Method and system for identifying key species by combining species nutrition levels and attributes thereof in food web

Abstract

The invention belongs to the field of protection biology and the field of ecology, and discloses a method and system for identifying key species by combining species nutrition levels and attributes thereof in a food web, and the method comprises the steps: constructing a directed and weighted large food web model, namely a food web, based on the species multi-source data of a region to be identified; based on the constructed food web, calculating a PageRank centrality index of each species in the food web; based on the constructed food web, calculating an E-shell centrality index improved by combining the PageRank and the species nutrition level; and based on the calculated PageRank centrality index, in combination with the improved E-shell centrality index, calculating the HCC-PRTL centrality index of each node so as to identify the key species. The objective of the invention is to identify key species more scientifically and more accurately and provide a scientific basis for protection decision making.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of conservation biology and ecology, and particularly relates to a method for identifying key species by combining species trophic level, hybrid characteristic centrality and PageRank centrality, and particularly relates to a method and system for identifying key species by combining species trophic level and its properties in food web. BACKGROUND

[0002] Changes in key species often lead to disproportionate large community effects. Therefore, accurate identification of key species is crucial for biodiversity conservation and effective ecosystem management. Since the concept of "key species" was proposed, its importance has been concerned. Key species refers to species with significant ecological impact, and their impact on the ecosystem is disproportionate to their abundance and biomass.

[0003] Currently, the mainstream method for identifying key species relies on complex network centrality theory, which calculates a series of centrality indicators to locate the position of species in complex ecological networks, and then identifies key species. Commonly used indicators include degree centrality, closeness centrality, PageRank centrality, etc. Degree centrality reveals the advantage of the number of connections of a species, and believes that important nodes have more neighbors; betweenness centrality highlights the ability of a species to act as a hub for information transmission in the network; PageRank centrality focuses on the global influence of a node, reflecting the complex network relationship of influence transmission through neighbor nodes.

[0004] However, the above indicators are based on local features of the network (such as degree centrality), or rely on global features of the network (such as PageRank centrality). Although in recent years scholars have proposed hybrid indicators that consider both local and global features of the network, such as the Hybrid Characteristic Centrality (HCC centrality), which considers both the Extended Degree and the hierarchical position of nodes based on E-shell decomposition: the former further incorporates the connection information of adjacent nodes on the basis of traditional degree centrality to more accurately quantify the local influence of nodes; the latter decomposes network nodes by transmission ability and structural depth through K-shell or E-shell decomposition method based on extended K-shell, effectively overcoming the problems of insufficient resolution and difficulty in distinguishing the influence of different hierarchical nodes of traditional hierarchical methods (Kitsaket al., 2010; He et al., 2020), but it often abstracts species as non-differentiated network nodes, ignoring the inherent key biological attributes of species that determine their ecological functions, such as trophic level.

[0005] In summary, the existing key species identification technology has the technical bottleneck of failing to integrate the key biological attributes of species, and there is an urgent need for a new technical method that can comprehensively consider the multidimensional characteristics of species in the food web to achieve more accurate and more ecologically principled identification of key species. SUMMARY

[0006] To solve the problems existing in the prior art, the present application provides a method and system for identifying key species by combining the trophic level of species and its attributes in the food web, which uses a multi-source data fusion analysis method to construct a directed and weighted "large food web". For the first time, it integrates three dimensions of trophic level (biological attribute), PageRank centrality (network influence), and E-shell hierarchical decomposition (global position), proposes the HCC-PRTL centrality index, and accurately quantifies the comprehensive influence of species in the food web. The purpose is to more scientifically and accurately identify key species and provide scientific basis for protection decision-making.

[0007] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0008] A method for identifying key species by combining the trophic level of species and its attributes in the food web, the method comprising:

[0009] Based on the multi-source data of species in the region to be identified, a directed and weighted "large food web" model, i.e. a food web, is constructed;

[0010] Based on the constructed food web, the PageRank centrality index of each species in the food web is calculated;

[0011] Based on the constructed food web, the improved E-shell centrality index combining PageRank and trophic level of species is calculated;

[0012] Based on the calculated PageRank centrality index and the improved E-shell centrality index, the HCC-PRTL centrality index of each node is calculated to identify key species.

