Method and system for calculating integrity of food web

Abstract

The invention belongs to the technical field of ecology and protection biology, and particularly relates to a method and system for calculating food web integrity, and the method comprises the steps: collecting species multi-source data of a to-be-evaluated region, and constructing a large food web model based on the species multi-source data; calculating a food web structure topology index based on a'large food web 'model; and determining the topological index weight of each food web structure by adopting a CRITIC weight method, and calculating the food web integrity of the to-be-evaluated region based on the food web structure topological index and the food web structure topological index weight. According to the method, the to-be-calculated food web is compared with the relatively stable reference food web, so that the capability of maintaining the original or near-original state of the food web in a specific ecological system or ecological geographic region can be evaluated.

Description

Technical Field

[0001] This invention belongs to the fields of ecology and conservation biology, and specifically relates to a method and system for calculating food web integrity. Background Technology

[0002] Under the dual pressures of global climate change and human activities, the health and stability of ecosystems are facing severe challenges. As the core framework for energy flow and material cycling within ecosystems, the integrity and stability of food webs are the cornerstone of maintaining stable ecosystems. However, factors such as climate change, habitat degradation, and human disturbance are continuously causing food web structural simplification and even functional decline by altering interspecies interactions. This not only accelerates biodiversity loss but also severely weakens the ecosystem's ability to resist external disturbances. Therefore, accurately assessing the health status of food webs has become a critical technical issue urgently needing to be addressed in the field of ecological conservation.

[0003] To assess ecosystem health, the academic community has long employed the concept of "biological integrity." First proposed by Karr in 1981, this concept refers to the ability of a balanced, integrated, and adaptive biological community whose species composition, diversity, and functional organization are comparable to the natural habitat of the area. Initially widely used in aquatic ecosystem assessments, this concept has recently been extended to other biological groups, such as birds. Although some studies have attempted to incorporate certain characteristics of food webs into the assessment, a standardized integrity assessment method specifically for food webs that can systematically integrate their "species composition" and "structural function" is still lacking.

[0004] In conclusion, given the current technological context, there is an urgent need for a new method that can systematically assess the overall state of food webs in order to accurately diagnose the health of ecosystems and provide a basis for decision-making in developing more scientific and efficient conservation strategies. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention provides a method and system for calculating food web integrity, which aims to calculate the ability of a food web within a specific ecosystem or ecological geographic region to maintain its original or near-original state.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for calculating food web integrity, the method comprising:

[0008] Collect multi-source data on species in the region to be evaluated, and construct a "large food web" model based on the multi-source data on species.

[0009] Based on the "large food web" model, calculate the topological indices of the food web structure;

[0010] The weights of each food web structure and topology index are determined using the CRITIC weighting method. Based on the food web structure and topology indexes and their weights, the food web integrity of the region to be evaluated is calculated.

[0011] Preferably, multi-source species data are collected from the region to be evaluated, and a "large food web" model is constructed based on the multi-source species data, including:

[0012] Collect multi-source data on species in the area to be evaluated;

[0013] Construct a predator-prey relationship matrix among species based on multi-source data of species;

[0014] Based on the predator-prey relationship matrix, calculate the food source ratio among species:

[0015]

[0016] Among them, F ij This represents the ratio of food source to prey species j that predator i preys. This represents the average probability that predator i will prey on node j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k;

[0017] Calculate the trophic level of a species based on the food source ratio among species:

[0018] Tr i =1+∑Tr j ×F ij ;

[0019] Among them, Tr i Tr represents the trophic level of predator i. j The trophic level of prey j;

[0020] Based on the trophic level and food source ratio of species, and combined with the predation relationships among food web species in the area to be evaluated, a "large food web" model is constructed.

[0021] Preferably, based on multi-source data of species, a predator-prey relationship matrix is ​​constructed, including:

[0022] Construct a predator-prey matrix A with dimension i×j:

[0023] A = (a ij ) i×j ;

[0024] Where i represents the total number of predators, j represents the total number of prey, and a represents the nutritional interaction between predator i and prey j. ijRepresented by 1 or 0, 1 if predator i preys j, otherwise 0;

[0025] Construct a predator-prey feature matrix P with dimension i×k:

[0026] P=(p ik ) i×k ;

[0027] Where k represents a species trait, and for each species' trait, a separate predator-prey trait matrix P is constructed, representing the relationship between predator i and prey trait k. ik Represented by the relative frequency of predator i preying on prey feature k;

[0028] Construct a species feature-species matrix T with dimensions k×j:

[0029] T=(t kj ) k×j ;

[0030] For each species' trait, a separate species trait-species matrix T is constructed, along with the attribution relationship t between species trait k and species j. kj Use 1 or 0 to represent species j; if species j possesses species trait k, then it is 1, otherwise it is 0.

