A method for assessing the exposure risk of cavefish communities to climate and human activities

Abstract

This invention provides a method for assessing the exposure risk of cavefish communities under the influence of climate and human activities, comprising the following steps: collecting historical location data of target cavefish; establishing a variable set by collecting annual average temperature (BIO1), annual average precipitation (BIO12), land use type (LC), and karst topography (KRT) data that influence cavefish distribution; testing for collinearity of variables using VIF and Pearson correlation coefficients; constructing a species distribution model for each genus in the cavefish community using the MaxEnt algorithm; simulating the cavefish community distribution based on SDMs; evaluating the performance of the SDMs model based on AUC, checking the relative contribution of modeling factors, and realizing model stacking and simulation; and assessing the cavefish exposure risk in groundwater and surface water environments based on karst topography information. This invention assesses and integrates the cavefish exposure risks in groundwater and surface water environments, solving the problem of insufficient adaptability of existing technologies in assessing the exposure risk of rare freshwater fish such as cavefish.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental protection technology, specifically a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities. Background Technology

[0002] Freshwater fish possess irreplaceable ecological and economic value, making the conservation of freshwater fish biodiversity crucial. Cave fish are rare freshwater species, defined as those that, in their natural state, complete all or part of their life cycle in caves or underground water bodies. The unique evolutionary characteristics of cave fish have significant implications for fields including evolution, development, neuroscience, and disease treatment.

[0003] Existing assessment methods primarily target common freshwater fish living in surface rivers. Their applicability to rare freshwater fish communities that inhabit both underground or cave environments as well as surface rivers is insufficient, potentially leading to a waste of human and material resources when developing targeted conservation measures. Combining exposure risk assessments of surface rivers and groundwater based on species distribution models is an effective solution to the challenges of risk assessment and conservation for rare fish like cave fish, enabling the development of effective and targeted conservation measures. However, conventional freshwater fish risk assessment methods are unsuitable for rare freshwater fish like cave fish, failing to effectively assess and quantify the exposure risk of cave fish communities and hindering conservation efforts, thus resulting in wasted human and material resources. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities, thereby solving the problems mentioned in the background. The present invention can comprehensively assess the exposure risk of cave fish from both surface water and groundwater environmental perspectives, thus solving the biodiversity conservation challenges in the face of multiple pressure sources.

[0005] To achieve the above objectives, the present invention provides 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 gather data on factors affecting cave fish distribution to establish a set of modeling variables;

[0007] Step 2: Use the variance inflation factor (VIF) and Pearson correlation coefficient to test the collinearity of the modeling factors, select the maximum entropy machine learning algorithm MaxEnt, construct species distribution models (SDMs) for each organism in the cave fish community, and stack all species distribution models (SDMs) to simulate the distribution of the cave fish community.

[0008] Step 3: Validate the model's performance using the area under the receiver operating characteristic (AUC);

[0009] Step 4: Collect surface exposure sources (ES) and subsurface 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.

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

[0013] Step 2.2: Randomly generate 10,000 background points, 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 points.

[0014] Step 2.3: Use the Bootstrap method to randomly divide the filtered combined dataset of historical 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 caused by dataset splitting due to 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, step three involves checking the relative contribution of the modeling factors to ensure excellent model performance and the rationality of the selected modeling factors.

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

[0019] Step 4.1: Select m surface exposure factors ES and n underground exposure factors EG that pose a threat to the distribution of cavefish;

[0020] Step 4.2: Convert the karst topography data (KRT) into binary data;

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

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

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

[0024]

[0025] In the formula, 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 groundwater exposure factors considered; and m is the number of surface water exposure factors considered.

[0026] Furthermore, when the grid represents a karst landscape, KRT=1, and when the grid does not represent a karst landscape, KRT=0.

[0027] Furthermore, the assessment method also includes the establishment of an assessment system, which includes a variable set module for collecting historical location data of cave fish communities and collecting data on annual average temperature (BIO1), annual average precipitation (BIO12), land use type (LC), and karst landform (KRT) that affect the distribution of cave fish to establish a variable set.