[0013] Preferably, the method for constructing a directed and weighted "large food web" model based on multi-source data of species in the region to be identified comprises:

[0014] Step 1: Collect the data required for constructing the food web, wherein the required data includes the predation relationship between species, species traits, and characteristic data;

[0015] Step 2: Based on the required data, construct a predation matrix and perform predation relationship prediction;

[0016] Step 3: Based on the probability of predation relationship occurrence in the predation matrix, calculate the food source ratio between species;

[0017] Step four: Calculate the trophic level based on the food source ratio between species;

[0018] Step five: Construct the quantitative and weighted food web by combining the predation relationship between species, the characteristics of trophic level of each node, and the food source ratio.

[0019] Preferably, in step two, the method for constructing the predation matrix and predicting the predation relationship based on the required data comprises:

[0020] Establish a predator-prey matrix A with dimensions i x j, as follows:

[0021] A = (a ij ) i×j ;

[0022] Where i is the total number of predators, j is the total number of prey, and the nutritional interaction a ij between predator i and prey j is represented by 1 or 0. Matrix A represents the recorded predation relationship between predators and prey.

[0023] Establish a predator-prey characteristic matrix P with dimensions i x k, as follows:

[0024] P = (p ik ) i×k ;

[0025] Where k is the species trait characteristic. For each species trait, a separate predator-prey characteristic matrix P is established, and the relationship p ik between predator i and prey trait k is represented by the relative frequency of predator i preying on prey trait k. Matrix P represents the predation preference of predators.

[0026] Establish a species trait-species matrix T with dimensions k x j, as follows:

[0027] T = (t kj ) k×j ;

[0028] For each species trait, a separate species trait-species matrix T is established, and the affiliation relationship t kj between species trait k and species j is represented by 1 or 0. Matrix T represents the characteristics of species.

[0029] Establish matrix A', which visualizes the predation preference of predators for species traits to each species, as follows:

[0030] M = P x T = (m' ij ) i×j ;

[0031]

[0032] wherein m' ij is the element of matrix P multiplied by matrix T, is the excess element, used to compare with matrix A to verify the existence of predator-prey relationship, and θ represents the minimum threshold value of predator-prey relationship, which is derived from the comparison between matrix M and matrix A, m' ij is the minimum value of the coincidence, wherein a' ij is the number of θ, and a' ij is the possible predator-prey relationship under the characteristic; for each species characteristic, a matrix A' is obtained; the predator-prey relationship that occurs under each species characteristic is screened out to construct a food web.

[0033] Preferably, in step three, the method for calculating the food source ratio between species based on the probability of the occurrence of the predator-prey relationship in the predation matrix comprises:

[0034] The average value of the probability of the occurrence of the predator-prey relationship under the prediction of the species trait characteristics is calculated, and the formula is as follows:

[0035]

[0036] wherein represents the average probability of the occurrence of the predation of node j by predator i, f ij is the probability of the occurrence of the predator-prey relationship under a single characteristic prediction, and n is the number of selected species characteristics;

[0037] The relative frequency of the occurrence of the predator-prey relationship of all prey of predator i is calculated as the food source ratio, and the formula is as follows:

[0038]

[0039] wherein F ij represents the food source ratio of the predation of species j by predator i, is the average value of the probability of the occurrence of the predator-prey relationship of predator i and prey k.

[0040] Preferably, in step four, the method for calculating the trophic level based on the food source ratio between species comprises:

[0041] Tr i = 1 + ∑ Tr j × F ij ;

[0042] wherein Tr i represents the trophic level of predator i, Tr j is the trophic level of prey j, and F ij is the food source ratio of the predation of node j by predator i.

[0043] Preferably, the method for calculating the PageRank centrality of each species in the food web based on the constructed food web comprises the following steps:

[0044] dw(i,j) = min(l / w ih + … + 1 / w hj );

[0045]

[0046] wherein dw(i,j) refers to the shortest distance from the functional group i to the functional group j, w ih is the weight of the link between the node i and the node h on the shortest path from the node i to the node j in the network, w hj is the weight of the link between the node h and the node j on the shortest path from the node i to the node j in the network, PR(i) is the PageRank centrality of the functional group i, PR(j) is the PageRank centrality of the functional group j, j is a functional group connected to the functional group i, k j,out is the out-degree of the functional group j, and λ is a damping coefficient, which is an empirical value of 0.85, and n is the number of functional groups.

