An aquatic organism habitat positioning prediction method

Abstract

The application discloses a kind of aquatic habitat positioning prediction methods, it is related to aquatic habitat distribution research field.The present application establishes the aquatic habitat positioning framework of suitable area + suitable river reach by integrating the target biological distribution point and the environmental condition of river reach in target area.The fine prediction of aquatic habitat is realized in the multi-scale of area plus river reach, when facing the need of target biological habitat prediction in wide range area, fine positioning can be realized, so as to help fine management of aquatic organism, which has important guiding significance and practical significance for maintaining ecosystem stability.The common characteristics of suitable river reach are obtained by comparing after predicting suitable river reach, so as to identify and judge the potential habitat of target organism, the identification of potential habitat can expand the positioning range of target biological habitat, which can effectively avoid habitat omission and protection in time.

Description

Technical Field

[0001] This invention relates to the field of aquatic organism habitat distribution research, and specifically to a method for predicting the location of aquatic organism habitats. Background Technology

[0002] Freshwater ecosystems play an irreplaceable role in nature and society. Freshwater biodiversity is rapidly declining across continents and watersheds, at a rate significantly faster than that of terrestrial ecosystems. In this context, in 2021, a group of 100 scientists jointly released an agenda for promoting freshwater biodiversity conservation globally, emphasizing that protecting freshwater biodiversity has become an urgent priority. Biodiversity conservation primarily involves in-situ conservation (habitat protection) and ex-situ conservation (species resource protection). In recent years, the impact of multiple stresses on aquatic habitats has become a research hotspot for scholars both domestically and internationally. Habitat damage and loss are among the major causes of biodiversity decline, and protecting habitats is fundamental to protecting biodiversity.

[0003] Currently, habitat location and potential habitat prediction for aquatic organisms (referring to benthic animals, fish, and amphibians) are mostly based on a single scale, using regional (watershed) or river segment scales. Using a single scale for habitat prediction often fails to accurately predict the habitats of target organisms, easily leading to omissions of habitats or potential habitats. This hinders the timely discovery and protection of habitats and potential habitats, which is highly detrimental to the protection of aquatic biodiversity. Especially given the vastness of my country's ecological reserves, using a single scale for prediction over large areas can easily result in a situation where an entire region / river segment is habitat / potential habitat, or only a very small area / river segment is habitat / potential habitat, which is illogical.

[0004] Therefore, a more accurate and faster method is needed to predict aquatic habitats over large areas, providing guidance for the establishment of biological nature reserves. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention provides an accurate and rapid method for predicting the location of aquatic organism habitats that can avoid omissions in the prediction of habitats or potential habitats.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] A method for predicting the location of aquatic organism habitats is provided, which includes the following steps:

[0008] S1: Collect data on the distribution of target organisms in the target area to obtain the distribution points of target organisms;

[0009] S2: Collect environmental factor data at the distribution points of the target organisms, and input the environmental factors into the structural equation model to obtain the key environmental factors;

[0010] S3: Input the target organism distribution data and key environmental factor data into the species distribution model to predict the threshold range of key environmental factors and the probability distribution map of the suitability of the target organism's habitat;

[0011] S4: Obtain suitable habitat areas for the target organisms based on the probability distribution map of habitat suitability.

[0012] S5: Measure the underwater topography of the river section within the suitable habitat area and use the hydrological analogy method to determine the inflow conditions of the river section;

[0013] S6: Input the suitability curve and inflow conditions of the target organism into the ecohydraulic model to obtain the distribution map of the suitability index of the target organism's habitat, and identify suitable river sections based on the distribution map of the suitability index of the target organism's habitat.

[0014] Furthermore, aquatic organisms include benthic organisms, fish, and amphibians; the methods for collecting biodistribution data of benthic organisms and fish include traditional fishing methods and environmental DNA testing; the methods for collecting biodistribution data of amphibians include tag-recapture, seedling extrapolation, non-destructive net trapping, questionnaire surveys, and environmental DNA testing.

[0015] Furthermore, the environmental factors incorporated into the structural equation model include geomorphological features, water quality, human disturbance, and food resources;

[0016] The geomorphic features include meandering degree, bank slope, vegetation coverage, river geomorphic unit type, riverbed composition, river width, water depth, and flow velocity.

[0017] The water quality includes turbidity, water temperature, pH, dissolved oxygen, conductivity, total hardness, chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, sulfide, and total coliforms.

[0018] The human interferences include the proportion of farmland, forest land, grassland, residential areas, and the distance from the sampling point to the tourist area;

[0019] The bait resources include the Shannon diversity index, Maglev richness index, and Pinault evenness index for fish.