[0028] Environmental Factor Testing Module: Used to test the collinearity of modeling factors using variance inflation factor (VIF) and Pearson correlation coefficient, and to simulate the distribution of cave fish communities based on the MaxEnt algorithm in the species distribution model, and to check the relative contribution of modeling factors.

[0029] Exposure Risk Assessment Module: Used to collect surface exposure sources (ES) and subsurface 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.

[0030] The beneficial effects of this invention are:

[0031] 1. This exposure risk assessment method solves the problem of insufficient adaptability of existing technologies in assessing the exposure risk of rare freshwater fish such as cavefish. It improves the accuracy of simulating the distribution of cavefish communities and assessing exposure risk, and provides a scientific basis for developing conservation and management strategies. It can accurately simulate the species richness of cavefish communities. Using the area under the receiver operating characteristic (AUC) as an evaluation index, and selecting the high-performance maximum entropy algorithm MaxEnt, it achieves the 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 technological advancements have been achieved in data processing, model construction, and result analysis.

[0032] 2. This exposure risk assessment method, through systematic data processing and model building, successfully addresses the challenge of insufficient adaptability of existing technologies in assessing the exposure risk of rare freshwater fish like cavefish. Its significant technological advancement lies in improving the adaptability of exposure risk calculation methods to cavefish, providing a scientific basis and practical tool for the protection and management of freshwater ecosystems. The widespread application of this method will have a profound impact on freshwater ecological conservation.

[0033] 3. This exposure risk assessment method ensures the stability and scientific validity of the model, overcomes the inaccuracy caused by variable collinearity in traditional methods, and significantly improves the accuracy and efficiency of variable selection. It also overcomes the limitations of existing models in simulating the distribution of rare species, significantly improving the model's predictive accuracy and stability.

[0034] 4. The separation method employed in this invention independently analyzes the exposure risk of cavefish to groundwater and surface water environments. Based on karst topography data, the exposure risk of cavefish communities in both groundwater and surface water environments is calculated separately. This method effectively solves the problem of inaccurate exposure risk assessment for rare species like cavefish in traditional methods, significantly enhancing the ability to identify exposure risks. Attached Figure Description

[0035] Figure 1 This is a flowchart of a method for assessing the exposure risk of cave fish communities under the influence of climate and human activities according to the present invention.

[0036] Figure 2 This is a map showing the risk distribution of cave fish exposure based on traditional methods.

[0037] Figure 3 A global cave fish species richness distribution map provided for embodiments of the present invention;

[0038] Figure 4 A global cavefish exposure risk distribution map provided for embodiments of the present invention;

[0039] Figure 5 This is a structural diagram of a cave fish community exposure risk assessment system under the influence of climate and human activities, provided as an embodiment of the present invention. Detailed Implementation

[0040] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0041] Please see Figures 1 to 5 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:

[0042] Step 1: Collect historical location data of cave fish communities, and collect data on annual average temperature (BIO1), annual average precipitation (BIO12), land use type (LC), and karst landform (KRT) that affect the distribution of cave fish to establish a set of modeling variables;

[0043] Step 2: Use the variance inflation factor (VIF) and Pearson correlation coefficient to test the collinearity of the modeling factors, select the maximum entropy machine learning algorithm MaxEnt, construct species distribution models (SDMs) for each organism in the cave fish community, and stack all species distribution models (SDMs) to simulate the distribution of the cave fish community.

[0044] Step 3: Validate the model performance using the area under the receiver operating characteristic curve (AUC), check the relative contribution of the modeling factors to the model, and ensure the excellence of the model performance and the rationality of the selected modeling factors.

[0045] Step 4: Collect surface exposure sources (ES) and subsurface 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.

[0046] Furthermore, the specific steps of step 2 are as follows:

[0047] Step 2.1: Calculate the variance inflation factor (VIF) and Pearson correlation coefficient values ​​for annual average odor (BIO1), annual average precipitation (BIO12), land use type (LC), and karst landform (KRT) data, ensuring that their VIF values ​​are all less than 10 and their Pearson correlation coefficients are all less than 0.8;

[0048] Step 2.2: Randomly generate 10,000 background points, 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 points.

