Mussel habitat adaptability evaluation method and system based on ecological niche model

Abstract

The invention discloses a mussel habitat adaptability assessment method and system based on an ecological niche model, and relates to the field of marine ecological modeling habitat assessment, and the method comprises the steps: obtaining the known distribution point data of a target mussel species and the data of multiple environmental factors of a research region, carrying out the preprocessing of the data, and obtaining a target mussel habitat adaptability assessment model; obtaining a species distribution data set and an environment variable data set; training an ecological niche model by using the species distribution data set and the environment variable data set to obtain a preliminary habitat suitability index distribution map; screening out a target functional character with the highest covariant strength with the spatial distribution of the initial habitat suitability index distribution map from a preset mussel functional character library; the spatial distribution covariance intensity is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of the functional character value. According to the method, deepening from statistical correlation to ecological mechanism explanation is realized, and the decision support capability of an evaluation result in precise protection and adaptive management is improved.

Description

Technical Field

[0001] This invention relates to the field of marine ecological modeling and habitat assessment, and in particular to a method and system for assessing the adaptability of mussel habitats based on a niche model. Background Technology

[0002] Niche models are important tools for species distribution modeling and habitat assessment. Algorithms, such as the maximum entropy model, can generate spatial distribution maps that reflect the relative suitability of habitats by integrating known species distribution points with environmental variable data. These statistical correlation-based methods provide effective technical support for habitat identification and conservation planning for marine organisms such as mussels at a macro scale and have become a commonly used technique in this field.

[0003] Existing technological approaches typically stop at obtaining preliminary suitability distribution maps, and their assessment results reflect more the degree of environmental matching. There is room for further exploration in terms of ecological mechanism interpretation and management application. Existing methods are unable to reveal the intrinsic driving mechanisms behind suitability, nor can they quantify the functional differences in the long-term maintenance of populations in different habitat areas. This, to some extent, limits the actual effectiveness of assessment results in supporting precision conservation and adaptive management. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a niche-based method for assessing the adaptability of mussel habitats to address the problems of insufficient explanation of ecological mechanisms in existing assessment results and difficulty in supporting precise management decisions regarding population dynamics.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for assessing the adaptability of mussel habitats based on a niche model, which includes acquiring known distribution point data of the target mussel species and multiple environmental factor data of the study area, preprocessing the data, and obtaining a species distribution dataset and an environmental variable dataset. A niche model was trained using species distribution datasets and environmental variable datasets to obtain a preliminary distribution map of habitat suitability index; From the pre-set mussel functional trait library, the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index distribution map are selected; the spatial distribution covariance intensity is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of the functional trait values. By using physiological and ecological experimental data of the target functional traits, a quantitative relationship function between environmental variables and the target functional traits is established. The adaptive distribution map of the target functional traits is then calculated using the quantitative relationship function and the environmental variable dataset. The adaptive distribution map of target functional traits is coupled with population connectivity data to conduct source-sink habitat analysis and output a source-sink habitat functional classification map. The adaptive distribution map of target functional traits and the source-sink habitat functional classification map are overlaid, integrated and visualized to generate an ecological management habitat adaptive assessment result map.

[0007] As a preferred embodiment of the niche-based habitat adaptation assessment method for mussels described in this invention, the method includes: acquiring known distribution point data of the target mussel species and multiple environmental factor data of the study area; preprocessing the data to obtain a species distribution dataset and an environmental variable dataset; and comprising the following steps: Collect known distribution data for the target mussel species. This known distribution data is sourced from global biodiversity data. Diversity information networks and field investigation reports with geographic coordinate records are used to clean known distribution point data, removing spatial clustering biases and obvious erroneous records from the known distribution point data; The study area acquired data on multiple environmental factors, including sea surface temperature and chlorophyll a concentration data extracted from remote sensing satellite products, ocean current velocity data obtained from hydrological models, and seabed sediment type data digitized from seabed geological maps. Spatial registration was performed on multiple environmental factor data, and sea surface temperature data, chlorophyll a concentration data, ocean current velocity data, and seabed type data were uniformly resampled to the same geographic coordinate system and pixel size; By integrating the cleaned known distribution point data with the spatially registered data of multiple environmental factors, a spatially aligned species distribution dataset and environmental variable dataset are obtained.

[0008] As a preferred embodiment of the mussel habitat adaptability assessment method based on the niche model described in this invention, the method includes the following steps: training the niche model using a species distribution dataset and an environmental variable dataset to obtain a preliminary habitat suitability index distribution map. The species distribution dataset is used as the existing data input to the maximum entropy niche model, and the environmental variable dataset is used as the environmental constraint layer input to the maximum entropy niche model. Run a maximum entropy niche model to learn the statistical relationship between the species distribution dataset and the environmental variable dataset; Based on the learned statistical relationships, the suitability index of each spatial unit in the study area is predicted, and a preliminary habitat suitability index distribution map is generated.

[0009] As a preferred embodiment of the mussel habitat adaptability assessment method based on the niche model described in this invention, the method involves: selecting target functional traits from a pre-defined mussel functional trait library that exhibit the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index distribution map, including the following steps: Extract each functional trait from the pre-set mussel functional trait library, perform spatial interpolation on the measured values ​​of each functional trait in the mussel functional trait library, and generate a spatial distribution map of the functional trait values ​​for each functional trait. The spatial covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of the functional trait values ​​for each functional trait was calculated using the Pearson product-moment correlation coefficient algorithm in spatial statistics. By comparing the spatial covariance values ​​between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits, the functional trait with the largest spatial covariance value in the preliminary habitat suitability index distribution map is obtained. The functional trait with the largest spatial covariance value relative to the preliminary habitat suitability index distribution map is selected to obtain the target functional trait with the highest spatial distribution covariance intensity.