[0020] Furthermore, the structural equation model was established using AMOS23 software. The path of human disturbance → geomorphological features → water quality → food resources → target organisms was input into the structural equation model. The structural equation model outputs the path coefficients of each environmental factor. By comparing the path coefficients of different environmental factors, the environmental factor corresponding to the path coefficient with an absolute value greater than 0.5 is the key environmental factor.

[0021] Furthermore, the output of the structural equation model also includes the chi-square significance probability value P, the chi-square value / degrees of freedom CHI / DF, the root mean square and square root of the asymptotic residuals RMSEA, and the fitness index GFI. P, CHI / DF, RMSEA, and GFI are used together to evaluate the fitness of the structural equation model. When P ≥ 0.05, CHI / DF ≤ 3, RMSEA ≤ 0.08, and GFI ≤ 0.9, the fitness of the structural equation model meets the requirements. Otherwise, the structural equation model is rebuilt by deleting or adding environmental factors until the fitness of the structural equation model meets the requirements.

[0022] Furthermore, S3 includes the following steps:

[0023] S31: Establish a species distribution model based on environmental variable data and key environmental factor data; the species distribution model is established using MaxEnt software;

[0024] S32: Randomly select 75% of the target organism distribution data as the training set and the remaining data as the test set, and use the training set to train the species distribution model;

[0025] S33: Use the area under the receiver characteristic curve (AUC) obtained from the test set to evaluate the model prediction accuracy. If the AUC value is greater than 0.8, the model prediction accuracy meets the requirements and training is completed; otherwise, adjust the species distribution model by screening environmental factors and return to step S32 for retraining.

[0026] S34: Input the target organism distribution data into the trained species distribution model to obtain the target organism habitat suitability probability distribution map and the relationship map between habitat suitability and key environmental factors;

[0027] S35: The threshold range of key environmental factors is obtained by reading the relationship diagram between habitat suitability and key environmental factors.

[0028] Furthermore, the method for obtaining suitable habitat areas includes: using the ArcGIS reclassification tool to reclassify the probability distribution map of the target organism's habitat suitability, dividing suitable habitats into: unsuitable areas [0, 0.2), low suitability areas [0.2, 0.4), moderate suitability areas [0.4, 0.6), high suitability areas [0.6, 0.8), and extremely high suitability areas [0.8, 1]; areas with a reclassification index ≥ 0.6, namely high suitability areas and extremely high suitability areas, are considered suitable habitat areas.

[0029] Furthermore, by using the River 2D model as an ecohydraulic model, the suitability curve of the target organism and the inflow conditions can be input into the River 2D model to obtain the distribution map of the habitat suitability index of the target organism. The river section with a habitat suitability index greater than 0.6 is the suitable river section.

[0030] Furthermore, methods for determining the suitability curve include: literature review and expert judgment.

[0031] Furthermore, in step S2, when collecting environmental factor data, collection and sampling points should also be set up in the tributaries of abiotic distribution points within the target area.

[0032] Furthermore, it also includes the identification of potential habitats for the target organisms, including the following steps:

[0033] A1: By comparing the key environmental factors of all suitable river sections, the common characteristics of the key environmental factors of each suitable river section can be obtained.

[0034] A2: Select key environmental factors as common environmental factors based on common characteristics;

[0035] A3: Based on the threshold range of common environmental factors obtained in step S3, compare the corresponding environmental factor data of all unsuitable river sections in the predicted suitable area with the threshold range of common environmental factors.

[0036] If the corresponding environmental factor data of a certain unsuitable river section falls within the threshold range of common environmental factors, then the unsuitable river section is a potential habitat for the target organism; otherwise, the unsuitable river section is not a potential habitat for the target organism.

[0037] The beneficial effects of this invention are as follows:

[0038] 1. This invention establishes a habitat location framework for aquatic organisms by integrating the distribution points of target organisms within the target area and the environmental conditions of river sections. It enables multi-scale, precise prediction of aquatic organism habitats across areas and river sections. When faced with habitat prediction for target organisms over large areas, it allows for rapid and precise location, thus facilitating refined management of aquatic organisms and providing significant guidance and practical value for maintaining ecosystem stability.

