Gini index-based quantitative evaluation method for habitat of fish eggs and larvae in estuary area

Abstract

The invention discloses an estuary region fish egg and larva habitat quantitative evaluation method based on a Gini index, and the method comprises the following operation steps: (1) sampling environment and biological data, and obtaining a fixed impact factor of a target research sea area; (2) data standardization: standardizing the fish catch per unit fishing effort; (3) calculating a fish suitability index, and carrying out normalization calculation on the suitability index; (4) environment factor heterogeneity quantitative evaluation: performing sea area environment factor heterogeneity quantitative evaluation; (5) quantitative evaluation of a habitat index: a habitat quantitative evaluation model based on a heterogeneity index; (6) screening key environmental influence factors, verifying the influence of an environmental factor combination on the habitat through an additive model, and screening significant factors; and outputting a habitat distribution map, and identifying optimal habitat areas of different species. According to the method, the model precision and ecological benefits are improved, the method can be expanded to other marine ecosystems with relatively high heterogeneity from an estuary region, and adaptive management under climate change is supported.

Description

Technical Field

[0001] The present invention relates to the fields of ecology and fishery resource management, and particularly to a method and system for evaluating the habitats of fish eggs, larvae and juveniles in estuaries based on quantitative analysis of environmental heterogeneity, which is applicable to the evaluation of fish habitat quality, ecological restoration and biodiversity conservation. Background Art

[0002] Based on the classical HSI model, combined with the Generalized Additive Model (GAM) to screen environmental factors (sea surface temperature, salinity, chlorophyll concentration, water depth), a habitat suitability index model is constructed by the weighted average method to analyze the spatial distribution of the habitats of gobies' fish eggs and larvae in the Yellow River Estuary. The research uses the horizontal trawl survey data and remote sensing data from April to May 2020, and finds that there are significant differences in the distribution of the most suitable habitats in April and May, and points out the weights of environmental factors (temperature has the greatest impact, followed by chlorophyll).

[0003] First, environmental heterogeneity has not been quantified: The estuarine environment has high heterogeneity, but in current research, environmental heterogeneity has not been quantified. The traditional habitat suitability index (HSI) model does not fully consider the spatial heterogeneity of environmental factors, resulting in a deviation between the evaluation results and the actual biological distribution.

[0004] Second, the universality of the model is poor: Existing methods mostly rely on the linear analysis of a single environmental factor (such as temperature, salinity), and it is difficult to capture the impact of the coupling of multiple factors on the habitat.

[0005] Third, the habitat evaluation index does not consider the problem of environmental factor heterogeneity: The existing HSI model averages and weights the environment. In fact, high environmental factor heterogeneity represents large changes in the habitat environment and is not suitable for biological survival, which will reduce the weight of this environmental factor itself and should be considered. Summary of the Invention

[0006] The present invention provides a method for quantitatively evaluating the habitats of fish eggs, larvae and juveniles in estuaries based on the Gini index, aiming to solve the problems of the inability to quantify the environmental heterogeneity in estuaries and its quantitative evaluation of the impact on habitats.

[0007] The present invention is achieved by the following technical solutions:

[0008] A method for quantitatively evaluating the habitats of fish eggs, larvae and juveniles in estuaries based on the Gini index specifically includes the following operating steps:

[0009] (1) Sampling environmental and biological data to obtain specific influencing factors in the target research sea area;

[0010] (2) Standardizing the data and standardizing the catch per unit effort.

[0011] (3) Calculation of fish suitability index and normalization calculation of suitability index (SI);

[0012] (4) Quantitative assessment of environmental factor heterogeneity, quantitatively assessing the heterogeneity of marine environmental factors through the Gini index;

[0013] (5) Quantitative assessment of habitat index, a habitat quantitative assessment model based on the heterogeneity index;

[0014] (6) Screening of key environmental impact factors, verifying the impact of environmental factor combinations on the habitat through the Generalized Additive Model (GAM), and screening significant factors (such as temperature, salinity, chlorophyll a); outputting a habitat distribution map to identify the optimal habitat areas for different species.