[0047] Preferably, the method for calculating the improved E-shell centrality index combined with the PageRank and the trophic level of each species based on the constructed food web comprises the following steps:

[0048] Step 1: input a food web G;

[0049] Step 2: initialize the iteration number p = 1 and G1 = G;

[0050] Step 3: find the minimum node set S

[0051] Step 4: find the minimum node set S p with the minimum trophic level pTL ;

[0052] Step 5: mark the position index of each node in the node set S pTL , and the position index index p = p;

[0053] Step 6: remove S p from S pTL to obtain a new S p+1 , and if S p+1 is not empty, update p = p + 1 until S p+1 is empty;

[0054] Step 7: if S p+1 is empty, obtain a new food web G p+1 , and update Gp+1 PageRank centrality value of the middle node;

[0055] Step 8: return to step 3 until G p+n is empty;

[0056] The node position index obtained in steps 1 to 8 is the improved E-shell centrality of the node.

[0057] Preferably, based on the calculated PageRank centrality index and the improved E-shell centrality index, the method for calculating the HCC-PRTL centrality index of each node to identify key species includes:

[0058]

[0059] In the formula, HCC-PRTL(u) is the HCC-PRTL centrality of node u, pos(u) is the position index of node u decomposed by the E-shell, PR(u) is the PageRank centrality of the node, PR(u) max and pos(u) max are the maximum PageRank centrality and the maximum position index of the node, respectively.

[0060] The application also provides a system for identifying key species in combination with species trophic levels and their properties in a food web, which is used to implement the aforementioned method, and comprises a construction module, a first calculation module, a second calculation module and an identification module.

[0061] The construction module is used to construct a directed and weighted “large food web” model, i.e., a food web, based on multi-source data of species in a region to be identified.

[0062] The first calculation module is used to calculate the PageRank centrality index of each species in the food web based on the constructed food web.

[0063] The second calculation module is used to calculate the improved E-shell centrality index in combination with the PageRank and the species trophic level based on the constructed food web.

[0064] The identification module is used to calculate the HCC-PRTL centrality index of each node based on the calculated PageRank centrality index in combination with the improved E-shell centrality index to identify key species.

[0065] Compared with the prior art, the application has the following beneficial effects:

[0066] The method for identifying key species in a food web by combining trophic level and its attributes within the food web provided by this invention can construct a food web model through a matrix model based on existing multi-source data on species and predation relationships between species. It integrates three dimensions: trophic level (biological attribute), PageRank centrality (network influence), and E-shell hierarchical decomposition (global position), and proposes the HCC-PRTL centrality index. This method analyzes the species from the dual perspectives of its own biological characteristics and its attributes in the network, more comprehensively reflecting the importance of species in the food web, and thus more scientifically and accurately identifying key species in the food web. Attached Figure Description

[0067] 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.

[0068] Figure 1 This is a flowchart illustrating a method for identifying key species by combining trophic levels and their attributes in a food web, according to an embodiment of the present invention.

[0069] Figure 2 This is a schematic diagram of the trophic levels of various species in the forest ecosystem of the Greater Khingan Mountains region, as described in an embodiment of the present invention.

[0070] Figure 3 This is a schematic diagram of the wildlife food web in the forest ecosystem of the Greater Khingan Mountains region, according to an embodiment of the present invention. Detailed Implementation

[0071] 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.

[0072] 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.

[0073] Example 1

[0074] like Figure 1 As shown, the present invention provides a method for identifying key species by combining trophic level and its attributes in a food web, comprising the following steps:

[0075] Based on the existing multi-source data of species (including the interspecific predation relationship, various species trait data), a directed and weighted "large food web" model is constructed.

[0076] Based on the constructed food web, the PageRank centrality index of each species in the food web is calculated.

[0077] Based on the constructed food web, the improved E-shell centrality index combined with PageRank and species trophic level is calculated.

[0078] Based on the calculated PageRank centrality index, combined with the improved E-shell centrality index, the HCC-PRTL centrality index of each node is calculated to identify key species.

[0079] The construction of the directed and weighted food web model uses a matrix model based on multi-source data of species, and the steps are as follows:

[0080] Step 1: Collect the data needed to construct the food web based on multi-source methods such as investigation, experiment, literature, and books, including the predation relationship between species, species traits, and characteristic data.

[0081] Step 2: Construct the predation matrix and make predation relationship prediction.

[0082] To predict the predation relationship between species, the matrix × matrix method is used. To create the matrix, all characteristic variables are converted to binary format. The specific process is as follows:

[0083] First, establish the predator-prey matrix A, which has a dimension of i × j, and the formula is as follows:

[0084] A = (a ij ) i×j ;

[0085] In the formula, i is the total number of predators, j is the total number of prey, and the nutritional interaction a ij between predator i and prey j is represented by 1 or 0 (1 if predator i preys on j, otherwise 0), and matrix A represents the recorded predation relationship between predators and prey.