[0039] 2. This invention identifies potential habitats for target organisms by predicting suitable river sections and comparing their common characteristics. On the one hand, identifying potential habitats expands the range of target organism habitats, effectively preventing habitat omissions and untimely protection. On the other hand, potential habitats reflect that the river section in the area only needs simple improvements to create a suitable habitat environment for the target organisms that need protection, thus transforming potential habitats into actual habitats. This has important guiding significance for protecting target organisms and expanding their populations. Attached Figure Description

[0040] Figure 1 A flowchart illustrating the method for predicting the location of aquatic organism habitats;

[0041] Figure 2 This is a schematic diagram illustrating the relationship between environmental factors;

[0042] Figure 3 A schematic diagram illustrating the relationship between habitat suitability and river width;

[0043] Figure 4 A schematic diagram illustrating the relationship between habitat suitability and forest land percentage;

[0044] Figure 5 A schematic diagram illustrating the relationship between habitat suitability and OD (Original Displacement).

[0045] Figure 6 A schematic diagram illustrating the relationship between habitat suitability and distance from tourist attractions;

[0046] Figure 7 A schematic diagram showing the distribution of habitat suitability index for wild giant salamanders;

[0047] Figure 8 A schematic diagram showing the distribution of suitable habitat areas for wild giant salamanders;

[0048] Figure 9 A schematic diagram showing the distribution of suitable habitats for wild giant salamanders;

[0049] Figure 10 This is a schematic diagram showing the distribution of suitable river sections for wild giant salamanders. Detailed Implementation

[0050] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0051] like Figure 1 As shown, a method for predicting the location of aquatic organism habitats includes the following steps:

[0052] Taking the prediction of wild giant salamander habitat in the Zhangjiajie Giant Salamander National Nature Reserve (hereinafter referred to as the Reserve) in Hunan Province as an example:

[0053] S1: Collect data on the distribution of wild giant salamanders within the protected area to obtain the distribution locations of wild giant salamanders;

[0054] The presence of wild giant salamanders can be determined by detecting DNA fragments from the water in an environmental DNA testing method.

[0055] S2: Collect environmental factor data at the distribution points of wild giant salamanders, and input the environmental factors into the structural equation model to obtain key environmental factors; when collecting environmental factor data, sampling points should also be set up in tributaries in the target area where there are no wild giant salamander distribution points.

[0056] Environmental factors include geomorphological features, water quality, human disturbance, and food resources;

[0057] Geomorphological features include meandering degree, bank slope, vegetation coverage, river geomorphological unit type, riverbed composition, river width, water depth and flow velocity;

[0058] Water quality includes turbidity, water temperature, pH, dissolved oxygen, conductivity, total hardness, chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, sulfide, and total coliforms.

[0059] Human disturbances include the proportion of farmland, forest land, grassland, residential areas, and the distance from the sampling point to tourist attractions;

[0060] Food resources include the Shannon diversity index, Maglev richness index, and Pinault evenness index;

[0061] A structural equation model was constructed using AMOS23 software, with the following influencing pathways: human disturbance → geomorphological features → water quality → food resources → wild giant salamanders. The AMOS23 software output is as follows. Figure 2 The diagram illustrates the relationships between environmental factors. Figure 2It can be seen that among the anthropogenic disturbance factors, the path coefficients of forest area and distance from tourist attractions are greater than 0.5; among the geomorphic features, the path coefficients of vegetation coverage, flow velocity and river width are greater than 0.5; among the water quality factors, the absolute values ​​of the path coefficients of dissolved oxygen, total phosphorus, total nitrogen and chemical oxygen demand (COD) are greater than 0.5; and among the food factors, the path coefficients of fish Shannon diversity index and Maglev richness index are greater than 0.5.

[0062] The key environmental factors include the proportion of forest land, distance from tourist attractions, dissolved oxygen, total phosphorus, total nitrogen, chemical oxygen demand (COD), Shannon diversity index, Margalef richness index, and Pielou evenness index.

[0063] The AMOS23 software output also includes the chi-square significance probability value P, chi-square value / degrees of freedom CHI / DF, asymptotic residual mean square and root square RMSEA, and fitness index GFI. P, CHI / DF, RMSEA, and GFI are used to jointly evaluate the fitness of the structural equation model. The fitness of the structural equation model is considered satisfactory when P ≥ 0.05, CHI / DF ≤ 3, RMSEA ≤ 0.08, and GFI ≤ 0.9. Otherwise, a new structural equation model is established in AMOS23 by deleting or adding environmental factors until the fitness of the established structural equation model meets the requirements.

[0064] S3: Inputting wild giant salamander distribution data and key environmental factor data into the species distribution model, we can predict the threshold range of key environmental factors and the probability distribution map of wild giant salamander habitat suitability.

[0065] S31: Establish a species distribution model based on environmental variable data and key environmental factor data; the species distribution model was established using MaxEnt software.