[0010] As a preferred embodiment of the mussel habitat adaptability assessment method based on the niche model described in this invention, the intensity of spatial distribution covariance is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of functional trait values, including the following steps: The Pearson product-moment correlation coefficient algorithm was used to process the preliminary habitat suitability index distribution map and the spatial distribution map of functional trait values. The covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits is used as a quantitative result of the spatial distribution covariance intensity between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits.

[0011] As a preferred embodiment of the niche-based habitat adaptation assessment method for mussels described in this invention, the method involves establishing a quantitative relationship function between environmental variables and target functional traits using physiological and ecological experimental data of the target functional traits, including the following steps: Physiological and ecological experimental data of target functional traits were obtained by measuring the functional traits of mussel individuals under different environmental gradients under controlled laboratory conditions. Environmental variable gradient data and target functional trait response data were extracted from the physiological and ecological experimental data of the target functional trait, and a regression model was fitted using the environmental variable gradient data and target functional trait response data. The quantitative relationship function between environmental variables and target functional traits is obtained through regression model.

[0012] As a preferred embodiment of the mussel habitat adaptation assessment method based on the niche model described in this invention, the method includes the following steps: calculating the adaptation distribution map of the target functional traits using quantitative relationship functions and environmental variable datasets. Input the value of each environmental variable in the environmental variable dataset into the quantitative relationship function between the environmental variable and the target functional trait; The fitness index of the target functional trait at each spatial location is calculated by using a quantitative relationship function between environmental variables and the target functional trait. Spatial mapping of the fitness index of the target functional traits at spatial locations generates a distribution map of the fitness of the target functional traits.

[0013] As a preferred embodiment of the mussel habitat adaptation assessment method based on the niche model described in this invention, the method includes the following steps: coupling the target functional trait adaptation distribution map with population connectivity data to perform source-sink habitat analysis and output a source-sink habitat functional classification map. Population connectivity data were obtained by simulating the larval dispersal model. The adaptive distribution map of the target functional traits and the population connectivity data were input into the cellular automaton population model. The cellular automaton population model was then run to simulate the growth, death and dispersal dynamics of the population. Based on the simulation results of the cellular automata population model, source habitat patches that make a net contribution to the regional population and sink habitat patches that depend on external replenishment were identified. Based on the identification results of source habitat patches and sink habitat patches, a source-sink habitat functional classification map is generated.

[0014] As a preferred embodiment of the mussel habitat adaptability assessment method based on the niche model described in this invention, the method involves overlaying, integrating, and visualizing the target functional trait adaptation distribution map and the source-sink habitat functional classification map to generate an ecological management habitat adaptability assessment result map, including the following steps: In a geographic information unit, the adaptive distribution map of target functional traits is overlaid with the source-sink habitat functional classification map. On the overlaid map, a color gradient is used to represent the suitability level of the adaptive distribution map of target functional traits. On the overlaid map, source habitat patches and sink habitat patches in the source-sink habitat functional classification map are labeled with different shaped legend symbols. The color gradient and legend symbols are integrated to generate an ecological management habitat adaptability assessment result map.

[0015] Secondly, the present invention provides a mussel habitat adaptability assessment system based on a niche model, including a data acquisition module, which acquires known distribution point data of the target mussel species and multiple environmental factor data of the study area, preprocesses the data, and obtains a species distribution dataset and an environmental variable dataset. The assessment module uses species distribution datasets and environmental variable datasets to train a niche model and obtain a preliminary habitat suitability index distribution map. The screening module selects the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index from the preset mussel functional trait library; the spatial distribution covariance intensity is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of the functional trait values. The trait adaptation mapping module establishes a quantitative relationship function between environmental variables and target functional traits using physiological and ecological experimental data of target functional traits, and calculates the adaptive distribution map of target functional traits using the quantitative relationship function and environmental variable dataset; The source-sink habitat analysis module couples the target functional trait adaptation distribution map with population connectivity data to perform source-sink habitat analysis and outputs a source-sink habitat functional classification map. It then overlays, integrates, and visualizes the target functional trait adaptation distribution map and the source-sink habitat functional classification map to generate an ecological management habitat adaptation assessment result map.

[0016] The beneficial effects of this invention are as follows: by integrating species distribution data and environmental factor data to construct a preliminary habitat suitability distribution map, a functional trait screening mechanism is introduced, key functional traits are screened based on the quantitative analysis of spatial covariance intensity, and a quantitative relationship between environmental variables and functional traits is established by combining physiological and ecological experimental data, generating a more mechanistically interpretable trait adaptation distribution map; by coupling population connectivity data to conduct source-sink habitat analysis, a comprehensive assessment result map integrating suitability level and population functional zoning is generated, realizing a deepening from statistical correlation to ecological mechanism explanation, and improving the decision support capability of the assessment results in precise protection and adaptive management. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for assessing mussel habitat adaptability based on a niche model.

[0019] Figure 2This is a schematic diagram of a mussel habitat adaptation assessment system based on a niche model.

[0020] Figure 3 A flowchart for data preprocessing.

[0021] Figure 4 A schematic diagram is generated from the results of the habitat adaptability assessment for ecological management. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0025] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for assessing the adaptability of mussel habitats based on a niche model, including the following steps: S1. Obtain known distribution data of the target mussel species and multiple environmental factor data of the study area. Preprocess the data to obtain species distribution dataset and environmental variable dataset.

[0026] S1.1 Collect known distribution data of the target mussel species. The known distribution data are sourced from around the world. Biodiversity information networks and field survey reports with geographic coordinate records are used to clean known distribution point data, removing spatial clustering biases and obvious errors.