[0086] Second, establish the predator-prey characteristic matrix P, which has a dimension of i × k, and the formula is as follows:

[0087] P = (p ik ) i×k ;

[0088] In the formula, k is the species trait, and for each species trait, a separate predator-prey characteristic matrix P is established, and the relationship pik The matrix P represents the predation preference of the predator on the prey characteristics k.

[0089] Step 3: Establish the species characteristic-species matrix T, which has a dimension of k x j, and is calculated as follows:

[0090] T = (t kj ) k×j ;

[0091] For each species trait characteristic, a separate species characteristic-species matrix T is established, and the relationship t kj between the species characteristic k and the species j is represented by 1 or 0 (1 if the species j possesses the species characteristic k, and 0 otherwise). The matrix T represents the characteristics of the species.

[0092] Step 4: To visualize the predation preference of the predator on the species characteristics to each species, the matrix A' is established, and is calculated as follows:

[0093] M = P x T = (m ij ) i×j ;

[0094]

[0095] In the formula, m ij is the element of the matrix M obtained by multiplying the matrix P with the matrix T, is the excess element, which is used for comparison with the matrix A to verify the existence of the predation relationship, and θ represents the minimum threshold value of the predator-prey predation relationship, which is derived from the comparison between the matrix M and the matrix A. The minimum value of m ij and a ij coincidence is the numerical value of θ, where a ij is the possible predation relationship under this characteristic; for each species characteristic, a matrix A' can be obtained. By screening the predation relationships that occur under each species characteristic, and removing the obviously unreasonable predation relationships, the food web is constructed.

[0096] Step 3: Accurately calculate the food source ratio between species based on the probability of the predation relationship occurring in the above matrix. First, calculate the average value of the probability of the predation relationship occurring under the prediction of the species trait characteristics, and the formula is as follows:

[0097]

[0098] In the formula, p represents the average probability of the predator i preying on the node j, f ij is the probability of the predation relationship occurring under a single characteristic prediction, and n is the number of selected species characteristics.

[0099] The relative frequency of all prey-predator relationships of predator i is calculated as the food source ratio, and the formula is as follows:

[0100]

[0101] In the formula, F ij represents the food source ratio of predator i preying on species j, is the average value of the occurrence probability of the prey-predator relationship between predator i and prey k.

[0102] Step four: Accurate calculation of the trophic level of species, based on the food source ratio between species to calculate the trophic level, the formula is as follows:

[0103] Tr i = 1 + ∑Tr j × F ij ;

[0104] In the formula, Tr i represents the trophic level of predator i, Tr j is the trophic level of prey j, and F ij is the food source ratio of predator i preying on node j. The trophic level of primary producers is set to 1.

[0105] Step five: Food web model construction, combining the prey-predator relationship between species in the food web, the trophic level characteristics of each node, and the food source ratio to construct a quantitative and weighted food web.

[0106] The PageRank centrality index of each species in the food web is calculated based on the constructed food web, and the steps are as follows:

[0107] PageRank centrality is proposed by one of the founders of Google, which is used to measure the importance of a web page in the Internet. This algorithm is mainly based on the hyperlink relationship between web pages, in other words, the importance of a web page depends not only on how many other web pages it links to, but also on the importance of the web pages that link to it. Therefore, based on the constructed food web, combined with the concept of shortest path, the PageRank centrality index of each species in the food web is calculated, and the formula is as follows:

[0108] dw(i,j) = min(1 / w ih + … + 1 / w hj );

[0109]

[0110] In the formula: dw(i,j) refers to the shortest distance from functional group i to functional group j, w ih is the weight of the link between node i and node h on the shortest path from node i to node j in the network, and similarly w hjis the weight of the link between node h and node j on the shortest path from node i to node j in the network. PR(i) is the PageRank centrality of functional group i, PR(j) is the PageRank centrality of functional group j, j is a functional group connected to functional group i, k j,out is the out-degree of functional group j, λ is the damping factor, which is an empirical value of 0.85, and n is the number of functional groups.

[0111] Based on the constructed food web, the improved E-shell centrality index combining PageRank and species trophic level is calculated, and the steps are as follows:

[0112] The improved E-shell centrality is based on network hierarchy decomposition, which determines the position of each node by decomposing the hierarchy of the network. In this method, in each iteration, the nodes with the minimum PageRank and trophic level are found and removed from the current network. Then, these nodes are labeled with a position index, which is represented by the iteration number here. The process of improved E-shell centrality hierarchy decomposition is as follows:

[0113] Step 1: input a food web G.