[0066] S32: Input the wild giant salamander distribution data into the MaxEnt software, randomly select 75% of the wild giant salamander distribution data as the training set, and use the remaining data as the test set. Use the training set to train the species distribution model using the training method built into the MaxEnt software.

[0067] S33: Use the area under the receiver characteristic curve (AUC) obtained from the test set to evaluate the model prediction accuracy. If the AUC value is greater than 0.8, the model prediction accuracy meets the requirements and training is completed; otherwise, adjust the species distribution model by screening environmental factors and return to step S32 for retraining.

[0068] S34: Input the wild giant salamander distribution data into the trained species distribution model to obtain the wild giant salamander habitat suitability probability distribution map and so on. Figure 3-6The diagram showing the relationship between habitat suitability and key environmental factors;

[0069] S35: The threshold range of key environmental factors is obtained by reading the relationship diagram between habitat suitability and key environmental factors.

[0070] S4: Based on the probability distribution map of the suitability of wild giant salamander habitats, the suitable habitat areas for wild giant salamanders are obtained;

[0071] The method for obtaining suitable habitat areas includes: using the ArcGIS reclassification tool to reclassify the probability distribution map of wild giant salamander habitat suitability, dividing suitable habitats into: unsuitable areas [0, 0.2), low suitability areas [0.2, 0.4), moderate suitability areas [0.4, 0.6), high suitability areas [0.6, 0.8), and extremely high suitability areas [0.8, 1]. Areas with a reclassification index ≥ 0.6, i.e., high and extremely high suitability areas, are considered suitable habitat areas. And generate... Figure 9 The diagram shows the distribution of suitable habitats for wild giant salamanders.

[0072] S5: Measure the underwater topography of the river section within the suitable habitat area and use the hydrological analogy method to determine the inflow conditions of the river section;

[0073] S6: The suitability curve for wild giant salamanders was determined using literature review and expert judgment. The suitability curve and inflow conditions were then input into an ecohydraulic model to obtain the following results: Figure 7 The map showing the distribution of the habitat suitability index of wild giant salamanders and as shown below Figure 8 The diagram shows the distribution of suitable habitat area for wild giant salamanders, and suitable river sections are identified based on the distribution diagram of wild giant salamander habitat suitability index. A graph is generated as shown below. Figure 10 The diagram shows the distribution of suitable river sections for wild giant salamanders.

[0074] It also includes the identification of potential habitats for wild giant salamanders, including the following steps:

[0075] A1: By comparing the key environmental factors of all suitable river sections, the common characteristics of the key environmental factors of each suitable river section can be obtained.

[0076] A2: Select key environmental factors as common environmental factors based on common characteristics;

[0077] A3: Based on the threshold range of common environmental factors obtained in step S3, compare the corresponding environmental factor data of all unsuitable river sections in the predicted suitable area with the threshold range of common environmental factors.

[0078] If the corresponding environmental factor data of a certain unsuitable river section falls within the threshold range of common environmental factors, then the unsuitable river section is a potential habitat for wild giant salamanders; otherwise, the unsuitable river section is not a potential habitat for wild giant salamanders.

[0079] Potential habitats were identified in the Hengxi River, Jindong River, and Huashui River in the upper reaches of the northern source of the Lishui River (as shown in Table 1 below). The proportion of deep pools within the protected area was identified as a common environmental factor. Based on the threshold range of the proportion of deep pools, it was determined that river sections with a deep pool area greater than 10% are unsuitable habitats for wild giant salamanders.

[0080] Table 1

[0081]

[0082]