[0027] Furthermore, known distribution data of the target mussel species are collected. This data mainly comes from public databases such as the Global Biodiversity Information Network and field survey reports with geographic coordinates. The known distribution data is then cleaned to address spatial clustering biases, such as overly dense sampling points in easily accessible sea areas and sparse records in remote sea areas. Obvious erroneous records, such as points whose coordinates fall on land, need to be removed. Through this cleaning process, the known distribution data can be made more representative in terms of spatial distribution and more accurately reflect the actual distribution pattern of the target mussel species.

[0028] S1.2 Acquire multiple environmental factor data for the study area, including sea surface temperature and chlorophyll a concentration data extracted from remote sensing satellite products, ocean current velocity data obtained from hydrological models, and seabed sediment type data digitized from seabed geological maps.

[0029] Furthermore, multiple environmental factor data for the study area are acquired. These data are variables with clear ecological significance collected from various sources. Sea surface temperature and chlorophyll a concentration data are typically extracted from remote sensing satellite products covering the study area, and these data can broadly characterize the thermodynamic conditions and basic productivity levels of the water body. Ocean current velocity data needs to be obtained from hydrological models that can simulate regional ocean dynamic processes, reflecting the key physical forces affecting the dispersal and attachment of mussel larvae. Seabed type data needs to be digitally interpreted and classified from detailed seabed geological maps, as seabed characteristics are directly related to the substrate selection and stability of adult mussels. The purpose of selecting multiple environmental factor data is to comprehensively capture the key habitat elements affecting the survival and distribution of mussels, ensuring that the environmental variable dataset can cover multi-dimensional information from the physicochemical properties of the water body to the physical structure of the seabed.

[0030] S1.3 Spatial registration of multiple environmental factor data, resampling sea surface temperature data, chlorophyll a concentration data, ocean current velocity data, and seabed type data to the same geographic coordinate system and pixel size.

[0031] Furthermore, since sea surface temperature data, chlorophyll a concentration data, ocean current velocity data, and seabed type data may originate from different sensors, models, or maps, and have different geographic coordinate systems, spatial references, and pixel sizes, spatial registration operation uses geographic information processing technology to unify all environmental factor layers to the exact same geographic coordinate system and pixel size. This ensures that the values ​​of different environmental factors on each spatial unit have a strict spatial correspondence, eliminates analytical errors caused by differences in data format and resolution, and provides a consistent and overlayable dataset of environmental variables.

[0032] S1.4 Integrate the cleaned known distribution point data with the spatially registered multi-environment factor data to obtain a spatially aligned species distribution dataset and environmental variable dataset.

[0033] Furthermore, the integration operation is carried out within a unified geographical framework formed by spatial registration, which enables each known distribution point to be accurately associated with all environmental factor values ​​corresponding to its location. Integration ensures that an accurate correspondence is established between species occurrence records and environmental conditions, generating spatially aligned species distribution datasets and environmental variable datasets.

[0034] S2. Train the niche model using the species distribution dataset and the environmental variable dataset to obtain a preliminary habitat suitability index distribution map.

[0035] S2.1. Input the species distribution dataset as the existence data into the maximum entropy niche model, and input the environmental variable dataset as the environmental constraint layer into the maximum entropy niche model.

[0036] Furthermore, the species distribution dataset is input into the maximum entropy niche model as the existence data. The cleaned coordinates of known distribution points are provided to the model and identified by the maximum entropy niche model as evidence of the existence of species. The environmental variable dataset is input into the maximum entropy niche model as an environmental constraint layer. That is, the spatially registered sea surface temperature data, chlorophyll a concentration data, ocean current velocity data, and substrate type data are used as the environmental background for the analysis of the maximum entropy niche model. The principle of the maximum entropy niche model is to find the habitat suitability probability distribution with the maximum entropy value, i.e., the most uniform distribution, under the environmental constraints of the species occurrence point. By inputting the species distribution dataset and the environmental variable dataset, the maximum entropy niche model can learn the environmental characteristics based on the actual distribution of species and establish a statistical correlation framework between environmental conditions and the probability of species existence.

[0037] S2.2 Run the maximum entropy niche model to learn the statistical relationship between the species distribution dataset and the environmental variable dataset.

[0038] Furthermore, the maximum entropy niche model is run to learn the statistical relationship between the species distribution dataset and the environmental variable dataset. The maximum entropy niche model uses an iterative algorithm to analyze the differences between the environmental conditions of the input data points and the environmental background of the entire study area described by the environmental variable dataset. The maximum entropy niche model assesses the limiting effect of each environmental factor on species distribution and learns how the combination of features of these environmental variables jointly determines the probability of species occurrence.

[0039] The specific maximum entropy niche model seeks a probability distribution model that best explains the species distribution dataset under given environmental constraints. By running the maximum entropy niche model for learning, a set of feature functions and their parameters that can quantify the correlation between environmental variables and species distribution can be obtained, revealing the dominant environmental factors affecting the distribution of the target mussel species and their response relationships.

[0040] S2.3 Based on the learned statistical relationship, predict the suitability index of each spatial unit in the study area and generate a preliminary habitat suitability index distribution map.

[0041] Furthermore, after the maximum entropy niche model completes training and obtains stable parameters, the environmental constraints represented by the parameters are applied to each spatial unit of the study area. For each unit, the maximum entropy niche model calculates a relative habitat suitability index based on the specific values ​​of each environmental factor in the corresponding environmental variable dataset. This index reflects the similarity between the environmental conditions of the spatial unit and the environmental characteristics of the known distribution points of the species. The higher the index, the more suitable it is. By spatializing the suitability indices of all spatial units, a preliminary habitat suitability index distribution map covering the entire study area is generated. The preliminary habitat suitability index distribution map provides a spatial pattern of potential suitable habitats based on environmental similarity.