[0031] To visualize predator preferences for species characteristics for each species, matrix A′ is established:

[0032] M = P × T = (m' ij ) i×j ;

[0033]

[0034] In the formula, m' ij The elements of matrix M resulting from the multiplication of matrix P and matrix T are transition elements, a'. ij For a predator-prey relationship, θ represents the minimum threshold at which a predator-prey relationship occurs.

[0035] Preferably, based on the "large food web" model, the topological indices of the food web structure are calculated, including:

[0036] Calculate the number of basic species in the food web of the region to be evaluated;

[0037] Calculate the number of apex predators in the food web of the region to be evaluated;

[0038] Calculate the number of intermediate species in the food web of the region to be evaluated;

[0039] Calculate the number of links in the food web of the region to be evaluated;

[0040] Calculate the maximum trophic level in the food web of the region to be evaluated.

[0041] Preferably, the CRITIC weighting method is used to determine the weights of each food web structure and topology index. Based on the food web structure and topology indexes and their weights, the food web integrity of the region to be evaluated is calculated, including:

[0042]

[0043] Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, FW i Let food web integrity be the food web integrity index of region i, and food web integrity(j) be the food web integrity index of the benchmark region j corresponding to region i.

[0044] This invention provides a system for calculating food web integrity, the system being used to implement the aforementioned method, the system comprising: a model building module, an index calculation module, and an integrity assessment module;

[0045] The model building module is used to collect multi-source species data of the region to be evaluated and to build a "large food web" model based on the multi-source species data;

[0046] The index calculation module is used to calculate the topological index of the food web structure based on the "large food web" model.

[0047] The integrity assessment module is used to determine the weights of each food web structure and topology index using the CRITIC weighting method, and to calculate the food web integrity of the region to be assessed based on the food web structure and topology indexes and their weights.

[0048] Preferably, the model building module includes: a data collection unit, a matrix building unit, a food source ratio unit, a species trophic level unit, and a model building unit;

[0049] The data acquisition unit is used to collect multi-source data on species in the area to be evaluated.

[0050] Matrix building unit, used to construct a predator-prey relationship matrix between species based on multi-source data of species;

[0051] The food source ratio unit is used to calculate the food source ratio between species based on the interspecies predator-prey relationship matrix.

[0052]

[0053] Among them, Fij This represents the ratio of food source to prey species j that predator i preys. This represents the average probability that predator i will prey on node j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k;

[0054] Trophic level units are used to calculate trophic levels based on food source ratios among species.

[0055] Tr i =1+∑Tr j ×F ij ;

[0056] Among them, Tr i Tr represents the trophic level of predator i. j The trophic level of prey j;

[0057] The model building unit is used to construct a "large food web" model based on the trophic level of the species, the food source ratio, and the predation relationships among food web species in the area to be evaluated.

[0058] Preferably, the matrix building unit includes:

[0059] Construct a predator-prey matrix A with dimension i×j:

[0060] A = (a ij ) i×j ;

[0061] Where i represents the total number of predators, j represents the total number of prey, and a represents the nutritional interaction between predator i and prey j. ij Represented by 1 or 0, 1 if predator i preys j, otherwise 0;

[0062] Construct a predator-prey feature matrix P with dimension i×k:

[0063] P=(p ik ) i×k ;

[0064] Where k represents a species trait, and for each species' trait, a separate predator-prey trait matrix P is constructed, representing the relationship between predator i and prey trait k. ik Represented by the relative frequency of predator i preying on prey feature k;

[0065] Construct a species feature-species matrix T with dimensions k×j:

[0066] T=(t kj ) k×j ;

[0067] For each species' trait, a separate species trait-species matrix T is constructed, along with the attribution relationship t between species trait k and species j. kj Use 1 or 0 to represent species j; if species j possesses species trait k, then it is 1, otherwise it is 0.

[0068] To visualize predator preferences for species characteristics for each species, matrix A′ is established:

[0069] M = P × T = (m' ij ) i×j ;

[0070]

[0071] In the formula, m' ij The elements of matrix M resulting from the multiplication of matrix P and matrix T are transition elements, a'. ij For a predator-prey relationship, θ represents the minimum threshold at which a predator-prey relationship occurs.

[0072] Preferably, the index calculation module includes: basic species unit, top predator unit, intermediate species unit, linking unit, and maximum trophic level unit;

[0073] Basic species unit, used to calculate the number of basic species in the food web of the region to be evaluated;

[0074] Top predator unit, used to calculate the number of top predators in the food web of the area to be evaluated;

[0075] Intermediate species unit, used to calculate the number of intermediate species in the food web of the region to be evaluated;

[0076] Link unit, used to calculate the number of links in the food web of the region to be evaluated;

[0077] The maximum trophic level unit is used to calculate the maximum trophic level in the food web of the region to be evaluated.