[0015] As a preferred embodiment, in the step (1), the target sea area is discretized into several spatio-temporal units, and the catch per unit fishing effort and marine environmental variables of each spatio-temporal unit are obtained;

[0016] As a preferred embodiment, the catch per unit fishing effort of fish eggs and larvae is expressed in density in the step (2). After sampling with a plankton net, the standardized density (unit: individuals per cubic meter (ind / m 3 )) is calculated according to the formula: DI = number of individuals counted in the laboratory / sampled water volume (m 3 ). The purpose is to eliminate the influence of sampling volume differences on density and achieve comparability of data across stations.

[0017] As a preferred embodiment, in the step (3), the optimal ranges of different marine environmental factors for fish are calculated.

[0018] As a preferred embodiment, in the step (4), the spatial heterogeneity of environmental factors is quantified through the Gini Index and Lorenz curve, and the heterogeneity levels are divided (very low heterogeneity: 0 - 0.2; low heterogeneity: 0.2 - 0.3; medium heterogeneity: 0.3 - 0.4; high heterogeneity: 0.4 - 0.5; extremely high heterogeneity: 0.5 - 1.0).

[0019] As a preferred embodiment, the quantitative expression of the habitat index in the step (5) is:

[0020]

[0021] where HSI gini is the habitat suitability index of each fish species based on the Gini index, P i is the observed value of the i-th environmental factor, C i is one end of the most suitable environmental value closest to P i , and M i is the end of the most suitable environmental value farthest from Pi At one end, the weight W is jointly determined by the Gini index of the i-th environmental factor and its influence weight on organisms, and the expression is:

[0022]

[0023] Among them, the influence weight W of the i-th environmental factor on organisms i is determined by the Generalized Additive Model (GAM).

[0024] Beneficial effects: The present invention provides a method for quantitatively evaluating the habitats of fish eggs, larvae and juveniles in estuarine areas based on the Gini index. By constructing a dynamic weight model and integrating the coupling effects of multiple environmental factors, it can quantify the spatial heterogeneity of estuarine environmental factors, improve the accuracy of habitat evaluation, and is applicable not only to estuaries but also to other sea areas with high environmental heterogeneity. It is a tool for quantitatively evaluating biological habitats based on environmental heterogeneity, specifically including the following aspects:

[0025] (a) Significantly improved the model accuracy: Verified with the actual survey data of gobies' fish eggs, larvae and juveniles and the environment in the Yangtze Estuary, the cross-validation R of the model 2 reached 87.3% - 94.1%, which was significantly better than the traditional HSI model.

[0026] (b) Improved ecological benefits: Accurately predicting the distribution of fish habitats in sea areas with prominent environmental heterogeneity such as estuaries can effectively improve the efficiency of resource management and effectively reduce the adverse impacts of estuary development projects on resource habitats. For research on aspects such as interspecific competition and ecological niche, accurately predicting the habitat range can provide reliable technical and data support.

[0027] (c) Wide application: The quantitative evaluation of highly heterogeneous marine environments and the evaluation of habitat indices based on the Gini index can be extended from estuarine areas to other marine ecosystems with high heterogeneity, supporting adaptive management under climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of a method for quantitatively evaluating the habitats of fish eggs, larvae and juveniles in estuarine areas based on the Gini index of the present invention.

[0029] Figure 2 is an evaluation schematic diagram of a method for quantitatively evaluating the habitats of fish eggs, larvae and juveniles in estuarine areas based on the Gini index of the present invention.

[0030] Figure 3 is a distribution diagram of a method for quantitatively evaluating the habitats of fish eggs, larvae and juveniles in estuarine areas based on the Gini index of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings: These embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given, but the protection scope of the present invention is not limited to the following embodiments.