[0042] S3. From the pre-set mussel functional trait library, select the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index distribution map.

[0043] S3.1 By integrating entries from public biological trait databases and specific experimental measurement data for target species, a mussel functional trait library is established by screening out quantifiable traits directly related to mussel attachment, growth, reproduction, and stress tolerance. Each functional trait in the mussel functional trait library is extracted, and the measured values ​​of each functional trait in the mussel functional trait library are spatially interpolated to generate a spatial distribution map of the functional trait values ​​for each functional trait.

[0044] Furthermore, each functional trait in the pre-set mussel functional trait library is extracted, and the measured values ​​of each functional trait in the library are spatially interpolated to generate a spatial distribution map of the functional trait values ​​for each functional trait. The pre-set mussel functional trait library contains measured data points of key traits such as byssal strength and filter feeding rate. The spatial interpolation process uses geostatistical methods such as Kriging interpolation, which is based on spatial autocorrelation and uses the functional trait measurements of known sampling points to estimate the functional trait values ​​of unsampled locations throughout the entire study area.

[0045] Specifically, discrete point-like functional trait measurement data are transformed into a continuous spatial distribution layer, so that each spatial unit has an estimated functional trait value. The purpose of generating a spatial distribution map of functional trait values ​​is to spatially express the biological attribute of functional traits, and to quantitatively compare and analyze the spatial patterns with the preliminary habitat suitability index distribution map, which also exists in raster form, to generate a spatial distribution map of functional trait values.

[0046] S3.2. Use the Pearson product-moment correlation coefficient algorithm in spatial statistics to calculate the spatial covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of the functional trait values ​​for each functional trait.

[0047] Furthermore, the Pearson product-moment correlation coefficient algorithm from spatial statistics was used to calculate the spatial covariance between the preliminary habitat suitability index distribution map and the spatial distribution maps of functional trait values ​​for each functional trait. The Pearson product-moment correlation coefficient algorithm calculates the covariance between the suitability index values ​​of the preliminary habitat suitability index distribution map and the functional trait values ​​of the functional trait values ​​in the spatial distribution map by traversing all paired spatial units within the study area. This algorithm measures the degree and direction of the linear correlation between two variables in space.

[0048] Specifically, for each pair of spatial units, the algorithm considers the degree of deviation of its initial habitat suitability index value and functional trait value from their respective average values, and standardizes the sum of the deviation products of all spatial units. By applying the Pearson product-moment correlation coefficient algorithm, the algorithm can quantify the intensity of the coordinated change of the two spatial distribution maps in the overall pattern, i.e., the spatial covariance. This provides an objective numerical basis for the subsequent selection of functional traits most related to the spatial pattern of habitat suitability, and obtains a quantified spatial covariance value to describe the spatial correlation strength between each functional trait and habitat suitability.

[0049] The expression for spatial covariance is: ; in, Preliminary habitat suitability index distribution map Spatial distribution map of functional trait values Spatial covariance between them This is a preliminary distribution map of habitat suitability index. This is a spatial distribution map of the values ​​of functional traits. To determine the total number of effective spatial units within the study area, For the index of spatial units, The first on the preliminary habitat suitability index distribution map The suitability index value of each spatial unit. On the preliminary habitat suitability index distribution map The arithmetic mean of the suitability index values ​​of each spatial unit The first one on the spatial distribution map of functional trait values The functional characteristics of each spatial unit are taken as follows: On the spatial distribution map of functional trait values The arithmetic mean of the functional traits of each spatial unit.

[0050] S3.3 Compare the spatial covariance values ​​between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits to obtain the functional trait with the largest spatial covariance value in the preliminary habitat suitability index distribution map.

[0051] Furthermore, after calculating the spatial covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of each functional trait using the Pearson product-moment correlation coefficient algorithm, a ranking comparison was performed. The purpose of the comparison was to identify which functional trait's spatial distribution map showed the strongest linear co-variance trend in spatial pattern with the preliminary habitat suitability index distribution map. The functional trait with the largest spatial covariance value had the most similar or complementary spatial distribution pattern to the spatial distribution pattern of habitat suitability. This meant that the functional trait was spatially influenced by the same or similar environmental gradients, and thus might be the key intrinsic factor driving the spatial differentiation of habitat suitability in terms of ecological mechanisms. The functional trait with the largest spatial covariance value was thus determined.

[0052] S3.4 Select the functional trait with the largest spatial covariance value from the preliminary habitat suitability index distribution map to obtain the target functional trait with the highest spatial distribution covariance intensity.

[0053] Furthermore, the functional trait with the largest spatial covariance value identified after comparison was formally selected as the target functional trait. The functional trait was determined to be the biological attribute most closely related to the spatial pattern of mussel habitat suitability in the current study area. The target functional trait with the highest intensity of spatial distribution covariance was obtained, which guided the focus of analysis from general environmental factor correlations to specific biological mechanisms supported by spatial evidence. This laid the foundation for a deeper understanding of the intrinsic driving forces of habitat suitability and the construction of a more mechanistic assessment model, thus identifying the target functional trait.

[0054] S4. The intensity of spatial distribution covariance is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of functional trait values.

[0055] S4.1 Use the Pearson product-moment correlation coefficient algorithm to process the preliminary habitat suitability index distribution map and the spatial distribution map of functional trait values.