[0114] Step 2: initialize the iteration number p = 1 and G1 = G.

[0115] Step 3: find the minimum node set S

[0116] Step 4: find the minimum node set S p with the minimum trophic level pTL .

[0117] Step 5: label the position index for each node in the node set S pTL , and the position index index p = p.

[0118] Step 6: remove S p from S pTL to obtain a new S p+1 , if S p+1 is not empty, update p = p + 1, until S p+1 is empty.

[0119] Step 7: if S p+1 is empty, obtain a new food web G p+1 , and update the PageRank centrality value of the nodes in G p+1 .

[0120] Step 8: return to step 3 until G p+n is empty.

[0121] The node position index obtained in the above step is the improved E-shell centrality of the node.

[0122] The calculated HCC-PRTL centrality index value of each node point is combined with the E-shell centrality index, and the key species is identified, and the steps are as follows:

[0123] The HCC-PRTL centrality combines the species trophic level, the PageRank centrality and the E-shell centrality. The PageRank centrality may overestimate the importance of some nodes due to some local link structure in the network, and the E-shell centrality combines the species trophic level to evaluate the nodes from the network level, so that the nodes with global influence and in the key level position can be more accurately screened out, and the trophic level characteristics of the species are integrated, so that the E-shell can more truly and effectively reflect the level of the species, and reduce misjudgment and omission. Therefore, compared with using PageRank or E-shell centrality alone, this combination can more accurately identify the key species in the network. The higher the HCC-PRTL centrality of the species, the higher the key degree, and the more suitable it is as a key species. The specific formula is as follows:

[0124]

[0125] In the formula, HCC-PRTL(u) is the HCC-PRTL centrality of node u, pos(u) is the position index of node u decomposed by the E-shell, PR(u) is the PageRank centrality of the node, PR(u) max and pos(u) max are the maximum PageRank centrality and the maximum position index of the node, respectively.

[0126] Example two

[0127] The application also provides a system for identifying key species in combination with the trophic level of species and its properties in a food web. The system is used to implement the method of example one, and the system comprises a construction module, a first calculation module, a second calculation module and an identification module.

[0128] The construction module is used to construct a directed and weighted “large food web” model, i.e., a food web, based on the multi-source data of species in the region to be identified.

[0129] The first calculation module is used to calculate the PageRank centrality index of each species in the food web based on the constructed food web.

[0130] The second calculation module is configured to calculate an improved E-shell centrality index combined with a PageRank and a species trophic level based on the constructed food web.

[0131] The identification module is configured to calculate an HCC-PRTL centrality index of each node based on the calculated PageRank centrality index combined with the improved E-shell centrality index, so as to identify key species.

[0132] Embodiment three

[0133] This embodiment takes the selection of key species of a wild animal food web in a forest ecosystem in the Daxing'anling region of a Chinese biogeographic subregion as an example, and it can be understood that the key species selection and optimization method of the present application can also be applied to other ecosystems.

[0134] Step one: For the Daxing'anling region, collect multi-source data required for constructing a food web based on books, literature, research reports and other multi-source channels, including the predation relationship between species and the characteristics of species. The basic information collected is shown in Table 1.

[0135] Table 1 Information of wild animals in the forest ecosystem in the Daxing'anling region (part of the species)

[0136]

[0137]

[0138] Step two: Construct a predation matrix and make predation relationship prediction. To predict the predation relationship between species, the matrix-by-matrix method is used. To create the matrix, all characteristic variables are converted to binary format. For the classification characteristics of species order, family and diet, data (0 or 1) is assigned according to the presence / absence of each classification characteristic of the species. For the species body weight which is a quantitative characteristic, Chinese terrestrial mammals are divided into three levels: small mammals, medium mammals and large mammals according to the Chinese terrestrial mammal size standard. On this basis, the present proposal further divides each size level of mammals into large, medium and small based on the upper limit and lower limit of the species size in all species. Finally, Chinese terrestrial mammals are divided into 9 categories based on the weight characteristics, and then converted into binary variables, and the matrix is constructed according to the above process, and the specific process is as follows:

[0139] First, a predator-prey matrix A is established, which has a dimension of i x j, and the formula is as follows:

[0140] A=(a ij ) i×j ;

[0141] In the formula, i is the total number of predators, and j is the total number of prey. The nutritional interaction aij Using 1 or 0 (1 if predator i preys j, 0 otherwise), matrix A represents the recorded predator-prey relationships.