[0078] Preferably, the integrity assessment module includes:

[0079]

[0080] Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, FW i Let food web integrity be the food web integrity index of region i, and food web integrity(j) be the food web integrity index of the benchmark region j corresponding to region i.

[0081] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0082] This invention provides a method and system for calculating food web integrity. Based on existing species data of different types, it constructs relatively complete food webs and food webs whose integrity needs to be calculated using a matrix model. By calculating five food web topological indicators characterizing species composition and structural function, and combining this with the CRITIC weighting method to determine the weights of each indicator, the food web integrity is finally calculated. Furthermore, based on the above results, the food web to be calculated is compared with a relatively stable benchmark food web, which can assess the ability of a food web within a specific ecosystem or ecogeographical region to maintain its original or near-original state. Attached Figure Description

[0083] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. 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.

[0084] Figure 1 This is a schematic flowchart of a method for calculating food web integrity according to an embodiment of the present invention;

[0085] Figure 2 This is a schematic diagram of a "large food web" according to an embodiment of the present invention. Detailed Implementation

[0086] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0088] Example 1

[0089] like Figure 1 As shown, the present invention provides a method for calculating food web integrity, comprising:

[0090] Collect multi-source data on species in the region to be evaluated, and construct a "large food web" model based on the multi-source data on species.

[0091] Based on the "large food web" model, calculate the topological indices of the food web structure;

[0092] The weights of each food web structure and topology index are determined using the CRITIC weighting method. Based on the food web structure and topology indexes and their weights, the food web integrity of the region to be evaluated is calculated.

[0093] Furthermore, the specific implementation process of this invention is as follows:

[0094] Specifically, multi-source data on species in the area to be evaluated are collected, and a "large food web" model is constructed based on this multi-source data, including:

[0095] Based on existing multi-source data on species (including predation relationships between species and phenotypic data of each species), a directed, weighted, and relatively complete "large food web" model is constructed. The specific steps are as follows:

[0096] Step 1: Collect multi-source data on species in the area to be evaluated: Collect multi-source data on species in the area to be evaluated, which is required for constructing the food web, based on multi-source data fusion analysis methods such as surveys, experiments, literature, and books. This includes data on predation relationships between species, species traits, and characteristics.

[0097] Step 2: Based on multi-source data of species, construct a predator-prey relationship matrix among species and predict predator-prey relationships among species.

[0098] To predict predator-prey relationships between species, a matrix-matrix method is used. To create the matrix, this invention converts all feature variables into binary format and constructs the matrix accordingly. The specific process is as follows:

[0099] The first step is to construct the predator-prey matrix A, which has dimensions i×j, as shown in the following formula:

[0100] A = (a ij ) i×j ;

[0101] Where i represents the total number of predators, j represents the total number of prey, and a represents the nutritional interaction between predator i and prey j. ij The matrix A represents the predator-prey relationship, represented by 1 or 0. If predator i preys j, the matrix is ​​1; otherwise, it is 0.

[0102] The second step is to construct the predator-prey feature matrix P, which has dimensions i×k, as shown in the following formula:

[0103] P=(p ik ) i×k ;

[0104] Where k represents a species trait, and for each species' trait, a separate predator-prey trait matrix P is constructed, representing the relationship between predator i and prey trait k.ik The matrix P represents the predator's predation preference, represented by the relative frequency of predator i preying on prey feature k.

[0105] The third step is to construct a species feature-species matrix T with dimensions k×j, as shown in the following formula:

[0106] T=(t kj ) k×j ;

[0107] For each species' trait, a separate species trait-species matrix T is constructed, along with the attribution relationship t between species trait k and species j. kj The value is represented by 1 or 0. If species j possesses species characteristic k, the value is 1; otherwise, it is 0. Matrix T represents the characteristics of the species.

[0108] The fourth step is to visualize the predator's predation preferences for species characteristics for each species, and then establish matrix A′, as shown in the following formula:

[0109] M = P × T = (m' ij ) i×j ;

[0110]

[0111] In the formula, m' ij Let m' be an element of matrix M, the product of matrix P and matrix T. This element is a transition element used to compare with matrix A to verify the existence of a predator-prey relationship. θ represents the minimum threshold for the occurrence of a predator-prey relationship, derived from the comparison of matrices M and A. ij With a ij The minimum value of the overlap is the value of θ, where a' ij Let A be the predator-prey relationships that may exist under this feature; for each species feature, a matrix A′ can be obtained. Predator-prey relationships that will occur under each species feature are selected, and obviously unreasonable predator-prey relationships are removed, thereby constructing a food web.

