered historical presence points and background points into a training set of 70% and a test set of 30%; Step 2.4: Repeat steps 2.2, 2.3, and 2.4 ten times to eliminate the bias of data set splitting caused by the uncertainty of a single model calculation.

4. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 3, characterized in that: The VIF values ​​in step 2.1 are all less than 10, and the Pearson correlation coefficients are all less than 0.

8.

5. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 1, characterized in that: In the step three, the relative contribution of the modeling factors is checked to ensure the excellence of the model performance and the rationality of the selected modeling factors.

6. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 1, characterized in that: The specific process of step 4 includes: Step 4.1: Select m surface exposure factors ES and n underground exposure factors EG that threaten the distribution of cave fish; Step 4.2: Convert karst landform data KRT into binary data; Step 4.3: Calculate the exposure risk of cavefish communities based on karst geomorphological information and species distribution model output.

7. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 6, characterized in that: In the step 4.2, the binary data 1 indicates that the land is a karst landform, and 0 indicates that the land is a non-karst landform.

8. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 6, characterized in that: The formula for calculating the exposure risk of cave fish communities is as follows: ER=SR×[KRT×max(EG i )+(1-KRT)×max(ES j )] Where SR is the species richness of cave fish output by the species distribution model; KRT is the karst landform information; EG is the groundwater exposure factor; ES is the surface water exposure factor, where i = 1, 2, ..., n, j = 1, 2, ..., m; n is the number of underground exposure factors considered; m is the number of surface exposure factors considered.

9. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 8, characterized in that: When the grid is karst landform, KRT=1, and when the grid is not karst landform, KRT=0.

10. The method for assessing the risk of cave fish community exposure to climate and human activities according to claim 1, characterized in that: The evaluation method also includes establishing an evaluation system, wherein the evaluation system includes establishing a variable set module: for collecting historical existence point data of cave fish communities, collecting annual average temperature, annual average precipitation, land use type and karst landform data that affect the distribution of cave fish to establish a variable set; Environmental factor test module: used to test the collinearity of modeling factors using variance inflation factor and Pearson correlation coefficient, simulate the distribution of cave fish communities based on the algorithm MaxEnt in the species distribution model, and check the relative contribution of modeling factors; Exposure risk assessment module: used to collect surface exposure sources and underground exposure sources that threaten the distribution of cave fish, and calculate the exposure risk of cave fish communities based on karst landform information and species distribution model output results.

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