[0032] As Figure 1 , 2 , shown in Figure 3, a method for quantitatively evaluating the habitat of fish eggs, larvae and juveniles in estuarine areas based on the Gini index specifically includes the following operation steps:

[0033] (1) Collection of biological samples: Use a plankton net with an opening width of 80 cm and a mesh size of 505 μm to collect larvae and juvenile samples. Drag the net horizontally at a speed of 2 - 3 knots for 10 minutes, and record the filtered water volume through a calibrated flowmeter installed at the net opening. After collection, the samples are immediately fixed with a buffered saline solution containing 5% formaldehyde. Identify the species of larvae and juveniles by morphological methods in the laboratory, and divide them into the pre-larval stage, post-larval stage, and juvenile stage. Use an SBE-19plus V2 SeaCAT thermosalinograph (CTD) to record environmental parameters. Calculate the average temperature (SST, °C), salinity (SSS), dissolved oxygen concentration (sdo, ml / L), and chlorophyll a concentration (schl, mg / m 3 ) at three meters below the sea surface. The water depth data is extracted from electronic nautical charts.

[0034] (2) Analysis of the topography and geomorphology of the study area: The environment in estuarine areas is often complex. Taking the Yangtze Estuary as an example. The Yangtze Estuary has a trumpet-shaped geographical form and is a multi-branch delta-type estuary. Chongming Island divides the inner area into the South Branch and the North Branch. The South Branch is divided into the South Channel and the North Channel by Changxing Island and Hengsha Island. The South Branch is the main flood discharge channel, and more than 80% of the net flow in the South and North Branches comes from freshwater runoff. August is the dry season of the Yangtze River. The runoff effect is less than the seawater upwelling caused by tides, and the North Branch even becomes the main channel for seawater to flow backward into the South Branch, and its salinity is about 10 - 25 times that of the South Branch throughout the year. A full analysis of the topography and geomorphology of the study area helps to deeply analyze the impact of environmental factors on biological habitats.

[0035] (3) Determination of the main research object: The estuarine area is a spawning and inhabiting place for various fish species. During the sampling process, various fish eggs, larvae and juveniles are often collected. First, it is necessary to analyze the relative importance index of the fish species identified in the laboratory to identify the target species. The formula is as follows:

[0036] IRI = N% × F% ………………(3)

[0037] Where: N is the fish density (number: ind / m 3 ), standardized by dividing the number of individuals counted in the laboratory by the volume of the sampled water body during the survey period; N% is the proportion of the density of a certain species in the total density of all species; F% is the proportion of the stations where this species appears in all the surveyed stations.

[0038] (4) Calculate the suitability index (SI) by the envelope method. The SI ranges from 0 to 1 and is used to characterize habitat suitability. When SI = 1, the environmental conditions are considered optimal for survival; when SI = 0, the environment is completely unsuitable for survival. The optimal ranges of each environmental factor for gobies are calculated through SI, and the formula is:

[0039]

[0040] where i is the ith environmental factor, k is the level of the environmental variable, DI is the biological density (ind / m 3 ), DI max and DI min represent the maximum and minimum densities. When SI > 0.8, the environment is most suitable.

[0041] (5) Use the generalized additive model to determine the environmental weights without considering environmental heterogeneity. In addition, based on the Gini coefficient, draw the Lorenz curve of different environmental factors. The greater the environmental heterogeneity, the greater the curvature of the curve, that is, the farther away from the 45° center line. The two together determine the weight of a certain environmental factor in evaluating habitat suitability.

[0042] The modeling steps of the habitat suitability index evaluation model based on the Gini coefficient are as follows:

[0043] First, the HSI gini model explores the spatial heterogeneity of each environmental factor using the Lorenz curve and the Gini index;

[0044] Second, draw the suitability index curve (SIC) of the environmental factor based on biological and environmental information to determine the optimal range of the habitat environmental factor;

[0045] Finally, construct the model using the principle that the distance between the measured environmental factor and the optimal environmental factor represents the habitat quality.