[0112] Step 3: Calculate the food source ratio between species. The food source ratio is calculated based on the probability of predation relationships occurring in the matrix above. First, calculate the average probability of predation relationships occurring under the predicted species traits, using the following formula:

[0113]

[0114] In the formula, f represents the average probability that predator i will prey on node j. ij represents the probability of a predator-prey relationship occurring under a single feature prediction, where n is the number of selected species features.

[0115] Calculate the relative frequency of predator-prey relationships among all predators of predator i, and use this as the food source ratio, as shown in the following formula:

[0116]

[0117] Among them, F ij This represents the ratio of food source to prey species j that predator i preys. Let be the average probability of a predator-prey relationship occurring between predator i and prey k.

[0118] Step 4: Calculate the trophic level of the species. The trophic level is calculated based on the food source ratio between species, using the following formula:

[0119] Tr i =1+∑Tr j ×F ij ;

[0120] Among them, Tr i Tr represents the trophic level of predator i. j For the trophic level of prey j, this invention sets the primary producer trophic level to 1.

[0121] Step 5: Food web model construction. A quantitative and weighted "large food web" model is constructed by combining the predation relationships among species in the food web, the trophic level characteristics of each node, and the food source ratio.

[0122] Specifically, based on the "large food web" model, the topological indices of the food web structure are calculated, including:

[0123] The steps for calculating food web structural topology indices characterizing species composition integrity and structural-functional integrity based on the constructed "large food web" are as follows:

[0124] Step 1: Calculate the number of basic species (B) in the food web of the area to be evaluated, using the following formula:

[0125] B = The number of species in the food web that are at the lowest trophic level and do not prey on other species.

[0126] Step 2: Calculate the number (T) of apex predators in the food web of the area to be evaluated, using the following formula:

[0127] T = The number of species in the food web that are at the highest trophic level and are not preyed upon by other species.

[0128] Step 3: Calculate the number (I) of intermediate species in the food web of the area to be evaluated, using the following formula:

[0129] I = SBT

[0130] In the formula, S represents the total number of species in the food web, B represents the number of basic species in the food web, and T represents the number of top-level species in the food web.

[0131] Step 4: Calculate the number of links (L) in the food web of the area to be evaluated. The number of links refers to the number of predator-prey relationships between different species in the food web. It can be regarded as the number of directed edges, and the formula is as follows:

[0132] The number of times each species is preyed upon;

[0133] or

[0134] The number of times each species preys on other species.

[0135] Step 5: Calculate the maximum trophic level (TLmax) in the food web of the area to be evaluated, using the following formula:

[0136] TLmax = trophic level of the top predator in a food web;

[0137] In the formula, TLmax is the trophic level value of the species with the highest trophic level in the food web.

[0138] Specifically, the CRITIC weighting method is used to determine the weights of each food web structure and topology index. Based on the food web structure and topology indices and their weights, the food web integrity of the region to be evaluated is calculated, including:

[0139] Step 1: Use the CRITIC weighting method to calculate the weight of each parameter in the food web integrity evaluation index.

[0140] Step 2: Based on the index and weight results obtained from the 5 topological indicators and the CRITIC weight calculation method, and combined with the food web integrity index proposed for the first time in this invention, calculate the food web integrity:

[0141]

[0142] Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, Food web integrity(i) is the food web integrity index of region i, and Food web integrity(j) is the food web integrity index of the baseline region j corresponding to region i. i For the food web integrity of region i.

[0143] In summary, this invention provides a method for calculating food web integrity. Based on existing species data of different types, it constructs relatively complete food webs and food webs whose integrity needs to be calculated using a matrix model. By calculating five food web topological indicators characterizing species composition and structural function, and combining this with the CRITIC weighting method to determine the weights of each indicator, the food web integrity is finally calculated. Furthermore, based on the above results, comparing the food web to be calculated with a relatively stable benchmark food web allows for the assessment of the ability of a food web within a specific ecosystem or ecogeographical region to maintain its original or near-original state.

[0144] Example 2

[0145] The present invention also provides a system for calculating food web integrity, for implementing the method of the foregoing embodiments, the system comprising: a model building module, an index calculation module, and an integrity assessment module;

[0146] The model building module is used to collect multi-source species data of the region to be evaluated and to build a "large food web" model based on the multi-source species data;

[0147] The index calculation module is used to calculate the topological index of the food web structure based on the "large food web" model.

[0148] The integrity assessment module is used to determine the weights of each food web structure and topology index using the CRITIC weighting method, and to calculate the food web integrity of the region to be assessed based on the food web structure and topology indexes and their weights.