[0056] Furthermore, the study analyzes the linear relationship between the suitability index value of each spatial unit on the preliminary habitat suitability index distribution map and the functional trait value of the corresponding unit on the functional trait value spatial distribution map. It calculates the average of all values ​​on the preliminary habitat suitability index distribution map and the average of all values ​​on the functional trait value spatial distribution map. For each spatial unit, it calculates the deviation of its suitability index value from the average and the deviation of its functional trait value from the average, and multiplies these two deviations. The sum of the products of deviations for all spatial units is standardized by the ratio to the square root of the sum of the squares of their respective deviations, yielding the Pearson product-moment correlation coefficient. The Pearson product-moment correlation coefficient algorithm effectively captures the linear co-variation trend of the two spatial distribution maps in the overall pattern, providing a standardized measure for subsequent comparison of the correlation strength between different functional traits and habitat suitability. This completes the calculation of the spatial covariance strength between a given functional trait and habitat suitability.

[0057] The expression for the Pearson product-moment correlation coefficient is: ; in, Preliminary habitat suitability index distribution map Spatial distribution map of functional trait values The correlation coefficient between the Pearson product moments.

[0058] S4.2. The covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits is used as the quantitative result of the spatial distribution covariance intensity between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits.

[0059] Furthermore, the numerical values ​​calculated by the Pearson product-moment correlation coefficient algorithm are directly given a clear interpretation, that is, they are defined as a quantitative representation of the intensity of spatial distribution covariance. This reflects the degree of consistency between the preliminary habitat suitability index distribution map and the spatial distribution map of functional trait values ​​in terms of spatial pattern. The larger the absolute value of the value, the more similar or complementary the spatial distribution patterns of the two layers are, and the stronger the linear correlation. The positive or negative value indicates the direction of the correlation. Using the covariance value as the quantitative result of the intensity of spatial distribution covariance transforms the complex comparison of spatial pattern similarity into a comparable single scalar. This makes it possible to objectively and quantitatively screen the target functional traits most related to the spatial pattern of habitat suitability from the pre-set mussel functional trait library. The calculated values ​​are confirmed as the quantitative result of the intensity of spatial distribution covariance.

[0060] S5. Using physiological and ecological experimental data of the target functional traits, establish a quantitative relationship function between environmental variables and the target functional traits.

[0061] S5.1 Obtain physiological and ecological experimental data of target functional traits by measuring the functional traits of mussel individuals under different environmental gradients under controlled laboratory conditions.

[0062] Furthermore, controlled laboratory conditions are used in artificial climate chambers or aquariums to precisely control and maintain the levels of a range of key environmental factors, such as setting different temperature gradients, salinity gradients, or food concentration gradients. Healthy mussels are then placed under these preset environmental gradients for a period of time to ensure that environmental factors are the main variables leading to differences in functional traits. The target functional traits exhibited by mussels in each treatment group are measured, for example, by using mechanical sensors to measure byssus strength or by quantifying filter feeding rate by measuring the removal rate of suspended particulate matter in the water.

[0063] Specifically, the physiological and ecological experimental data of the target functional traits obtained by the formula can clearly reveal the causal relationship between environmental conditions and the performance of functional traits, eliminate the interference of multiple environmental factors in field surveys, and obtain accurate response data of the target functional traits under different environmental gradients.

[0064] S5.2 Extract environmental variable gradient data and target functional trait response data from the physiological and ecological experimental data of the target functional trait, and fit a regression model using the environmental variable gradient data and target functional trait response data.

[0065] Furthermore, the extraction process involves organizing experimental records into a structured dataset, where each row corresponds to an experimental unit, containing specific environmental variable levels such as temperature and salinity, as well as the measured values ​​of target functional traits under those conditions, such as byssalinity strength. Using regression analysis as a statistical method, environmental variable gradient data are used as independent variables, and target functional trait response data are used as dependent variables. A regression model is fitted to find a mathematical function that optimally describes the trend of target functional trait response when environmental variables change. The regression model used can be linear, polynomial, or nonlinear. By fitting the regression model with environmental variable gradient data and target functional trait response data, discrete experimental observation data can be transformed into a continuous and predictable mathematical relationship, thereby achieving theoretical prediction of functional trait performance under any given environmental conditions. This completes the regression model fitting process.

[0066] S5.3 Obtain the quantitative relationship function between environmental variables and target functional traits through regression model.

[0067] Furthermore, once the regression model is fitted, the mathematical expression itself constitutes a quantitative relationship function between environmental variables and the target functional trait. This function explicitly defines how the predicted value of the target functional trait changes with the values ​​of one or more environmental variables. For example, a simple linear regression model generates a linear equation containing a slope and an intercept, while more complex models may include exponential or interaction terms. This quantitative relationship function obtained through the regression model encapsulates biological mechanisms within a mathematical framework, allowing us to input any set of environmental conditions and calculate the corresponding theoretical functional trait performance level. Establishing a quantitative relationship function between environmental variables and the target functional trait is the core step in connecting environmental data with biological responses, elevating qualitative ecological knowledge to a quantitative predictive tool, and resulting in a mathematical function that can be used for prediction.

[0068] S6. Calculate the adaptive distribution map of the target functional traits using quantitative relationship functions and environmental variable datasets.

[0069] S6.1 Input the value of each environmental variable in the environmental variable dataset into the quantitative relationship function between environmental variables and target functional traits.

[0070] Furthermore, the environmental variable dataset contains specific values ​​of multiple environmental factors, including sea surface temperature, chlorophyll a concentration, ocean current velocity, and sediment type, at each spatial location within the study area. For each spatial location in the dataset, all corresponding environmental factor values ​​need to be extracted as input items and substituted one by one into the quantitative relationship function between environmental variables and target functional traits established through regression analysis. This ensures that the environmental conditions on which the quantitative relationship function is calculated completely correspond to the actual environmental conditions at each spatial location, thus completing the preparation of environmental data input for all spatial locations.

[0071] S6.2 Calculate the fitness index of the target functional trait at each spatial location using the quantitative relationship function between environmental variables and the target functional trait.