[0142] The second step is to construct the predator-prey feature matrix P, which has dimensions i×k, as shown in the following formula:

[0143] P=(p ik ) i×k ;

[0144] In the formula, k represents the species trait. For each species' trait, a predator-prey trait matrix P is established separately, and the relationship between predator i and prey trait k is p. ik The matrix P represents the predator's predation preference, represented by the relative frequency of predator i preying on prey feature k.

[0145] The third step is to construct the species characteristic-species matrix T, which has dimensions k×j, as shown in the following formula:

[0146] T=(t kj ) k×j ;

[0147] For each species' trait, a separate species trait-species matrix T is constructed, and the attribution relationship t between species trait k and species j is established. kj The matrix T represents the characteristics of a species, represented by 1 or 0 (1 if species j possesses species characteristic k, otherwise 0).

[0148] 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:

[0149] M = P × T = (m' ij ) i×j ;

[0150]

[0151] 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.

[0152] Step three: Calculate the food source ratio between species, based on the probability of the occurrence of the predator-prey relationship in the above matrix. First, calculate the average value of the probability of the occurrence of the predator-prey relationship under the prediction of the characteristics of the species, as follows:

[0153]

[0154] In the formula, represents the average probability of the occurrence of the predation of species j by predator i, f ij is the probability of the occurrence of the predator-prey relationship under the prediction of a single characteristic (i.e., order, family, trophic level, body weight), and n is the number of selected species characteristics.

[0155] Calculate the relative frequency of the occurrence of the predator-prey relationship of all prey of predator i, and thus calculate the food source ratio (F Figure 2 ) between species in the forest ecosystem of Daxing'anling region, as follows:

[0156]

[0157] In the formula, F ij represents the food source ratio of species j by predator i, is the average value of the probability of the occurrence of the predator-prey relationship between predator i and prey k.

[0158] Step four: Calculate the trophic level of species, based on the food source ratio between species, as follows:

[0159] Tr i = 1 + ∑ Tr j × F ij ;

[0160] In the formula, Tr i represents the trophic level of predator i, Tr j is the trophic level of prey j, and F ij is the food source ratio of prey j by predator i. In this proposal, the trophic level of primary producers is set to 1, and the trophic level of each species in the food web of the forest ecosystem of Daxing'anling region is obtained (Tr Figure 2 ), with the overall trophic level of each consumer spanning 2.00-3.30.

[0161] Step five: Construct the food web model, combine the predator-prey relationship between species in the food web, the characteristics of the trophic level of each node, and the food source ratio to construct a quantitative and weighted food web (W Figure 3 ).

[0162] Step six: Calculate the PageRank centrality of each species in the food web based on the constructed food web. Based on the constructed food web, the PageRank centrality of each species in the food web is calculated using the concept of shortest path. The formula is as follows: The formula is as follows:

[0163] dw(i,j) = min(1 / w ih + … + 1 / w hj );

[0164]

[0165] In the formula, dw(i,j) represents the shortest distance from functional group i to functional group j, w ih is the weight of the link between node i and node h on the shortest path from node i to node j in the network, and similarly w hj is the weight of the link between node h and node j on the shortest path from node i to node j in the network. PR(i) is the PageRank centrality of functional group i, PR(j) is the PageRank centrality of functional group j, j is a functional group connected to functional group i, k j,out is the out-degree of functional group j, λ is the damping coefficient, which is an empirical value of 0.85, and n is the number of functional groups.

[0166] According to the results of the PageRank centrality index (Table 2), wolves have the highest PageRank centrality (0.10), and black bears rank second (0.09).

[0167] Table 2: PageRank centrality results of forest ecosystem in Daxing'anling region (partial species)

[0168]

[0169]

[0170] Step seven: Based on the constructed food web, calculate the improved E-shell centrality index combined with PageRank and species trophic level. The detailed process is as follows:

[0171] Step 1: Input a food web G.

[0172] Step 2: Initialize iteration number p = 1 and G1 = G.

[0173] Step 3: Find the minimum node set

[0174] Step 4: Find the minimum node set S p with the smallest trophic level pTL .

[0175] Step 5: Label the position index for each node in S pTL , the position index index p = p.

[0176] Step 6: Remove S p from S pTL , resulting in a new S p+1 , if S p+1 is not empty, update p = p + 1, until S p+1 is empty.