[0149] Furthermore, the model building module includes: a data collection unit, a matrix building unit, a food source ratio unit, a species trophic level unit, and a model building unit;

[0150] The data acquisition unit is used to collect multi-source data on species in the area to be evaluated.

[0151] Matrix building unit, used to construct a predator-prey relationship matrix between species based on multi-source data of species;

[0152] The food source ratio unit is used to calculate the food source ratio between species based on the interspecies predation relationship matrix.

[0153]

[0154] Among them, F ij This represents the ratio of food source to prey species j that predator i preys. This represents the average probability that predator i will prey on node j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k;

[0155] Trophic level units are used to calculate trophic levels based on inter-species food source ratios.

[0156] Tr i =1+∑Tr j ×F ij ;

[0157] Among them, Tr i Tr represents the trophic level of predator i. j The trophic level of prey j;

[0158] The model building unit is used to construct a "large food web" model based on species trophic level, food source ratio, and predation relationships among food web species in the area to be evaluated.

[0159] Furthermore, the matrix building units include:

[0160] Construct a predator-prey matrix A with dimension i×j:

[0161] A = (a ij ) i×j ;

[0162] Where i represents the total number of predators, j represents the total number of prey, and a represents the nutritional interaction between predator i and prey j. ij The matrix A represents the predator-prey relationship, represented by 1 or 0. If predator i preys j, the matrix is ​​1; otherwise, it is 0.

[0163] Construct a predator-prey feature matrix P with dimension i×k:

[0164] P=(p ik ) i×k ;

[0165] Where k represents a species trait, and for each species' trait, a separate predator-prey trait matrix P is constructed, representing the relationship between predator i and prey trait k. ik The matrix P represents the predator's predation preference, represented by the relative frequency of predator i preying on prey feature k.

[0166] Construct a species feature-species matrix T with dimensions k×j:

[0167] T=(t kj ) k×j ;

[0168] For each species' trait, a separate species trait-species matrix T is constructed, along with the attribution relationship t between species trait k and species j. kj The value is represented by 1 or 0. If species j possesses species characteristic k, the value is 1; otherwise, it is 0. Matrix T represents the characteristics of the species.

[0169] To visualize predator preferences for species characteristics for each species, matrix A′ is established:

[0170] M = P × T = (m' ij ) i×j ;

[0171]

[0172] In the formula, m' ij Let m' be an element of matrix M, the product of matrix P and matrix T. This element is a transition element used to compare with matrix A to verify the existence of a predator-prey relationship. θ represents the minimum threshold for the occurrence of a predator-prey relationship, derived from the comparison of matrices M and A. ij With a ij The minimum value of the overlap is the value of θ, where a' ij Let A be the predator-prey relationships that may exist under this feature; for each species feature, a matrix A′ can be obtained. Predator-prey relationships that will occur under each species feature are selected, and obviously unreasonable predator-prey relationships are removed, thereby constructing a food web.

[0173] Furthermore, the indicator calculation module includes: basic species unit, top predator unit, intermediate species unit, linking unit, and maximum trophic level unit;

[0174] Basic species unit, used to calculate the number of basic species in the food web of the region to be evaluated;

[0175] Top predator unit, used to calculate the number of top predators in the food web of the area to be evaluated;

[0176] Intermediate species unit, used to calculate the number of intermediate species in the food web of the region to be evaluated;

[0177] Link unit, used to calculate the number of links in the food web of the region to be evaluated;

[0178] The maximum trophic level unit is used to calculate the maximum trophic level in the food web of the region to be evaluated.

[0179] Furthermore, the integrity assessment module includes:

[0180]

[0181] Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, FW iLet food web integrity be the food web integrity index of region i, and food web integrity(j) be the food web integrity index of the benchmark region j corresponding to region i.

[0182] Example 3

[0183] This embodiment illustrates the implementation process of the aforementioned embodiments using specific data.

[0184] This embodiment first constructs a large food web for wildlife in the Southwest Mountainous Subregion of China's biogeographical regionalization using multi-source data fusion analysis, calculates its food web integrity, and uses this integrity as a benchmark. Second, it constructs a large food web for wildlife in the Gongga Mountain National Nature Reserve, a representative protected area within the Southwest Mountainous Subregion, and calculates its food web integrity. Finally, it compares the food web integrity of the Gongga Mountain National Nature Reserve with that of the Southwest Mountainous Subregion to systematically assess the food web integrity of the Gongga Mountain National Nature Reserve. It is understood that the food web integrity evaluation and optimization method of this invention can also be applied to other ecosystems.