[0072] Furthermore, the mathematical expression for the fitness index of the target functional trait explicitly states that the index is the output value of the quantitative relationship function between environmental variables and the target functional trait under specific environmental conditions. For the j-th spatial location within the study area, the environmental variable data for that location is concentrated... Specific values ​​of each environmental factor Substitute it into the function as input. The calculation is performed in the [location name], and the output result is the fitness index of the target functional trait at that location. This index quantifies the theoretical performance level or suitability of a target functional trait under the environmental conditions of its location.

[0073] Specifically, by calculating the quantitative relationship function between environmental variables and target functional traits, landscape-scale environmental heterogeneity data can be transformed into spatial distribution data of functional trait adaptability at the corresponding scale, providing the possibility for explicit spatial simulation of ecological processes, and calculating the corresponding target functional trait adaptability index for each spatial location.

[0074] The expression for the fitness index of the target functional trait is: ; in, For the first in the study area The target functional trait fitness index for each spatial location For the first The specific value of the first environmental factor at each spatial location. For the first The specific value of the second environmental factor at each spatial location. For the first At the first spatial location Values ​​of environmental factors, For spatial location index, This is a quantitative relationship function between environmental variables and target functional traits. These are environmental factor values.

[0075] S6.3. Spatialize the fitness index of the target functional traits in spatial locations to generate a fitness distribution map of the target functional traits.

[0076] Furthermore, each spatial location and the fitness index of the target functional trait are treated as data pairs, reorganized and rendered according to the geographic coordinates of the spatial locations. By arranging and visualizing all the fitness index values ​​of the target functional trait according to their spatial relationships, a continuous spatial distribution map is formed. This target functional trait fitness distribution map, in the form of raster data, intuitively displays the spatial differentiation of the theoretical suitability of the target functional trait at different locations within the study area, reflecting the geographic pattern of the potential influence of environmental gradients on functional trait expression. Generating the target functional trait fitness distribution map transforms numerical calculation results into visualized spatial information, providing ecologists and managers with an intuitive tool for understanding the spatial variation of functional traits and their environmental drivers.

[0077] S7. Couple the adaptive distribution map of the target functional traits with the population connectivity data to perform source-sink habitat analysis and output a source-sink habitat functional classification map.

[0078] S7.1. Based on the larval dispersal model, population connectivity data is obtained through simulation. The adaptive distribution map of the target functional traits and the population connectivity data are input into the cellular automaton population model. The cellular automaton population model is then run to simulate the growth, death, and dispersal dynamics of the population.

[0079] Furthermore, population connectivity data was obtained by simulating the dispersal trajectory and settlement probability of mussel larvae under the influence of ocean currents, reflecting the migration potential of individuals between different habitat patches. The cellular automata population model discretizes the study area into regular grids, with the state of each grid cell represented by the local population density. The target functional trait adaptive distribution map serves as the input parameter for the local carrying capacity or basic growth rate of each grid cell in the cellular automata population model, determining the potential growth capacity of the population. Population connectivity data serves as the rule for individual migration between grid cells in the cellular automata population model, controlling the population dispersal process. Running the cellular automata population model to simulate the growth, death, and dispersal dynamics of the population can dynamically reproduce the distribution changes of the population in spatially heterogeneous habitats, thereby capturing the dynamic equilibrium state of population distribution and completing the spatiotemporal simulation process of population dynamics.

[0080] S7.2 Based on the simulation results of the cellular automata population model, identify source habitat patches that make a net contribution to the regional population and sink habitat patches that depend on external supplementation.

[0081] Furthermore, after the cellular automata population model reaches a stable state, the functional roles of each grid cell are identified by analyzing the net change in population size and the net flux of individual migration during the simulation. Source habitat patches refer to the set of grid cells where the internal population growth rate is higher than the mortality rate and individuals are continuously exported. These patches play a core supporting role in maintaining the regional population size. Sink habitat patches, on the other hand, refer to the set of grid cells that mainly rely on the input of external individuals to maintain the existence of the population. Their internal growth is insufficient to offset the losses. The process of identifying source and sink habitat patches is based on spatial statistical analysis of the output data of the cellular automata population model. This can reveal the functional spatial structure formed by the interaction between habitat suitability patterns and population dynamics, thus completing the spatial identification and classification of habitat functions.

[0082] S7.3. Based on the identification results of source habitat patches and sink habitat patches, generate a source-sink habitat functional classification map.

[0083] Furthermore, by using a geographic information system, source habitat patches, sink habitat patches, and other areas are assigned different classification codes or legend symbols and integrated into a complete spatial distribution map. The source-sink habitat functional classification map intuitively displays the functional roles of different habitat patches in regional population maintenance, highlighting key areas with important source functions. The generation of the source-sink habitat functional classification map transforms dynamic population simulation results into static spatial management guidance maps, providing direct scientific basis for prioritizing the protection of core source habitats and optimizing habitat network structure, and outputting the source-sink habitat functional classification map.

[0084] S8. Overlay, integrate, and visualize the adaptive distribution map of target functional traits and the source-sink habitat functional classification map to generate an ecological management habitat adaptive assessment result map.

[0085] S8.1 In the geographic information unit, the target functional trait adaptation distribution map and the source-sink habitat functional classification map are overlaid as layers. On the overlaid map, the color gradient is used to represent the suitability level of the target functional trait adaptation distribution map.