[0046] The habitat suitability index evaluation model based on the Gini coefficient assumes: 1) When determining the environmental factor weights, the spatial heterogeneity level of the environment should be appropriately considered. The more unstable the environment, the greater the impact on the biological distribution; 2) The optimal environmental range is obtained from the biological density. The closer the habitat environment is to the optimal range, the higher the habitat quality. The Gini coefficient is between [0, 1], where 0 represents absolute equality and 1 represents absolute inequality. Based on the distance between the actual environment and the optimal interval, the HSI gini model was developed, as shown in formulas (1) and (2).

[0047] This model was verified through cross-validation. 80% of the samples were used as the training set and 20% as the test set. This process was repeated 100 times for each model. Determine R2 The sum of squares and root mean square error (RMSE) are used to examine the fit between the observed and predicted values and the accuracy of model predictions.

[0048] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A quantitative evaluation method for the habitats of fish eggs and larvae in estuarine areas based on the Gini index, characterized in that, Specifically, it includes the following operation steps: (1) Sampling environmental and biological data to obtain specific impact factors in the target research sea area; (2) Data standardization, standardizing the catch per unit effort; (3) Calculating the fish suitability index and normalizing the suitability index; (4) Quantitatively evaluating the heterogeneity of environmental factors, quantitatively evaluating the heterogeneity of sea area environmental factors through the Gini index; (5) Quantitatively evaluating the habitat index, a habitat quantitative evaluation model based on the heterogeneity index; (6) Screening key environmental impact factors, verifying the impact of the environmental factor combination on the habitat through a generalized additive model, screening significant factors; outputting a habitat distribution map to identify the optimal habitat areas for different species.

2. The method for quantitatively evaluating the habitat of fish eggs and larvae in estuarine areas based on the Gini index according to claim 1, wherein, In step (1), the target sea area is discretized into several spatio-temporal units, and the catch per unit effort, random effect factors, and marine environmental variables of each spatio-temporal unit are obtained.

3. The method for quantitatively evaluating the habitat of fish eggs and larvae in estuarine areas based on the Gini index according to claim 1, characterized in that, The catch per unit effort of fish eggs and larvae in step (2) is expressed as density. After sampling with a plankton net, the standardized density is calculated according to the formula (unit: individuals per cubic meter (ind / m 3 )): DI = number of individuals counted in the laboratory / sampled water volume (m 3 ). The purpose is to eliminate the influence of sampling volume differences on density and achieve comparability of data across stations.

4. The quantitative evaluation method for the habitats of fish eggs and larvae in estuarine areas based on the Gini index according to claim 1, characterized in that, In step (3), the optimal ranges of fish for different sea area environmental factors are calculated.

5. The method for quantitatively evaluating the habitat of fish eggs and larvae in estuarine areas based on the Gini index according to claim 1, characterized in that, In step (4), the spatial heterogeneity of environmental factors is quantified through the Gini index and Lorenz curve, and the heterogeneity levels are divided as follows: extremely low heterogeneity: 0 - 0.2; low heterogeneity: 0.2 - 0.3; medium heterogeneity: 0.3 - 0.4; high heterogeneity: 0.4 - 0.5; extremely high heterogeneity: 0.5 - 1.

0.

6. The method for quantitatively evaluating the habitat of fish eggs and larvae in estuarine areas based on the Gini index according to claim 1, wherein In step (5), the quantitative expression of the habitat index is: Among them, HSI gini is the habitat suitability index based on the Gini index for each fish species, P i is the observed value of the i-th environmental factor, C i is the end where the most suitable environmental value is closest to P i and M i is the end where the most suitable environmental value is farthest from P i The weight W is jointly determined by the Gini index of the i-th environmental factor and its influence weight on the organism, and the expression is: Among them, the influence weight \(W\) of the \(i\)-th environmental factor on organisms i is determined by the generalized additive model.

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