[0185] Step 1: Collect data needed to construct a food web through multiple sources such as surveys, experiments, literature, and books. This includes data on predation relationships between species, species traits, and characteristics, laying the foundation for constructing a "large food web" for wild animals. The collected species information is shown in Table 1.

[0186] Table 1. Basic Information on Wildlife in the Southwest Mountainous Subregion (Partial Species)

[0187]

[0188]

[0189] Step 2: Construct an inter-species predation relationship matrix and predict inter-species predation relationships.

[0190] To predict predator-prey relationships between species, a matrix-matrix method is used. To create the matrix, this invention converts all feature variables to binary format and constructs the matrix accordingly. The specific process is as follows:

[0191] The first step is to establish the predator-prey matrix A.

[0192] The second step is to establish the predator-prey feature matrix P.

[0193] The third step is to establish the species characteristic-species matrix T.

[0194] The fourth step involves visualizing the predator's predation preferences for species characteristics onto each species, establishing a matrix A′. A matrix A′ can be obtained for each species characteristic. Predation relationships that occur under each species characteristic are then identified, and obviously unreasonable predation relationships are removed. Based on this, a food web is constructed.

[0195] Step 3: Accurately calculate the food source ratio between species. The food source ratio is calculated based on the probability of predator-prey relationships occurring in the matrix above. First, calculate the average probability of predator-prey relationships occurring under the predicted species traits.

[0196] Calculate the relative frequency of predator-prey relationships among all predators of predator i, and use this to calculate the food source ratio F among species in the Southwest Mountain Subregion and the Gongga Mountain Nature Reserve within that region. ij ( Figure 2 ).

[0197] Step 4: Accurately calculate the trophic level of the species, based on the food source ratio between species.

[0198] This invention sets the trophic level of primary producers to 1, and obtains the trophic levels of various species in the Southwest Mountain Subregion and the Gongga Mountain Nature Reserve within the region (Table 2). The overall trophic level range of consumers in the Southwest Mountain Subregion is 2.00-4.00, while the overall trophic level range of consumers in the Gongga Mountain Nature Reserve is 2.00-3.32, with the red fox having the highest trophic level (3.32).

[0199] Table 2. Trophic levels of species in the Southwest Mountainous Subregion and the Gongga Mountain Nature Reserve within the region (partial list of species)

[0200] Species Nutritional level area red fox 3.28 Southwest Mountainous Subregion civet cat 3.27 Southwest Mountainous Subregion Yellow weasel 3.25 Southwest Mountainous Subregion Leopard 3.25 Southwest Mountainous Subregion Leopard cat 3.25 Southwest Mountainous Subregion ferret badger 3.25 Southwest Mountainous Subregion Tibetan fox 3.22 Southwest Mountainous Subregion Little Civet 3.20 Southwest Mountainous Subregion snow leopard 3.20 Southwest Mountainous Subregion Wolf 3.20 Southwest Mountainous Subregion Yellow-throated marten 3.14 Southwest Mountainous Subregion brown bear 3.13 Southwest Mountainous Subregion Black bear 3.12 Southwest Mountainous Subregion red fox 3.32 Gongga Mountain Nature Reserve Leopard cat 3.31 Gongga Mountain Nature Reserve Yellow weasel 3.30 Gongga Mountain Nature Reserve civet cat 3.28 Gongga Mountain Nature Reserve Leopard 3.28 Gongga Mountain Nature Reserve ferret badger 3.28 Gongga Mountain Nature Reserve Tibetan fox 3.26 Gongga Mountain Nature Reserve Wolf 3.22 Gongga Mountain Nature Reserve Little Civet 3.20 Gongga Mountain Nature Reserve snow leopard 3.20 Gongga Mountain Nature Reserve Yellow-throated marten 3.16 Gongga Mountain Nature Reserve brown bear 3.16 Gongga Mountain Nature Reserve Black bear 3.15 Gongga Mountain Nature Reserve

[0201] Step 5: Food web model construction. Combining predation relationships among species, trophic level characteristics of each node, and food source ratios, a "large food web" for wildlife in the Southwest Mountainous Subregion and Gongga Mountain Nature Reserve is constructed. Figure 2 As shown, the food web of the Southwest Mountainous Subregion (left) and the Gongga Mountain Nature Reserve (right) within the region.

[0202] Step Six: Calculate various integrity indices of the constructed food web, as follows:

[0203] Step 1: Calculate the number B of basic species in the food web.

[0204] Step 2: Calculate the number T of apex predators in the food web.

[0205] Step 3: Calculate the number I of intermediate species in the food web.

[0206] Step 4: Calculate the number of links L in the food web.

[0207] Step 5: Calculate the maximum trophic level (TLmax) in the food web.