[0086] Furthermore, a geographic information unit (GIS) refers to the working environment within a GIS software. Layer overlay operations ensure complete matching of spatial references and extents between the two layers. The target functional trait fitness distribution map provides the potential fitness index for each spatial unit based on functional traits, while the source-sink habitat functional classification map identifies the functional role of each patch in population dynamics. The overlaid map retains all spatial information. A color gradient is used to represent the fitness level of the target functional trait fitness distribution map. Continuous fitness index values ​​are divided into several discrete intervals, and a color is assigned to each interval, for example, from a cool color representing low fitness to a warm color representing high fitness. This visualization method can intuitively display the spatial distribution pattern of habitat fitness, enabling observers to quickly identify areas with superior environmental conditions, thus completing the overlay of spatial layers and the visualization of fitness levels.

[0087] S8.2 On the overlaid map, use different shaped legend symbols to label the source habitat patches and sink habitat patches in the source-sink habitat functional classification map, integrate the color gradient and legend symbols, and generate an ecological management habitat adaptability assessment result map.

[0088] Furthermore, based on the map that has already been overlaid with color gradients, functional role information is added. For source habitat patches identified in the source-sink habitat functional classification map, a specific shape of legend symbol is used for labeling, such as a star; for sink habitat patches, a distinctly different shape is used for labeling, such as a triangle. The labeling method does not cover the color gradient of the base map, but is presented in the form of overlaid dot symbols. This integrates the static quality assessment of habitats with the dynamic functional role analysis into the same map, so that the ecological management habitat adaptability assessment result map contains suitability information and functional information on which aspects are more important for population survival. The generated ecological management habitat adaptability assessment result map provides a comprehensive and intuitive spatial science basis for management decisions such as the delineation of marine protected areas and the prioritization of ecological restoration, and generates an ecological management habitat adaptability assessment result map that integrates dual information.

[0089] This embodiment also provides a mussel habitat adaptability assessment system based on a niche model, including: a data acquisition module, which acquires known distribution point data of the target mussel species and multiple environmental factor data of the study area, preprocesses the data, and obtains a species distribution dataset and an environmental variable dataset; The assessment module uses species distribution datasets and environmental variable datasets to train a niche model and obtain a preliminary habitat suitability index distribution map. The screening module selects the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index from the preset mussel functional trait library; the spatial distribution covariance intensity is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of the functional trait values. The trait adaptation mapping module establishes a quantitative relationship function between environmental variables and target functional traits using physiological and ecological experimental data of target functional traits, and calculates the adaptive distribution map of target functional traits using the quantitative relationship function and environmental variable dataset; The source-sink habitat analysis module couples the target functional trait adaptation distribution map with population connectivity data to perform source-sink habitat analysis and outputs a source-sink habitat functional classification map. It then overlays, integrates, and visualizes the target functional trait adaptation distribution map and the source-sink habitat functional classification map to generate an ecological management habitat adaptation assessment result map.

[0090] This embodiment also provides a computer device applicable to the mussel habitat adaptation assessment method based on the niche model, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the mussel habitat adaptation assessment method based on the niche model proposed in the above embodiment.

[0091] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0092] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the mussel habitat adaptation assessment method based on the niche model proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0093] In summary, this invention constructs a preliminary habitat suitability distribution map by integrating species distribution data and environmental factor data, introduces a functional trait screening mechanism, quantitatively screens key functional traits based on spatial covariance intensity, and establishes a quantitative relationship between environmental variables and functional traits by combining physiological and ecological experimental data, generating a more mechanistically interpretable trait adaptation distribution map. By coupling population connectivity data to conduct source-sink habitat analysis, a comprehensive assessment result map integrating suitability levels and population functional zoning is generated, achieving a deepening from statistical correlation to ecological mechanism explanation, and enhancing the decision support capability of the assessment results in precise conservation and adaptive management.

[0094] The embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the adaptability of mussel habitats based on a niche model, characterized in that: This includes acquiring known distribution data of the target mussel species and multiple environmental factor data of the study area, preprocessing the data, and obtaining species distribution datasets and environmental variable datasets; A niche model was trained using species distribution datasets and environmental variable datasets to obtain a preliminary distribution map of habitat suitability index; From the pre-set mussel functional trait library, the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index distribution map are selected; The intensity of spatial distribution covariance is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of functional trait values. By using physiological and ecological experimental data of the target functional traits, a quantitative relationship function between environmental variables and the target functional traits is established. The adaptive distribution map of the target functional traits is then calculated using the quantitative relationship function and the environmental variable dataset. The adaptive distribution map of target functional traits is coupled with population connectivity data to conduct source-sink habitat analysis and output a source-sink habitat functional classification map. The adaptive distribution map of target functional traits and the source-sink habitat functional classification map are overlaid, integrated and visualized to generate an ecological management habitat adaptive assessment result map.

2. The method for assessing mussel habitat adaptability based on a niche model as described in claim 1, characterized in that: Acquire known distribution data of the target mussel species and multiple environmental factor data of the study area. Preprocess the data to obtain species distribution datasets and environmental variable datasets, including the following steps: Collect known distribution data for the target mussel species. This known distribution data is sourced from global biodiversity data. Diversity information networks and field investigation reports with geographic coordinate records are used to clean known distribution point data, removing spatial clustering biases and obvious erroneous records from the known distribution point data; The study area acquired data on multiple environmental factors, including sea surface temperature and chlorophyll a concentration data extracted from remote sensing satellite products, ocean current velocity data obtained from hydrological models, and seabed sediment type data digitized from seabed geological maps. Spatial registration was performed on multiple environmental factor data, and sea surface temperature data, chlorophyll a concentration data, ocean current velocity data, and seabed type data were uniformly resampled to the same geographic coordinate system and pixel size; By integrating the cleaned known distribution point data with the spatially registered data of multiple environmental factors, a spatially aligned species distribution dataset and environmental variable dataset are obtained.