Claims

1. A method for predicting the location of aquatic organism habitats, characterized in that, Includes the following steps: S1: Collect data on the distribution of target organisms in the target area to obtain the distribution points of target organisms; S2: Collect environmental factor data at the distribution points of the target organisms, and input the environmental factors into the structural equation model to obtain the key environmental factors; S3: Input the target organism distribution data and key environmental factor data into the species distribution model to predict the threshold range of key environmental factors and the probability distribution map of the suitability of the target organism's habitat; S4: Obtain suitable habitat areas for the target organisms based on the probability distribution map of habitat suitability. S5: Measure the underwater topography of the river section within the suitable habitat area and use the hydrological analogy method to determine the inflow conditions of the river section; S6: Input the suitability curve and inflow conditions of the target organism into the ecohydraulic model to obtain the distribution map of the suitability index of the target organism's habitat, and identify suitable river sections based on the distribution map of the suitability index of the target organism's habitat. The environmental factors incorporated into the structural equation model include geomorphic features, water quality, human disturbance, and food resources. Geomorphic features include meandering degree, bank slope, vegetation cover, river geomorphic unit type, riverbed composition, river width, water depth, and flow velocity. Water quality includes turbidity, water temperature, pH, dissolved oxygen, conductivity, total hardness, chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, sulfides, and total coliforms. Human disturbance includes the proportion of farmland, forest, grassland, and residential areas, and the distance from the sampling point to tourist attractions. Food resources include the Shannon diversity index, Maglev richness index, and Pinault evenness index for fish. The structural equation model was established using AMOS23 software. The path of human disturbance → geomorphological features → water quality → food resources → target organisms was input into the structural equation model. The structural equation model outputs the path coefficients of each environmental factor. The path coefficients of different environmental factors were compared. The environmental factor corresponding to the path coefficient with an absolute value greater than 0.5 is the key environmental factor. The output of structural equation modeling also includes chi-square value and significance probability value. P Chi-square value / degrees of freedom (CHI / DF), root mean square and square root of asymptotic residuals (RMSEA), and fitness index (GFI); use P CHI / DF, RMSEA, and GFI are used together to evaluate the fit of structural equation modeling; when all conditions are met... P If the environmental factors are ≥0.05 and CHI / DF≤3, RMSEA≤0.08 and GFI≤0.9, then the structural equation model fits the requirements; otherwise, the structural equation model is rebuilt by deleting or adding environmental factors until the structural equation model fits the requirements.

2. The method for predicting the location of aquatic organism habitats according to claim 1, characterized in that, Aquatic organisms include benthic organisms, fish, and amphibians. Data collection methods for the distribution of benthic organisms and fish include traditional fishing methods and environmental DNA testing. Data collection methods for the distribution of amphibians include tag-recapture, seedling extrapolation, non-destructive net trapping, questionnaire surveys, and environmental DNA testing.

3. The method for predicting the location of aquatic organism habitats according to claim 1, characterized in that, S3 includes the following steps: S31: Establish a species distribution model based on environmental variable data and key environmental factor data; the species distribution model is established using MaxEnt software; S32: Randomly select 75% of the target organism distribution data as the training set and the remaining data as the test set, and use the training set to train the species distribution model; S33: Use the area under the receiver characteristic curve obtained from the test set (AUC) to evaluate the model prediction accuracy. When the AUC value is greater than 0.8, the model prediction accuracy meets the requirements and training is complete. Otherwise, after adjusting the species distribution model by screening environmental factors, return to step S32 for retraining; S34: Input the target organism distribution data into the trained species distribution model to obtain the target organism habitat suitability probability distribution map and the relationship map between habitat suitability and key environmental factors; S35: The threshold range of key environmental factors is obtained by reading the relationship diagram between habitat suitability and key environmental factors.

4. The method for predicting the location of aquatic organism habitats according to claim 1, characterized in that, The method for obtaining suitable habitat areas includes: using the ArcGIS reclassification tool to reclassify the probability distribution map of the target organism's habitat suitability, dividing suitable habitats into: unsuitable areas [0, 0.2), low suitability areas [0.2, 0.4), moderate suitability areas [0.4, 0.6), high suitability areas [0.6, 0.8), and extremely high suitability areas [0.8, 1]; areas with a reclassification index ≥ 0.6, namely high suitability areas and extremely high suitability areas, are considered suitable habitat areas.

5. The method for predicting the location of aquatic organism habitats according to claim 1, characterized in that, Using the River 2D model as an ecohydraulic model, the suitability curve of the target organism and the inflow conditions can be input into the River 2D model to obtain the distribution map of the habitat suitability index of the target organism. The river section with a habitat suitability index greater than 0.6 is the suitable river section.

6. The method for predicting the location of aquatic organism habitats according to claim 1, characterized in that, In step S2, when collecting environmental factor data, collection and sampling points should also be set up in the tributaries of abiotic distribution points within the target area.

7. The method for predicting the location of aquatic organism habitats according to claim 1, characterized in that, It also includes the identification of potential habitats for the target organisms, including the following steps: A1: By comparing the key environmental factors of all suitable river sections, the common characteristics of the key environmental factors of each suitable river section can be obtained. A2: Select key environmental factors as common environmental factors based on common characteristics; A3: Based on the threshold range of common environmental factors obtained in step S3, compare the corresponding environmental factor data of all unsuitable river sections in the predicted suitable area with the threshold range of common environmental factors. If the corresponding environmental factor data of a certain unsuitable river section falls within the threshold range of common environmental factors, then the unsuitable river section is a potential habitat for the target organism; otherwise, the unsuitable river section is not a potential habitat for the target organism.

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