[0049] Step 2.3: Use the Bootstrap method to randomly divide the filtered combined dataset of historical points and background points into a training set (70%) and a test set (30%);

[0050] Step 2.4: Repeat steps 2.2, 2.3 and 2.4 ten times to eliminate the bias caused by dataset splitting due to the uncertainty of a single model calculation.

[0051] Furthermore, the specific steps of step 4 are as follows:

[0052] Step 4.1: Select two surface exposure factors ES and two subsurface exposure factors EG that threaten the distribution of cave fish (Table 1).

[0053] Step 4.2: Convert the karst landform data KRT into binary data, where 1 indicates that the area is a karst landform and 0 indicates that the area is a non-karst landform.

[0054] Step 4.3: Calculate the exposure risk (ER) of the cave fish community based on karst topography (KRT) information and species distribution model output; the formula for calculating the exposure risk (ER) of the cave fish community is as follows:

[0055] (1)

[0056] In the formula, (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 a 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 all 0-1 and have probabilistic meaning, where i=1,2, j=1,2.

[0057] Table 1. Surface and subsurface exposure factors faced by cave fish communities

[0058]

[0059] like Figure 5 As shown in the illustration, this embodiment also provides a risk assessment system for cave fish communities exposed to the influence of climate and human activities, used to further illustrate the above method, specifically including:

[0060] A variable set module was established to collect historical data on the existence of cave fish communities. Data on annual average temperature (BIO1), annual average precipitation (BIO12), land use type (LC), and karst landform (KRT) that affect the distribution of cave fish were collected to establish the variable set.

[0061] The environmental factor testing module is used to test the collinearity of modeling factors using the variance inflation factor (VIF) and Pearson correlation coefficient. Based on the MaxEnt algorithm in the species distribution model, it simulates the distribution of cave fish communities and checks the relative contribution of modeling factors.

[0062] The exposure risk assessment module is used to collect surface exposure sources (ES) and subsurface exposure sources (EG) that threaten the distribution of cavefish, and calculates the exposure risk (ER) of cavefish communities based on karst topography (KRT) information and species distribution model output.

[0063] This study covers seven regions in Asia, Oceania, Africa, North America, and South America. Because cave fish receive less attention than common freshwater fish, yet are more sensitive to environmental changes, coordinating the protection of cave fish communities is extremely difficult. Therefore, assessing the exposure risk of cave fish globally is of great significance. Since cave fish (including typical and atypical cave fish in parts of their physiological cycle) primarily live in underground environments, while atypical cave fish inhabit surface water environments for parts of their physiological cycle, this invention presents 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) globally as an example, species distribution models (SDMs) are established based on the MaxEnt algorithm, representing karst regions (representing groundwater environments) and non-karst regions (representing surface water environments). Figure 3 The process of assessing the exposure risk of cave fish communities in a specific area helps to better develop conservation strategies. The details are as follows:

[0064] Historical location data of all cavefish species worldwide from 2001 to 2020 were collected. These locations were categorized by cavefish genera and filtered into seven study regions. Only cavefish genera with more than five historical locations in a single study region were retained as modeling units. 1000 background points were generated using a randomization method and merged into the final dataset. Global-scale annual mean temperature (BIO1), annual mean precipitation (BIO12), land use type (LC), and karst topography (KRT) data were collected as the modeling factor set. Before modeling, the variance inflation factor (VIF) and Pearson correlation coefficient were used to test for collinearity of the modeling factors, ensuring that VIF < 10 and Pearson correlation coefficient < 0.8. The merged dataset and modeling factor set were input to run the SDMs model for 31 genera 10 times (based on the MaxEnt algorithm). Model performance was evaluated based on the area under the receiver operating characteristic (AUC) curve (mean AUC > 0.85), and the relative contribution of the modeling factors was examined. The outputs of SDMs models from 31 genera were stacked with species as weights to obtain the species richness of cave fish in the seven study areas. Figure 3Using karst topography information for classification, and based on the species richness results of cavefish, the exposure risk of cavefish in groundwater and surface water environments was calculated and assessed respectively. Figure 4 ).