[0177] Step 7: If S p+1 is empty, get a new food web G p+1 , and update the PageRank centrality values of the nodes in G p+1 .

[0178] Step 8: Return to step 3 until G p+n is empty.

[0179] The node position index obtained in the above steps is the improved E-shell centrality of the node (Table 3). The black bear has the highest improved E-shell centrality (18.00), followed by the leopard cat (17.00) and the wolf (16.00).

[0180] Table 3 Results of improved E-shell centrality of species in forest ecosystem in Daxing'anling region (partial species)

[0181]

[0182]

[0183] Step eight: Based on the calculated PageRank centrality index, combined with the improved E-shell centrality index, the HCC-PRTL centrality index value of each node point is calculated, and the key species is identified. HCC-PRTL centrality combines the species trophic level, PageRank centrality and E-shell centrality. PageRank centrality may overestimate the importance of some nodes due to some local link structure in the network, while E-shell centrality combines the species trophic level to evaluate the nodes from the network level, so that those nodes with global influence and in key level position can be more accurately screened out. At the same time, the trophic level characteristics of the species are integrated into the improved E-shell, so that the improved E-shell can more truly and effectively reflect the level of the species, reduce misjudgment and omission. Therefore, compared with using PageRank or E-shell centrality alone, this combination can more accurately identify key species in the network. The higher the HCC-PRTL centrality of a species, the higher its key degree, and the more suitable it is as a key species. The specific formula is as follows:

[0184]

[0185] In the formula, HCC-PRTL(u) is the HCC-PRTL centrality of node u, pos(u) is the position index of node u decomposed by E-shell, PR(u) is the PageRank centrality of the node, PR(u) max and pos(u) max are the maximum PageRank centrality and maximum position index of the node, respectively.

[0186] The HCC-PRTL centrality index values of each species are shown in Table 4, in which the black bear has the highest HCC-PRTL centrality (1.92), so the black bear is more suitable as the key species of the ecosystem in the Greater Khingan Mountains region.

[0187] Table 4 HCC-PRTL centrality index results of each species in the forest ecosystem in the Greater Khingan Mountains region

[0188]

[0189]

[0190] The above-described embodiments are only descriptions of the preferred modes of the present application and do not limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements to the technical solutions of the present application made by those skilled in the art shall fall within the protection scope determined by the claims of the present application.

Claims

1. A method of identifying keystone species in a food web by combining species trophic level and its attributes in the food web, characterized by, The method comprises: Based on the multi-source data of species in the region to be identified, a directed and weighted "large food web" model, i.e. a food web, is constructed; Based on the constructed food web, the PageRank centrality index of each species in the food web is calculated; Based on the constructed food web, the E-shell centrality index improved by combining PageRank and species trophic level is calculated; Based on the calculated PageRank centrality index, the HCC-PRTL centrality index of each node is calculated by combining the improved E-shell centrality index, so as to identify key species.

2. The method of claim 1, wherein, The method for constructing a directed and weighted "large food web" model based on multi-source data of species in the region to be identified comprises: Step 1: Collect the data required for constructing the food web, wherein the required data includes the predation relationship between species, species traits, and characteristic data; Step 2: Based on the required data, a predation matrix is constructed, and predation relationship prediction is performed; Step 3: Based on the probability of predation relationship occurrence in the predation matrix, the food source ratio between species is calculated; Step 4: Based on the food source ratio between species, the trophic level is calculated; Step 5: A quantitative and weighted food web is constructed by combining the predation relationship between species in the food web, the trophic level characteristics of each node, and the food source ratio.

3. The method of claim 2, wherein, In the step 2, the method for constructing a predation matrix based on the required data and performing predation relationship prediction comprises: A predator-prey matrix A is established, which has a dimension of i x j, and the formula is as follows: A = (a ij ) i×j ; where i is the total number of predators, j is the total number of prey, a ij The matrix A represents the known predatory relationships between predators and prey using 1 or 0. A predator-prey characteristic matrix P is established, which has a dimension of i x k, and the formula is as follows: P = (p ik ) i×k ; where k is a species trait characteristic, and for each species trait characteristic a separate predator-prey trait matrix P is established, the relationship p ik The matrix P represents the predation preferences of the predators, expressed as the relative frequency with which predator i preys on prey trait k; An all-species characteristic-species matrix T is established, which has a dimension of k x j, and the formula is as follows: T = (t kj ) k×j ; For each species trait, a separate species trait-species matrix T is created, with the membership t between species trait k and species j kj The matrix T represents the traits of the species, with 1 or 0. A matrix A' is established, which visualizes the predation preference of the predator for the species characteristics to each species, and the formula is as follows: M = P x T = (m ij ) i×j ; where m' ij is the element of matrix P multiplied by matrix T, is the excess element, used for comparison with matrix A to verify the existence of predator-prey relationship, and θ represents the minimum threshold value of predator-prey relationship, which is derived from the comparison between matrix M and matrix A, m' ij is the minimum value of a ij coincidence, which is the value of θ, where a' ij is the possible predator-prey relationship under this feature; for each species feature, a matrix A' is obtained; the predator-prey relationship that occurs under each species feature is screened out to construct a food web.