[0208] Step 7: Calculate food web integrity. First, the CRITIC weighting method is used to calculate the weights of each food web integrity index in the food web integrity assessment. The calculated weights are: number of basal species (0.120525), number of intermediate species (0.125445), number of apex predators (0.351972), number of links (0.111716), and maximum trophic level (0.290342). Combining this with the food web integrity index proposed for the first time in this invention, food web integrity is calculated using the following formula:

[0209]

[0210] Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, FW i Let food web integrity be the food web integrity index of region i, and food web integrity(j) be the food web integrity index of the benchmark region j corresponding to region i.

[0211] The calculated food web integrity indices for the Southwest Mountainous Subregion and the Gongga Mountain Nature Reserve are shown in Table 3. The food web integrity index for the Gongga Mountain Nature Reserve is 55.45, which is significantly different from the food web integrity index for the Southwest Mountainous Subregion (326.65). Furthermore, based on the Southwest Mountainous Subregion as the benchmark, the food web integrity of the Gongga Mountain Nature Reserve is 16.98%, indicating that the food web integrity of the Gongga Mountain Nature Reserve is relatively low.

[0212] Table 3. Topological indices and food web integrity indices of the Southwest Mountainous Subregion and Gongga Mountain Nature Reserve

[0213]

[0214] The calculated results of the two food web integrity indices are shown in Table 3. It can be seen that the current food web integrity of wildlife in the Gongga Mountain Nature Reserve is significantly lower than that of the food web in the southwestern mountains. Although the Gongga Mountain food web contains relatively stable apex predators and long food chains, which are generally considered indicators of a highly complex and mature food web, the significant lack of basic and intermediate species results in a low overall food web connectivity, thus restricting the integrity and functional stability of the food web. This structural imbalance may make the ecosystem lack sufficient resilience and buffering capacity in the face of environmental disturbances, increasing the risk of system collapse. Furthermore, the survey results on species composition reveal a significant absence of Leporidae species among small mammals in the Gongga Mountain area, with only a few records of the Grey-tailed Rabbit. This may be attributed to the fact that the local small mammal survey methods (such as trapping) typically target smaller animals with limited ranges, such as shrews and pikas, resulting in a lower capture rate for larger, more mobile Leporidae species. Underestimation of basic species diversity will significantly weaken the basic structure of the food web, reducing its overall stability and integrity.

[0215] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for calculating food web integrity, characterized in that, The method includes: Collect multi-source data on species in the region to be evaluated, and construct a "large food web" model based on the multi-source data on species. Based on the "large food web" model, calculate the topological indices of the food web structure; The weights of each food web structure and topology index are determined using the CRITIC weighting method. Based on the food web structure and topology indexes and their weights, the food web integrity of the region to be evaluated is calculated.

2. The method according to claim 1, characterized in that, Collect multi-source species data from the region to be evaluated, and construct a "large food web" model based on the multi-source species data, including: Collect multi-source data on species in the area to be evaluated; Construct a predator-prey relationship matrix among species based on multi-source data of species; Based on the predator-prey relationship matrix, calculate the food source ratio among species: Among them, F ij This represents the ratio of food source to prey species j that predator i preys. This represents the average probability that predator i will prey on node j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k; Calculate the trophic level of a species based on the food source ratio among species: Tr i =1+∑Tr j ×F ij ; Among them, Tr i Tr represents the trophic level of predator i. j The trophic level of prey j; Based on the trophic level and food source ratio of species, and combined with the predation relationships among food web species in the area to be evaluated, a "large food web" model is constructed.

3. The method according to claim 2, characterized in that, Based on multi-source data from various species, a predator-prey relationship matrix was constructed, including: Construct a predator-prey matrix A with dimension i×j: A=(a ij ) i×j ; Where i represents the total number of predators, j represents the total number of prey, and a represents the nutritional interaction between predator i and prey j. ij Represented by 1 or 0, 1 if predator i preys j, otherwise 0; Construct a predator-prey feature matrix P with dimension i×k: P=(p ik ) i×k ; Where k represents a species trait, and for each species' trait, a separate predator-prey trait matrix P is constructed, representing the relationship between predator i and prey trait k. ik Represented by the relative frequency of predator i preying on prey feature k; Construct a species feature-species matrix T with dimensions k×j: T=(t kj ) k×j ; For each species' trait, a separate species trait-species matrix T is constructed, along with the attribution relationship t between species trait k and species j. kj Use 1 or 0 to represent species j; if species j possesses species trait k, then it is 1, otherwise it is 0. To visualize predator preferences for species characteristics for each species, matrix A′ is established: M=P×T=(m' ij ) i×j ; In the formula, m' ij The elements of matrix M resulting from the multiplication of matrix P and matrix T are transition elements, a'. ij For a predator-prey relationship, θ represents the minimum threshold at which a predator-prey relationship occurs.