[0065] BIO1, BIO12, LC, and KRT all passed the collinearity test, with relative contributions of 34.2%, 15.8%, 12.5%, and 37.3%, respectively. The mean AUC of the SDMs models from the 31 cavefish genera was 0.91, with most models having an AUC higher than 0.85, indicating excellent model performance. Figure 3 This indicates that southwestern China is the region with the richest concentration of cave fish in the world, while South America (SA) is the region with the widest distribution of cave fish. Figure 4 This indicates that southwestern China is also the region with the highest risk of cave fish exposure.

[0066] I. Evidence related to the technical effects obtained by the embodiments of the present invention.

[0067] Cave fish are a rare species of freshwater fish. Assessing their exposure risk as a common freshwater fish using traditional methods would severely underestimate the existential threats they face (compare this to other species). Figure 2 and Figure 4 This is detrimental to the formulation and implementation of protection policies.

[0068] This invention 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 This can improve the efficiency of cave fish conservation and avoid wasting human and material resources.

[0069] This embodiment has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0070] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments 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, Includes the following steps: Step 1: Taking 31 genera and 263 species worldwide as an example, collect historical presence and absence data of cave fish communities, classify and summarize these points according to the genera of cave fish, filter them into 7 study areas, and collect data on the influence of 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, and construct species distribution models of 31 genera in the cave fish community. Stack the output results of the species distribution models of the 31 genera with species as weights to obtain the species richness of cave fish in 7 study areas. Step 3: Validate the model's performance using the area under the receiver operating characteristic (ROC) curve; Step 4: Collect surface and underground exposure sources that threaten the distribution of cavefish, and calculate the exposure risk of cavefish communities based on karst landform information and species distribution model output results; The data affecting the distribution of cave fish in step 1 include average annual temperature, average annual precipitation, land use type, and karst landform; The specific process of step 4 includes: Step 4.1: Select m surface exposure factors ES and n underground exposure factors EG that pose a threat to the distribution of cavefish; Step 4.2: Convert the karst topography data (KRT) into binary data; Step 4.3: Calculate the exposure risk of cave fish communities based on karst landform information and species distribution model output results; 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 )]; In the formula, 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 groundwater exposure factors considered; m is the number of surface water exposure factors considered. When the grid is a karst landform, KRT = 1; when the grid is a karst landform, KRT = 0.

2. The method for assessing the exposure risk of cave fish communities 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 annual average temperature, annual average precipitation, land use type, and karst landform data; Step 2.2: Randomly generate 10,000 background points, 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 points. Step 2.3: Use the Bootstrap method to randomly divide the filtered historical points and background points dataset into a training set (70%) and a test set (30%). Step 2.4: Repeat steps 2.2, 2.3 and 2.4 ten times to eliminate the bias caused by dataset splitting due to the uncertainty of a single model calculation.

3. The method for assessing the exposure risk of cave fish communities under the influence of climate and human activities according to claim 2, characterized in that: In step 2.1, the VIF values ​​are all less than 10, and the Pearson correlation coefficients are all less than 0.

8.

4. The method for assessing the exposure risk of cave fish communities under the influence of climate and human activities according to claim 1, characterized in that: Step 3 involves checking the relative contribution of the modeling factors to ensure excellent model performance and the rationality of the selected modeling factors.

5. An assessment system based on the exposure risk assessment method of claim 1, characterized in that: The evaluation system includes a variable set module for establishing variables: it is used to collect historical data on the existence of cave fish communities and to collect data on annual average temperature, annual average precipitation, land use type and karst landform that affect the distribution of cave fish to establish a variable set. Environmental Factor Testing Module: Used to test the collinearity of modeling factors using variance inflation factor and Pearson correlation coefficient, simulate cave fish community distribution based on MaxEnt algorithm in species distribution model, and check the relative contribution of modeling factors. Exposure Risk Assessment Module: This module collects surface and underground exposure sources that threaten the distribution of cavefish and calculates the exposure risk of cavefish communities based on karst topography information and species distribution model outputs.

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