4. The method of claim 2, wherein, In the step 3, the method for calculating the food source ratio between species based on the probability of predation relationship occurrence in the predation matrix comprises: The average value of the probability of predation relationship occurrence under the prediction of species trait characteristics is calculated, and the formula is as follows: wherein, represents the average probability of predation of node j by predator i, f ij is the probability of predation relationship occurrence under single feature prediction, n is the number of selected species features; The relative frequency of predation relationship occurrence of all prey of the predator i is calculated as the food source ratio, and the formula is as follows: where F ij represents the food source ratio of predator i preying on prey species j, is the average value of the occurrence probability of the predator-prey relationship between predator i and prey k.

5. The method of claim 2, wherein, In the step 4, the method for calculating the trophic level based on the food source ratio between species comprises: Tr i = 1 + ∑Tr j × F ij ; wherein Tr i represents the trophic level of the predator i, Tr j is the trophic level of the prey j, F ij is the food source ratio of the predator i preying on the prey j.

6. The method of claim 1, wherein, The method for calculating the PageRank centrality index of each species in the food web based on the constructed food web comprises: dw(i,j) = min(l / w ih +... + 1 / w hj ); where dw(i, j) is the shortest distance from functional group i to functional group j, w ih is the weight of the link between node i and node h on the shortest path from node i to node j in the network, w hj is the weight of the link between node h and node j on the shortest path from node i to node j in the network, PR(i) is the PageRank centrality of functional group i, PR(j) is the PageRank centrality of functional group j, j is a functional group connected to functional group i, k j,out is the out-degree of functional group j, λ is a damping factor with an empirical value of 0.85, and n is the number of functional groups.

7. The method of claim 1, wherein, The method for calculating the E-shell centrality index improved by combining PageRank and species trophic level based on the constructed food web comprises: Step 1: Input a food web G; Step 2: Initialize iteration number p = 1 and G1 = G; Step 3: Find the smallest node set Step 4: Find the smallest set of nodes S p The set S of nodes with the smallest trophic level pTL ; Step 5: For the node set S pTL Each node is labeled with a location index, where the location index is... p =p; Step 6: Remove S p from S pTL , resulting in a new S p+1 If S p+1 is not empty, update p = p + 1 until S p+1 is empty. Step 7: If S p+1 is empty, get a new food web G p+1 and update the PageRank centrality values of the nodes in G p+1 . Step 8: Go to Step 3 until G p+n is empty; The node position index obtained in steps 1 to 8 is the improved E-shell centrality of the node.

8. The method of claim 1, wherein, The method for calculating the HCC-PRTL centrality index of each node based on the calculated PageRank centrality index and the improved E-shell centrality index to identify key species comprises: In the formula, HCC-PRTL(u) is the HCC-PRTL centrality of node u, pos(u) is the position index of node u decomposed by E-shel, PR(u) is the PageRank centrality of the node, PR(u) max and pos(u) max are the maximum PageRank centrality and the maximum position index of the node, respectively.

9. A system for identifying keystone species in a food web in combination with the trophic level and its attributes, said system for implementing the method according to any one of claims 1 to 8, characterized in that, The system comprises a construction module, a first calculation module, a second calculation module, and an identification module; The construction module is used for constructing a directed and weighted "large food web" model, i.e. a food web, based on multi-source data of species in the region to be identified; The first calculation module is configured to calculate a PageRank centrality index of each species in the food web based on the constructed food web. The second calculation module is configured to calculate an improved E-shell centrality index combined with the PageRank and the trophic level of each species based on the constructed food web. The identification module is configured to calculate an HCC-PRTL centrality index of each node based on the calculated PageRank centrality index and the improved E-shell centrality index, and identify a key species.

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