4. The method according to claim 1, characterized in that, Based on the "large food web" model, the topological indices of the food web structure are calculated, including: Calculate the number of basic species in the food web of the region to be evaluated; Calculate the number of apex predators in the food web of the region to be evaluated; Calculate the number of intermediate species in the food web of the region to be evaluated; Calculate the number of links in the food web of the region to be evaluated; Calculate the maximum trophic level in the food web of the region to be evaluated.

5. The method according to claim 1, characterized in that, The CRITIC weighting method is used to determine the weights of each food web structure and topology index. Based on the food web structure and topology indexes and their weights, the food web integrity of the region to be evaluated is calculated, including: Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, FW i Let food web integrity be the food web integrity index of region i, and food web integrity(i) be the food web integrity index of region i, and food web integrity(j) be the food web integrity index of the benchmark region j corresponding to region i.

6. A system for calculating food web integrity, said system for implementing the method of any one of claims 1-5, characterized in that, The system includes: a model building module, an indicator calculation module, and a completeness assessment module; The model building module is used to collect multi-source species data of the region to be evaluated and build a "large food web" model based on the multi-source species data; The index calculation module is used to calculate the topological index of the food web structure based on the "large food web" model. The integrity assessment module is used to determine the weights of each food web structure and topology index using the CRITIC weighting method, and to calculate the food web integrity of the region to be assessed based on the food web structure and topology indexes and their weights.

7. The system according to claim 6, characterized in that, The model building module includes: data collection unit, matrix building unit, food source ratio unit, species trophic level unit, and model building unit; The data acquisition unit is used to collect multi-source data on species in the area to be evaluated. Matrix building unit, used to construct a predator-prey relationship matrix between species based on multi-source data of species; The food source ratio unit is used to calculate the food source ratio between species based on the interspecies predator-prey relationship matrix. Among them, F ij This represents the ratio of food source to prey species j that predator i preys. This represents the average probability that predator i will prey on node j. Let be the average probability of a predator-prey relationship occurring between predator i and prey k; Trophic level units are used to calculate trophic levels based on food source ratios among species. Tr i =1+∑Tr j ×F ij ; Among them, Tr i Tr represents the trophic level of predator i. j The trophic level of prey j; The model building unit is used to construct a "large food web" model based on the trophic level of a species, the food source ratio, and the predation relationships among food web species in the area to be evaluated.

8. The system according to claim 7, characterized in that, Matrix building units include: Construct a predator-prey matrix A with dimension i×j: A=(a ij ) i×j ; Where i represents the total number of predators, j represents the total number of prey, and a represents the nutritional interaction between predator i and prey j. ij Represented by 1 or 0, 1 if predator i preys j, otherwise 0; Construct a predator-prey feature matrix P with dimension i×k: P=(p ik ) i×k ; Where k represents a species trait, and for each species' trait, a separate predator-prey trait matrix P is constructed, representing the relationship between predator i and prey trait k. ik Represented by the relative frequency of predator i preying on prey feature k; Construct a species feature-species matrix T with dimensions k×j: T=(t kj ) k×j ; For each species' trait, a separate species trait-species matrix T is constructed, along with the attribution relationship t between species trait k and species j. kj Use 1 or 0 to represent species j; if species j possesses species trait k, then it is 1, otherwise it is 0. To visualize predator preferences for species characteristics for each species, matrix A′ is established: M=P×T=(m' ij ) i×j ; In the formula, m' ij The elements of matrix M resulting from the multiplication of matrix P and matrix T are transition elements, a'. ij For a predator-prey relationship, θ represents the minimum threshold at which a predator-prey relationship occurs.

9. The system according to claim 6, characterized in that, The indicator calculation module includes: basic species unit, top predator unit, intermediate species unit, linking unit, and maximum trophic level unit; Basic species unit, used to calculate the number of basic species in the food web of the region to be evaluated; Top predator unit, used to calculate the number of top predators in the food web of the area to be evaluated; Intermediate species unit, used to calculate the number of intermediate species in the food web of the region to be evaluated; Link unit, used to calculate the number of links in the food web of the region to be evaluated; The maximum trophic level unit is used to calculate the maximum trophic level in the food web of the region to be evaluated.

10. The system according to claim 6, characterized in that, The integrity assessment module includes: Among them, W i It is the weight of each food web structural topology index, I i These are the values ​​of various food web structure and topology indicators, where n is the number of food web structure and topology indicators, FW i Let food web integrity be the food web integrity index of region i, and food web integrity(j) be the food web integrity index of the benchmark region j corresponding to region i.

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