3. The method for assessing mussel habitat adaptability based on a niche model as described in claim 2, characterized in that: A niche model was trained using species distribution datasets and environmental variable datasets to obtain a preliminary habitat suitability index distribution map, including the following steps: The species distribution dataset is used as the existing data input to the maximum entropy niche model, and the environmental variable dataset is used as the environmental constraint layer input to the maximum entropy niche model. Run a maximum entropy niche model to learn the statistical relationship between the species distribution dataset and the environmental variable dataset; Based on the learned statistical relationships, the suitability index of each spatial unit in the study area is predicted, and a preliminary habitat suitability index distribution map is generated.

4. The method for assessing mussel habitat adaptability based on a niche model as described in claim 3, characterized in that: From the pre-set mussel functional trait library, the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index distribution map are selected, including the following steps: Extract each functional trait from the pre-set mussel functional trait library, perform spatial interpolation on the measured values ​​of each functional trait in the mussel functional trait library, and generate a spatial distribution map of the functional trait values ​​for each functional trait. The spatial covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of the functional trait values ​​for each functional trait was calculated using the Pearson product-moment correlation coefficient algorithm in spatial statistics. By comparing the spatial covariance values ​​between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits, the functional trait with the largest spatial covariance value in the preliminary habitat suitability index distribution map is obtained. The functional trait with the largest spatial covariance value relative to the preliminary habitat suitability index distribution map is selected to obtain the target functional trait with the highest spatial distribution covariance intensity.

5. The method for assessing mussel habitat adaptability based on a niche model as described in claim 4, characterized in that: The intensity of spatial distribution covariance is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of functional trait values, including the following steps: The Pearson product-moment correlation coefficient algorithm was used to process the preliminary habitat suitability index distribution map and the spatial distribution map of functional trait values. The covariance between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits is used as a quantitative result of the spatial distribution covariance intensity between the preliminary habitat suitability index distribution map and the spatial distribution map of functional traits.

6. The method for assessing mussel habitat adaptability based on a niche model as described in claim 5, characterized in that: Using physiological and ecological experimental data of the target functional traits, a quantitative relationship function between environmental variables and the target functional traits is established, including the following steps: Physiological and ecological experimental data of target functional traits were obtained by measuring the functional traits of mussel individuals under different environmental gradients under controlled laboratory conditions. Environmental variable gradient data and target functional trait response data were extracted from the physiological and ecological experimental data of the target functional trait, and a regression model was fitted using the environmental variable gradient data and target functional trait response data. The quantitative relationship function between environmental variables and target functional traits is obtained through regression model.

7. The method for assessing mussel habitat adaptability based on a niche model as described in claim 6, characterized in that: The adaptive distribution map of the target functional trait is calculated using quantitative relationship functions and environmental variable datasets, including the following steps: Input the value of each environmental variable in the environmental variable dataset into the quantitative relationship function between the environmental variable and the target functional trait; The fitness index of the target functional trait at each spatial location is calculated by using a quantitative relationship function between environmental variables and the target functional trait. Spatial mapping of the fitness index of the target functional traits at spatial locations generates a distribution map of the fitness of the target functional traits.

8. The method for assessing mussel habitat adaptability based on a niche model as described in claim 7, characterized in that: Couple the adaptive distribution map of the target functional traits with population connectivity data to perform source-sink habitat analysis and output a source-sink habitat functional classification map, including the following steps: Population connectivity data were obtained by simulating the larval dispersal model. The adaptive distribution map of the target functional traits and the population connectivity data were input into the cellular automaton population model. The cellular automaton population model was then run to simulate the growth, death and dispersal dynamics of the population. Based on the simulation results of the cellular automata population model, source habitat patches that make a net contribution to the regional population and sink habitat patches that depend on external replenishment were identified. Based on the identification results of source habitat patches and sink habitat patches, a source-sink habitat functional classification map is generated.

9. The method for assessing mussel habitat adaptability based on a niche model as described in claim 8, characterized in that: The adaptive distribution map of target functional traits is overlaid, integrated, and visualized with the source-sink habitat functional classification map to generate an ecological management habitat adaptive assessment result map, including the following steps: In a geographic information unit, the adaptive distribution map of target functional traits is overlaid with the source-sink habitat functional classification map. On the overlaid map, a color gradient is used to represent the suitability level of the adaptive distribution map of target functional traits. On the overlaid map, source habitat patches and sink habitat patches in the source-sink habitat functional classification map are labeled with different shaped legend symbols. The color gradient and legend symbols are integrated to generate an ecological management habitat adaptability assessment result map.

10. A niche-model-based system for assessing the adaptability of mussel habitats, based on the niche-model-based method for assessing the adaptability of mussel habitats according to any one of claims 1 to 9, characterized in that: This includes a data acquisition module, which acquires known distribution data of the target mussel species and multiple environmental factor data of the study area, preprocesses the data, and obtains species distribution datasets and environmental variable datasets. The assessment module uses species distribution datasets and environmental variable datasets to train a niche model and obtain a preliminary habitat suitability index distribution map. The screening module selects the target functional traits with the highest covariance intensity with the spatial distribution of the preliminary habitat suitability index distribution map from the preset mussel functional trait library. The intensity of spatial distribution covariance is quantified by calculating the covariance between the spatial distribution of the preliminary habitat suitability index and the spatial distribution of functional trait values. The trait adaptation mapping module establishes a quantitative relationship function between environmental variables and target functional traits using physiological and ecological experimental data of target functional traits, and calculates the adaptive distribution map of target functional traits using the quantitative relationship function and environmental variable dataset; The source-sink habitat analysis module couples the target functional trait adaptation distribution map with population connectivity data to perform source-sink habitat analysis and outputs a source-sink habitat functional classification map. It then overlays, integrates, and visualizes the target functional trait adaptation distribution map and the source-sink habitat functional classification map to generate an ecological management habitat adaptation assessment result map.

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